We recently received and reviewed an excellent book summarizing practical findings from academic marketing research.  It is called Consumer Insights: Findings from Behavioral Research. It is published by the Marketing Science Institute and we highly recommend it.  Why is it so good and useful for corporate researchers and marketers?

• It provides a quick overview of what we know in various areas of marketing
• It is organized into 42 useful topics, most of which are relevant to nearly all marketers
• Each topic provides universal findings based on research from hundreds of studies, not just one or two
• Each topic/chapter is short and to the point (just two or three pages)
• Each topic outlines insights, the evidence base, and managerial implications

You will find important applications to almost any marketing effort you are engaged in.  For example, here is one that applies to what we are doing right now:  “Consumers are becoming more and more suspicious of online bloggers touting products.  Thus, a blogger’s attempt to tout a product may actually backfire and reduce consumer trust in both the blogger and the product.”  So to allay concerns, it is important to note that we have no connection to the authors, publishers, or sellers of this book!  Nor was the book pitched to us, or provided to us free (alas, we paid full price).

Here are the topics covered in the book:

Information Search
1. Effects of Product Knowledge on information Search
2. In-store Decision Making and Unplanned Purchases
3. Perceptions of Product Assortment
4. Variety-seeking Behavior
5. Consumer Search on the Internet
6. Buyers’ Post-purchase Information Biases Pricing
7. Perception of Price Deals
8. Biases in Processing Price Information
9. Effects of the Internet on Consumer Price Sensitivity
10. Effects of Transaction Structure on Price Perceptions & Consumption
11. Perceptions of Price Fairness

Advertising
12. Consumer Attention to Advertising
13. Effects of Ad Likability

Brand Effects
14. Consumer Brand Recall
15. Perceptions of Brand Extensions
16. Brand Dilution and Protection

Consumer Inferences
17. Consumer Inferences and Assumptions
18. Perceptions of Quality Signals
19. Causal Inferences and Consumers’ Judgments
20. Consumer Use of Persuasion Knowledge

Feelings, Attitudes, and Persuasion
21. Effects of Mere Exposure on Brand Liking
22. Influence of Feelings & Emotions on Consumers’ Judgments
23. Persuasion: Elaboration Likelihood Model
24. Consumer Goal Orientation
25. Consumer Attitudes Toward Marketing

Decision Making and Purchase
26. Pre-choice Bias in Brand Choice
27. Consumers’ Intertemporal Preferences
28. Loss Aversion and Consumer Choice
29. Protected Values
30. Purchase Intentions and Purchasing
31. Consumer Habits and Purchase Behavior
32. Impulsive and Compulsive Buying

The Social Consumer
33. Social Contagion and Word-of-mouth
34. Consumer Identity and Purchase Behavior
35. Perceptions of Advisors

Vulnerable Consumers
36. Children and Advertising
37. Aging Consumers
38. Effects of Low Literacy on Consumer Decision Making

Health and Well-being
39. Effects of Nutrition Information and Health Claims on Consumption
40. Mass-media Campaigns and Health-related Behaviors
41. Perceptions of Health Risks
42. Effects of Portion/Package Size and Shape on Consumption

The book is excellent and useful.  Get it.  It is now available through Amazon at a substantially lower price than we paid.

We learned many things we did not know, and we learned that some of the things we have come to know about marketing through our own experiences now have a wider research base to back them up.

Happy insights, from Versta Research!

–Joe Hopper, Ph.D.

Friends often solicit from me quick advice about conducting do-it-yourself customer satisfaction surveys.  What questions should they ask?  How many questions should they ask?  What measures and scales should they use?  And, of course, shouldn’t they be using NPS (Net Promoter Score) like everyone else?

I tell them that, by far, the most useful question they can ask is an open ended question that would be something like this:

If there were one thing you would like us to improve, what would it be?

