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PREDICTIVE ANALYTICS: WHAT IT IS, APPLICATIONS, INDUSTRY USES, HOW TO GET STARTED

 

“The dilemma is, as it is often said, correlation does not imply causation. The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly. No way, nohow.”  - Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die

What is predictive analytics?

Predictive analytics is the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.

The goal is to go beyond descriptive statistics and reporting on what has happened to providing a best assessment on what will happen in the future. The end result is to streamline decision making and produce new insights that lead to better actions.

Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. This is different from descriptive models that help you understand what happened or diagnostic models that help you understand key relationships and determine why something happened.

More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage using predictive analytics. Why now?

• Growing volumes and types of data and more interest in using data to produce valuable information.
• Faster, cheaper computers and easier-to-use software.
• Tougher economic conditions and a need for competitive differentiation.

With interactive and easy-to-use software becoming more prevalent, predictive analytics is no longer just the domain of mathematicians and statisticians. Business analysts and line-of-business experts are using these technologies as well.

What is predictive analytics?

A 2014 TDWI report found that the top five things predictive analytics are used for is to:

• Identify trends.
• Understand customers.
• Improve business performance.
• Drive strategic decision making.
• Predict behavior.

Some of the most common applications of predictive analytics include:

• Fraud detection and security – Predictive analytics can help stop losses due to fraudulent activity before they occur. By combining multiple detection methods – business rules, anomaly detection, link analytics, etc. – you get greater accuracy and better predictive performance. And in today’s world, cybersecurity is a growing concern. High-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate occupational fraud, zero-day vulnerabilities and advanced persistent threats.
• Marketing –  Most modern organizations use predictive analytics to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow the most profitable customers and maximize their marketing spending.
• Operations – Many companies use predictive models to forecast inventory and manage factory resources. Airlines use predictive analytics to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
• Risk – One of the most well-known examples of predictive analytics is credit scoring. Credit scores are used ubiquitously to assess a buyer’s likelihood of default for purchases ranging from homes to cars to insurance. A credit score is a number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

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Predictive analytics use across industries -- real life examples

Any industry can use predictive analytics to optimize their operations and increase revenue. Here are a few examples:

• Credit card, banking and finan­cial services. Detect and reduce fraud, measure credit risk, maximize cross-sell/up-sell opportu­nities, retain customers and optimize marketing campaigns. Commonwealth Bank can reliably predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction being initiated.
• Governments and the public sec­tor. Improve service and performance; detect and prevent fraud, improper payments and the misuse of funds and taxpayer dollars; and detect criminal activities and patterns. The Hong Kong government visualizes and analyzes big, unstructured data to anticipate and address public complaints.
• Health care providers. Predict the effectiveness of new procedures, medical tests and medications, and improve ser­vices or outcomes by providing safe and effective patient care. Taipei Medical University executives analyze and monitor performance across all hospitals in its system.
• Health insurers. Detect and handle insurance claims fraud, identify which patients are most at risk of chronic diseases and know which interventions make the most medical and financial sense. One of the largest pharmacy benefits companies in the US, Express Scripts, uses analytics to identify patients not adhering to their prescribed treatments, resulting in a savings of $1500 to $9000 per patient.
• Insurance companies. Determine insurance premium rates, detect claims fraud, optimize claims processes, retain customers, improve profitability and optimize marketing campaigns. Within two hours of an earthquake striking rural New Zealand, Farmers Mutual Group assessors were headed to affected areas. With SAS Analytics, they knew who their most at-risk policy holders were and chartered a helicopter to get to them quickly.
• Manufacturers. Identify factors leading to reduced quality and production failures, and optimize parts, service resources and distribution. Lenovo used predictive analytics to better understand warranty claims, leading to a 10 to 15 percent reduction in warranty costs.
• Media and entertainment. Deepen insight into audiences by identifying influencing attributes, trends, drivers and desires across properties, and score visitors to determine appropriate audience seg­ments and behavior value. How is the slot floor doing every day? How is the gaming floor performing? How are the nonsmoking tables compared to the smoking tables? The answers – which previously could take numerous weeks and many dollars to find out – are now coming in minutes and at a far lower cost for Foxwoods Resort Casino. 
• Oil, gas and utility companies. Predict equipment failures and future resource needs, mitigate safety and reliability risks, and improve performance. Salt River Project is the second-largest public power utility in the US and one of Arizona's largest water suppliers. A sophisticated forecasting model helps them know the best time to sell excess electricity for the best price.
• Retailers. Assess the effectiveness of promotional events and cam­paigns, predict which offers are most appropriate for consumers, deter­mine which products to stock where and how to build brand loyalty. Staples analyzes online and offlilne consumer behavior to provide a complete picture of their customers, and realized a 137% ROI.
• Sports franchises. Sports analytics is a hot area, thanks in part to Nate Silver and tournament predictions. The NBA’s Orlando Magic uses SAS predictive analytics to improve revenue and determine starting lineups.

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What do you need to get started?

• Problem to solve. The first thing you need to get started using predictive analytics is a problem to solve. What do you want to know about the future based on the past? What do you want to understand and predict? You’ll also want to consider what will be done with the predictions. What decisions will be driven by the insights? What actions will be taken?
• Data. Second, you’ll need data. In today’s world, that means data from a lot of places. Transactional systems, data collected by sensors, third-party information, call center notes, web logs, etc. You’ll need a data wrangler, or someone with data management experience, to help you cleanse and prep the data for analysis. To prepare the data for a predictive modeling exercise also requires someone who understands both the data and the business problem. How you define your target is essential to how you can interpret the outcome. (Data preparation is considered one of the most time-consuming aspects of the analysis process. So be prepared for that.)
• Modeling building tools.  After that, the predictive model building begins. With increasingly easy-to-use software becoming more available, a wider array of people can build analytical models. But you’ll still likely need some sort of data analyst who can help you refine your models and come up with the best performer. And then you might need someone in IT who can help deploy your models. That means putting the models to work on your chosen data – and that’s where you get your results.
• Predictive modeling requires a team approach. You need people who understand the business problem to be solved. Someone who knows how to prepare data for analysis. Someone who can build and refine the models. Someone in IT to ensure that you have the right analytics infrastructure for model building and deployment. And an executive sponsor can help make your analytic hopes a reality.

COMMENTARY:   According to Malene Haxholdt, Global Marketing Manager, Business Analytics, SAS, there are several good reasons why businesses should start predictive analytics initiatives.  

• Growing revenue.
• Lowering costs.
• Establishing governance and compliance.

Companies are looking to get value from predictive analytics in many business areas. The value comes when you can take data, apply analytics, and act on the results. The value ultimately means growing revenue, lowering costs, or establishing governance and compliance. Growing revenue is often associated with customer analytics and being better at retention modeling and cross-sell and up-sell activities. Lowering costs often comes from analytics used to improve processes as well as from using internal capacity in any form. Especially in the financial industry, the driver for predictive analytics can be part of a compliance and governance strategy.

Predictive analytics is relevant and useful across all industries. Some industries are more mature in their use and implementation of predictive analytics. The most common applications of predictive analytics come from a need to better manage fraud, risk, equipment failure, or customer interactions. What are common across industries are the accelerating growth of data and the desire to turn it into valuable information. Predictive analytics is a component of that journey.

It is key to remember that predictive analytics is only valuable if you can turn the results of analytical models into actions. The business process, the people, and the technology need to be aligned to successfully deploy predictive analytics. Think of predictive analytics as part of a life cycle that consists of (1) managing all of your data, (2) exploring all of your data, (3) building your models with the best techniques, and (4) deploying and monitoring your models.

It is important that the organization believes and understands that predictive analytics is driving better decisions. The software tools that allow you to start building predictive models are very approachable and do not require you to be a technical expert. We see a tremendous uptick in interest in interactive predictive modeling using visual analytics and visual statistics. Without any coding required, you can start exploring your data and get value. More mature companies are hiring more data scientists to further grow and explore the possibilities predictive analytics brings. Having human skills that can align the technology and business understanding is key to success.

With the reality of big data, new techniques are being explored by companies to leverage the value hidden in new types of data. Being able to explore all of your data quickly and in an interactive manner is driving the need for data visualization techniques and interactive predictive modeling on very big amounts on data—fast. Also, we see a growing interest in machine-learning techniques such as Random Forest and techniques to handle unstructured data. Take, for example, SAS Contextual Analysis. This next-generation text modeling software combines the ease of machine learning with subjectmatter expertise, enabling powerful text models to easily be defined from unstructured data.

To get sustainable value from predictive analytics, IT and business users are both key in the process. A modern CIO and IT department work closely with the business to enable predictive analytics throughout the organization by providing data access and approachable analytics tools to the right users. Think of it as self-service analytics. The IT department is, in some cases, the true driver of innovation because it can enable the use of all data and help get even more predictive power in the models used by the business. IT’s role is critical in selecting an architecture that will meet future demands around diverse analytical data preparation tasks, reducing model building latency and quickly deploying models into operational systems.

All companies, no matter the size, will benefit from making better decisions in a world of uncertainty. We see small companies that apply predictive analytics as a key strategy to growth. In fact, companies that embed predictive analytics as a cornerstone in their business tend to be more successful in the long run.

Getting A Predictive Analytics Initiative Started

Please read my previous blog post dated October 11, 2015 titled "The Ultimate How-To Guide For Planning A Predictive Analytics Project and Results From An Actual Case Study." if you would like to begin a predictive analytics project at your company. 

Courtesy of an article titled, "Predictive Analytics: What It Is and Why It Matters," by SAS Analytics and the TDWI eBook by SAS titled, "Predictive Analytics: Revolutionizing Business Decision Making"

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WALMART'S U.S. SALES ARE DOWN, ITS STOCK FELL 10% AND LOST $20 BIL IN VALUE, IS WALMART'S BUSINESS MODEL BROKEN?

This week, Walmart's shares crashed on October 14 after the company reported a disappointing profit outlook. Profits will fall 6% to 12% next year, the company said.

Walmart founder Sam Walton wrote in his 1992 memoir "Sam Walton: Made in America".

"Now, when it comes to Wal-Mart, there's no two ways about it: I'm cheap," 

If the notoriously cheap Walton were alive today, he would probably be surprised that his pioneering profit formula -- keep costs low so that prices are as low as possible and customers buy more -- is now virtually impossible to maintain. Full blame for that can be pinned on employees demanding higher wages and the rise of e-commerce giants such as Amazon (AMZN), which are open 24 hours a day, seven days a week. 

Walmart founder Sam Walton (Click Image To Enlarge)

Perhaps the retail legend saw some of this on display from up above on what was a dark day in Walmart's history on Wednesday. Walmart's usually stable stock price tumbled 11% as the company said at its annual investor day that higher wages for associates will dent profits by about $1.5 billion in the 2017 fiscal year, following a $1.2 billion hit this fiscal year.

For years, Walmart has had the upper hand with its employees, paying them state minimum wages, offering basic healthcare plans, and using computerized systems to control their hours. But with the rise of social media and the internet giving employees a free platform to express views, and the rising cost of living causing state governments to push through higher minimum wages, Walmart has had to change with the times.

As a result, it has lost a key element of its business model put in place by Walton that has supported the company's profits since its founding. Further, rising employee costs are arriving as online shopping is also limiting the ability of Walmart to raise prices at all to help deliver sustainable profit growth.

Walmart added on Wednesday that it expects earnings per share to decline between 6% and 12% next year. Wrote Sterne Agee analyst Charles Grom in a note to clients on Wednesday :

"What's surprising to us is that the new outlook is incorporating roughly $20 billion in share buybacks in the next two years, which implies significant margin contraction along with modest sales growth." 

Employee costs aren't the only line item on the income statement Wal-Mart is battling to control.

Walmart's investments in e-commerce and digital initiatives are expected to total about $1.1 billion in the 2017 fiscal year. This year, Walmart is projected to shell out $1.2 billion to $1.5 billion on e-commerce and digital.

