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Founder of Firm Acquired by Omnicom Starts New Marketing and Market Research Analytics Company

Logo with subtitle 3Now nearly five years since Omnicom Group, Inc. acquired The Modellers, LLC, founder Dr. Chris Diener has started a new company, The Analytics Team, Inc. ( When Dr. Diener originated the idea and co-founded the prior firm in 1998, the aim was to provide advanced analytics services to other market research, advertising and consulting firms. That was the main roadmap for the first eight years. It was a “wholesale” analytics approach. It then grew into a full service market research firm creating potential channel conflict with its original clientele. Now, it has been merged with Hall and Partners, Inc. leading to further channel concerns.


The Analytics Team, Inc. is returning to this pure “wholesale analytics” service model, giving service providers a new analytics option, yet based on over 20 years experience in the industry and having done it before.  On the market research side, It provides high quality multivariate analytics from maps and driver models, through chaid and segmentation, to MaxDiff and choice modeling or choice-based conjoint. But it also combines these more traditional approaches with database, social media, and marketing analytics helping internal and external MR teams to integrate all the data at hand.


Though similar to the first company, The Analytics Team, Inc. has several unique elements shaped for the needs of today’s market. Dr. Diener, the President and founder, describes the positioning of the company,


Chris Diener“When I first started nearly 20 years ago our choice modeling approaches were leading edge and we thus positioned the company as the one that could solve your toughest problems — sort of the pinnacle of advanced analytics. We invested heavily in very custom approaches, because there were no alternatives for what we were aiming to do. We also acquired a level of exclusivity to our brand. This also required premium fees for our work. Most research firms don’t need the ultimate complex solution. And many of the existing needs are now more standard.”


“Our partners don’t want to differentiate on complexity or handling the hardest projects. Rather they want to differentiate based on unique, innovative solutions and custom-fit insights. They want to build their own brands and potentially design their own signature or hallmark services. They want to build their own internal analytics resources across time. They are looking for a partner who can help build their business with branding, and attracting the type of work and type of client best suited for profitable growth.”


“The Analytics Team, Inc. acts as an integrated partner — consulting with them to provide reliable, predictive, custom, even white-label branded, analytics products and services.  We can help them sell their services through assistance with design and in the sales process. We also serve our clients as an internal resource for their teams, training them to competently use analytics tools and think through design and application. We grow with them, but there is a time when they will use us less and rely more on their internal resources.”


You can contact Dr. Diener at for more details.


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Facts or Emotions — Which is Better to Communicate?

Image result for rational or emotionalWhat should you focus on? Facts or emotions? Best answer: it depends. Both together can be a very powerful combination and this avenue should typically be pursued first. In doing so, its critically important to keep the message simple, consistent and bold or distinguished from the field so as to attract attention and aid in automatic recall. One prolific writer with Verge noted recently the refreshing nature of Samsung’s Galaxy advertising — which focused on important, differentiating facts — instead of the kind of advertising that may challenge the intelligent viewer’s patience with emotion centered advertising that does not cut to the chase and help with the task of evaluation and purchase.  Here are Vlad’s words:

“Gone are the live orchestras and grandiose theatrics of former product presentations. The new Samsung gets to the point quickly and delivers a clear and concise message. You want amazing photos in all circumstances? Here’s a pair of cameras with f/1.9 lenses and lightning-quick operation. You want performance, power efficiency, and the best possible display? Here’s the world’s first 14nm processor, multi-standard wireless charging, and the most pixel-dense display ever put on a smartphone.

In the place of strained metaphors about quad-core processors being akin to four wind turbines, Samsung is now appealing to consumers with facts and numbers that matter. The Galaxy S6 recharges twice as fast as the iPhone 6. Samsung’s metal is “50 percent stronger” than that used in other phones and “will not bend.” These are the things that people care to know. Instead of trying to sell us on gimmicky and overwrought features, Samsung has returned to the more reliable strategy of addressing the needs we already have. That’s the same approach that Apple took when it expanded its iPhone lineup last year with some long overdue larger devices, and the payoff for that move was the biggest sales success in corporate history.” — Vlad Savov, Verge writer from “The new Samsung is arriving just in time,”

Vlad’s comments get us thinking about what we want to hear from advertisers, at least on a rational level. Samsung did a great thing in focusing on key attributes. That focus is itself “key”. If they were not relevant attributes or differentiating ones, the ad would fall flat in its effectiveness.  When presenting these attributes, does Samsung ignore emotion and create just a fact filled, rational ad? No. They combine the elements. And for a given purpose, it works and works well.