You can tailor the wording to your specific market, service, product, or situation.  Best of all, you do not need to code and tabulate all the responses, although you could and might want to.  You just need to read through them and act on them if it makes sense.

I offer this advice with conviction based on the experience of a business colleague who implemented an ongoing web satisfaction survey ten years ago.  I suggested he include a question like this.  He has told me several times over the years it is the only data he regularly reviews and acts upon.  Almost invariably a customer will point him towards a functional glitch on his website or offer a simple suggestion that would make it easier for customers to buy.

He asks other survey questions as well, measuring satisfaction across several dimensions.  And surely a robust effort to analyze that data might tell him what drives satisfaction and how to boost his levels of satisfaction from 85% to, say, 90%.  But what he really wants to know day in and day out is what he can fix to keep improving, and whether any of his customers have a great idea he never thought of that he can easily incorporate.  He gets that with this one question.

His experience is a testament to the idea that one question can provide a great deal of useful insight.  Sometimes one question is all you need.  If so, you’re all set.  If not, give us a call.  We’ll advise you on the next nine questions you need on your survey as well.

–Joe Hopper, Ph.D.

]]> http://www.verstaresearch.com/blog/the-one-question-you-need-on-your-survey/feed/ 0 http://www.verstaresearch.com/blog/nielsens-legacy-tons-of-data/ http://www.verstaresearch.com/blog/nielsens-legacy-tons-of-data/#comments Thu, 20 Oct 2011 14:59:50 +0000 Joe Hopper http://www.verstaresearch.com/blog/?p=1691

Earlier this month Arthur C. Nielsen, Jr. died.  He left behind a giant and reputable market research company and a brand name recognized throughout the world.  The A.C. Nielsen company was started by his father and in its early years tracked the sales of goods through grocery and drug stores.  The company then moved into media tracking and became the authoritative source for measuring audience size and demographics.  Nearly every company with an advertising budget continues to rely on Nielsen data to determine where to advertise and how much to spend.

Nielsen’s legacy is that he demonstrated the value of collecting and tracking data, and lots of it.  Every item we purchase is now logged, counted, and tracked.  Every television and radio show is tracked for how many viewers it has and in what markets they live.  And of course everything we do on the Internet is recorded and tracked.  Even our bodily locations are tracked via GPS or cell phone signals.  Most market research firms today generate the bulk of their revenue simply by collecting, tracking, tabulating, and reporting data.

This important legacy has left us with tons of data, growing at an exponential rate,  and a monumental challenge of how to synthesize it and move beyond mere tabulation and reporting.  The question is, how do we meet that challenge and take Nielsen’s legacy to the next frontier?  In our view, it will involve two key efforts:

1.  Understanding data in much deeper ways and analyzing it with data mining tools, new algorithms, and new approaches that go beyond traditional statistics, including Bayesian analysis, neural networks, and machine-learning techniques.

2. Interpreting and communicating data in ways that are more practical, relevant, meaningful, and useful.  In other words, turning data into stories that real people, real managers, and real businesses understand and can use.

To be sure, much of the research industry is still (appropriately) focused on implementing technologies to better manage, tabulate, and report volumes of data.  But automated tables and charts with ever-expanding levels of detail are reaching their limits of utility.  Versta Research is proud to be on the next frontier, where better interpretation and understanding of data is key.

–Joe Hopper, Ph.D.

Two researchers at the U.S. Census Bureau recently outlined an emerging innovation in survey research that could reverse the trend towards passive, boring, self-administered surveys that characterizes much online research.  The idea is to use internet avatars in real-time interviewing with survey respondents.

Beyond just the heightened interest of having an animated survey, the avatars would be programmed to register and interpret respondents’ verbal answers, facial expressions, and body language through webcams.

Suppose, for example, that a respondent answers a question with detailed information that answers a follow-up question as well.  The avatar would use natural language processing to insert that data into the subsequent question, and then avoid asking the follow-up.  Or if the respondent looks away from the screen and pauses for time longer than is typical, the avatar can offer a rephrased question or a reassuring comment to re-engage the participant and to put him or her at ease.  This type of innovation  could bring many of the advantages of live interviewing back into the realm of internet surveys, which are far more efficient in terms of time and cost.