These investments are a necessary evil to stay competitive with Amazon, as well as with Best Buy (BBY) and Target (TGT) , which are continuing down their own paths to reduce prices, improve checkout speeds and offer a range of delivery options.  

When Walton opened the first Walmart store in Rogers, Arkansas in 1962, though, it's unlikely he foresaw a day when a sweater could be purchased off a handheld device. Today it can be, however, and to make it happen seamlessly and at the lowest cost is proving to be a major profit headwind for the once mighty Walmart.

Even with such enormous investments in digital, however, Walmart still sees sales as being under pressure. Walmart said it now expects net sales growth for the current fiscal year to be relatively flat, down from a forecast of between 1% and 2% in February. Excluding the impact of currency exchange fluctuations -- mostly the stronger dollar -- net sales growth is estimated to be about 3%.

Goldman Sachs analyst Matthew Fassler in a note on Wednesday pointed out.  

"New guidance reflects that Walmart's competitive edge -- historically largely assortment and price -- has faded relative to purveyors of extreme value (warehouse clubs, hard discounters) or extreme convenience (dollar stores, hard discounters), as e-commerce has neutralized the impact of selection." 

The deterioration of Walton's pioneering business model has been playing out for some time in Walmart's numbers. 

According to Bloomberg data, Walmart's operating profit margins, which takes into account its costs to run stores and pay for wages and healthcare, has fallen from 7.1% in 1987 to 5.6% in 2015.  From 1988, Walmart's return on assets -- an indicator of how profitable a company is relative to its total assets -- has gone from 13.7% to 8.0% in 2015.

The alarming downtrends in these measures, partly fueled by investments to compete online and compensate workers better, have been overlooked for years by Walmart's investors who have focused on the retailer's healthy dividend and relative stability.

Walmart CEO Doug McMillon (Click Image To Enlarge)

But the stock's nosedive on Wednesday, and an 11% drop for the year prior to that point, suggests Walmart's investor base is growing concerned that the retail giant's business is broken. Wrote Stifel analyst David Schick in a note,

"The market is reacting to meaningful evidence that Walmart has substantially over-earned."

In other words, Walmart is now being forced to play catch up on investments it neglected to make in the past to keep the profits flowing. 

And if Walmart's business model is broken, profits are likely to stay under pressure for some time, and future dividend hikes may not be as robust as in years past. 

Now, Walmart has to find a way to channel Walton and become cheap once more. Indications have emerged that it may be willing to take drastic actions to return to some sense of frugality. 

 

Walmart CEO Doug McMillon said at the investor event.

"We are more than open to re-shaping our portfolio."

McMillon added the company continues to "evaluate its portfolio" of assets, and pointed to Walmart's history of exiting non-strategic assets and closing underperforming stores.

For example, the retailer exited Germany in 2006 and took a $1 billion hit to profits as a result. By shedding assets such as Sam's Club or lagging international operations, Walmart would also be shedding costs related to employees and maintaining physical stores. In turn, those savings could be used to offset the investments being made in core assets such as the online business and Walmart U.S., or simply be brought to the bottom line to appease investors.

Walton wrote.

"You can make a lot of different mistakes and still recover if you run an efficient operation -- or you can be brilliant and still go out of business if you're too inefficient."

Words of wisdom from beyond the grave for McMillon and the execs tasked with turning around the largest ship in retail.

Walmart did not respond to request for comment for this story.

COMMENTARY:   When I heard that Walmart's stock had plummeted 10% in mid-October over its shitty earnings report, decling profit margins and the CEO's statements that earnings will continue to be stagnant due to higher employee wages and investments in ecommerce, I shed crocodile tears and managed a smile.

Walmart has been the "king of exploiters." For years it has exploited its employees, vendors and foreign suppliers.

Walmart has been the subject of numerous lawsuits from employees seeking payment for overtime and unfair labor practices, including paying female managers less than their male counterparts, among other sins.

For years Walmart has squeezed its vendors for lower prices, often forcing some into bankruptcy, while others have fled the retail giant, and swore never to return again.  

The pricing deals that it has extracted from Chinese suppliers has cost hundreds of thousands of US jobs, and many U.S. small businesses have gone out of business.

So, if you were to ask, I don't feel the slightest remorse for this disgusting company?  Nope.  

The Walton family, prior to the recent stock market decline, were worth $140 billion, the wealthiest American family, and representing the very top of that 1% which owns 90% of America's total net worth. After the stock price crash, they are still worth at least $125 bllion.  Now I hear they are blaming the far left's campaigns to increase the minimum wage for cause of their problems.

From my viewpoint, the cause of Walmart's problems is mostly about Walmart's values, and they are all about profits, and more profits, at any cost.  

Do you think the Walton family cares about all the small retailers they have put out of business, or the Chinese workers being exploited by suppliers so that they can pay bottom dollar for their goods, the US vendors they squeezed for lower prices then put out of business, or even their employees who are finally getting paid minimum wage (a paltry $9.00 per hour)?  

This is more of a condemnation of Walmart's values coming back to bite them in the ass.  It is not about their business model going to pieces.  You can bet that Walmart's CEO Doug McMillon and his team will find ways to restore the viability of their dirty business model by screwing somebody.  Stay tuned for more bad news about this indecent company.   

The Wharton Business Radio ran an excellent broadcast titled Marketing Matters Walmart, that details where Walmart fucked up, their failure to react to the dollar discount stores, their botched entry into Canada, their badly executed store remodelling, and failure to gain traction in online sales to combat Amazon, and what is really going on, to get them into this disasterous hole.  Definitely worth listening to.

Courtesy of an article dated October 16, 2015 appearing in The Street, an article dated February 19, 2015 appearing in Newsmax, an article dated October 16, 2015 appearing in Business Insider, and an article dated October 14, 2015 appearing in Fortune Magazine

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THE STATE OF DATA-DRIVEN MARKETING, MARKETING BENEFITS, KEY GROWTH DRIVERS AND THE 15 ESSENTIAL MARKETING METRICS

Data-driven marketing, in theory, is about better using the flood of customer-related data to integrate and optimize marketing efforts in the age of an empowered consumer and Big Data.

What matters most is the optimization of the customer experience, relevance and (perceived) customer value as a driver of business value. Data-driven marketing certainly is not (just) about advertising and programmatic ad buying as some believe. Nor is it just about campaigns. On the contrary: if done well, data-driven marketing is part of digital marketing transformations whereby connecting around the customer across the customer life cycle is key.

The Role and Evolution of Data-Driven Marketing

One of the pioneers and leading voices is data analytics and marketing software vendor Teradata. A while ago, the company recently released the results of its Teradata 2015 Global Data-Driven Marketing Survey. According to Teradata, marketers who adopt an integrated and data-driven marketing approach become more empowered across all areas including the various stages of “engagement” as described in the illustration below from the page of the 2013 survey the company conducted.

The Various Stages of Customer Engagement - Teradata (Click Image To Enlarge)

At the occasion of the launch of the 2015 edition of the survey, Teradata said it saw some “dramatic shifts in how companies and marketers are deriving business value from data, integrated marketing platforms, and customer-centric data-driven marketing strategies” as compared with its 2013 report.

Among the key findings of the Teradata 2015 Global Data-Driven Marketing Survey:

• A large majority of marketers (90%) wants to move beyond segmentation towards one-to-one personalization in a real-time interaction context.
• Faster decisions and more accurate decisions are reported as key benefits of using data in this real-time economy. Two-thirds of respondents cite speed and accuracy in decisions as key benefit.
• For 38% of respondents the major challenge is improving both customer acquisition and customer retention.
• 78% of marketers in the survey claim to use data systematically. In 2013 this was only 36%.

Omni-channel consistency and silos remain hurdles

Respondents see data-driven marketing as a means to an end and for several an integrated marketing cloud approach seems like the way forward from a platform perspective. Of course, Teradata is one of several major providers of such a marketing cloud (like Adobe, Oracle, Salesforce.com, IBM, Microsoft etc.).

Obviously there is a gap between what marketers would like to achieve with data-driven marketing and what they actually achieve. Many hurdles remain, among others regarding consistency in an omni-channel marketing context whereby silos are the eternal challenge, also from a campaign marketing perspective.

More about the evolutions, challenges, customer data issues (privacy, regulation, ownership) etc. in the press release, the report and the infographic below.

Given the huge attention for data-driven marketing (and, admittedly, the big marketing cloud wars we see happening), there is of course more research looking at the state of data-driven marketing and the drivers of it. In a second infographic below the one from Teradata, we’ll take a closer look at more research – with a second infographic.

Data-driven marketing: customer-centricity and customer experience first?

It’s not just the vendors and marketing cloud providers such as Teradata who come up with research.

Recently, the GlobalDMA, “an organisation that represents, supports and unites marketing associations from around the globe that focus on data-driven marketing”, looked at the state of data-driven marketing and data-driven marketing practices in 17 global markets.

It did so in collaborations with strategic consultancy for the advertising and marketing industry, Winterberry Group.

You can access the report for free on the website of the GlobalDMA (registration required). Just like Teradata, the report found that marketers – obviously – increasingly see the importance of a smart use of data in marketing, customer experience and advertising (the whole programmatic advertising concept).

In an infographic on AdWeek, infographist Carlos Monteiro summarized some of the key takeaways from that research (and from Teradata’s 2015 Global Data-Driven Marketing Survey).

How Data-Driven Marketers Outperform Their Competition - Adweek by Carlos Monteiro (Click Image To Enlarge)

Data-driven marketers outperform their competitors according to The State of Marketing 2013 by IBM – another contender in the marketing cloud wars – source: presentation by Jason Heller of McKinsey at Integrated Marketing Week 2013

Among the key takeaways of the data-driven marketing report by the GlobalDMA:

• 77% of marketers are confident in the data-driven approach and 74% expect to increase data marketing budgets this year.
• Data efforts by far focus on offers, messages and content (marketing) first (69% of respondents). Second ranks a data-driven strategy or data-driven product development. Customer experience optimization unfortunately only ranks third with 49% of respondents.
• Among the key drivers of increased data marketing: first of all a need to be more customer-centric (reported by 53% of respondents). Maximizing efficiency and return ranks second followed by gaining more knowledge of customers and prospects.

The question is what will be done with this knowledge. And here we see that 20% reports a need to align with digital consumer preferences.

So, it seems the digital customer experience does matter as it’s essential for a more customer-centric approach and the – for many in practice – extremely urgent need to align with (digital) consumer preferences or, maybe better, catch up with the changed customer reality, in the end the essence of what digital marketing transformation is all about in the first place.

Infographic – via AdWeek and made by Carlos Monteiro – below. You can download the Winterberry Group/ GlobalDMA report here.

 Click Image To Enlarge

COMMENTARY:  Customers today expect—and demand—a seamless and relevant experience. They have grown accustomed to marketers’ knowledge of their preferences and anticipation of their needs. Fractured or conflicting messages from a brand make marketers seem unorganized and annoy customers, sometimes even driving them away.

For marketers, the only solution is data-driven marketing for individualized insights. Teradata defines data-driven marketing as

"the process of collecting and connecting large amounts of online data with traditional offline data, rapidly analyzing and gaining cross-channel insights about customers, and then bringing that insight to market via a highly personalized marketing campaign tailored to the customer at his/her point of need."

Data-driven marketing is the means that leads to the end of individualized insights: moving beyond segmentation to true one-to-one personalization in a real-time context— achieving the capability to distill insights at the individual level, and the ability to target known customers in a digital marketing ecosystem that often settles for “close enough.” 

A well-integrated data-driven marketing program that provides a single view of the customer is the path to such individualization, and can result in business benefits that are just waiting to be tapped. The data in this report is based on a survey of 1,506 marketing and communications executives worldwide, representing all major industries. The survey was conducted in the fall of 2014, and follows a similar survey conducted in 2013.

According to Mark Jeffrey, Kellogg School of Management, Northwestern University, and author of "Data Driven Marketing," there are 15 essential marketing metrics that all marketers should know.