Focusing just on emotions without the “facts” lends itself to higher level brand advertising which is meant to trigger automatic emotional reactions towards a brand. While these kinds of ads are very important they are not appropriate as the main vehicle to drive sales in a competitive marketplace with other emotionally imbued brands. However, the pure emotional messaging provides the platform of trust and social appeal that more fact-based, product-based advertising can effectively leverage.

In terms of driving sales or having a bottom line impact, advertising that is purely emotional will not move the needle much in the immediate term. This is where facts need to be included. But not just any facts. They must be important and differentiating and targeted to create a given kind of market and competitive reaction.

On the other hand, pure fact-based marketing will be effective for specific needs. But it will likely not live up to its full potential because of lack of viewer attention, engagement and recall. It needs to be enhanced by story-telling, thematically consistent imagery, brand equity cues, etc. These kinds of enhancements bring emotion into the equation.

So, its nice to say, focus on the key emotional cues and focus on the key features for the factual elements — but harder to implement. How do you know what to focus on, in either domain? The bottom line is that you need to find out. Do market research. And use approaches that will give you valid results. Find a market research consultant either inside your current organization or through an external vendor who understands not only market research, but also communications strategy.

Market research done without communications strategy and communications done without market research will fall short of their potential impact. But together, they will exceed expectations.

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Conjoint on Mobile? Yes! … but be wise

In 2013, 20% of surveys were taken on a mobile device. That is double from 2012 .. and the pace of change increases into 2014. And yet, over 60% of researchers avoid surveys on the mobile platform. One of the reasons for this low use by researchers is that they don’t trust mobile surveys to be able to handle the complex kinds of questions that they employ in regular surveys.

What they don’t realize is that with 60% of American adults owning smartphones, most of these folks are doing complex tasks on their phones already. Cell phone owners have the capability and capacity to do complex surveys on their phones.

One of the most complex of survey tasks is Conjoint — also more specifically referred to as Choice-Based Conjoint or Choice Modelling. Last year, with the help of the AbsolutData team, I designed and tested conjoint on mobile. The results showed that both in the US and internationally, people are ready to do complex surveys on the mobile platform. Other than specific sampling needs, there is no reason to avoid mobile and many reasons to embrace it more consistently.

See the presentation I gave at the 2013 Sawtooth Software conference on this topic that details the findings of our international study. 

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Updated Book Edition Release: Supercharging Your Business using Choice Modeling

This article is a cross-post from Murphy Research:

By Chris Diener

It has been a few years in the making, but I finally updated my book, Using Choice Modeling to Supercharge Your Business. While the fundamentals of the book remain unchanged, the increased prevalence and continued evolution of choice modeling necessitated a few tweaks to ensure continued relevance for market research professionals.

Choice modeling is the most advanced and powerful predictive modeling approach to critical issues in market research insights.  Because of this, the method is still surging in its use and breadth of application areas. New people are learning about it every day and are being exposed to it in various capacities at large volumes. From communications to product development and brand strategy, it is the premier approach to knowing what your consumers want and the best way of giving it to them.

The ways in which choice modeling differs from and is better than other approaches remains the same.  In a choice model, preferences for product attributes, rather than abstract concepts, are researched to model the decision making process of an individual or segment.  It is not a panacea, but it does have specific areas of application that are most beneficial, and in these areas, it functions second to none. Need to know how to best price your product or service? Or how to best configure, update or change an existing product or service? Need to know how to manage your product line to minimize cannibalization and maximize revenue and profits? Or how to best position your products and among which consumers? These are all virtually timeless questions that choice modeling effectively answers.

The challenge is that it really is a complex and advanced approach. For those who are not experts in it or do not deal with it very often, it may not be intuitive and there are dangers when choice modeling is not used to its best value. It can also be hard to communicate with others about the technique and outcomes. That’s where this book comes in. Using Choice Modeling to Supercharge Your Business is a unique resource to the market research industry. It fits the needs of non-technical professionals who are seeking to drive the creation of value from the application of choice modeling.

