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BIG DATA IS NOW A TOP MANAGEMENT ISSUE WITH THE CIO AND CTO TAKING OWNERSHIP OF BIG DATA STRATEGY SAYS NEW SURVEY

A new study by the Economist Intelligence Unit has just been released that shows how big data is moving from its infancy to “data adolescence,” in which companies are increasingly meeting the challenges of a data-driven world.

The report, called “Big Data Evolution,” details the ways in which companies’ attitudes and activities have changed over the past four years with regards to big data — collecting it, storing it, analyzing it, and using it to make business decisions about strategy.

Data is becoming a corporate asset

The report shows that, since 2011, substantially more companies are treating their data as what it actually is: a strategic corporate asset. The initial excitement about the possibilities presented by big data is morphing into a more strategic approach, defining which data initiatives will have the biggest and most immediate impact.

I refer to this as asking the right questions. Companies are getting a bit savvier, and on the whole, are not asking for more data, but rather the right data to help solve specific problems and address certain issues.

Because of this greater understanding, executives are more likely to report they are making good, fact-based decisions about their data and their business.

In addition, data strategy has been elevated to the C-level, usually centralized with a CIO/CTO or a newly-appointed Chief Data Officer (CDO). Outside that position, executives across the board are more likely to be in charge of their departments’ particular data initiatives and instrumental in putting those resources to use.

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Another important finding of the survey points to a strong correlation between good data management practices and financial success.

Companies with a well defined data policy, are much more likely to report that they are financially competitive with their peers and rivals.  In addition, they’re more likely to report that their data initiatives are successful and effective at resolving real business problems.

The reason for this could be that data initiatives are moving out of the realm of theoretical possibility and well into reality, demonstrating the ability to focus on real business problems and provide practical solutions.

Less about volume and velocity than value

One final encouraging trend the report finds is that the “bigness” of big data is starting to wear off. Companies are less focused on the quantity of data they can collect and the speed at which they can access it, and more focused on the value the data can provide for their business.

I strongly believe that the right data is much more important and valuable than simply collecting more data, and the report bears that out.

As technology continues to improve, the “bigness” of big data will become less and less of a factor. Companies are becoming more comfortable with the idea that they will need to scale up to allow the value of data initiatives to reach all sectors of the business, and so they are becoming more comfortable with approximation, agility and experimentation.

From my point of view, all of these are positive signs that big data is moving out of its infant stages and is well on the way to data maturity.

Bernard Marr is a best-selling author, keynote speaker and business consultant in big data, analytics and enterprise performance. His new books are 'Big Data' 'Key Business Analytics'

COMMENTARY:  The tone of corporate conversations about big data continues to shift from initial excitement to expecting long-term business impact.

Over the past four years, executives have not only become better educated about the technology behind big data, but have fully embraced the relevance of data to their corporate strategy and competitive success.  It could be said that most companies are experiencing their “data adolescence”, increasingly rising to the challenge of executing and delivering against the promise and potential of big data.

What are the hallmarks of this current stage of evolution, and what does the path to “data adulthood” look like from here?

In February 2015, the Economist Intelligence Unit (EIU) conducted a global survey of 550 senior executives sponsored by SAS, to follow up on our 2011 and 2012 executive surveys. By comparing the results, we were able to examine the evolution of companies’ views, capabilities and practices regarding big data as a corporate asset, and explore the future implications as companies continue to mature as strategic data managers.

Additionally, EIU conducted six in-depth interviews with leading corporate big data thought leaders and practitioners.  Two of these interviews revisited specific big data–related issues these companies faced beginning in 2011.