Click Image To Enlarge

People-Centric Targeting Across All Screens

What is it: Media consumption habits are more fragmented and diverse than ever, as consumers engage with multiple screens and devices. And in this multi-channel world, isolated, one-off marketing strategies and closed technologies no longer make sense. Advertisers don’t need a social plan or a mobile plan; they need to take full advantage of all available data to develop a people-centric plan that zeroes in on customers wherever they are and when they are most likely to engage. This requires platforms that can integrate data across channels and provide transparent, objective analysis of performance across every marketing touch point.

In the following schematic, Mark Jeffrey illustrates the process of gathering customer data from multiple sources, the flow of that data through the processes of validation, cleaning, transformation, aggregation and loading that data into a centralized data warehouse for business intelligence purposes.   

Click Image To Enlarge

Why it’s important: People-centric marketing is even reaching the world of linear TV, where new streams of data from set-top boxes and connected televisions are making it possible for advertisers to programmatically buy specific audience segments rather than focusing only on shows and time slots, which is how TV has traditionally been bought and sold.

Data will continue to blur the lines between TV and digital, between mobile and display, and even between direct response and branding. It all comes down to the person — whether the screen they are looking at is on their wall, at their desk or in the palm of their hand.

Why it’s hard: Cross-channel engagement is the cornerstone of people-centric marketing. The challenge, though, lies in evaluating attribution — an understanding of how well channels performed in engaging targets. Several years ago, this degree of accountability was impossible to achieve because data analysis tools hadn’t yet innovated to the point of granular attribution. Today, however, multi-touch attribution platforms, driven by more recent advances in machine learning and predictive analytics, have made this a reality.

Courtesy of an article appearing in i-scoop, an exerpt from the Teradata's 2015 Data-Driven Marketing Survey and an article dated August 28, 2015 appearing in VentureBeat

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THE ULTIMATE HOW-TO GUIDE FOR PLANNING A PREDICTIVE ANALYTICS PROJECT AND RESULTS FROM AN ACTUAL CASE STUDY

The explosion of interest in predictive analytics and in particular sophisticated  predictive analytics has led to renewed interest in the topic of how to plan for an predictive analytics project.

The topic is hardly new but anyone involved in planning for, or conducting a serious analysis project needs to cognizant of the fundamentals. Here we abstract from insights and ideas from two classic discussions, the 1996 paper by Fayyad, Piatetsky-Shapiro, and Smyth "From Data Mining to Knowledge Discovery in Databases,” and the 1999 “Cross-Industry Standard Process (CRISP) for Data Mining” developed by analytical practitioners at Daimler-Chrysler, ISL (later acquired by SPSS and now part of IBM) and NCR (Teradata). While these first-generation discussions clearly date from an earlier technological era the principle ideas are still relevant and are being rediscovered and translated into modern terminology daily. Here we offer a summary of the essentials viewed from our perspective of 20 years of experience in the world of data mining and predictive analytics.

Stage #1: Business Understanding (Specification of an Objective)

To successfully conduct an analytics project it is first necessary to have a notion of what is hoped to be accomplished, described in the practical language of business or decision-making. Often the objective can be clearly stated. Examples include:

• In an election campaign, identify the swing voters most likely to ultimately vote for a specific candidate (precisely what the Obama campaign undertook in 2012).
• Rank the offers you might provide to a website visitor by the probability of acceptance or expected value of the response (something Salford Systems undertook on behalf of a client).

Not all projects have such a clear and focused objective and may be more generic:

How can I make my overall grocery store operation more efficient logistically? 

In this discussion we have in mind the more clearly defined, and thus more narrowly scoped, projects. The CRISP approach calls this first prerequisite “Business Understanding” which as Fayyad et. al. point out, includes the identification of the objective from the point of view of the client.

Stage #2: Data Inventory and Understanding

Getting a good grasp of exactly what data is available for analysis is critical and involves canvassing both internal (proprietary) and external (publicly available or purchasable) data sources. Identifying, locating, and gaining access to potentially useful data within an organization can be challenging, and a project may be forced to proceed using less data than actually exists due to institutional constraints isolating data silos. An effort to identify all sources of potentially useful data is generally worthwhile as many large organizations have a myriad of data stores, some of which are generally unknown outside the department that collected it.  External data is increasingly available via the Internet and can come from government sources and/or private organizations.  Such external data can be extremely helpful in improving the predictive performance of a model and refining of the analytical insights it can yield. Examples of such data include:

• Popularity of certain Internet search terms
• Stock market index movements
• Snowfall in Fairbanks, Alaska

Supplemental data can be of enormous value in providing the relevant context for what is tracked from the perspective of a single enterprise. For example, supplemental data may include prices charged by competitors for the same or similar products offered by a retailer.

In our consulting experience for financial services enterprises, we have generally found that the data such enterprises amass on their own customers is far more valuable than anything that could be collected or purchased about those same customers externally, but the added value of such data could still justify the cost of the acquisition of this data.

 

Stage #3: Assessment of Data for Suitability

Having identified objectives and located potential data the next step involves an assessment of the data with the objective of determining whether the available data can in fact support the original objective or perhaps some modified and even scaled-back objectives. One may discover that the available data is missing essential details (descriptors for the ad actually shown to a web site visitor) or that data is available only for a subpopulation (e.g. only data for the clicks was retained and all non-click information was discarded). Such data omissions make the job of predictive analytics vastly more difficult if not impossible. The topic of mishandled or mismanaged data is a large one and all experienced analysts have their own catalog of horror stories. Our point here is simply that we have to allow for the possibility that data problems may complicate the project if not stop it in its tracks. 

The data may also offer too small a sample size for meaningful analysis. This may appear to be a thing of the past, something that will never occur again in the brave new world of big data.  But even with huge data stores the fraction that is relevant to a specific analysis may be small. 

If you need to build a click model for an ad that has only been clicked on a 15 times you are short of data even if you have one billion impressions for that ad.  In sum, we need both a sufficient quality and a sufficient quantity of data.

Stage #3: Assessment of Data for Suitability

Having identified objectives and located potential data the next step involves an assessment of the data with the objective of determining whether the available data can in fact support the original objective or perhaps some modified and even scaled-back objectives. One may discover that the available data is missing essential details (descriptors for the ad actually shown to a web site visitor) or that data is available only for a subpopulation (e.g. only data for the clicks was retained and all non-click information was discarded). Such data omissions make the job of predictive analytics vastly more difficult if not impossible. The topic of mishandled or mismanaged data is a large one and all experienced analysts have their own catalog of horror stories. Our point here is simply that we have to allow for the possibility that data problems may complicate the project if not stop it in its tracks. 

The data may also offer too small a sample size for meaningful analysis. This may appear to be a thing of the past, something that will never occur again in the brave new world of big data.  But even with huge data stores the fraction that is relevant to a specific analysis may be small. 

If you need to build a click model for an ad that has only been clicked on a 15 times you are short of data even if you have one billion impressions for that ad.  In sum, we need both a sufficient quality and a sufficient quantity of data.

Stage #4: Pilot Project 

During the last five years or so, all of our consulting projects have begun with a significantly scaled-down dry run or pilot project; one major year-long project we conducted in 1996 began with a 3-month scaled down trial. This is something of an innovation in the practice of advanced analytics even though the notions are anything but new. Engineers and architects build scale models, jet engines are tested in stationary mounts on the ground long before they are placed on an airplane, and new web pages and marketing campaigns are typically first exposed to just tiny fractions of a major web site’s traffic. Obvious or not, the notion that data analysts should consciously opt for a trial run may be new to some practitioners and is well worth considering.  At the very least, analytics could be conducted on a radically slimmed down version of the data set to accelerate modeling run times.

Real-World Example: In a project to predict the sales of individual promoted products in a chain of large grocery stores we started with data for a few dozen products for one department, and then expanded with further selected products from other departments. When done with the pilot project we moved on to making the system work for 122,000 products.

Stage #5: Prepare (and Explore) Data 

From a manager’s point of view the important point here is that the preparation of the data (“beating it into shape”) so that it can be successfully modeled may require considerable time and resources.  David Vogel et. al. were the winner’s of the first (and second) round progress prizes in the $3 million Heritage Provider Network predictive modeling competition. In their paper explaining their approach and methods, about one third of the paper is devoted to documenting their preparation of the data. While some data mining and predictive analytics tools can work remarkably effectively with unprepared data (TreeNet is clearly one such tool), careful data preparation invariably leads to better, and often substantially better, performance.

Much of the data preparation is guided by rather straightforward examination of the main components of the data expected to be relevant to modeling.

Routine first Steps:

1. Ensuring that the data display appropriate values
2. Scanning for extreme values and/or invalid values
3. Locating oddities in the data such as unexpected gaps

Often special care needs to be devoted to handling of missing information, which might be encoded in different ways in different parts of the data. Data that has been collected over a fairly lengthy period of time may also suffer from changes in data coding conventions over time rendering the overall inconsistent. A simple example would be post codes initially stored as 9 digit zip codes and then later stored always in the ZIP+4 format. Without paying attention to such details certain aspects of the analysis will suffer.

There is nothing especially glamorous about data preparation and perhaps for this reason there are very few books or courses devoted specifically to this topic. The data cleaning process is crucial for achieving the best possible results, however, and we expect to offer a series of blog entries on this topic in the future.

Stage #6: Modeling

For the professional data analyst, modeling is the most enjoyable and stimulating part of the project.  It s here that we get to show off our skills and the magic of modern technology, delivering results with modern analytical tools that were simply unthinkable using only standard statistical methods. In SPM 7.0 we offer CART, MARS, TreeNet, RandomForests, GPS Generalized Pathseeker (Lasso style regression, logistic regression, and the extended elastic net), ISLE model compression, and conventional regression which gives the modeler a fair of options to choose from.

Although modelers enjoy building and assessing models, the process of moving from first drafts to final versions can involve a fair bit of experimentation and trial and error. To accelerate this search process we recommend making use of Modeling Automation such as embedded in the SPM BATTERY feature.

By modeling automation we do not mean letting the machine do all the work, but in allowing the machine the assist the modeler by automatically running and summarizing a series of experiments in which variations on a theme, tailored by the modeler, are run. This allows the modeler to keep analytical servers running 24 hours a day, building model variations that can be quickly reviewed in graphical summaries.

Stage #7: Evaluation, Interpretation, Understanding

Although any model can be treated as a black box prediction machine there are usually valuable insights that can be extracted from the model and it is worthwhile spending the time to secure them. We might learn for example that certain subpopulations of customers are unusually favorable or unfavorable for our objectives or we might learn that we can shift selling effort from one segment to another. Evaluation, interpretation, and insight are by definition the domain of the thoughtful and informed reviewer and we do not expect this stage of the project to be automated any time soon (with or without Watson).

The traditional large consulting companies have always understood this very well and have typically devoted the lion’s share of their efforts to telling the story unveiled by the technical analysis in any project. When analytics are undertaken in house the responsible department should take pains to perfect this portion of the project as well.

Stage #8: Full Project

The pilot project may yield results good enough to justify moving forward with the resulting models and insights.  In this case, we would move immediately to “Deployment” below and then consider expansion of the pilot.

If the pilot is successful but the business requires the full project before deployment can be considered, then the steps above must be repeated but in the context of a much larger scope. While much should have been learned from the pilot the full project may reveal a host of new problems and challenges and several conclusions derived form the pilot may need to be revised.

Pilot projects often benefit from the luxury of allowing detailed study of a narrowly defined set of data, supporting very high quality results. A risk for the expansion of the project is that insufficient resources are allocated under the mistaken understanding that the simple scaling up of the project is routine. Salford Systems has been part of several projects in which we thought the pilot was of far higher quality than the full project in all aspects of its execution due to resource allocation.

Stage #9: Deployment

Unless a model is intended to guide only strategic thinking its real payoff comes only when it is deployed in some real-world process. Our retail sales prediction models are used to guide the logistics of a grocery store chain, dictating the number of cans of tuna fish to ship to each store in the network next week. Our ad optimization system recommends which of possibly many thousands should be displayed right now for a given visitor of a given web page. Our lifetime customer value model scores credit card applicants on the basis of their likelihood to have nonzero balances on their card 12 months from now. These models all needed to be embedded in business processes to speed and improve the quality of decisions. Without deployment, models become little more than academic exercises or illustrations of what might be accomplished.