The use of effective avatar interviewers is at least several years away, however, because it involves not only evolving internet technologies, but also advanced linguistic processing, facial and voice recognition technologies, and so on.  In fact, the sheer technological difficulty of truly replacing human interviewers reminds us of how absurd it is for research companies to make claims about technology replacing higher-order activities in the research process, such as providing analysis and insight.

At least for now, software and services with “actionable insight” buttons generate yet more mountains of data in need of human synthesis and interpretation.  If anything, the role for smart and experienced researchers who can turn all that data into a story is growing.  It is growing for researchers who work on the client side and who have direct accountability to the executives who need data-driven insights.  And it is growing for firms like Versta Research where the highest levels of intellectual and human capital are central to our work.

–Joe Hopper, Ph.D.

one shortcut is to become reckless: to act impulsively instead of expending the energy to first think through the consequences. . . .  The other shortcut is the ultimate energy saver: do nothing. Instead of agonizing over decisions, avoid any choice.

But give the brain a hit of glucose (the basic fuel that runs cell functioning), and our willpower and rational decision-making are restored.

The findings are from multiple experiments over the past decade that relied on a variety of scenarios that both academic researchers and marketing people care a great deal about: selecting (and paying for) options on new car purchases, buying computers, shopping in malls or grocery stores, selecting fabrics for customized products, and making critical financial decisions that involve trade-offs between short-term rewards and long-term gains.

The findings also have implications for all kinds of marketing decisions and strategies.  How many choices should consumers be offered?  How difficult should those choices be?  Should you offer a default or recommended option?  Where in the decision process should the recommended option be offered?

One area in which Versta Research has done a great deal of work is in consumer attitudes and behaviors when it comes to saving, spending, and investing, particularly for retirement.  We all know that few consumers are saving enough.  Most consumers know this, too, but they struggle to make the most effective decisions.  Bombarding them with education and tools seems to go only so far.

Fortunately, many financial services companies are doing a better and better job helping people save and invest more wisely.  In part, they are doing it by taking advantage of our proclivities to “do nothing” when fatigued by choice, though nobody has understood until now why this works.  The recent research cited here offers new insights that may help configure better choices and contexts for financial decision making, in part because it suggests an intriguing link to brain physiology:

Apparently ego depletion causes activity to rise in some parts of the brain and to decline in others. Your brain does not stop working when glucose is low. It stops doing some things and starts doing others. It responds more strongly to immediate rewards and pays less attention to long-term prospects.

So before you are faced with the task of configuring your 401k or making other long-term financial and investing decisions, here are a few tips at least suggested by the research cited here: Be well rested.  Set aside time early in the day when your decision-making abilities are fresh.  Avoid times right after you have made other significant choices or decisions, either at work, when shopping, or in your personal life.  And the best suggestion of all?  Have a cookie with your 401(k).

—Joe Hopper, Ph.D.

]]> http://www.verstaresearch.com/blog/have-a-cookie-with-your-401k/feed/ 0 http://www.verstaresearch.com/blog/the-pitfalls-of-auto-coding-text-responses/ http://www.verstaresearch.com/blog/the-pitfalls-of-auto-coding-text-responses/#comments Thu, 28 Jul 2011 13:56:27 +0000 Joe Hopper http://www.verstaresearch.com/blog/?p=1523 An issue we continually struggle with at Versta Research is how to automate the research process and leverage new technologies without losing the essence of what good research does.  Good research does not report data, build charts, or generate dashboards. It learns, answers new questions, interprets data, and helps users focus on information and findings that are relevant to their needs.