Key highlights of the research include the following:

• Since 2011, a significantly larger proportion of companies have come to regard and manage data as a strategic corporate asset. The ranks of companies with well-defined data-management strategies that focus on identifying and analysing the most valuable data (referred to here as “strategic data managers”) have swollen impressively since 2011. No longer indiscriminate data collectors or wasters, companies are entering a period when the initial excitement over the possibilities presented by big data gives way to the need to prioritise and develop on data initiatives with the biggest payoff. More companies have ventured further into this stage of their data evolution, and their executives are more likely to feel that they are better at making good, factbased business use of their information.  
• Strategic data management is correlated with strong financial performance. Our survey points to a clear correlation between managing data strategically and achieving financial success. Companies with a well-defined data strategy are much more likely to report that they financially outperform their competitors. In addition, they are more likely to be successful in executing their data initiatives and effectively applying their data and analytics to resolve real and relevant business problems.
• Data-strategy ownership has been elevated and centralised, while engagement and demand from the business is at an all-time high. Across industries, data strategy has been elevated and centralised to the C-level, most often with the CIO/ CTO or the newly minted chief data officer (CDO) role. At the same time, senior executives across functions and business units are increasingly in the driver’s seat of their data initiatives, and not just relying on IT leadership to design and execute them.
• Data initiatives have moved from theoretical possibilities to focus on solving real and pressing business problems. Companies approach data initiatives today with a clear focus on their purpose—putting business value first. They are much more likely to start by articulating and finding a consensus on the high-priority business problems the organisation will solve by leveraging its data assets. Financial resources available for big data initiatives remain scarce, so there is a pronounced need to prioritise which initiatives to invest in, as well as how to demonstrate the financial return on these investments.
• Technical challenges associated with quality, quantity and security persist. Even top performers continue to struggle with a number of technical aspects of big data. These foundational aspects of data management still drown out the more advanced, higher-value-add aspects of data management, such as governance, compliance and converting data into actionable insights.
• The future of big data is less about volume and velocity, and more about the value that the business can extract from it.  Going forward, companies will have to shift their attention away from the “bigness” of big data and focus on its business value. Data and analytics will be increasingly applied to predict future outcomes and automate decisions and actions. Most importantly, many companies will have to continue to evolve their structure and culture to scale up successful data pilots across the entire organisation. This means becoming more comfortable with approximation, agility and experimentation, and reinventing themselves into a new kind of information-driven, data-centric business—closer to data adulthood.

CIO's Now Consider Big Data Analytics A Game-Changer

Greg Taffet, CIO of U.S. Gas & Electric, when The Economist Intelligence Unit interviewed him back in 2011, said.

“It is going to be a game changer.”

He was referring to fast-moving, real-time “big data”—which, at that time, was a novel buzz word.

In EIU's first comprehensive study in 2011 of how companies perceive and handle big data as a corporate asset, just 9% of survey respondents said data had completely changed the way they do business, while 39% believed data had become an important tool that drives strategic decisions at their organisation. But more than half of executives saw data in less critically important terms.

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As you can see from the above EUI survey, 58% (First two colums for 2015 survey: 14% + 44%) of respondents now see data as a game-changing asset, or at least, an important decision-making tool.  

In the EUI 2011 study, four categories of companies were identified based on their level of sophistication of their thinking and strategy vis-à-vis corporate data:

• Strategic data managers: companies that have well-defined data-management strategies that focus resources on collecting and analysing the most valuable data;
• Aspiring data managers: companies that understand the value of data and are marshalling resources to take better advantage of them;
• Data collectors: companies that collect a large amount of data but do not consistently maximise their value; and
• Data wasters: companies that collect data, yet severely underuse them.

EUI plotted the four corporate categories and compared 2011 vs 2015 in the following chart: 

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The above chart (Figure 2) shows that, in the last four years, companies have advanced along the evolutionary curve and, compared with 2011, many more now have developed a well defined data strategy.  The ranks of strategic data managers have swollen impressively (18% vs 33%), and actually showed the only growth among the four categories, while the number of data collectors and wasters is shrinking (28% to 20%).

Further evidence that companies are moving beyond strategy development and are tackling the adoption, or implementation, stage of data evolution is the fact that executives today put more of their valuable data to good use (see Figure 3).

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Alan Feeley, managing director of global shared services at Siemens, a global engineering firm, points out.

“Data and analytics are no longer opportunistic.  They are now formal research areas for our company.”

The CIO and CTO Have Now Taken Ownership of Big Data

The ownership of data strategy and the sponsorship of data initiatives have evolved throughout the organisation. Responsibility for the organisation’s data strategy has been elevated and centralised to the C-level, but at the same time, the pull and energy are increasingly coming from the lower levels of the corporate pyramid.  Over half of companies surveyed make sure that data are available to employees who need them, and offer the appropriate technology and training programmes. Data strategy has become “everybody’s business”—senior executives across functions and business units are increasingly in the driver’s seat of their data initiatives, instead of relying on the CIO or CTO to design and execute them in a top-down manner.

