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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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Anticipating Needs in Real Time: Are You Failing?

We love search, but do we really? What we are really saying is: I wish someone would find what I want. When we make people search, are we failing?

I am cross-posting a blog entry I authored for my company, AbsolutData, this month entitled: Identifying Customer Needs with Big Data.

“By the time you search, something’s already failed,” Phil Libin, chief executive of Evernote, said in an interview recently with the New York Times.

Brands can’t afford to fail to anticipate needs. With customer interaction, brands must respond before being explicitly asked – and the window of anticipatory response is shrinking every day. Before customers put energy into searching the site or asking for a product, brands that win in this ultra-competitive environment will have already anticipated – and created – a solution. As marketers, if we want to lead, we must provide customers with a situation that cuts out uncertainty.

Most businesses think this is rocket science, but we’ve had the tools to do it for a long time. Today it’s getting easier and becoming more of a mandate with the benefits of Big Data.

Big Data is already being used to identify customer needs

For 60 years, marketers have been building products that anticipate the needs of consumers. The difference now is that we have the data and tools to understand these needs in various contexts. Customers have been segmented by groups for more than 10 years, but Big Data allows us to classify individual customer needs and act in real time.

What used to be one-way is now a two-way social channel. Digitally and socially, marketers have new opportunities for relationships to increase loyalty through trust and wonder.

Big Data also allows for geographic opportunities to anticipate customer needs. Whether they’re on the highway, visiting a shopping center or out to dinner, we have an opportunity to communicate with customers at virtually any time using a mobile device. No, it’s not rocket science. In fact, some companies are already using analytics to anticipate their customers’ next moves.

Google Now’s Google Cards are a great example of how data is already being used to understand customers’ needs before they search. Google Cards works on Android phones to search existing user content, in addition to other data patterns including geographic location, to anticipate what the user will need.

For example, when your alarm goes off in the morning the application pulls up directions with the approximate time your route will take, suggesting when to leave and even providing alternative routes, if traffic is heavy. Google knows you go to work at the same time from your appointments and knows the location from your past map searches. It will even tell you stop hitting snooze or you’ll be late! The application also pulls up other things you may need for the day, including weather and important appointments.

A wider known example is Expedia’s use of data to suggest other items you may be interested in booking based on your previous behavior and others who have taken a similar trip. Even Amazon suggests new books, based on your interests and what others have read.

How businesses can move this forward within their organization

Most companies already have what they need, but they’re either not capturing it or aren’t using it in the correct way. Businesses have access to customer behavior data as well as social and transactional data. This gives them the tools to understand their customers on a much deeper level.

The first step in using this data is figuring out the vision – then you can make an argument for collecting, aggregating, storing and even mining the data. This comes from thinking about the company over a transformative period. How do you want the customer experience to differ? How can you improve the business? Larger, strategic changes can be greatly improved by the use of data.

Once you’re ready to integrate customer data, take it a step further and ask for feedback. Then, learn from behavior and refine the process accordingly to create an assertive interaction.

The tools you need and the team you’ll build depends on your strategy. There are a number of data platforms that allow for easy data access and management. New flexible analytics workspaces allow you to discover data and patterns you might not have known are important.

Finally, having the right talent is an imperative part of the process. A third-party analytics team, such as AbsolutData, can provide additional talent and tools support, or even take on projects externally.

How will this be used in the future?

The important thing to remember is that this is already happening now as the leading edge of current applications.

I don’t think we’re necessarily at the point where it’s a failure to have a customer search for a product on a brand’s website – but that’s where it’s headed, and quickly. Businesses must rise to overcome this statement of failure.

The cold, hard facts of life in this accelerating sphere of customer interaction show that if you are not taking steps now to learn and engage more, you will likely find yourself behind the pack. On the other hand, there is ample opportunity to get in the game or deepen the efforts now and doing so will pay handsome dividends.

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CMO, are You on the Real-Time CRM Big Data Applications Bus?

The times are changing and changing fast. Just like the data for these times. It comes fast. It changes often. This opens up new opportunities for savvy marketers. However, these kinds of opportunities need a structural and organizational mandate. These opportunities are big and the best competitors are capitalizing on them. What are the opportunities? Real-time automated customer interaction — customized customer interaction.  That’s a mouth full but very meaningful — real-time automated customized customer interaction.

This is the peak of the big data mountain value. Its a mountain and that’s why it takes serious organizational commitment. Building or moving mountains. First, you need the vision of where you want to go. Then you have to make the mountain. That involves getting your data assets and resources properly organized. Then you need an infrastructure of BI and reporting to glean low hanging fruit value and initiate best practices organization governance with the right information flows. Then you take this data further through exploration and analytics. Finally you apply those analytics into your automated, customized framework.