The last couple of weeks we have been working with a group that specializes in coding and tabulating text responses to open-ended questions on surveys.  They have tools and technology that undoubtedly make the process easier and more efficient (we have used those tools, and they are impressive).  They are also have a singular focus and expertise that is supposed to help streamline the process, cut costs, and improve speed and efficiency.

The results have been mediocre at best, even with human coders working the technology and making the critical decisions. They efficiently and accurately coded each response into one or more  buckets.  But they created buckets that give hardly any insight into what the client wants to know.  “What do you love most about this product?”  The coders accurately identified all consumers who mentioned the physical size of the product.  But they lost critical distinctions not only about big versus small, but also about size being a constraint (not enough room for a larger product) versus a preference for how consumers wanted the product to look (an aesthetic choice).  They got the topic right, but did not answer the question in a meaningful way.  So what good was all that coding?

I asked a colleague (highly paid, less efficient) to fix and re-code the data, and I asked her to think not in terms of topics but in terms of answers to questions.  She did, and remarked, “You have an advantage, because you know more about this product and what is relevant to the research than the people (and machines) who did the coding.”

Yes, that’s the point, and that’s the struggle for technology and automation.  Smart researchers who know the right questions and continually think about how best to answer them with data will always have an advantage.  Even when pitted against the best and most efficient technologies, we will always win the insight contest.

There are lots of places where technology is helping us do our work faster, smarter, and at a lower cost.  But no matter what the innovators in tools and technology tell you, it makes a huge difference to have smart people with expertise slogging through the data, deciding how to analyze and present it, and transforming that into a story you can use.  We, at Versta Research, consistently and substantially outperform machines and outsourced labor, which means that you, the client, win as well.

—Joe Hopper, Ph.D.

The study suggests that entrepreneurs are more focused on immediate and practical questions that will help them get their products into the hands of customers, and that traditional market research may not be the best way to get the right data and answers.  That makes sense.

But according to an article in the February issue of Inc. magazine, “when asked what kind of market research they would conduct for [a] hypothetical start-up, most of Sarasvathy’s subjects responded with variations on the following:

“OK, I need to know which of their various groups of students, trainees, and individuals would be most interested so I can target the audience a little bit more. What other information… I’ve never done consumer marketing, so I don’t really know. I think probably…I think mostly I’d just try to…I would…I wouldn’t do all this, actually. I’d just go sell it. I don’t believe in market research. Somebody once told me the only thing you need is a customer. Instead of asking all the questions, I’d try and make some sales. I’d learn a lot, you know: which people, what were the obstacles, what were the questions, which prices work better. Even before I started production. So my market research would actually be hands-on actual selling.”

We heartily agree that sometimes you should get out there and sell rather than conducting more market research (see our article What You May Need Is Marketing, Not Market Research).  But the problem with taking this quote at face value is that a really good market researcher would never say “How would you use market research?”  She would say, “What do you need to know?   What answers to questions would help you achieve your most critical business objectives?” (See our article The Art of Asking Questions.)  Then she would decide whether and how market research can best be used.

At Versta Research we believe that no matter what size company you are, you should be thinking about research more like an entrepreneur.  Great entrepreneurs are using, gathering, analyzing, and interpreting data all the time to help them make decisions.  But their data might be coming from reports on sales calls rather than standard satisfaction or tracking surveys or another new product benchmarking study.  That is a good thing, and a smarter way to approach research even if you are an established firm with a substantial research budget.

So when somebody offers you the shiny new market research tool which is now the “best practice” or the “benchmarked metric,” set it aside.  Instead, outline your questions.  Describe the data and information that would help you achieve your most critical business objectives.  Ask whether research is the most efficient and insightful tool to deliver answers.

If you need help with that process, we are happy to advise.

—Joe Hopper, Ph.D.