The vertical migration to centralised leadership of data strategy and strong ownership from the C-suite is an emerging best practice today. Ram Chandrashekar, executive vice-president of operational excellence and IT and president of Asia Pacific and Middle East region at ManpowerGroup, a global human-resources consulting company, says.

“Clearly, a top-down data strategy driven and articulated by the CEO is a critical success factor.”  

The EUI Survey data support his observation.

Over the past four years, ownership of corporate data strategy has migrated upwards from executives at the business-unit level to C-suite members—particularly, the CIO. In 2011, 23% of respondents said their CIO is primarily responsible for all data initiatives. This proportion jumped to 30% in 2012, and continued to rise to 39% in 2015 (Figure 7 below).

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A recent appearance in our 2015 survey is the increasingly popular chief data officer (CDO) role. This C-level position was virtually unknown in 2011—limited mostly to government and heavily regulated industries such as banking and insurance following the 2008 financial crisis. In our 2015 survey, some 9% of respondents pointed to their CDO as the custodian of the corporate data strategy and capabilities. Emergence of this role comes at a good time, especially as business executives from across the functional spectrum have become much more technology-literate and involved in the design and execution of their data strategy and initiatives.

Emergence of the Chief Data Officer (CDO)

Increased involvement from the business comes with the challenge of co-ordinating agendas, aligning priorities and communicating effectively with all stakeholders.  Mr Chandrashekar of ManpowerGroup says. 

“There is strong alignment and articulation at the C-level. People on the frontline, such as sales and operational staff, are also data-driven.  The disconnect often happens in the middle, and the challenge is to make the data flow from top to bottom. Engaging the business is critical—data strategy cannot be seen as just a central initiative.”

And few today excel at engaging the business. In the EUI 2015 survey, when asked to rate their company’s competence across different datarelated corporate capabilities, respondents expressed the least confidence in their ability to engage employees across the organisation to use data in day-to-day decision-making (only 26% rated their company as “very competent”, while 22% saw themselves as “not at all competent”). High-quality, consistent engagement across layers of the organisation and among horizontal functional lines is in high demand, and in short supply.

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Enter the CDO.   Mr Krishnamurthy of Cognizant Technology Solutions says.

“The CDO has emerged as the embodiment of ‘integrated leadership’.”

He points out that the best-designed CDO roles are focused on three top-level priorities:

• Ensuring availability and integrity of data across the organisation;
• Driving adoption—from small-scale pilots to company-wide rollouts; and
• Driving the monetisation of new data capabilities.

Most importantly, the role is about organisational engagement, brokering between agendas and balancing priorities among big data initiatives. Thus, finding the right senior talent to fill the CDO role can be tricky, as Edd Dumbill, vice-president of marketing and strategy at Silicon Valley Data Science, a big data consulting firm, points out:

“They have to know technology, they have to know the business, and they have to be a political wiz.”

Foundational and talent challenges

Companies have made great strides in embracing data as a strategic asset, making the necessary technology investments, and even beginning to evolve their corporate structure. Centralised leadership allows for better co-ordination in strategy and execution of initiatives. And executives, both on the business side and in IT, are much more focused on deploying their limited resources on top-`priority data projects that extract tangible business value from these investments.

However, significant challenges still plague most companies—and that’s true even for companies with the financial resources. The most daunting challenges companies face relate to three.  highly technical and operational aspects of big data—quality, quantity and security (see Figure 8).   These are fundamental aspects of data management. Yet companies are far from having resolved them completely and with full confidence, leading to a lack of progress to more advanced, value-added aspects of data management.

In the last four years, the problems posed by the overwhelming amount of data companies can access and collect have only been exacerbated further. In 2011, one in eight companies said they had so much data that they struggled to make sense of them—in 2015 this was nearly one in four companies. And today, more than half of executives (54%) say they probably leverage only half of their valuable data (Figure 3). 