In the ideal world — you can piece it together like this. But more likely than not, you must work in the context of legacy structures. You must incrementally build. You must generate short term wins, rally the troops, generate greater commitment and build into a vision. This is where the rubber meets the road for most of the folks who dive into big data.

Its a constantly evolving world with new tools and technologies. Its a challenging world where intellectual resources and manpower is constrained. Its a world where going it alone has some serious risks — risks of doing it with less effectiveness but also risks of not getting it done at all.

This article, authored by me, at CMO.COM, motivates the need to be on this journey and to plan your partners carefully.

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Mobile Apps Will Rock Your Analytics World

Mobile apps will rock your marketing and your marketing analytics world.  If they haven’t done so already or are not in process — you are behind. And, if you are behind, unfortunately you are not alone. I have talked before about assertively creating your own intelligent anlytics leadership opportunities. I have talked about actively influencing your data streams and not just being a passive recipient. Mobile apps drive active management and enable you to create rich opportunities. However, the opportunity only comes when the apps are used and when they are coded in a way to collect not only direct but indirect data.

In a recent article I was quoted about how to develop an impactful mobile app.

“The app must provide value. It must link with other apps … It needs to be social and it should be gamified … So the first thing marketers need to do is develop and deploy custom, linked, value-providing apps.”

via The mobile phenomenon: How to stand out – Mobile Marketer – Strategy.

In addition to building your own app, which gives you the greatest degree of flexibility and interaction, you will increasingly be able to take advantage of location-based app platforms — like the recently released Facebook Home. These kinds of apps will limit your flexibility, but probably give you much better access to non- or infrequent customers giving you a fresh customer acquisition channel among those who would not have your app.

So, where is your organization? Do you have an app? How well is it being used or shared? How well is it targeted? Was it developed with a data strategy in mind? How are you using the data that it generates? Have you linked it to other data streams? What solutions are you producing from it? What value do you add through the data it generates? Have you looked for other location-based app platforms you can leverage?

Answer these questions well and you are ahead of the game. If not, use them to guide your location-based app strategy, development,  or refinement.

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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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11 Big Data No Brainers in QSR — “Quick Serve Restaurants”

In addition to quick service, QSR has quick data and a lot of it. They also have the advantage of frequent interactions with customers and with the same customer. This adds up to a huge Big Data opportunity. However, many QSR companies are not very analytically oriented or they have complicating franchisee relationships to navigate for investment in data systems and analytics. The article linked below comes, in part, from an interview I had with John Morell — who was very interested in exploring the role of Big Data in QSR. He is finding that Big Data best practices and applications, while being applied well in pockets, still has a lot of opportunity in QSR.

McDonalds is exploring social media in greater depth. Dominos wowed the pizza ordering world with their online information system that lets users track pizza making progress. Taco Bell is working wonders operationally with their inventory management systems. Even given these pockets of application, few, yet, are harnessing the power of the data they have at their fingertips. What can they do?

Here is a quick list of “No Brainer” ideas. They are “No Brainers” because not because they are obvious nor because they are simple. They will all lead to increased revenues and generate a high ROI both in the short and long term if approached right.

  • Start or deepen loyalty programs and combine with social, apps and gamification elements to generate rich data — so much opportunity here to create value
  • Organize individual level purchase data and combine with digital, social and media information for rich data — the basis for real time interaction with customers
  • Combine unit performance data trade-area geo-demo-psycho information, social, individual level purchase and loyalty data — for much more effective targeting
  • Correctly attribute sales to online, digital and offline activities and tie this attribution to the individual level for more powerful cross-channel integration
  • Build recommendation systems for promotion customization, loyalty deepening, word of mouth generation, online ordering, in-person prompting
  • Optimize inventory systems to decrease costs and increase quality
  • Customize local menu options for increased sales
  • Track and respond to customer service issues before they become big issues — by area; curate your brand
  • Optimize your online and offline media spending and efforts for better ROI and increased sales

And there are many more Big Data opportunities and priorities. When considering Big Data investment, especially for QSR, here are a few guidelines:

  1. Always include short term (1-3 month) wins that can be used to generate momentum
  2. Define win in terms of ROI or even direct impact on stakeholder decisions or KPIs
  3. Put in place a larger vision and plan that can motivate deep emotional commitment and advocacy

The key is to start down the path and begin to gain experience and get your head in the game. Folks who keep putting it off, at this point, are at risk of just being in a different league of success than their competitors who are trying to play in this arena.  While these are not quite “No Brainers” by some definitions, they should be if they were on your radar.

Restaurants Activate Big Data to Leverage Customer Dining Data | QSR magazine.

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