Deployment is a complex topic deserving of an extended discussion all of its own. The only point here is that a modeling system should support easy deployment, by, for example, allowing export of models into reusable code such as Java, C, or PMML. (SPM offers all three and more).

Conclusion

Here are the stages we described once again:

1. Business Understanding  (Specification of an Objective)
2. Data Inventory and Understanding
3. Assessment of Data for Suitability
4. Pilot Project
5. Prepare (and Explore) Data
6. Modeling (Build Predictive Models)
7. Evaluation, Interpretation, Understanding
8. Full Project
9. Deployment

We might also add a final stage: Extract lessons learned and refine “best practices” documents to guide future projects. 

COMMENTARY:     As consultant I often discover that my business clients have only a very general and superficial understanding of who their customers are.  Many claimed they know their customers, but they are only seeing the first layer or veneer of their core customers.  They lack a deeper understanding of their customers.  They are surprised when they discover that their are many different layers of customers that makeup their core customers.

It is no longer sufficient to target customers based solely on demographics. Targeting has become a lot more complicated and difficult than in the past because customers are found both online and offline.   They read less newspapers for their news, listen to less radio broadcasts for their music, watch less television programs than in the past and don't go to movie theaters like your parents did. We are living in the "digital age" and the internet has changed everything.  Customers are spending more time online now, consuming more online content and spending more time sharing and engaging with friends and brands on social networks.   Customers are shifting from the desktop to mobile devices like smartphones, tablets and wearable devices adding an additional layer of complexity in targeting them.  Finally, your customers must not only be targeted demographically but by customer segments and lifestyles -- in short, how they spend their time and money.       

I cannot stress the importance of gaining a deeper understanding and knowledge of your customers in order to effectively target them.  Customer data is no longer homogenous, but originates from many different sources and comes in different formats, and this data must be captured, stored, validated, cleaned, transformed and aggregated, then it must be procesed through a business intelligence system for data mining purposes, online analytical processing (OLAP), data visualization, reports and management dashboards.

Data Sources and Data Flows Essential For an Efficient Business Intelligence System (Click Image To Enlarge)

As a former CFO for Solar Planet, a very successful indoor tanning salon franchise headquartered in San Francisco, I speared the effort to gain a deeper understanding and knowledge of the tanning customers of our tanning salon franchisees located across the U.S.  This required esablishing a data warehouse, then going through numerous steps (see above schematic) to get our customer data in a format that could be used for business intelligence analysis and reporting purposes.  

When we began this effort, we had a pre-conceived notion of what we thought our tanning salon customers were like, but when we analyzed the customer data through our business intelligence system, we discovered that our customers were not all the same.  True, they enjoyed tanning for different reasons, but they were different as night and day, and this deeper understanding, valuable insights and knowledge allowed us to more accurately target them while using the most efficient media channels to reach them.  Armed with this information we were able to provide our existing Solar Planet franchisees with better service and marketing advice and information, and identify the best locations for future Solar Plant franchise locations.  Solar Planet was able to customize the advertising and promotions campaigns for each franchisee based on the knowledge we gained about their customers.  Our franchisees were able to gain measureable improvements in new customers, increases in revenues and lower marketing expenses, and the results of each marketing campaign became much more predicable.  Below is a presentation that I prepared for our management team and potential investors.   I hope you find it valuable.

Solar Planet Company Owned Salon Presentation 3 from Tommy Toy

Courtesy of Salford Systems how-to guide titled, "The Ultimate How-To Guide For Planning A Predictive Analytics Project." 

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HOW, WHEN, AND HOW MUCH IT WILL COST TO OUTSOURCE YOUR SOCIAL MEDIA, PLUS A LIST OF THE TOP SOCIAL MEDIA MARKETING FIRMS

Many large companies worldwide are facing a conundrum when it comes to using social media to help generate new business.

On the one hand, firms of all stripes are pouring large sums of money into using paid media to do outreach to consumers on social networks such as Facebook and Twitter — and are allocating larger slices of their digital ad pies for that purpose.

In the United States alone, advertisers will spend $9.59 billion on social ads this year, according to the market research firm eMarketer. This represents a 31 percent surge from the 2014 outlays on social media, and more than double what was put into this segment in 2013.

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By 2017, U.S. advertisers will boost their social ad outlays to $14.4 billion, eMarketer projects. By that point, social ads are expected to consume nearly $1 out of every $5 in U.S. advertisers' budgets.

This year, social will account for 15.9 percent of North American ad budgets — up from 10.5 percent just two years ago. But while eMarketer has estimated that nearly 90 percent of companies with 100-plus employees did social marketing last year, many firms are also having trouble achieving their goals in that realm.

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A study that Ascend2 published in December 2014 discovered that 43 percent of respondents found a lack of in-house resources and skills as their most challenging obstacle to success from social media marketing. Another 42 percent complained about an inability to measure return on investment, and 39 percent cited a lack of effective strategy.

These results suggest that for many companies it may make sense to outsource the job of social outreach to prospective customers (something that the Ascend2 study found 53 percent of respondents were doing, in whole or in part).

However, as companies worldwide learned during the great outsourcing boom of the previous two decades, farming out important jobs carries risk.

As Hootsuite has pointed out, having someone else handle your social marketing can result in everything from an added layer of bureaucracy for customers to navigate to slower response times to consumer inquiries.

In social media outsourcing, as in every area of business, it's important to pick partners wisely — and to follow sensible practices in managing your relationships with those vendors.  

Five Keys to Social Media Outsourcing Success

Success in outsourcing social marketing boils down to five considerations:

1. Setting realistic goals: Before talking to prospective vendors, executives need to establish what they want to achieve from a social campaign and how outreach in this realm can help achieve them it. Up-front research is key to know what's doable.
2. Doing due diligence: A relationship with a key vendor can bear certain similarities to marriage. As any divorce lawyer can attest, bad marriages can wreak havoc on the lives of everyone involved. It's thus vital to do as much up-front checking on prospective vendors as possible.  
3. Being reasonable: As CIO.com has pointed out, hardball business tactics (e.g., beating vendors down on price or paying bills slowly) can lead to hard feelings — and problems down the road. Maintaining good relationships with vendors can lead to better results for both parties. 
4. Using a carrot and stick: In social media, as in any area of outsourcing, it makes sense to pay for performance. This means structuring the vendor's compensation so it has an incentive both to work only in the client's best interests and to help achieve business goals.  
5. Getting everything in writing — including an escape hatch: If client and vendors both leave extensive paper trails on their key interactions, it provides both parties with strong motivations to live up to their sides of the bargain, along with a quick and fair way to resolve disagreements. Every vendor agreement should provide the customer with some type of "kill switch" to get out of the deal if things aren't working out.

Ultimately, a relationship between a company and its social media vendor is just that — a relationship. And like any interaction between people, the client will only get out of the association what it puts in.

The more a vendor can learn about a client's business and its goals, the better the vendor can serve.

And isn't that what outsourcing is about in the first place?

COMMENTARY:    As has been pointed out in the above article, the biggest obstacle small business owners face in managing social media inhouse is a lack personnel with sufficient expertise in social media marketing and management systems.

Simply having a social media presence on social networks like Facebook, Twitter or Pinterest, does not provide sufficient experience for small business owners to manage their social media inhouse. Managing your social media means a lot more than just posting updates in your newsfeed, generating Likes and retweets for your content, or sending messages with your fans and followers.  

At some point, small business owners are faced with the decision to do their social media inhouse or outsource their social media.  So, let's look at a few cost estimates.

Social Media Management Costs

According to Mack Collier, a social media marketing thought leader and blogger, here's what outsourcing social media PR costs for Twitter and Facebook platforms:

• Twitter - Launching a new Twitter account, complete with setup and outsourcing content creation/consumer interaction (all 140 characters at a time), costs an average of $2,000-$4,000 per month – and that's just for Twitter. With that said, the total price range was $1,000-$7,500 per month.   If you already have an existing Twitter account, but need some help to take it to the next level, restructuring an existing Twitter account with "limited coaching" to achieve client goals is still going to cost you between $1,000-$2,500 per month, with some charging as much as $4,000 per month…just for Twitter! That's right, if you want Facebook, you're paying extra.
• Facebook - If you thought the costs of marketing with Twitter were high, you might want to grab your fainting chair — Facebook marketing rates will give you a case of the vapors. To set up a new Facebook account and provide limited ongoing training to business partners, online PR agencies charge an average of $2,500-$5,000 per month, with some going as high as $9,000. Of course, this also includes status updates and interacting with customers. 

If you're shelling out a minimum of $4,500 per month for Facebook and Twitter management, you're not getting any strategy behind the efforts.  Social media strategy is an additional cost. 

Social Media Strategy Costs

According to Mack Collier, creating a comprehensive strategy for social media marketing and outsourcing all work for all channels (with a minimum of two social networks) costs anywhere from $3,000-$20,000 per month, with the industry average settling between $4,000-$7,000 per month. If you want the social media agency to start the accounts from scratch and consult on a 4- to 12-month contract, you'll pay between $3,000-$15,000 per month. What do they mean by channels, minimum of two? That's just a fancy way of saying that the cost includes both Facebook and Twitter…I'm betting there's probably an upcharge if your company wants Google+, too.

Other Costs To Consider

• Customer Personas - If you're doing market research to figure out your customer/client's persona(s) and preferences, it's possible to legitimatize a $20,000 spend to gather the background info necessary to excecute a killer social media campaign. But otherwise, you can probably get much of the same for less money.  I also recommend that you read my previous blog posts on developing customer personas dated June 20, 2015, July 20, 2015, September 2, 2014, and March 6, 2014.  I have actually helped clients develop customer personas, and it is a grueling process, which is why it costs so much to do. Truth be told, many clients think they know their customers, but they really don't.  Knowing customer basics such as gender, age, what they buy, and how often they buy, barely scratches the surface.  To do it right, you need more indepth ways of describing and segmenting your customers, hence customer personas.
• Social Media Audit -  What if you just want an audit of your current social media strategy, with a few pointers on how to improve?  This is going to cost you anywhere from $2,000-$10,000. The lower end of this spectrum seems reasonable, especially if you add in the cost of social media training and competitive analysis. The only way to justify a $10k spend is if the social media consultant actually flies out to your office and does an in-depth, in-person training over the course of several days. 

Before jumping into the fray and making the decision to go inhouse or outsource or your social media, it might be very helpful to know what small business owners are spending on social media. VerticalResponse conducted a survey of small businesses, and the created this wonderful infographic that shows what SMB's are actually spending.

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If you are a small business owner, you are probably experiencing advanced stages of sticker shock, and probably ready to drop the whole idea of outsourcing social media.  However, keep in mind that 62% of small business owners outsource some or all of their social media functions.  Having said this, if you are serious about outsourcing, and willing to make the necessary investment, the next question is:  Who do I contact to outsource my social media management and strategy.  Well, I just happen to have that information for you, thanks to the good folks at Clutch Firms That Deliver. Clutch has done all the work of reviewing and rating the top social media marketing firms.  I am not recommending any particular firm.  They are all good.  The best of the best.

Make sure that you do your homework when evaluating firms to outsource your social media.  You should be asking the following questions:

1. How long have you been in business?
2. Who are your clients and can you provide references?
3. What sets you apart from your competition?
4. What social media services to you provide?
5. Do you offer month-to-month or fixed term contracts and what is your minimum term?
6. Are there any penalties for early termination of contracts?
7. Do you offer pricing plans for each social media service and how much does each cost per month?
8. Do you offer package pricing plans, what is included and how much does each cost per month?
9. How long will it take to accomplish our goals and objectives?
10. What exactly will I get for my money?  
11. How many people will be assigned to my account and what services do they provide?
12. How often can we contact you about problems and issues, needs and requests?
13. How much interaction do you require from the client?
14. Do you offer clients a dashboard where we can view our social media efforts in real-time?
15. Can the client prepare their own newsfeed content and tweets?   