Given the sheer volumes, ensuring the integrity and quality of data, and arriving at the proverbial “single source of truth”, are still major problems. And thus, the ultimate challenge of extracting meaningful and actionable business knowledge from data is still a significant one for most companies, even slightly more so for companies that say that they are strong financial performers as they may be more ambitious with their data strategy. But only 16% of companies these cite extracting business insights as a top challenge—for reported poor financial performers, this was 24%. Despite strong or poor financial performance, 33% of all survey respondents continue to struggle with managing the vast amount of data and 41% struggle with maintaining quality (Figure 9).

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On the organisational front, companies have made strides in both creating the right structures and roles, as well as recruiting key talent to enable them to formulate and begin executing their data strategy. However, the talent market in the data and analytics field is still very tight.

This is especially still true in the market for data strategists—executives who are expected to speak the languages of both technology and data science, as well as understand the business, the markets and the customers (see section Paving the way for the CDO). These rare and invaluable executives—the “effective engagers”, as Ms Merkel of Zurich Insurance calls them—are in short supply and high demand. As Mr Feeley of Siemens puts it,

“There’s a war for talent, particularly for people who combine data expertise with domain knowledge.”

In a blog post dated December 8, 2015, in which I wrote about the market for data scientists, I mentioned the serious talent shortage in big data and analytics.

Courtesy of an article dated November 30, 2015 appearing in Forbes and the whitepaper prepared by The Economist Intelligence Unit titled, "Big Data Evolution"

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THE 43 BEST PPC MANAGEMENT TOOLS AS VOTED FOR BY EXPERTS AND THE PPC METRICS YOU SHOULD MEASURE

Paid advertisements are a great way to drive targeted traffic and relevant people to a website.

However, if you’re new to the Paid-Per-Click PPC game or have not optimised your campaigns to their full potential you could be throwing money away!

There’s a whole bunch of management tools out there that can help with optimising your PPC campaigns but how do you know which ones are the best?

In this article, Studio-40 asked 21 PPC experts for the tools that they could not live without.

Their answers include a wide range of free, paid and free trial option tools. Take a look…

Studio 40’s 43 Best PPC Management Tools As Voted For By Experts from Tommy Toy

 

COMMENTARY:   

Using PPC Metrics to help measure the success of your PPC Marketing?

The second question out of the 8 questions to ask before you hire a PPC expert is how will you measure success? When I wrote about setting pay per click goals, I said setting goals before you start is critical. Just as critical is understanding how you are going to measure whether you reached your goals or not.

Since goals are specific, you should be able to figure out what metrics you need in order to measure progress. For example, if your goal is to spend $1000 and get 10 conversions, you need to measure your total cost and total conversions. If you goal is to get 1000 visitors, then you will be measuring clicks. If you want to be shown on the first page of Google for specific keywords, then you measure average position and impression share for each of those keywords or ad groups.

These are some simple ideas but don’t let them fool you. As you begin to dig deeper into managing PPC campaigns you will want to start measuring other metrics or segmenting data into device types or time of day in order to understand how well your campaigns perform. For the sake of keeping this post from becoming a book, I am going to stick with the high level metrics and refrain from others such as bounce rates and the various types of conversions.

What PPC metrics are available?

Google and Bing provide plenty for you to analyze. To get you started, there are a handful of metrics that you need to know. Below are some basic definitions to help you understand them. Google details more of these and other definitions in their Adwords Glossary.

1. Impressions – The number of times your ad was displayed in a search results page.
2. Clicks – The number of times someone clicked on your ads.
3. Click Through Rate (CTR) – The percentage of clicks divided by Impressions.
4. Cost – The total cost for all clicks.
5. Average Cost per Click (CPC) – Total cost divided by the total number of clicks.
6. Average Position – How high in the results page is your ad showing. 1 is the highest.
7. Impression Share – The percentage of the total number of times your ad could have been shown vs the actual number of times it was shown.
8. Conversions – The number of times someone clicked on an ad and completed an action you defined as a conversion within a specific period of time.
9. Quality Score – Relevancy of your combined keyword, ad and landing page which affects your CPC. Quality scores vary but the higher the better.
10. % Served – When analyzing your ad copy, how often an ad was served vs other ads in the same ad group.