To celebrate the new edition and printing and to introduce more people to this fascinating and incredibly useful methodology, MURPHY RESEARCH IS OFFERING A FREE CRITICAL CHAPTER FOR IMMEDIATE DOWNLOAD AS WELL AS A SIGNIFICANT DISCOUNT ON THE ENTIRE BOOK. JUST CLICK HERE TO START SUPERCHARGING YOUR BUSINESS.

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Most Impactful Ways to Leverage Big Data in CPG Insights

Often experts pit Big Data and market research (MR) against each other, as if Big Data will make MR increasingly irrelevant. It’s a misguided match-up. Recently, At AbsolutData, in my capacity as SVP, Analytics, I had a discussion with a CPG senior executive in charge of insights, market research, for a large international CPG company.  He is vitally interested, as with many leaders in the industry, in how to best combine Big Data with market research insights: Finding the best ways of bringing the two together.

I’ve taken the liberty of organizing that discussion into a Q&A format to bring out the most useful points. The discussion addresses common concerns about types of Big Data and illustrates high impact opportunities for it when combined with MR.

Question 1: How representative is Big Data, particularly for categories like kids yogurt where the core target is mothers who may or may not be the people expressing opinion/views on social media, chats, blogs etc?


Big Data can quite well represent a population. On a high level, some countries, like the US, have very high internet penetration and social media use by moms, suggesting that geographic internet penetration is a factor in determining degree of representation for given populations. Having said that, extent of representation is not a one-size-fits-all concept but is applied differently based on the context. So to more fully address the issue, we need to step back and put this question into the context of the intended use of the Big Data –

  • Will we be trying to forecast demand?
  • Assess overall brand or product satisfaction?
  • Decide to make a product or a product line change?
  • Understand the market-wide impact of product or advertising changes?
  • Trigger more in depth research activity?
  • Understand trends?
  • Clarify consumer language and perception, etc?

There are many potential uses of Big Data and depending on the kind of Big Data we are referring to, it can have an impact on each of these areas.

The extent of representation depends on the kind of Big Data and the brand category. Furthermore the appropriateness of a given level of representativeness depends on the application of the Big Data.

Illustration –

If we define Big Data as Social Media – which includes tweets, FB comments and likes, Pintrest, Instagram, blogs, reviews, chats etc. these kinds of data will be representative of a given kind of population. For a category like kids yogurt, customers who mention the category or specific brands will be those who are involved in the category and are likely heavy users. These people are also most likely influencers in their circles of friends.

In fact, these people would well represent a valid target for the purposes of such issues as

  • Assessing brand image
  • Success or problems with product changes
  • Tracking or anticipating extremes of satisfaction (which really is the zone of interest when examining satisfaction over the longer term)
  • Exploring usage occasions, and
  • Identifying most compelling features and benefits for communications

As another example, innovative companies like BlueFin Labs (recently acquired by Twitter) have shown a direct correlation between twitter comments and advertising effectiveness when correlated in real time with video streams in TV.

The application has a bearing on the appropriateness of representation – but the kind of data also bears upon the issue of representativeness. As detailed below in response to the second question, Big Data means more than Social Media and the data has different properties of representativeness, especially when integrated with disparate data sources.

Question 2: Therefore where and how can Big Data be used? And where can it not be used? What, if any, are its limitations?


In addition to the Social Media aspect of Big Data, and its associated specific uses, broader use cases become relevant when considering aspects of Big Data. We often think only in terms of social or web activity but we can include other data streams such as

  • Daily or hourly sales by location
  • Stock out information and distribution information
  • Temperature and weather
  • Socio-psycho-demographic information based on location
  • Daily information on promotions, coupons, mailers, etc.

Because of the velocity, resulting volume, and variety of these kinds of data they can be used in solutions which are particular to what we consider “Big Data” these days. Not only can we map, in real time, these different elements together but we can model them as well to create early warning systems, recommendation systems, and communications and operations real time interventions. Social Media monitoring with text analytics can be overlaid geographically against sales, promotion usage and distribution information to react in real time with retail partners on pricing decisions or display options. We can then include demographic and even psychographic zip+4 information, and traffic information to maximize sales or communications effectiveness.