Courtesy of an article dated September 16, 2015 appearing in eMarketing and Commerce , the Content Factory's article titled, "How Much Does Social Media Marketing Cost," and Clutch's "Top Social Media Marketing Companies - 2015 Reviews"

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UBER'S BUSINESS MODEL IN PERIL AFTER CALIF JUDGE RULES DRIVERS ARE EMPLOYEES, AND THE POTENTIAL COSTS OF THAT DECISION

When a California judge ruled earlier this month that a lawsuit brought by Uber drivers could go forward as class-action, it re-opened the biggest question about the hottest company in Unicornland: Is its business model legal? And if not, can Uber survive?

The ruling has also reignited questions about whether the sharing economy, of which Uber is by far the largest participant and its poster boy, is fair to workers. Last week, workers’ advocacy group the National Employment Law Project released a report that equated Uber and other sharing economy companies to “turn of the century sweatshops” because of a lack of employee benefits and protections.

Investors think that Uber will not just survive, but thrive. They have driven Uber’s valuation up to $51 billion, much higher than what Facebook was worth when it was a six-year-old private company. But the recent lawsuits against Uber raise questions about what seemed like its inevitable success.

By now, many people know the debate. Uber currently classifies its drivers as independent contractors, or “1099” workers, rather than (W-2) employees. Uber says its drivers prefer the flexibility of being independent contractors. But that classification also allows Uber to avoid the taxes and insurance and other administrative expenses that companies usually have to pay to have a workforce.

How much would it cost Uber if it was forced to reclassify its drivers as employees, rather than independent contractors? By my calculations, with help from some experts, I crunched the numbers and got an answer, and it’s a huge amount: $4.1 billion.

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Uber declined to comment on Fortune's calculations. A spokesperson for the company said it’s difficult to know what additional costs Uber would have to pay if it were required to turn all of its drivers into employees because doing so would force the company to completely change its business model, which is exactly the point.

The California lawsuit was brought by four former drivers who think they deserve the status of employees and reimbursement of their driving expenses. But by certifying it as a class action, the judge said the eventual ruling in the case will apply to all of Uber’s California drivers, excluding only those who had specifically waved their right to participate in the current suit. Uber is fighting the lawsuit, as it has other efforts to force the company to put its drivers on its books. On Tuesday, Uber filed an appeal in the California case, asking the court to reverse the class action status.

Uber critics say the real reason Uber has opposed the shift is that it can’t afford it. Miriam Cherry, a St. Louis University law professor who has long followed the sharing economy, says Uber’s current business model is one part technology company and one part labor law workaround. The question is which part is bigger.

To figure that out, Fortune spent the past two weeks digging into Uber’s finances, employment law, and payroll realities. It enlisted the help of experts from two companies that specialize in payroll costs, Paychex and StaffOne. It also talked to SherpaShare, a third party application that helps ride-share drivers track their work, for data on the size and composition of Uber’s workforce, which SherpaShare estimates at around 200,000 drivers. (Uber says it has 160,000 drivers, but those numbers were from the beginning of this year.)

To get the $4.1 billion figure, Fortune started at the top. Uber’s recently leaked numbers—the company is private, so it doesn’t disclose its financials, but some have emerged—put the company’s expected sales this year at about $10 billion. Uber splits what passengers pay 80-20. Uber takes a 20% cut for allowing drivers to use its platform. The rest, $8 billion, is what the drivers get, which would be their salaries if they were employees, though Uber currently does not include that figure in its finances.

Two caveats: Uber has licensing deals for its technology with other companies, and those deals may be contributing to its $2 billion in revenue. So Fortune may be overstating driver salaries by a bit—but not by much. And, just to clarify, Fortune's calculations are based on what it would hypothetically cost Uber if it had to hire all of its drivers as employees. But even a legal loss in California won’t automatically mean it will have to change the status of all of its drivers, though it will add to the pressure on the company to do so.

Let’s move on. As 1099 workers, Uber drivers are responsible for paying all of their payroll taxes. If they were employees, Uber would have to split those taxes with its employees. Uber’s share: 6.2% of what it pays its drivers for Social Security and another 1.45% for Medicare taxes. And it has to pay those taxes for both its part-time and full-time workers.   Uber's share of Social Security and Medicare taxes, one of its largest new costs, would total $612 million a year. 

The next biggest cost: workers’ compensation insurance. Workers’ comp is regulated by each state, and not all states require it, though most states where Uber is active do. The one exception is Texas—but even there, a worker can demand that his or her employer pay for any work-related injuries, so Uber would be smart to have insurance. Rates vary by state and occupation. Those tend to be relatively high for taxi drivers, and particularly high in California, where about 20% of Uber’s drivers are based, according to estimates by SherpaShare. Total workers' compensation insurance bill: $512 million a year.

Next up, medical insurance. Under Obamacare, employers don’t have to provide medical insurance, but starting next year that decision comes with a $2,000 penalty per employee per year. Paychex says that on average employers spend $5,000 for single employees and $12,000 for family coverage medical insurance. But employers can provide basic medical insurance (and not pay a penalty) for about $3,000 per employee. According to a filing with the federal government, Uber spent $1,275,066 on its healthcare plan, which included prescription drug benefits, for the workers (non-drivers) that it does classify as employees from July 2013 through June 2014. The filing said the plan covered 442 employees, which means it cost $2,885 per employee.

The good news for Uber is that companies only have to provide healthcare to full-time employees. Uber says that many of its drivers log less than 10 hours a week, though it’s not clear if Uber is counting just the hours when the drivers have passengers, or total drive time. SherpaShare estimates only about 20% of Uber’s drivers work more than 40 hours a week. (Some might even qualify for overtime, but Fortune hasn't factored that cost in.) Nonetheless, providing medical insurance for its full-time drivers at the same rate that it paid in 2014 for other employees would cost Uber $115 million a year.

NerdWallet, which published an earlier story looking at what Uber drivers stand to gain from becoming employees, said that the company would likely have to provide drivers with nine vacation days, based on what it currently offers its employees. Paychex says providing vacation days and sick leave typically costs employers 6.9% of a workers salary. For Uber, again only for its full-time drivers, providing vacation days and sick leave would be another $110 million a year.

Unemployment insurance varies by state. The average of what employers pay in the four states with the most Uber drivers, according to SherpaShare, was $363 per employee.  Unemployment insurance would cost Uber an additional $72 million for Uber a year, plus just over $8 million a year for federal unemployment.

Let's recap Uber's exposure to additional costs as a result of the California ruling classifying drivers as employees:

• Additional Social Security and Medicare payroll taxes per year:  $612 million 
• Additional workers' compensation insurance per year: $512 million
• Additional medical insurance for full-time drivers per year:  $115 million
• Additional vacation days and paid sick leave for full-time drivers per year: $110 million
• Additional unemployment insurance (California) per year:  $72 million
• Additional federal unemployment insurance (Federal) per year: $8 million

If you tally all of the above, the total so far comes to:  $1.429 billion.   

OK, we’re nearly there.

Companies don’t have to provide a retirement plan. But if they do, they have to provide it for all of their full-time employees. According to Glassdoor, Uber has a 401(k) plan for its current employees, and offers a 3% match, meaning it will match its employees’ 401(k) contributions up to 3% of their salary. If all of Uber’s full-time drivers-turned-employees contributed at least that much, that would be another $72 million in 401(k) plan costs for Uber.

The last one is a big one: millage reimbursement. Not all states require companies to reimburse their employees for using their personal car for business. But California does. And since that is where Uber is based, there is a good chance that it will have to reimburse all of its employees, not just the ones based in California. Calculating this figure is tricky. Based on survey data from SherpaShare, the average Uber driver, full-time and part-time, puts in nearly 24 hours a week for the ride hailing company. The average speed in cities around the country where Uber is most used appears to be around 20 miles per hour. That means the average Uber driver covers about 475 miles a week. The IRS’s mileage reimbursement rate is 57.5 cents per mile. If Uber were to reimburse its workers at that rate, which it has been ordered to do in some cases, that would be an average of $273.13 a week, or just over $13,000 a year per driver. The total mileage reimbursement bill for Uber: $2.6 billion a year.

Add all of those costs up and you get to just over $4.1 billion. Sound too high?  Fortune thought so, too. So it had StaffOne’s Donna Meek, a payroll and HR expert, run her own independent calculation. Meek estimated the costs from the bottom up, looking at what it would cost Uber per worker, rather getting the total figures for each cost as Fortune did. Meek’s conclusion: nearly $1.5 billion before the cost of reimbursing drivers for their miles. So pretty close to what Fortune estimated.   Fortune also calculated how much more Uber's operating costs would increase if all its drivers were classified as employees.

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Could Uber afford an additional $4.1 billion in costs a year? Depends on how you measure that. Based on profits, no. Uber, by its own admission, is not profitable. So it really can’t afford its current expenses, let alone another $4 billion. But as Uber has told others, startups aren’t run on profits—they’re run on investors’ money. And they can afford their losses as along as those people are willing to keep funding them.

How long will investors be willing to fund Uber? According to leaked financial statements obtained by Gawker, Uber was on track to lose about $300 million a year, though those losses appeared to be growing. It had about $1.2 billion of cash at the end of June 2014. And it just raised an additional $1 billion this summer. That means Uber will likely have about $1.75 billion in cash in the bank at the end of this year.

Sounds like a lot. But if Uber had to swallow an additional $4 billion in costs, they would need to raise more money by the middle of next year, possibly sooner, or face extinction. And that doesn’t include any money it might have to pay out in damages, or past compensation. Uber could force its workers to take a pay cut. Many of them will be getting new benefits. And Uber would be covering some expenses, like gas and payroll taxes, that its workers are covering now.

But James Kimbrough, who drives about 20 hours a week in Austin as a second job, says that Uber has already cut what its drivers make by nearly a half in the past year in order to attract more customers. Any further reduction in rates—”even just a few more cents,” says Kimbrough—and driving for Uber is probably no longer worth it for him. He believes other drivers would feel the same way. So Uber doesn’t have a lot of wiggle room with the drivers.

At a $51 billion valuation, investors obviously believe that, given enough time, Uber is going to make money, and likely a lot of it. The question is how much longer will that take if the company faces an additional $4 billion in expenses—and are investors prepared to stick around for that long. Uber’s cash meter is running, and it’s running fast.

COMMENTARY:  If you missed it, the California Labor Commission ruled on Wednesday, June 17, 2015 that an Uber driver who brought suit was an employee, not an independent contractor. The decision was made after a San Francisco driver, Barbara Ann Berwick, filed a claim against Uber. The commission sided with her largely because it deemed Uber was "involved in every aspect of the operation."

Four more drivers subsequently sued Uber citing the case of Uber driver Barbara Ann Berwick, that won her case with the CLC.  A judge finally ruled that Uber drivers could file a class action suit, paving the way for all Uber drivers to file suit as a group.  Uber is appealing the ruling.  This case is so important to Uber, that losing at the appellate courts, and if necessary, in the US Supreme Court (if necessary), could become very costly for Uber.    

I have actually been involved in a case similar to Uber where the State of California ruled against a former employer mine.  California applies the economic realities test or amount of control the employer exerts over employees in the performance of their work.  In applying the economic realities test, the most significant factor to be considered is whether the person to whom service is rendered (the employer or principal) has control or the right to control the worker both as to the work done and the manner and means in which it is performed. Additional factors that may be considered depending on the issue involved are:

1. Whether the person performing services is engaged in an occupation or business distinct from that of the principal.
2. Whether or not the work is a part of the regular business of the principal or alleged employer;
3. Whether the principal or the worker supplies the instrumentalities, tools, and the place for the person doing the work;
4. The alleged employee’s investment in the equipment or materials required by his or her task or his or her employment of helpers;
5. Whether the service rendered requires a special skill;
6. The kind of occupation, with reference to whether, in the locality, the work is usually done under the direction of the principal or by a specialist without supervision;
7. The alleged employee’s opportunity for profit or loss depending on his or her managerial skill;
8. The length of time for which the services are to be performed;
9. The degree of permanence of the working relationship;
10. The method of payment, whether by time or by the job; and
11. Whether or not the parties believe they are creating an employer-employee relationship may have some bearing on the question, but is not determinative since this is a question of law based on objective tests.