What should you measure?

Although every metric is important when evaluating PPC marketing performance, your goals will determine which metrics you should focus on first. As a general rule, I don’t recommend any of the following goals by themselves but together they will help you understand the basics.

I want to be seen on the first page of Google!

Being seen in the search engines is easy as long as you know what you need to spend per click and you are willing to spend it. Decide which keywords matter to you and bid them high enough to be competitive. Monitor the number of impressions as well as your impression share. You want your impression share to be at 98% or better. To be consistently seen, you should try to maintain an average position of 5 or higher. If your impression share is low, you need to increase your bids or increase your daily budget. If you are using up your daily budget, you might also consider limiting when your ads are running to weekdays, weekends or working hours only.

I want to be at the top of Google!

If you want to be at the top of the first page on Google you must be willing to spend more per click than everyone competing in your market space. Select keywords that you know people use when looking for the service or product you sell. Bid high and be sure you have the budget to afford being in the top spot. First position ads get more clicks and can eat up your daily budget really fast.  Once your campaign starts, monitor your average CPC, average position and impression share. If your average position is not 1 or 1.x, you should bid higher. There are other things you can do to improve performance, but by simply bidding higher than your competitors you can be assured that your ad will appear in the top position as long as you have not exhausted your daily budget.

I want to sell more widgets or get more leads!

If you are selling widgets or looking for leads you will want to setup conversion tracking. You can do this in Google Adwords and Microsoft adCenter but recent changes make tracking conversion (goals) easier in Google Analytics. In order to know whether your PPC campaign is doing its job, conversions should be your number one metric with everything else secondary. Most companies track form completions or e-commerce purchases but you can also track phone call conversions. Phone call tracking is especially important for companies focused on leads rather than sales. With custom scripts loaded on your website, you can present unique phone numbers to visitors who clicked on your PPC ads. For as little as $100 a month, you can track how many phone-based conversions come from your ad campaigns as well as organic SEO and other online channels. It’s another dimension that helps you understand how your online marketing affects your real world sales.

Variety is the spice of life

Every marketer has their own goals and their own ways of managing PPC campaigns. And everyone has their own ways of measuring success. As you begin to learn the ins and outs of PPC marketing, you will learn how campaigns work and what metrics are important to your success. Remember that PPC marketing combines art and science to deliver results. It’s not perfect but it can deliver great results as long as you keep working at it and keep an eye on the metrics. Understand the metrics and you will know whether your campaign is working or not. Understand the metrics and you will know if your goals are being met.

Courtesy of an article dated December 17, 2015 appearing in Studio-40 and an article dated June 26, 2013 appearing in FindAndConvert

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WHO DOES WHAT IN THE DATA SCIENCE INDUSTRY, HOW MUCH THEY EARN AND A MASTER GUIDE TO HIRE OR OUTSOURCE

What are the key roles in data science? Here's a look at the various job positions in the data science industry and what they mean.

A data scientist cleans, massages, and organizes Big Data, according to DataCamp in the following infographic.

A database administrator, on the other hand, focuses on backup and recovery, data modeling and design, distributed computing, database systems, and data security.

Another role is statistician. This person "collects, analyzes, and interprets qualitative as well as quantitative data with statistical theories and methods," according to DataCamp.

To find out more about the different roles in data science, click or tap on the infographic.

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COMMENTARY:   Data-driven business processes are not a nice-to-have but a need-to-have capability today. So, if you’re an executive, manager, or team leader, one of your toughest assignments is managing and organizing your analytics and reporting initiative.

The days of business as usual are over.  Data generation costs are falling everyday.  The cost of collection and storage is also falling.  The speed of insight-to-action business requirement is increasing.  Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.

With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.

Data Science And Analytics Outsourcing – Vendors, Models, Steps by Ravi Kalakota - May 28, 2015 from Tommy Toy

Courtesy of an article dated December 8, 2015 appearing in MarketingProfs and an article dated May 28, 2015 appearing in Ravi Kalakota's  Practical Analytics

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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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DATA-DRIVEN MARKETING: TOP OBJECTIVES, CHALLENGES, QUALITY OF DATA AND SOURCES OF DATA

Businesses are increasingly incorporating data into their marketing efforts in order to create more personalized customer experiences and better measure ROI, according to a recent report from Ascend2.