In short, when taken in its entirety and integrated together, the different aspects of Big Data that have varying degrees of representativeness when individually applied can be blended for general representativeness and applied innovatively to very important decisions. And, innovation is the fertile ground of competitive advantage.

Big Data can further be applied when approached more assertively. Big Data is a flow of information. It is somewhat reactive (but there is more to be said about this aspect) and, if we think in terms of social media, not particularly specific in all the ways we would prefer.

It must be harvested, filtered and interpreted. Or it can be blended with more traditional data to imbue more meaning.

If you are looking to test specific ideas, new product concepts, advertising copy, new product line configuration or pricing, then Big Data generally, used passively, will not provide the answers.

If you want to dive deeply into your target market to connect lifestyles, behaviors, attitudes and needs for segmentation purposes on a strategic level, you will need more than what is commonly available in Big Data.

This does bring up an interesting point, that of using Big Data passively or more actively creating data flows to more directly address specific issues.

On a very high level this is what we do with A/B testing of offers, promotions, web site enhancements, etc. We actually put something out there that customers react to in their normal activity and we measure those reactions and mine them for insights.

Likewise in the Social Media sphere much can be done to elicit more targeted and in depth information. Contests, viral announcements, online events, and such can be used to find out what people think or how they feel about very specific kinds of issues, products, features or benefits.

Question 3: How can one integrate Big Data with primary data and what would be its applicability?


Many feel very excited about the combination of Big Data with primary data.

On one level the excitement stems from the promise of enriching Big Data flows with the intimate and more detailed information that comes from primary data.

And vice-versa, the ability to further enrich our primary data insights by appending or blending in associated Big Data information.

There are many levels of integration that are possible now and yet will be possible in the near future. Here are a few examples:

  • Augmenting a large portion of brand and advertising effectiveness tracking with social media – the best solution is a blended solution now
  • Sampling for primary research from people who are part of a multi-device panel so that we know their primary data as well as their web and social interactions across a period of time
  • Sampling for primary research directly from social media platforms, and therefore being able to tie into that users past data on that site and maybe even be able to glean information from their social-graph
  • It is more typical to use social media listening and text analytics to identify trends, problems, and opportunities that can then be further explored using the more precise tools of primary data collection.

More specifically, AbsolutData has created several tools that specifically link Big Data and primary data. We can:

  • Mine the social sphere to come up with an initial set of brands, attributes and levels to offer the most realistic testing using conjoint
  • Track brands allowing the issues and image items we are tracking to take form from what people are talking about
  • Develop a more realistic set of brand driver models based on social media information
  • Administer surveys on consumer’s mobile devices allowing us to tie into social and local data both parts of the Big Data information flow.

In short the main areas that currently can blend Big and primary data would include

  • Tracking
  • Product development
  • Advertising and promotional testing and effectiveness
  • Segmentation and
  • U&A type landscape work.

We can further combine primary data with other kinds of geo-demographic Big Data which could also include data on sales, inventory, pricing, promotion and distribution to more fully explain patterns observed in the primary data. In this area we have specific experience in

  • Combining transactional data with behavioral data from survey for a segmentation exercise undertaken for a leading US based sporting goods retailer
  • Combining internal CRM data with survey data to predict travel wallet share for a leading global hospitality major
  • And other studies for our global clients


These ideas represent some of the increasing ways that Big Data and MR work hand in hand to generate more valuable insights. The two types of data do not contend for the insights but complement each other in deepening those insights. Each brings its own critical elements of value.

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Optimize for Action: How to Drive Value in MR and Analytics Consulting

Are you optimizing? If not, start … now. Most research and analytics efforts fall short because they don’t go the extra step from descriptive to prescriptive results.  This presentation I gave at the 2013 MRA Insights and Strategies conference a couple of weeks ago focuses on this message.