Even where there is an absence of control over work details, an employer-employee relationship will be found if (1) the principal retains pervasive control over the operation as a whole, (2) the worker’s duties are an integral part of the operation, and (3) the nature of the work makes detailed control unnecessary.(Yellow Cab Cooperative v. Workers Compensation Appeals Board (1991) 226 Cal.App.3d 1288).

In conclusion, if you utilize the services of independent contractors in your workplace, are providing workspace facilities (offices, desks, equipment, supplies, etc.), set specific work hours and work schedules, set specific guidelines and instructions for the performance of the work to be completed, provide regular supervision and guidance, and control as to where, when and how the work is to be performed, your indepndent contractors are probably employees.  Wow, this describes most of Silicon Valley employers, particularly startups, who as a general rule, employ many independent contractors, in order to avoid paying payroll taxes and providing employee benefits.

The panic at Uber HQ after the latest California decision ruling their drivers as employees is probably something like this:

 

Courtesy of an article dated September 17, 2015 appearing in Fortune and an article dated June 17, 2015 appearing in Business Insider and the California Department of Industrial Relations article titled, "Independent Contractor versus Employee" 

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THE RISE AND FALL OF FUBU: A LESSON IN BRANDING AND IGNORING MARKET SIGNALS AND CHANGING FASHION TRENDS

FUBU co-founders J. Alexander Martin, Daymond John, Carl Brown anKeith Perrind in 1998 (Click Image To Enlarge)

I’m a sucker for Shark Tank – in which wide-eyed entrepreneurs pitch their “once-in-a-lifetime” ideas to a group of rich men and women running the gamut from owners of an NBA franchise to some schmuck they call “Mr. Wonderful,” who I like to imagine made his money selling doll furniture. One guy in particular, Daymond John, always appealed to me. While most of his fellow gurus earned their massive fortunes in the tech space, John had built the fashion empire known as FUBU. I thought. 

”Holy shit.  If he’s sitting next to Mark Cuban who made enough money to own the Dallas Mavericks, Daymond must be swimming in it!”

The "Shark Tank" TV series cast for 2015 from L-to-R: Mark Cuban, Barbara Corcoran, Daymond John (FUBU founder), Kevin O'Leary (Mr. Wonderful), Lori Greiner and Robert Herjavec (Click Image To Enlarge)

But as we all know, FUBU isn’t exactly flying off the shelves these days. What exactly happened to the company before, during, and after its massive success?

Daymond John was raised in the Hollis neighborhood of Queens. He didn’t come from a family with money. His parents divorced when he was 10 years old, and he would never see his father again after the separation.  He says.

“We went from middle class to poor.  I became the man of the house and started working at that age.”

 

Prior to launching his own line, John still looked to fashion as a means to make money. According to the Washington Post,

“Initially, it was through printed T-shirts, but not my own brand. I went and printed up some shirts when the Rodney King riots were happening in Los Angeles, with lines like ‘What happened to poor Rodney King?’ Then Mike Tyson got incarcerated, and we did the whole ‘Free Mike Tyson’ shirts. We would sell them at events and on street corners. It showed me something about the reason people buy clothes – that when there’s an emotional slogan or an emotional connection, products sell quicker. That’s when I started thinking about this concept of ‘For Us, By Us.’”

The original founders of FUBU in 1998 from left to right -- Keith Perrin, Carl Brown, Daymond John, J. Alexander Martin (Click Image To Enlarge)

By the end of the day, he had sold out of the hats and turned a $800 USD profit. He knew he was on to something – although he didn’t exactly have the capital to immediately capitalize on the momentum he had seemingly conjured out of thin air.  He told CNN.

“The next step could be how do I finance all of this?  Do I sell one or two hats and then re-up? Do I take out a loan from a bank or do I get an angel investor? Do I just chug along at $50,000 a year, $100,000 a year? When do I go balls out? Quit the job. Mortgage the house. Sell the car. Do I do this at all?”

While those were all smart questions to ask, $800 USD dollars worth of sales didn’t necessarily mean that he should quit everything. But that would soon change. According to CNBC,

“During his off hours, however, John would hit music video sets and try to coax rappers to wear FUBU apparel in the shoot, a move that would ultimately pay off as millions of fans saw their favorite stars wearing FUBU clothing.” 

John explains, 

“First it started with Brand Nubian in one of their videos, then Ol’ Dirty Bastard wore it in a Mariah Carey video, then Busta Rhymes wore it on one of his videos, and LL Cool J decided to wear FUBU on the “Hey Lover” video with Boyz II Men. Our product was front and center on the biggest and most influential personalities for our core consumers.”

Daymond John (center) with model Tyson Beckford (left) and rapper LL Cool J at a FUBU event in 2000 (Click Image To Enlarge)

As legend has it, John also used a bit of guerilla marketing in his attempt at reaching the top. John remembers.

“One of the most well-known hits we had with LL was during a Gap commercial.  He was wearing a pair of Gap jeans and a Gap shirt, but he was somehow able to sport one of our hats during the commercial. Then during his thirty-second freestyle rap, he looks directly into the camera and says, ‘For Us, By Us, on the low.’ No one at Gap nor any of their ad execs thought anything of it. It wasn’t until a month later that someone at the Gap found out, pulled the commercial, and fired a whole bunch of people after they had spent about $30 million running this campaign.”

 

After an appearance at a trade show in Las Vegas, he and his partners – J. Alexander Martin, Carl Brown and Keith Perrin – had managed to sell $400,000 USD worth of clothes that didn’t exist yet. Ultimately, his mother took out a second mortgage on her home after John was turned down by 27 banks for a business loan.

Soon after, his partners moved in, and they turned the home into a makeshift factory. John remembers.

“It was a typical Queens house, the kind you see on All in the Family.  It had three levels: A basement, the first level with a dining room, living room and the kitchen, and then three bedrooms upstairs. After we took out the mortgage, we took all of the furniture out of the house, sold what we could, and the rest we burned in the backyard. We put all the raw materials down in the basement, and on the first floor, we converted the living room and put eight sewing machines there and we hired some seamstresses. In the dining room, we put a cutting table where we cut all the fabrics. The kitchen, well, the kitchen was still the kitchen.”

 

By 1998, FUBU’s reached its peak with sales over $350 million USD.  John and his partners had used hip-hop culture and stars like LL Cool J – a fellow Queens native – to put FUBU’s clothing in seemingly ever rap video at the time. John and crew thought to themselves, “we should put out a record too…”

FUBU released the compilation album The Good Life on September 25, 2001 which featured the likes of LL Cool J, Nate Dogg, and Keith Murray. It lost the company an estimated $5 million USD. John told Fast Company.

“We didn’t know our numbers, we didn’t look at our numbers, we were spending money like drunken sailors, we were getting caught up.  What was the reason we were doing it? Did we get exposure? Yes, but from the business model, we died.”

 

“Fatty Girl” was the lead single from FUBU’s The Good Life album, prominently featuring the apparel in the music video directed by Hype Williams 

In a book he co-authored, The Brand Within, John explained that one of the major factors that led to the company’s demise is that they had too much product. He writes.

“Once you hit mark-down bins, it’s tough to climb out, because you’ve lost the sense that your clothes are fresh and vibrant.”

By 2003, FUBU left the U.S. market completely – except for its footwear division – and built business in Europe and Asia. Additionally, they acquired up and coming brands Heatherette, Drunkn Munky, Kappa USA, Coogi, and Crown Holder. Worldwide sales for those brands reached $200 million USD in 2009. Despite being absent from the marketplace, there was no catastrophic collapse or new people coming on board.

NSYNC wearing FUBU when the brand went mainstream (Click Image To Enlarge)

In 2009, John announced that FUBU would be making a comeback after a six-year hiatus with an aesthetic similar to “Carhartt meets Abercrombie.”  In speaking with WWD John said.  

“I wasn't too worried about losing FUBU’s brand identity since the kids nowadays have a three-year memory span, so most don’t have a sense of the brand’s roots.”

COMMENTARY:   Daymond John blamed excessive inventories as the chief cause for FUBU's failure.  Excessive inventories is not a cause, but a symptom that something bigger is going on. It is my belief that John did not have a good understanding of fashion retail marketing and the need to identify changing trends in fashion.  In short, John's ownership of FUBU's marketing strategy and failure to listen to marketplace signals is really what did in FUBU.    

John started FUBU as a fashion brand that appealed to and targeted young urban black males. Young urban black men were the biggest fans of rap and hip-hop music when it began to take off in the late 1990's.  John's love of rap and hip-hop music and associaiton with several well known rap and hip-hop music artists like LL Cool J, Salt-N-Pepper, Run-DMC, and others, provided the perfect vehicle to reach his target market.

John did not have a marketing budget and relied mostly on guerilla marketing to get the FUBU brand recognized in the marketplace.   Providing many of these black rap and hip-hop music artists with FUBU apparel that they wore on stage and on music videos was a great strategy to gain brand recognition.  The FUBU brand quickly become part of the rap music culture.  Young urban black males wanted to become part of this music culture and the FUBU brand was the perfect way for them to express their association with that culture.  As the FUBU brand grew in popularity among rap and hip-hop music fans, the brand took off, eventually peaking with revenues of $350 million in 1998.  

Many nascent rap groups undoubtedly embraced FUBU and Cross Colours due to accessibility and their finger-on-the-pulse relevance.  Later on many hip-hop insiders launched their own lines, with Wu Tang and Wu Wear, Russell and Kimora Lee Simmons with Phat Farm and Baby Phat, Nelly with Apple Bottoms, and more.  This crop of urban wear newcomers quickly became the "next big thing" in urban and hip-hop fashion, and the results were obvious:  FUBU, Tommy Hilfiger and Abercrombie & Fitch, who had grown and prospered due to the early popularity of rap and hip-hop music, began to lose favor with young urban black males.  The handwriting was on the wall, and John either missed these market signals or completely ignored them.

Clearly, J0hn failed to realize that fashion trends can change quickly and dramatically.  Like many startups that experience dramatic and early success, John learned too late, that success is a lousy teacher.  Those that assume that success will continue by doing the same things they have been doing often have a rude awakening.   

John's announcement in 2006 that FUBU would make a comeback, obviously has not been realized. Market forces in the fashion industry quickly change, and young urban blacks no longer emulate their counterparts of the late 1990's.  Though urban brands certainly enjoyed their moment, their popularity seems to be waning. Instead of Jay-Z wearing Rocawear, you’re most likely to see him in Givenchy. Though Beyonce is the face of House of Deréon (and is wearing it in their new ad!), we know the R&B and pop songstress truly has a soft spot for Balmain,  Lanvin  and Alexander McQueen.   Nicki Minaj loves her Versace as much as Kim loves her Kanye West.     

Courtesy of an article dated June 30, 2015 appearing in TheHundreds.com, an article dated November 17, 2014 appearing in Business Insider, an article dated October 9, 2012 appearing in Fashion Bomb Daily, and an article dated March 27, 2009 appearing in Marketings411

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A PARADIGM SHIFT FROM BRANDS TO CUSTOMERS MEANS BRANDS MUST PROVIDE PERSONALIZED EXPERIENCES OR RISK LOSING THEM

It’s true that you never get a second chance to make a first impression.

The proof? Customers are breaking up with retail brands that can’t deliver on their evolving expectations. The tech-savvy generation and their time-pressured parents are rapidly adapting (and perhaps outpacing) retail brands.  Customers are demanding a shopping experience that is intuitively seamless, fast, authentic and personalized.

So how do you answer the increasing demands of your customers?

The paradigm shift: from brand to customer

The customer experience has become the “final frontier” for retailers. It used to be all about the brand. But a lot has changed. Because of increasing customer expectations and falling store sales, retailers are learning that they have to make a big shift. It’s not about the retailer’s brand any longer.  It’s all about the customer—and how the brand can create a shopping experience built on this new-found focus.

