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HOW TO HIRE A GREAT DATA SCIENTIST AS THE DEMAND FOR BIG DATA ANALYTICS RISES AND A SERIOUS TALENT SHORTAGE LOOMS

 

The huge demand for big data analytics, and scarcity in individuals with the right skills and experience, is driving the starting salaries for data scientists over $200,000, and it is going to get worse

A new species of techie is in demand these days—not only in Silicon Valley, but also in company headquarters around the world.  Pascal Clement, the head of Amadeus Travel Intelligence in Madrid, says.  

“Data scientists are the new superheroes.”

The description isn’t exactly hyperbolic:  The qualifications for the job include the strength to tunnel through mountains of information and the vision to discern patterns where others see none. Clement’s outfit is part of Amadeus IT Holding, the world’s largest manager of flight bookings for airlines, which has more than 40 data scientists on its payroll, including some with a background in astrophysics. The company recently launched Schedule Recovery, a product that tracks delays and automatically rebooks all affected passengers.

A study by McKinsey projects that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.”  The shortage is already being felt across a broad spectrum of industries, including aerospace, insurance, pharmaceuticals, and finance. When the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year.  However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle.  Narendra Mulani, senior managing director at Accenture Analytics, says.

“It will get worse before it gets better.” 

Many data scientists have Ph.D.s or postdoctorates and a background in academic research, says Marco Bressan, president for data and analytics at BBVA, a Spanish bank that operates in 31 countries and has a team of more than 20 data scientists. He says.

“We have nanotechnologists, physicists, mathematicians, specialists in robotics.  It’s people who can explore large volumes of data that aren’t structured.”

So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them.  Stan Humphries, chief economist at Zillow, the real estate listings site, says:

“You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.”

Universities are taking note. MIT, where graduate students in physics, astronomy, and biology are fielding offers from outside their chosen fields, is in the process of setting up a dedicated data-science institute. Marilyn Wilson, the university’s associate director for career development, says the center will begin enrolling graduate degree candidates in 2016.

In the U.K., the University of Warwick introduced a three-year undergraduate data-science program last year, which David Firth, the program’s mastermind, says may well be the first of its kind.  He says.

“Big Business was complaining about the lack of people.  Finance is a major employer, but also large-scale insurers, large online commercial retailers, high-tech startups, and government, which has huge data sets.”

Accenture’s Mulani says he’s tallied some 30 new data-science programs in North America, either up and running or in the works. The University of Virginia began offering a master’s in 2014, as did Stanford. Many of those students may be tempted to drop out before collecting their degree.  Margot Gerritsen, director of Stanford’s Institute for Computational & Mathematical Engineering, says.

“Companies are scrambling.  We have second- and third-year students getting offered salaries much higher than what I get.”

Starting pay for some full-time jobs is above $200,000, she reports. Summer internships, meanwhile, pay anywhere from $6,000 to $10,000 a month.   To make these stints memorable, many employers offer perks such as free meals, complimentary gym memberships, and occasionally temporary housing.   Gerritsen says.  

“Sometimes you read about students getting abused in internships and working like slaves.  We don’t see that.”

The bottom line: McKinsey projects that by 2018 demand for data scientists may be as much as 60 percent greater than the supply.

COMMENTARY:  There is no doubt that data scientists are in high demand.  There is simply not enough talent to fill the jobs.  Why? Because the sexiest job of 21st century requires a mixture of broad, multi-disciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business.   Finding a data scientist is hard.  Finding people who understand who a data scientist is, is equally hard. 

Jean-Paul Isson, Monster Worldwide, Inc. says.

“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.” 

It is very unlikely that you will be able to hire a data scientist who can solve all your data problems. The skill-set presented below is a guide on how the modern data team should be equipped.

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What Makes a Great Data Scientist?

What are the personality traits of today’s data scientists? Do they have the skills needed to support businesses’ changing needs? How can senior management build cohesive teams to drive the most value from big data? Is this fast evolving discipline causing stress to its practitioners?   These are some of the questions that SAS Analytics tried to answer when it conducted a survey in 2014 of 596 respondents in the UK and Ireland who identified themselves as part of the data science profession. The survey highlights 10 key personality types as well as some of the challenges data scientists are facing today.