The report was based on data from a survey of 234 marketing, sales, and business professionals from around the world.

Some 60% respondents say creating more personalized customer experiences is an important objective of using data as part of their marketing efforts; 51% say measuring data-driven ROI is a top goal.

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Top Obstacles

More than half (59%) of respondents say poor data quality is a top obstacle to their data-driven marketing efforts; 51% say integration across platforms is a major challenge.

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Data Quality

Just 16% of respondents say the quality of the data they use for marketing purposes is very good; 57% say it is somewhat good, 23% say somewhat poor, and 4% say very poor.

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Data Sources

Some 85% of companies surveyed say they use data they collect themselves; 49% use data from marketing partners, and 40% purchase data from third-party sources.

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About the research: The report was based on data from a survey of 234 marketing, sales, and business professionals from around the world.

COMMENTARY:  If you are interested in learning more about big data, data mining, predictive analytics and data driven marketing, please click HERE.

Courtesy of an article dated November 23, 2015 appearing in MarketingProfs

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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THE TOP 20 MOST POPULAR SURVEY SOFTWARE SOLUTIONS [INFOGRAPHIC]

SurveyMonkey is the most popular software program for conducting online surveys and analyzing the resulting poll data, according to a recent report from Capterra.

The researchers ranked the popularity of survey software solutions based on three criteria: the number of customers (unique companies/organizations that are actively using the software), the number of users (employees who create and manage surveys), and social media presence (Facebook likes, Twitter followers, Google +1s, LinkedIn followers, Klout scores, and number of online reviews).

SurveyMonkey has the most customers (2.35 million) of any survey program examined, as well as the largest social media presence; it is tied with QuestionPro for the most number of users (2.5 million).

QuestionPro received the second-highest composite popularity score, followed by Qualtrics, SurveyGizmo, and FluidSurveys.

Check out the infographic for the full rankings from the report.

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Courtesy of an article dated October 22, 2015 appearing in MarketingProfs

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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.

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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.

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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.   

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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 49% ROI MISTAKE MARKETERS DON'T KNOW THEY'RE MAKING DUE TO THE MOBILE MARKETING BLIND SPOT

With growing consumer adoption of smartphones and increased usage of "click to call," consumers are creating a blind spot for marketers. The blind spot? Phone calls. DialogTech, formerly known as Ifbyphone, a call analytics and automation company, created the infographic below to help marketers navigate the rise of customer service phone calls, as well as offer tips to eliminate the blind spot in your mobile marketing attribution.

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COMMENTARY:  Breaking down which mobile channels generate the most phone calls, we found that search (51.9%) and landing pages (29.4%) are driving a combined 81% of calls for our customers. Compared to display (16.2%) and social advertising (2.5%), mobile searchers and visitors to mobile landing pages both have a higher level of intent to call a business.

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If a consumer searches on their phone for a local business, they can easily click a call extension to speak to the business right away from the search ad. Or if they are researching a business and reach a landing page, they can immediately talk to an agent instead of flling out the form and waiting to hear back. Callers from these channels know what they are looking for and are ready to talk.

The same cannot yet be said about calls from social media, but there is investment in its growth. Take Facebook and Twitter: both have recently introduced call buttons within their advertising. As people continue to adopt features like this, it’s helping power the 5X growth in calls from mobile social ads that BIA/Kelsey predicts will occur by 2019.

Opportunity #1:  Attribution  (You May Be Misattributing 49% of Your Conversions)

Accurately measuring mobile ROI is a challenge for many marketers – whether they know it or not. When marketers rely solely on measuring conversions from landing page web forms, they may be misattributing nearly half of their conversions. The missing piece of the attribution puzzle?  Phone calls.

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While conversion rates vary widely based on factors such as industry or product, DialogTech found the average web form conversion rate for landing pages to be 2.4%. DialogTech also found the average phone-through rate (PTR) to be 2.3%. Combining the two conversion paths, the result is an average total conversion rate of 4.7%. Without call attribution, 49% of conversions could be missed or misattributed to the wrong source.