Let’s face it. Most shoppers can find similar or better alternatives online. A large part of why they are coming to the store is for the experience itself.  

"Customers not only want, but expect a personalized experience. They expect retailers to know their preferences and interests. A recent Infosys study reported that 78 percent of consumers are more likely to be a repeat customer if a retailer provides them with targeted, personalized offers.”

But the pendulum swings both ways. The CMO Council reported that more than half of North American consumers would consider breaking ties with retailers that do not provide offers that are relevant and personalized.

So we’ve come to a place in time where retailers are forced to change their ways. The shift of focus must come away from the brand and, instead, highlight the customers’ expectations and needs at an individual level. Brands that succeed in this change will see increased loyalty and profit margins. But those that do not will be faced with a serious decline in sales and brand loyalty.

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Unless you’ve been hiding under a rock, you probably already know all of this. But the real question is:  What do you do about it? How do you create an experience for each and every one of your shoppers that will make them feel that when they enter your brand’s store, they are entering their store?

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Today most retailers realize the power of personalization and the in-store experience. The problem is that they don’t know how to craft an experience that is unique, and they don’t have the information they need to get started.

The materials for change: personas, shopper history and real-time decisioning

To get started along the journey of creating a shopping experience that is built around your
customer, you will find that personas, your compiled shopper history and an organized real-time message engine are your keys to success. However, before you can cross these items off your to-do list, you need one very important thing—data.

Data is the building block for creating shopper personas built in reality. Data that you store of your
shoppers’ past transactions with your brand will provide critical information that will deliver a truly
personal experience for them. And data will help you to make decisions about what message or offer your shopper should receive in any given moment. To be successful, you need to be sure that data is your foundation for all things moving forward and that your decisions are grounded in actionable insights.

Personas: Going beyond segmentation

Retailers have long used customer profiles as a representation of their marketing segments. personas take these profiles beyond the basic demographic makeup of the customers. A well-developed persona incorporates the basic demographics with financial information (e.g., ability to pay, credit worthiness, net worth), interests and—perhaps most importantly—transactional behavior and categorical spend.  True, it does take work (and often money) to create accurate shopper personas. But once this task is done, these can be powerful tools for getting marketers and store employees to understand and obsess about customer needs.

With personas, your brand has a holistic view of who is shopping at your stores. To be successful,
marketers have to use personas consistently.  They should serve as a reference at every step of the design process so that it’s clear who they are designing the experience for.

It’s also important to note, that building personas is not just a one-time job. Once you have created
your personas, they should serve as living, breathing documents that need to be evaluated on a regular basis. If your customer base changes, then so should your personas. Measurement is crucial to ensure that the personas remain an effective guide.

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Shopper history: the more you know

Personas are only one element of the personalized shopping experience.  Shopper history is another. Capturing a customer’s history with your brand empowers you with hugely valuable information. What have they purchased in the past? What are their preferences? What are they buying with your competitors?

Having the answer to these questions can make you a master of the retail universe.  But, knowing them is not enough. You have to ensure that they are easy to access.  How many databases are the answers to these questions stored in currently? Do your store associates have access to them? Are they fueling the messages that are being served on your ecommerce site? If your answer is no, then you have some work to do.

It is one thing to capture this information—but the most important thing you can do is to be sure that it is readily available so you can use it at every step of your customers’ shopping journey.  

Real time: Making it happen in the moment that matters 

Now that you have created your personas and you can access your shopper history at any stage of your customers’ journey, you have one more very important thing to do. You need a real-time decisioning engine.

Sound complicated? Well, in a way, it is. But you already have most of your legwork done with your personas and your shopper history database. Now it’s time to map all of the possible journeys your shoppers may take and think about their experiences.  At every step, what offer would be relevant to them in that moment?

This exploration takes time and a lot of thought. But in this mapping process, you’re actually creating a database of offers and messages for your customers that will make their shopping experience feel personal and relevant. After all, that’s the goal, right?

Bringing it together: Joining the elements of your new shopping experience

Forrester notes,

“Combining the user-archetype information that personas contain with an individual’s history, preferences and current environment can help companies engineer experiences that feel like they were designed for a specific user, even if they were not.”

In essence, this is the secret formula. It’s not possible to make every experience individually and uniquely personal to a single customer.  But if you can combine the persona + shopper history + real-time decisioning engine, you will be delivering on an experience that feels custom fit for your shoppers.

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Your customers don’t have patience for you to figure it out. They just want an intuitive experience that feels as if it was authentically created with them in mind. Shoppers’ expectations of instant gratification aren’t changing. If you can deliver on their expectations, you’ll reap the marketshare of the brands that are not.

It’s a lot to tackle if you haven’t already started. Remember, you don’t have to climb the mountain in one attempt. Focus on one aspect of your customer experience and use it as a beta project. Then build upon your customer experience from there.

According to Forrester,

“Organizations that have failed in personalization initiatives often tried to do too much at once. Instead, successful organizations we spoke to avoided these pitfalls in new contextualization initiatives by starting small and adding a personal touch for certain experiences before ramping up to large personalization initiatives.”

Avoiding the breakup: how to build trust with your customers

Just as in real-life relationships, your consumers are looking for authentic relationships with the brands and products that they buy. The evolving digital landscape has created competition and endless shopping opportunities. With all of these endless options, consumers want to be inspired.

However, through all this evolution, the premise of buying and selling goods hasn’t fundamentally changed. The old adage “Nothing happens until someone decides to purchase something” still applies. The difference is that now shoppers are more equipped to demand an intelligent, transparent, unique and meaningful experience with the brand of their choice. They have more choices than they ever did before when selecting a brand to have a relationship with.

Where the brand used to hold the power, now it is the consumer with all the power of choice. To be successful, you need to be strategically prepared to set your brand apart from the competition. To do this, you need to know your customer in a way that feels unconsciously unique and authentic. In the end, this is what will inspire trust for your shoppers. It’s that trust that will kindle a loyal relationship with your brand.

Conclusion

Today the customer is in control. They decide if, when and how to interact with their favorite retail brands. With all the channels of interaction available to the consumer, retailers need the ability to develop well-grounded engagement strategies to maximize the customer experience every minute of every day.

As a new breed of marketing partner for this customer-empowered world, our approach harnesses the power of rich data, world-leading technologies, engaging creativity and transformative ideas to connect customers to brands and deliver dramatic results.

Customers today have many options. We can help you create meaningful customer connections, resulting in increased revenue, more frequent engagement and more efficient marketing spend.

COMMENTARY:   Improving the customer experience is a strategic priority for 73% of businesses, yet only one percent of companies deliver an excellent customer experience, according to the 2015 Forrester Research Customer Experience Index™., 

Based on a survey of 46,000 consumers, the CX Index measured and ranked 299 large US brands, across 17 industries, on the quality of their customer experience. USAA is ranked number one. Other companies among the top 15 include (in alphabetical order): Ally Bank, Amazon.com, Charles Schwab, Discover, Edward Jones, Etsy, HSN, JetBlue Airways, Lexus, Navy Federal Credit Union, Newegg, PNC, QVC, and Zappos.com.

In the Forrester index for 2015, retailers remain the best at providing a great customer experience, but it is interesting to see that parcel shippers and hotels dropped way down the index – last year they were up there at the top with retailers. And there is an important distinction to make with retailers. Digital-only online retailers are much better at providing a great customer experience than those with a store network.

Megan Burns, Forrester Research principal analyst serving customer experience professionals said.

“The emotion that an experience elicits influenced loyalty in nearly every industry.  And making customers feel valued is the holy grail of CX emotions. Customer experience leaders made customers feel valued almost twice as often as CX laggards did.”

Megan explained that this year's CX rating used different methodology that now incorporates customer loyalty in determing the overall CX rating.    

"The biggest change in our new approach is the way we judge CX excellence. To hit a home run, the 299 brands we studied had to do more than make customers happy. They had to design and deliver a CX that actually helps the business by creating and sustaining customer loyalty.  Our new rules shook up the standings and pulled a bit of a Billy Beane, tipping conventional CX wisdom on its head.  For example:  Customer loyalty is undergoing a rapid change from being the way that you manage a points-based program with plastic cards to a strategy that requires engagement with customers and an ability to improve their experience with your brand."

Megan said that there is a shakeup on the CX leaderboards for 2015. 

"For the first seven years of the CX Index, retailers, hotels, and parcel shippers dominated — but not this year. As industries, hotels and shippers dropped to the middle of the pack. Retailers held on to first place, but there’s an asterisk on that particular stat. We broke the retail industry into two groups in 2015 — those with physical stores and those without. Retailers without stores (we call them digital-only retailers) kept the top spot. Those with stores fell multiple places in the industry standings."

Megan cautions that a high CX rating may not always be worth it and that there maybe a point of diminishing returns that brands may not want to cross.   She says.

"In most of the industries, we saw a strong positive link between CX quality, loyalty, and loyalty-driven revenue. But that wasn’t always the case. Our models for one industry showed a very clear and compelling point of diminishing return. Taking CX from poor to okay is more profitable for brands in this space than going from okay to excellent. That doesn’t mean that anyone can afford to ignore CX. Many aren’t even hitting the okay mark right now. But it gives executives something to think about as they refine their company’s overarching CX strategy."

Courtesy of the Epsilon's "It's Not You, It's Me" , an article dated April 20, 2015 appearing in MarketWatch, and an article dated April 20, 2015 appearing in Forrester Blog

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6 TIPS FOR COMMUNICATING CONTENT MARKETING ROI TO YOUR EXECUTIVE TEAM; PLUS THE FUNDAMENTAL PILLARS OF CONTENT MARKETING

Marketing teams persuade and captivate audiences every day. They convince people to buy everything from $160 pens to coffee beans that pass through elephants. But a CMO’s toughest sell is always his own boss — especially when it comes to content marketing.

Executives want to know a marketing strategy’s expected ROI before they commit money and resources to it. But content is a different animal than other marketing initiatives. It doesn’t produce impressive short-term results; it bolsters long-term profits. Once a content marketing strategy proves its worth, executives feel more confident investing in it. The challenge is convincing them to take the initial plunge.

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Here are six selling points you can use to persuade your company leaders that content marketing will go the distance for your brand:

1. Leads generated from content: Anything that requires visitors to submit their contact information — whether that’s subscribing to your newsletter, downloading a piece of content, or signing up for webinars and other offers — will ultimately capture leads. Use tools such as Google Analytics to track and document engagement with internal and external content. Then, take this data to your boss to demonstrate how different types of content generate new leads. Analytics also give you clear, actionable information on how to improve your ROI and pivot from poorly performing strategies.

2. Sales generated from content: Executives are especially interested in these numbers. Use your analytics systems to track how many people sign contracts or purchase your products after downloading content from your website.

Encourage your salespeople to ask prospects and clients how they found your company. They may have read an article you posted but didn’t click through to a download or sign-up page. It’s useful to know which articles compel prospects to get in touch.

Ask your sales staff how often content comes up organically in customer conversations or how frequently they use it to educate leads. All of these insights will demonstrate how content indirectly influences the sales process.

3. The performance of referral traffic: By tracking your content’s referral traffic over time, you can make fairly accurate assumptions about how it’s impacting your business. My company saw a151% increase in conversions in the first quarter of 2015. Thanks to our analytics system, we can attribute that jump to the guest contributions, blog posts, and gated content we developed and executed this year.

Lead generation and referral traffic numbers help you set benchmarks for your content. Build a strategy using Google Analytics and marketing automation software to tie a definitive ROI to your content. By showing a favorable comparison between content production costs and the value of sales generated, you can make a strong case for a content strategy to your CEO.

4. Non-quantitative metrics: Analytics don’t tell the full story. Great content creates ripples in your industry, which can’t always be measured by social media shares or lead generation numbers.

For example, I often reference American Express’ OPEN Forum content marketing strategy during keynote speeches. Amex doesn’t track those types of mentions, but small wins for brand visibility like these can add a powerful punch to its ROI. Hard data is important, but don’t overlook content’s role in brand storytelling and positioning.