Summary of Findings

• Data scientists with “traditional” traits (analytical, logical, technical) make up the largest group (41 per cent), yet a significant proportion of data scientists display other traits, such as strong communication and creativity skills.
• We expect a lot from data scientists: to be technically proficient, mathematically agile, business savvy and good at communicating. Yet 55 per cent of data scientists have fewer than three years of experience in the discipline, and more than a quarter are adapting their behaviours to fulfil roles that are not well matched to their skills or work personality profiles.
• In most personality types there is a 70:30 split between men and women. The Geeks, the profile with technical bias, strong logic and analytical skills, shows a higher percentage of women at 37 per cent compared to 63 per cent for men.
• Data scientists are exhibiting high levels of work-related stress, with a total of 55 per cent of the respondents showing a level of stress, with 1 in 4 male and just under a third of female data scientists being heavily stressed.
• Organisations must better identify and define what they need from data scientists. Only then can they build and develop multi-faceted teams with the complementary skills needed to realise the full value of big data.  

This SAS survey report looked at the aggregated data of the initial 596 responses, received from 405 men (68 per cent) and 191 women (32 per cent).  Respondents’ experience ranged from less than a year to a handful of data science pioneers who have committed more than 21 years to the discipline.

The survey was based on the well-established DISC profiling methodology, which has been used for more than half a century to categorise respondents into a range of personality types with recognised characteristics.

By using DISC, SAS able to create the typical profiles of today’s data scientists, and explore how these compare to skills and personalities required by organisations.

The DISC Personality Profiling Methodology

The DISC methodology has been widely used to assess personality types for decades. This information is often utilised to analyse and build effective teams, improve communication and increase productivity. DISC is based upon a two-axis matrix:

• Vertical Axis:  Proactive to Reactive  
• Horizontal Axis: Introvert to Extrovert 

This generates four possible combinations – named D, I, S and C.  Individuals possess measures of all four of these combinations but in varying amounts. The values for each combination is calculated and plotted on a graph to illustrate the relative strengths of each.  These values can be matched to known behavioural characteristics to describe an individual’s personality profile.

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The Data Scientist Personality Mix

The SAS analysis identified ten psychometric profiles evident within the data scientist community, based on the responses to the survey so far.   These profiles are characterised by definable patterns that are well known to scientists and were consistently apparent in the survey sample.  

The ‘traditional’ traits associated with data scientists – such as technical, analytical and logical skills – still dominate. However, other less technical traits – such as project management, creativity and good communication skills – are also present. Organisations need data savvy individuals who are technically proficient, mathematically minded, business oriented and strong communicators.

It’s unlikely that any individual will have all of the skills required to maximise the value of big data.  So it’s important for managers to identify the particular skills needed and build a cohesive team of individuals with complementary skills and traits.  As we will see later in the report, failure to do so can result in individuals trying to fulfil roles to which they are not suited – which may lead to stress and burn-out.

Distribution of Personality Types in SAS Survey Sample

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Final Conclusions Drawn From The Survey

There is huge pressure on data scientists to display a range of skills: technical, mathematical, creative, business aptitude and communication. Unsurprisingly, organisations struggle to find this mix of skills in just one person, particularly when the majority of data scientists have only a few years of experience.

It is therefore important for managers to build cohesive, diverse teams that can answer all the needs of the organisation – not just data scientists but also data savvy managers to interpret the insights and transform them into actions. Organisations will need to:

• Manage the business’ expectations from data scientists
• Find and develop individuals with the appropriate skills and personality traits
• Create teams of individuals with complementary skills and experience
• Encourage peer to peer support, learning and development
• Match training to the business’ requirements.

It’s possible that, as technology develops, there will be less of a weighting towards the more traditional, technical personality traits – while demand for the less frequently found profiles such as the Ground Breakers, the Teachers and the Seekers will grow. As more respondents take part in SAS surveys, it is hoped to be able to explore this interesting development further.

Courtesy of an article dated June 4, 2015 appearing in Bloomberg Businessweek, an article dated August 30, 2014 appearing in Marketing Distillary and the results of a SAS survey titled, "What Makes A Great Data Scientist?"