Consider a search marketer with a monthly Google AdWords budget of $50,000. Assuming they generated 250 web leads in one month, the result is a cost per lead of $200. But that’s with no call attribution. With call attribution the search marketer discovers a 49% ROI mistake: in addition to 250 web leads, they are also receiving 245 phone leads each month. Now with 495 total monthly leads, the cost per lead decreases 49% to $101.

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Without call attribution data, the search marketer in this scenario has a 49% ROI blind spot that inhibits their ability to measure what’s really working and accurately bid on the right keywords driving both web and phone leads. The result: inaccurate ROI data with an infated cost per lead and potentially lost sales opportunities and revenue.

Opportunity #2:  Personalization (Personalize the Call Experience to Close More Sales)

Callers are more likely to become customers: when they call they are ready to purchase. In fact, 62% of mobile searchers fnd it extremely/very important to be able to call a business during the purchase phase.

It’s no surprise then that 66% of sales managers rate phone leads as good or excellent, more than any other lead type. Not when call conversions become revenue 10x more frequently than web conversions.

Mobile marketing is all about personalized and relevant ads, content, and experiences. The same holds true for the call experience: offering consumers a personalized, one-to-one call experience can go a long way in helping businesses convert more callers to revenue.  There is signifcant metadata around each caller that marketers can use to create a more relevant call experience, including the marketing campaign and referrer information that drove the call, the search inquiry they used, the caller’s location, the webpages they viewed before calling, what device they are calling from, and more.

Opportunity #3:  Disposition (Understand Intent to Increase Customer Acquisition)

When digital marketing was focused exclusively on desktops, understanding a lead’s behavior and intent was simpler. Consumers often began and ended their journey on one device, with conversions occurring via a web form or online purchase. With everything confned to the desktop, tracking each consumer’s digital footprint -- where they came from, the webpages they viewed, if and how they converted -- was easier.

In a mobile-frst world, however, measuring the customer journey is more challenging. While some mobile purchases are simple (buying music, for example), many are more considered due to their complex, infrequent, or expensive nature. Buying life insurance, enterprise software or hardware, a new car, real estate, or senior care services are just a few of the many considered purchases for which buyers need more detailed information before making a decision. The customer journey for these goods and services often includes mobile, desktop, and offine touch points.

It was mentioned earlier the average phonethrough rate for DialogTech customers is 2.3%. In the chart below there are customers with a PTR over 10%, and others as high as 10X the average. Their common denominator? They are industries that are either considered purchases (e.g., insurance, consulting) or require conversation (e.g., listings, pest control, repair).

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When leads convert by calling, marketers need the same level of analysis to understand what happens as they do with desktop-exclusive purchases. What was their digital footprint? What web pages did they visit? What exactly happened on the call?

The chart below shows DialogTech data on the industries with the longest average call duration. Longer calls are often a strong indicator of quality sales leads. However, long calls aren’t a guarantee of a sale.

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Marketers need insight into what happens on calls from their marketing to truly measure call quality and ROI. Did a call result in a sale or an appointment? What was said that infuenced the caller to convert? Call recordings and transcriptions – and technology to scan conversations for specifc words and phrases spoken on the call – give marketers more insight to help them improve campaigns, optimize marketing messaging for the words callers actually use, or measure agent performance on the call.

Conclusion

The mobile opportunity is clear: call volumes are exploding and callers are ready to buy. Marketers are shifting their digital ad budgets accordingly, yet many are making a critical ROI mistake without even knowing it. By 2019, inbound calls from mobile advertising will increase 114% and marketers who fail to track these leads may be misattributing half of their conversions.

Call attribution and optimization is key to capitalizing on this opportunity. By correcting the 49% ROI mistake they are making without tracking calls marketers will have closed-loop attribution for every conversion, and be able to prove exactly how their mobile marketing is driving revenue. They can then optimize spend for what’s really working, increasing their impact on the business. And by controlling how each caller is routed and analyzing what happens on calls, marketers can help sales agents convert more callers to revenue faster.

Courtesy of an article dated October 2, 2015 appearing in eMarketing+Commerce and DialogTech's whitepaper "The 49% ROI Mistake Marketers Don’t Know They’re Making"

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