5. Budget opportunities: Content marketing isn’t cheap. Getting it right requires a substantial financial investment that might not show a visible ROI for a while, which can make it a hard sell to budget-conscious executives. Anticipate your boss’s reservations, and suggest ways to make the numbers work.

Make the case for pulling funds from the sales, recruitment, and research and development budgets. Content serves as a great asset in social media campaigns, recruiting efforts, public relations strategies, and sales, so marketing shouldn’t be the only department shouldering the financial burden.

But that doesn’t mean marketing is off the hook. Revise your own budget to see where you can redirect funds to more productive channels, such as content marketing. Too many companies throw money at outdated or ineffective SEO strategies like link building and keyword stuffing rather than content creation.

But creating valuable, engaging articles and guest posts is the surest way to rise in the SEO rankings. Plus, good content yields a number of other benefits: lead generation, sales conversions, thought leadership, great customer relationships — the list goes on.

The money for a great content strategy is already there. You just need your boss to rethink how she’s using it.

6. Competitors’ content: With 78% of CMOs saying custom content is the future of marketing, you know your competitors are hard at work crafting industry-leading content. And the more powerful and compelling their content strategies become, the harder it will be for your company to catch up. Your boss doesn’t want competitors controlling the conversation, so she’ll come around quickly when she realizes what’s at stake.

Once you’ve sold your company leaders on the value of content marketing, you can get the ball rolling with implementation. Your leadership team is an invaluable asset to your marketing game plan, but time constraints often prevent top-level executives from contributing their insights. Create a custom knowledge management process that allows you to leverage their experience without eating up their time.

Content marketing is a no-brainer when you consider all the ways it supports your bottom line. Solid content drives traffic and positions your brand as a hub for information. As a marketing professional, you already know this. You just need to help your boss see the light, too.

COMMENTARY:  According to a marketing survey, 43 percent of respondents said “blatantly promotional and self-serving content” is the characteristic they dislike most in B2B content.  Another survey found that ending a blog post or newsletter with a product pitch reduced the content’s credibility by 29 percent.  Julie Fleischer from Kraft stated that 90 percent of data is “crap.” So keep in mind your own unique situation when considering how this information relates to your content efforts.)

To create effective content that builds trust, breaks down sales barriers, and keeps your company top of mind, you need to focus on providing value to your audience. The best way to do this is by educating them. The information you provide will hopefully lead customers to do business with your company, but if it doesn’t, the problem likely is your product or service — not your content.

Here are a few ways to sell without being promotional or compromising credibility:

1. Choose a medium that aligns with your goals. Every content medium serves a specific purpose, and choosing one that fits your goals is the first step in effective content creation. Third-party publications generate visibility and boost credibility, whereas blog posts, whitepapers, and case studies create hyper-targeted engagement.

If you want to take an in-depth look at a confusing aspect of your industry for people further along in the buying journey, you can create a whitepaper that’s available for download from your website. For example, we created “The Insider’s Guide to Tackling Content Marketing Like a Pro,” which offers a step-by-step guide to creating and implementing a successful content strategy. It serves as a great resource, shares our company’s tried-and-true methods, and even includes concrete examples of how our clients find success using our process. If you want people to know how great your product or service is, ask your customers to sing your praises in a case study or testimonial.

2. Eliminate company mentions that don’t enhance understanding.The company name drop, the “backdoor brag,” the shameless product plug — we’ve all seen content creators commit these promotional sins. Even “subtle” brand mentions can make a bad impression when they’re simply there for promotion and don’t serve to help educate the reader. In general, you should avoid all mentions of your company that don’t enhance the reader’s understanding of the message you’re trying to convey.

3. Don’t try to sneak in links. Nothing destroys credibility faster than populating an article with dozens of links to your company’s product or service. Even if you don’t mention your product or service outright, you’re violating the social contract with your readers by trying to push them to your offering when they’re seeking information.

It’s fine to link back to one of your blog posts or whitepapers that builds on the information you provided in the article, but it must be extremely relevant and further understanding of the topic. Otherwise, readers will feel betrayed and click away.

4. Share unique experiences, but avoid irrelevant rambling. Writing about your company’s challenges humanizes your brand and offers your readers valuable learning experiences. It can also be an excellent way to subtly draw attention to your company’s unique differentiator without creating an article that reads like website copy. Organize your expertise and key experiences, and house them in a company knowledge bank using this custom template to streamline the process. My team relies on this tool to help supplement my articles and keep the content flowing.

However, there’s a big difference between sharing a helpful experience and bragging. Your company might have the world’s largest in-office bar, or you might be a world Judo champion. But unless you’re using those experiences to teach readers something or to provide a little entertainment, you’re the digital equivalent of the guy who corners people at the company Christmas party to show them pictures of his cat.

5. Focus on information that’s most valuable to your audience. Don’t tackle content from the standpoint of “What can I write about to position my product or service best?” Instead, tailor your content to the topics your audience wants to read about.

What are your readers’ burning questions? What are their biggest pain points? What obstacles do you encounter most often on sales calls? All these questions should naturally lead you to an information vacuum that you can help fill with educational content. You can also write about important industry trends, timely events that impact your audience, or a product/service category (hint: not just your product) to help customers along the buying journey.

If you’re creating content with clear business goals in mind, it can be difficult to walk the line between sharing helpful information that builds credibility and being promotional. But it’s important to shift from the “me” marketing mentality to considering what’s best for your audience. When in doubt, go with what will deliver the most value to your readers. After all, the best ways to build credibility are by sharing generously and demonstrating true expertise.

Here's an excellent infographic that outlines the key elements of how to measure content marketing ROI:

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Courtesy of an article dated June 14, 2015 appearing in Forbes an an article dated June 14 2015 appearing in Forbes

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HOW TO DRIVE A CUSTOMER EXPERIENCE-FRIENDLY CULTURE AND THE TEMKIN EXPERIENCE RATINGS FOR 2014

The challenges of creating a great customer experience extend from the tactical to the strategic, from remaking processes to encouraging innovation and from building new skills to integrating an array of technologies.

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Despite the varying maturities and consumer habits that are bound to exist on a regional basis, the criticality of customer centricity has remained consistent. The challenge is for organizations worldwide to create memorable experiences that also make a difference to the bottom line.

Further evidence of the ubiquitous focus on customer experience can be found in Figure 7. More than three-quarters (78%) of company respondents are attempting to differentiate through CX, with 28% strongly agreeing with the statement. In comparison, only 5% of companies are not trying to use customer experience as a point of differentiation.

It is clear that the coming year will see plenty of activity in this area. Organizations have spent considerable time getting their respective houses in order. The next step for many is execution; pulling together mobile, content, data and the various other building blocks and deploying them in a harmonious fashion.

Creating an excellent customer experience is a complex process, but the respondents’ enthusiasm and dedication to using it as a point of differentiation bodes well. Companies have committed to customer experience for the long term. This is critical, as a commitment to customer experience means a commitment to a structured, longterm view on delighting the customer.

Simply put, customer experience isn’t simple.

For many organizations, the goal of turning customer experience (CX) into a competitive advantage means recreating themselves. That’s because the customer’s experience is the result of every part of the company working together, or failing to. Decisions made in fi nance and human resources can have just as much impact as those in marketing and product teams.

This interconnection is one reason that a cultural shift should underlie any strategic priority around CX. It’s not easy to get hundreds or thousands of employees to realign their thinking, but it’s essential if the shift to the customer is genuine.  Here are some fundamental building blocks necessary to create a CX culture at your company.

• Unified Team or Company - Perhaps the most powerful element of culture is the potential for unity. When teams or entire companies are unified around a vision, it energizes them and can counteract some of the normal tensions between departments.
• Common Customer Experience Perspective - In a business environment where there are far more options for how to spend time and money than the ability to follow through, nothing is more useful than focus. When the organization begins to share a common perspective that the customer experience drives growth, it focuses everything from budgets to daily priorities.
• Customer-Centric Business Organization - Declaring that CX will be a defining organizational characteristic is a choice that means prioritizing customer-centric qualities over other business goals. As this permeates the organization, this new direction can galvanize innovation. In a business, creativity is often at its best (or most useful) when it’s given context and boundaries. The move to build a great CX opens the door for creativity in every team and at every level.

The five-year plan…

How important is CX? Thousands of companies tell us that it’s the number one way they hope to differentiate themselves over the next fi ve years. The product and service commoditization in many sectors means that while offerings must be competitive, they’re less likely to be a competitive advantage. Even starker is the end of the price war; only 5% of respondents see themselves as being able to set themselves apart with their pricing strategies.

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Experience starts with strategy and culture

Strategy is the easy part. As challenging as it is to discover and define corporate direction, strategy is a malleable set of ideas that depends on the buy-in of a relatively small group of people.

It’s within the context of culture that strategy becomes practice. Its success or failure depends on whether the wider audience of employees, partners and other stakeholders understand and support it.

Redefining culture

A cross-team approach – building a truly customer-centric enterprise means working together. It’s not simply a marketing initiative. Product, fi nance, customer service, sales, marketing and management have to work together. The good news is that a cross-team approach that transcends the silos of the past is a powerful and more effi cient way to work.2 The bad news is that it’s very hard work to change internal structures.

Realignment through incentives – at the heart of any effective transition is using fi nancial and other incentives to reset priorities. For example, this might mean a simple redefi nition of how bonuses are calculated to include a customer satisfaction component. But, it could also be using a more sophisticated set of goals around cross-team cooperation and customer evaluation.

Supporting experimentation and understanding failure – a great customer experience combines existing best practices with new discoveries and innovations. One of the hardest challenges for some organizations to overcome is to make experimentation a part of the culture. Risk and failure-averse companies actually risk failure when they don’t embrace a vigorous campaign of experiments in the name of superior customer experience.

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Everything is part of CX. From the moment a prospect sees an ad the customer experience has begun and it doesn’t end until the relationship does. Although Figure 9 shows organizational priorities around CX, it doesn’t draw a line between what’s important and what isn’t. Everything is important, but some are differentiators while others are simply part of the new cost of doing business. Elements of the CX with the potential to separate a brand from its competitors:

• Personalization depends on technology, but it is human insight that moves it beyond the algorithms relating products and people. Great personalization can be fundamental to relevance, while bad personalization feels intrusive and unnecessary.
• Value is an expression of cost and benefit. The benefits to a customer are a mix of what they get and how they perceive it, which is why marketing has to be both the voice of the customer to the enterprise, and the voice of the enterprise to the customer.
• Making customer experience fun or fulfilling is a challenge and only 7% cite it as their path to differentiation (Figure 10). It’s powerful for those that can connect brand with experience in ways that delight customers3 but most companies should first focus on getting the basics just right.

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Fundamentals for every CX-focused company:

• Customers want to know what to expect. Consistency in experience should go far enough to make them feel safe and to fulfil on expectations set in other media. At the same time, every medium has its unique traits. A mobile site should be simple and useful above all, which should be balanced with overall brand consistency.
• Safety and speed are the first steps of customer experience. Digital customers who are concerned for their data will never buy, nor will they return if they find the experience slow or awkward.
• Similarly, while mobile can be a differentiator today, marketers looking at the future recognize that it won’t be one for long. The growth in visitors and buyers is forcing every digital marketing team to be mobile-ready.

2014 Temkin Experience Ratings

Bottom line, marketers know great customer experiences are good for business, and the 2014 Temkin Experience Ratings report shows consumers rank several grocers and fast food chains as the best providers of overall excellent customer experiences. The report, based on a survey of 10,000 consumers in the U.S., evaluates 268 companies across 19 industries— including everything from healthcare and cable service to airlines and hotel chains. Rising above the pack are grocer H.E.B. (88%), Trader Joe's (84%), Chick-fil-A (83%), Publix (83%), and Aldi (82%), each rated “excellent” by customers.  Here's their list of the top and bottom organizatons:

Courtesy of eConsultancy's Quarterly Digital Intelligence Briefing Digital Trends 2015 and the Timkin Customer Experience Ratings