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HOW KLOUT LEVERAGES MACHINE LEARNING TO CALCULATE SOCIAL INFLUENCE VIA THE 'KLOUT SCORE'

For the first time, Klout has published a paper on how they calculate and measure a person’s influence as an attempt to provide more transparency with its service.

Klout, owned by Lithium Technologies, is a website and mobile app that uses social media analytics to rank its users according to online social influence via the “Klout Score”, which is a numerical value between 1 and 100. For many, its a measure of how active and influential their social profile is across several social networks such as LinkedIn, Facebook, Twitter, Tumblr, and popular blog hosts such as WordPress.

The Klout scoring system assigns scores to 750 million users using machine learning that examines;

• 9 different social networks on a daily basis
• Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user
• Over 45 billion interactions from social networks every day

Adithya Rao, lead research engineer behind the Klout score said.

“Though the Klout Score has been around for years, this is the first time we have published a paper that provides this much detailed information on what’s behind it.  Getting this paper accepted is a step towards a more transparent view into the Klout score, and by publishing this information in a public forum others will be able to build upon the work we’ve done.”

Although there are multiple ways to consider what constitutes ‘influence’, how Klout defines it is as a way to to measure a users ability to drive action from their posts or social interactions. Many ‘influencer lists’ for example pull together people active on social media platforms based on the number of followers, ratio between followers and users they follow or simply by how many times they appear via a hashtag search. Klout digs a lot deeper by listening to the signals behind the noise using machine learning and data science to distinguish between people who claim to be an influencer and others who are more insightful the real influencers.

Who responds to your messages and interactions also adds to the algorithms behind Klout, for example, if Elon Musk responds to a message posted then they are influenced by your message and have acted on it.  This is weighted far differently than if someone who holds little influence across multiple domains, since Klout also validate that users with higher Klout Scores are able to spread information wider in a network.

When a user registers on Klout they associate their social identities on different social networks with the Klout profile;

• For Twitter, public data is collected via the Mention Stream;
• Anonymised data for opted-in Lithium Communities comes from in-house datastores
• Data for other social networks is collected via REST APIs on the user’s behalf, based on the granted permissions.

Data is collected continuously from interactions in a trailing window of 90 days using the Play Framework. The collected data is written out to a distributed file system, where batched parsing and processing is done using Hadoop MapReduce and Hive. From there the users Klout score is calculated and weighted according to further factors;

• The difference between the current time and the time at which the reaction occured.
• The social network on which the reaction was performed.
• The unit of original content or action on which the reaction was performed.
• The type of reaction.

Of course, there are still some elements missing from the scoring mechanism itself which Klout is working on integrating. When I spoke with Adithya he said that in the future Klout would be looking to attribute content from reputable sources like Forbes and weight this accordingly along with the other networks (Forbes has a Klout score of 99 for example, being associated with Forbes content would credit a contributing writer like myself with additional influence.)

Sentiment also isn’t tracked currently – negative or positive, which Adithya also acknowledged is a measure of influence but one which is very nebulous to calculate, since people will react differently to certain content. And it all hangs on the availability of the information to power the framework itself through the APIs provided by the social networks. Real-time and near real-time measurement is possible with some, but for example in the case of LinkedIn, Klout’s abilities are limited to certain interactions and data, such as the users job title and status updates. A users LinkedIn blog posts for example aren’t taken into account yet.

Where Klout’s strengths now lie is in the way it will tie in a users Klout score with transactional information and their overall digital footprint online, allowing businesses who use Klout to reach audiences and customers with a more personalised and contextual experience. 

Years ago Klout was purely used to measure and compare people online in a digital bragging rights context. There were other examples where a potential recruitment candidate’s Klout score was looked at for certain role profiles which demanded a command of an online audience or deep understanding of social networks.

Their next challenge now is to educate the wider online community and average user that they need Klout as part of their own generated data as retail and consumer companies seek to create and engage on deeper and personal levels. To an extent, many current users haven’t outgrown trying to play the system just for higher scores, and wear a Klout score like a birthday badge. I wrote about this on Forbes a while back in a previous article on social media habits.

"People have become obsessed with chasing meaningless numbers and Klout scores rather than just enjoy life. There was at one point a movement to use Klout as a means of measuring a candidate against a job application. Thankfully it failed.