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The Market Research Tortoise and the Big Data Hare … or a Misguided Analogy

If you are in market research (MR), you may debate the which is tortoise and which is hare, but you probably do see a race and competition for the prize. Or, more realistically, you may fear that others see it as a competition. The prize may be budget, recognition as the source of valuable customer insights, or winning jobs. This may be a limiting perspective. If you are in the Big Data space could see it as a race but MR is probably not on your radar or you consider the outcome a foregone conclusion. You probably believe that MR could be a valuable appendage but ultimately discount MR when viewing the main role and impact of Big Data. There may be some blind spots. However, the two areas do overlap and treating it emotionally or functionally as a competitive situation will be counter-productive and lead to lost opportunity.

Some say that focus group and survey-based market research has reached the end of its life-cycle  and will be replaced by Big Data analytics. Those who fully promote this position usually have an agenda to push or they are not well informed about market research. However, it does seem that these two areas of market intelligence, customer insight, and decision support both offer valuable and maybe even critical decision support. The million, or billion, dollar question is how to integrate them and leverage them together. Will this integration be the basis for the next round of killer market research applications or the means of getting the most value from your big data investments? My thoughts in this area were triggered by the linked article that contains some comments, including one application that combines the two areas of MR and Big Data.

In both areas, those who wish to capitalize the most will figure out how to combine them to leverage value. In Big Data, folks will come to realize that there are many holes in the data, many missing links, many areas where current or past data will not suffice in predicting the future, and many areas where the value of the insights can be magnified through the leaven of custom MR.

On the other hand, MR folks either have or will come to recognize that the in-context reality and pure organic validity of Big Data adds immensely to the precision inquiry possible in MR. Big Data integration will increase MR value by bridge limitations or biases created by interviewer/researcher subjectivity, sampling frames, respondent behavioral self-report inaccuracies, perceived insights credibility, perception of frequent lack of tangible real-world action-ability, to name a few.

Seeing or specifically identifying the areas of combined value will be the first and most important step to making this happen. The next step, and arguably the more difficult one, is to actually combine the functions related to Big Data and MR to bring about a profitable union. Most corporations have these functions silo-ed separately, based on their historical origins and historically separate functions, skill-sets and processes. In fact it goes even deeper in that these two areas may compete for the same budget and in fact may feel quite competitive with each other for recognition in the organization. Executives not only have an organizational issue to resolve but likely an emotional one as well.

On the supplier side the difficulties are multiple for being able to combine these areas. First is that most MR firms do not have an understanding of or a captive skill set that encompasses Big Data analytics. They don’t have the mindset for what it is, how to get it, and what to do with it. Next, most emerging Big Data firms come from a programming, IT, data mining, or highly technical area of strength. They do not have the insights background or training and skills. The foundations of marketing strategy and consumer behavior and psychology is lighter than it needs to be. The softer, more granular or individually based understanding of motivations, attitudes, drivers, etc. and how they mix together to generate action and commitment are usually missing with folks who have been devoted to algorithmic programming or econometric modeling and mining.

The future in this area belongs to people and organization who can bridge these domains. This is a process and the sooner it begins the better and the more likely you can capitalize on it for competitive advantage. Identify the needs, find the relevant gaps in addressing the needs, prioritize and act on filling the holes and showing tangible progress through a series of quick wins.

To motivate these changes, we need to see more examples of how the two domains combine for added value. The referenced article (noted below) provides one lightly sprinkled example. I believe we will begin seeing more.

How will it play out? Will Big Data analytics be the hare? Will the hare win? Maybe fundamentally, is there a race? Maybe, we see the races either being set up or already in process and the question is how do we change the nature of the race so that instead of competitors, the tortoise and the hare are teammates?


Referenced article:

“The general consensus seems to be that “big data” is never going to replace traditional research – that is, specific research methods like surveys and focus groups that deal with particular topics will always be around.  These specific research methods answer the question of “what” – that is, they are concerned with empirical details.  For example, an online survey may indicate that compared to 2011, this year 5% of soda drinkers no longer drink Coca-Cola on a regular basis.  Where “big data” aims to change research is in the “why” – that is, the broad trends and underlying reasons why certain results have been obtained.  Using our previous example, “big data” may be able to tell us that key nutritional influencers have recently been saying that carbonated sugary drinks reduce life expectancy by an average of 4 years in healthy individuals.  By having both the “what” results and the “why” results, researchers can use this combination of data to have a much clearer picture of a particular situation, and potentially be able to advise their clients on how to act to obtain the results they wish to achieve.”

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