Klout is a classic example of behavioural modification, where emphasis is placed on posting content that you know will hit the spot for a larger audience to get a push up the ladder, rather than post something you know will be engaging to the people that really matter (and consequently won’t even garner a blip on the Klout graph.)"

But Klout has now matured beyond this kind of audience now, and as a first step the publication of this paper of how their data scientist team employs sophisticated algorithms and machine learning techniques proves that Klout and Lithium Technologies is much more than just a number.

COMMENTARY:    This is not the first time that Klout has changed its social influence algorithm. After numerous complaints, Klout changed the algorithm in 2011, 2012 and 2014.  What I find very disturbing is that Klout places too much emphasis on the number of followers or fans that you have. I keep reading that users who bought followers on Twitter raised their scores significantly.  Here are a few examples:  

1. @BarackObama - President Barack Obama is the #POTUS, but according to Klout he commands a score of 98.  However, twitteraudit reports that the #POTUS has 37% audit score, claiming that over 41 million of his Twitter followers are "fake."
2. @HillaryClinton - Former Secretary of State Hillary Clinton has a Klout score of 94.  However, twitterdit reports that Madam Secretary has a 59% audit score, claiming that over 1.8 million of her Twitter followers are "fake."
3. @SenSanders - Senator Bernie Sanders has a Klout score of 81 with a 90% audit score, with only 90,000 "fake" followers. 

Granted, both Barack, Bernie and Hillary command tremendous influence by the nature of their current and past political resumes.  However, a lot of their tweets, if not most of them, are automated. This is not a level playing field, if you tweet 3 to 5 times per day like I do.  Does Klout take the volume of tweets into account?

When I first joined Klout, I got a score of 40, which is considered an average social influence score.  At that time, I spent most of my time tech blogging, and at one time achieved 40,000 unique visitors to my blog over a period of about one year.  I was averaging about two to three blog posts per day at that time.  Half of my fans were from the U.S. the remainder were international, primarily from the U.K. Canada, the E.U. and India.   I average 1.5 blog posts per day, and now average about 18,000+ unique visitors.  My Klout score has gradually increased from 40 when I started, to 44, 46, and 47 as of today. 

I spend most of my time on Twitter where I now have 2,800 followers (96% authentic).  I really never liked Facebook, so I only have 64 fans on Facebook, and this is deliberate on my part, and most of my posts from Twitter are fed to my Facebook page.  I also have a separate Facebook page, my Sharon Stone page where I am getting about 300-360 likes per week. The Basic Instincts star is quite a draw.  Klout can only key on one Facebook page to measure social influence.  As a result, I have switched from my Facebook home page to my Sharon Stone page.  I also have a lot of views on my LinkedIn page.  Hopefully, the combination of Twitter, LinkedIn an Sharon Stone (Facebook) will increase my Klout score. Get back to you on this one, but it should be interesting.  My goal is to hit a Klout score of 50 within 30 days.   

Courtesy of an article dated October 30, 2015 appearing in Forbes Tech

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STUDY FINDS TOP B2B MARKETERS USING PREDICTIVE ANALYTICS OUTPERFORM COUNTERPARTS USING TRADITIONAL DATA ANALYTICS

As the cold weather starts to roll in for many of us, it’s easy to get curious about what the near future has in store, temperature-wise. The Farmer’s Almanac claims that it can predict the weather with 80% accuracy months in advance, based on a top-secret mathematical formula that takes into account tidal and sunspot activity. In reality, their predictions fall pretty short of perfect most of the time. Modern meteorologists fare a little better, looking at atmospheric conditions in the present and the past to make their predictions. It’s all about the tools that you use.

So what tools can you use in business to help you figure out the temperature of your market? Predictive analytics are the hot new trend helping marketers to identify risks and opportunities by looking at existing data, but how well do they really work? Enough to justify your investment? This infographic was created through a partnership between CustomerThink and Forrester, and is based on surveys and conversations with the CMOs of over 100 B2B companies over the course of 3 months. They talked about their experiences with using (or not using!) this marketing approach, and the sort of impact it made on their businesses. Let’s take a look at what they had to say.

The CustomerThink and Forrester survey revealed the following findings.

• 2% of those polled denied having any interest in predictive analytics.
• 61% had already adopted the technology or were already expanding their investment in it.
• 37% were interested in implementing predictive analytics at their company.

It’s pretty easy to see why this interest is justified – 83% of users see a significant impact on their business, with twice the annual revenue growth, market share, and likelihood to exceed marketing value contribution goals than non-users! Use of predictive analytics, not surprisingly, predicts a more modern approach to marketing by the companies that use it.

• They are most likely than non-users to have a standardized marketing platform (79% v. 34%).
• They are most likely to implement analytics solutions in under 2 weeks (36% v. 21%).
• They track their conversation rates (55% v. 36%).

Predictive analytics can be used to address tough challenges faced by B2B businesses. Those polled named limited visibility into addressable markets as the #1 hurdle they face, and 86% agreed that predictive analytics helped them to see where their companies could fit in these new markets. Predictive analytics helps marketers understand not only markets, but also customers. 97% said that it helped them see how and why their best customers buy, 89% said it helped them identify new leads, and 92% said it helped them adjust their marketing mix to best reach new buyers.

With all of this new information about predictive analytics in front of you, I wager that you’re starting to seriously think about adding this tool to your marketing strategy. That’s my prediction, anyway. Have you already been looking into or using this technology? Let me know in the comments.

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COMMENTARY:  Radius, the B2B predictive marketing software company, commissioned Forrester Consulting to survey 106 B2B marketing executives and CMOs in the U.S. The findings revealed predictive analytics is going mainstream, with a majority of marketers (61 percent) using predictive analytics today, allowing them to anticipate outcomes with a significant probability of accuracy.

Published in September 2015, the study, “From Insight To Action: How Predictive Analytics Improves B2B Marketing Outcomes,” yielded three key findings:

• B2B marketers who use predictive analytics outperform their
  counterparts who do not.
• Predictive analytics users are twice as likely to outperform non-users on several key business metrics, including the following:
  • Exceeding revenue growth target;
  • Exceeding marketing goals for revenue contribution;
  • Commanding a higher market share than competitors.
• Predictive analytics helps B2B marketers identify and address new
  markets.
  • According to the study, marketers’ top challenge is limited visibility into addressable markets, but:
    • 86 percent agree that predictive analytics help them evaluate new market opportunities;
    • 84 percent say that insights from these marketing-focused analytics have become the primary driver in their go-to-market strategies.
• Predictive analytics impacts the entire customer lifecycle, not just pre-sales acquisition.
  • While most marketers (89 percent) see value in using predictive analytics to identify new opportunities and to better qualify leads, the more advanced users also realize upstream and downstream benefits:
    • 97 percent of predictive analytics users analyze their best customers and understand how/why they buy;
    • 92 percent of predictive analytics users optimize the marketing mix to reach the right types of buyers.

Radius Director of Product Marketing John Hurley said.

“As the findings of this study prove, the best B2B marketers are predictive analytics users.  This is because they can pursue new market opportunities and attract, analyze and engage customers with insights backed by data science, not guesswork. Given our goal for this study was to evaluate the use of predictive analytics among B2B marketers, we were excited to find the majority of companies have already incorporated these systems into their marketing arsenals and are actively driving business growth. We’ve entered an age where technology and data science can deliver the power of analytics to marketers.”

Courtesy of an article dated October 21, 2015 appearing in Online Sales Guide Tips and an article dated September 29, 2015 appearing in Radius

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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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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.

Click Image To Enlarge

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.

Click Image To Enlarge

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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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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THE STATE OF B2B SALES AND MARKETING DATA INCLUDING OVERALL DATA HEALTH RATINGS

Most B2B marketing/sales databases lack key information about prospects and customers, according to a recent report from NetProspex.

The report was based on an analysis of 223 million records in the databases of B2B companies. The researchers examined which commonly required fields were "missing" from each record (the report makes the assumption that each of these data points is necessary, which may not actually be the case for all companies).

Top industries represented in the research study:

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The demographics of the consumers encapsulated in this b2b research study are as follows:

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Some 84% of the records analyzed were missing revenue information and 82% were missing employee information about the lead's/customer's company, the analysis found.

Other common data points lacking from records included:

• Website domain (72% did not include).
• Industry information (71%).
• Phone number (54%).
• Title information (34%).

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The researchers also gave B2B companies overall "health ratings" in various marketing/sales data categories, including email deliverability, record completeness, phone connectivity, and record duplication.

B2B companies in general are doing a good job of dealing with duplicate leads/customers, with 90% of databases containing fewer than 10% duplicates, the analysis found.

In contrast, more than half (54%) of the records analyzed did not include a phone number, and many others were inaccurate, making "phone connectability" poor overall.

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Here's a summary of the overall health of b2b email by deliverability, record completeness, phone connectabilty and record dupication:

• Email Deliverability:  Based on the findings of the b2b research study, overall email deliverability rates continue to introduce unnecessary risk into email marketing programs. While there is an overall trend of improvement over the past three years, the average company database deliverability was still less than optimal, with a Health Scale Rating of 3.2.
• Record Completeness:  Lead scoring, lead routing, effective content personalization and web customization are all highly dependent on having actionable information about each prospect or customer. Most marketers don't have the time to wait for progressive profiling to kick in, and can't afford to compete against fake form data.
• Phone Connectability:  When it comes to improving the demand generation funnel, the ability to reach buyers by phone is fundamental to closing the loop between sales and marketing — and keeping that relationship as healthy as possible. Yet, all too often, marketing databases are filled with inaccurate telephone numbers — or no numbers at all.
• Record Duplication:  Internally, record duplication hinders effective account-based marketing strategies, marketing attribution and ROI analysis. It also increases storage and maintenance costs while decreasing the effectiveness of lead scoring.

Industries scoring the lowest health ratings were as follows:

• Email Deliverability:  Retail and Manufacturing
• Record Completeness:  Media and Software
• Phone Connectability (By Revenue Size):  $5Mil to <$25Mil and $1Bil and Above
• Record Duplication (By No of Employees):  <100

About the research: The report was based on an analysis of 223 million records in the databases of B2B companies.

Courtesy of an article dated June 1, 2015 appearing in MarketingProfs

GAMEMAKER ZYNGA ANNOUNCES 18% STAFF CUT AND FOCUS ON FEWER GAMES JUST TWO WEEKS AFTER RETURN OF CEO MARK PINCUS

Just weeks after returning to the helm of the videogame company he founded, Zynga Inc. CEO Mark Pincus is slashing nearly a fifth of the workforce.

The job cuts, announced Wednesday, come as Zynga grinds away at a turnaround. The company reported a 9.1% increase in its first-quarter revenue and a narrower loss, both better than what Wall Street expected. It also forecast a second quarter slightly stronger than analysts’ projections.

Shares jumped hours before the close in New York, finishing up 4.4% to $2.61. In after-hours trading, Zynga’s shares were up 6.1%.

Zynga’s stock price over the nearly two-year period since the hiring of former CEO Don Mattrick on July 1, 2013 (Click Image To Enlarge)

Zynga is cutting 364 employees, or about 18% of its workforce. It is one of the first strategic moves from Mr. Pincus since he returned in early April to retake the reins from Don Mattrick, a former Electronic Arts Inc. and Microsoft Corp. executive who held the top job nearly two years.

Departed Zynga CEO Don Mattrick (left) with founder Mark Pincus (right) who returned from a nearly two-year hiatus to retake the reins as CEO (Click Image To Enlarge) 

The cuts, expected to be completed before year-end, will result in charges of between $18 million and $22 million in the second quarter, Zynga said. The company expects annualized savings of about $45 million excluding those charges, with another $55 million down the road through other belt-tightening, such as letting Amazon.com Inc. manage its servers.

It’s not the first time Zynga has cut into its ranks. In June 2013, it let go of nearly a fifth of its payroll. This latest round will leave Zynga with about 1,600 employees, less than half its peak of 3,400 in 2012, when Zynga acquired OMGPOP Inc. for $180 million.

In an interview, Mr. Pincus said Zynga was trying to do too much. He plans to focus on a few popular areas like casino games. One casualty is sports, a category Zynga entered less than a year ago with a new studio in Orlando, Fla. That studio is closing, stopping development of a Tiger Woods golf game and putting a countdown clock on an NFL game.

Mr. Pincus said.  

“We just have been spread too thin.  Zynga hasn't done a great job of funding our best opportunities.”

One hole in Zynga’s mobile portfolio is action-strategy games. Earlier this week, Zynga released “Empires & Allies” to compete with the likes of Supercell Oy’s “Clash of Clans,” a top-grossing game on app stores.  It took two years to develop, compared with less than three months to make one of Zynga’s first-ever hits, “Mafia Wars” for PCs, Mr. Pincus said.

Sean McGowan, an analyst at Needham & Co., called the ax-wielding a positive step.  He said.  

“Even after all the rounds of cost reductions that we’ve seen, this was still a company with a cost structure that was higher than you’d expect.” 

But he questioned the strategy of making fewer games.

“There isn’t a lot of evidence yet that fewer games will lead to bigger games.”

Zynga expects to launch six to eight new mobile games in 2015, including a second strategy game called “Dawn of Titans.”

Getting strategy games out the door is just one step toward an elusive finish line—profitability. That slow march continued in the first quarter, when Zynga reported a loss of $46.5 million, or five cents a share, compared with a year-earlier loss of $61.2 million, or seven cents a share.

Excluding certain items, the loss was a penny a share, on par with a year earlier but better than the two-cent loss analysts expected, according to Thomson Reuters.

Revenue climbed to $183.3 million, ahead of the $147.7 million expected by analysts, according to Thomson Reuters. Zynga said mobile accounted for 63% of its $167.4 million in bookings—money spent on games—in the quarter.

For the second quarter, Zynga expects revenue of between $175 million and $190 million, better than the $156 million Wall Street expects, according to Thomson Reuters. The company expects another net loss, of between $54 million and $50 million, in line with Wall Street’s forecasts. In the second quarter 2014, Zynga had a loss of $62.5 million.

Write to Sarah E. Needleman at [email protected]

COMMENTARY:   When Zynga announced that former CEO Mark Pincus, who was on a nearly year and a half hiatus, was returning to replace departing CEO Don Mattrick, I was flabbergasted and very disappointed.  It was during Pincus' watch that Zynga tried to make the transition from a desktop social game company that used Facebook as its user platform to attract gamers to an indepenent mobile game company.   There was also a lot of controversy surrounding Mark Pincus' treatment of employees, most notably taking back stock options from staffers he considered poor performers.     

Under Pincus leadership, Zynga's active users, revenue growth and share prices dropped precipitously.  Here's what I am talking about:

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In an article dated June 3, 2013, Business Insider's Jay Yarow and Nicholas Carlson discuss in great detal what went wrong with Zynga under CEO Mark Pincus.  Richard Greenberg, an analyst covering technology stocks, like many analysts on Wall Street were "stunned" by Zynga's board of directors announcement that Mark Pincus was returning to replace departing CEO Don Mattrick.

  

Let's hope that the "new" and "improved" Mark Pincus can turn things around at Zynga.  Zynga's financials for the year ending December 31, 2014 and Q1 2015 show declines and red ink across all important company financial performance metrics.

Financial Data FY Ending December 31, 2014 

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Financial Data Quarter Ending March 31, 2015

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Under departed CEO Don Mattrick, Zynga's both daily and monthly active users have suffered significant declines. This is a condition that must be reversed in order to avoid even larger operating losses.  It will require new games with the potential to become hits and revenue generators.      

 

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As you can readily see, turning around Zynga will present a huge challenge for Mark Pincus.   In Zynga's Q1 2015 Earnings Report, Mark will employ a "Narrowed Focus" strategy that will focus on five game categories:

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Social games like "Farmville", "Farmville 2" and "Mafia Wars" which were Zynga's "bread-and-butter" revenue sources  have matured and lost favor with gamers and have shown dramatic declines in active users and revenue contributions.  Pincus must now hope that its two newest "Action" games "Empires & Allies" and "Dawn of Titans," become big hits.     

Courtesy of an article dated May 6, 2015 appearing in The Wall Street Journal, an article dated April 8, 2015 appearing in the New York Times, an article dated May 28, 2014 appearing in ValueWalk, an article dated June 3, 2013 appearing in Business Insider, and data reported in Zynga's Q1 2015 Earnings Report