The cartoon brings to mind three lessons worth pondering:

1. Customer satisfaction research is often “not fun”—but it can be
2. Internal data can be a goldmine of insight and there is often a lot of it lying around
3. Ethical considerations dictate that just because research can be done does not mean it should be done

1. Customer satisfaction research is often “not fun.” Why?  In our view it is because so many customer satisfaction surveys are done for the wrong reasons, focus on the wrong issues, and ask about the wrong people.  What could be more boring—and more unenlightening—than tracking the percentage of customers who like (or dislike) you month after month and year after year?

To make customer satisfaction research far more interesting and useful, focus not on your products and not on you, but instead on your customers, what they need, and what will delight them.  Then assemble a team that is committed to thinking hard and adding value to every report they deliver.  Tracking studies can be fun if focused on the challenge of consistently providing an insightful story.

2. Internal data can be a goldmine of insight.  Organizations often send out survey after survey without ever taking stock of what they have already learned, and without considering the wealth of data they have internally.  Yet, the volume of useful, untapped internal data that most organizations have is astonishing.  You can learn a great deal about your customers and satisfaction, for example, simply by analyzing historical data.  Who are you losing or gaining as customers?  What segments, industries, or geographies characterize them?  You don’t always need surveys to answer these questions.

3. Ethical approaches to research are essential.  Data should never be collected and archived without the people supplying that data knowing about it and knowing how it will be used.  This is a fundamental ethical requirement all social scientific research, and it extends to all market research and public opinion polling as well.  We recommend that research teams adhere strictly to the strictest standards of ethics and privacy as outlined by The Council of American Survey Research Organizations (CASRO) and the American Association of Public Opinion Research (AAPOR).

Need help on any or all of these fronts?  Definitely stay away from Mordac the Preventer of Information Services, and give us a call instead.

—Joe Hopper, Ph.D.

Here is an example of how critical it can be to look closely at your data, and in this case, very early in the data collection process.  We launched an online survey last week and got reports back from our sample supplier that incidence was just one-third of what we expected, which would have serious feasibility and cost implications.

But once we looked at their report portal, we saw that for every qualified respondent completing the survey, two qualified respondents quit before finishing.  That’s an unusually high ratio of “suspends” as we call them.  So what was the problem?  Were we just getting lousy respondents who did not want to seriously participate in a survey?  Was the survey was too difficult, tedious, boring, or confusing?  One source of answers (rarely examined) is to look at the data question by question to identify where in the survey people are quitting.

The story in this data: Something is wrong with your survey

Nearly every respondent who quit got close to finishing and then dropped off at exactly the same point, which was odd because the most difficult questions were earlier in the survey.  In fact, the question where most ended up quitting was an interesting drag-and-drop interactive exercise.  Ah, that was the problem.  The programming for the interactive piece was flawed and respondents were being kicked out.

It wasn’t without a good deal of angst that the programming team tested, re-tested, and confirmed the error.  Everybody involved in this effort resisted: the sampling provider, the programmers, the survey tool developers, the questionnaire designers—they have all done this work hundreds of times, so there can’t be anything wrong with their piece of it, right?

Let the data speak.  It will tell you where the mistakes are. There are lots of places and moments where things may go wrong.  If the top people responsible for the project do not have immediate visibility into the data, they are unable to suggest smart solutions, and bad research will just keep happening.

—Joe Hopper, Ph.D.

Finding Gold in Your Data Mine

The truth, however, is that while technology and tools can multiply our capabilities and help us work better and faster, they cannot discover meaningful patterns or find hidden insights. Only smart people can do that.  The reason is that market research data only become meaningful within a context of questions that need to be answered, or stories that need to be told.  Tools and technology cannot supply that context.

We are working with a client who has been struggling for the last five months to find a story in survey data.  They commissioned the survey to generate data for a whitepaper for presentation to business level clients and prospects.  They’ve been staring at tables and banner tabs, pie charts and bar charts, correlations and gap analyses.  But squeeze the data as they might, the story will not emerge.

Here was our counsel:  Before you can find a story in the data, be more explicit about the context that will bring it to life.  So we asked each person on the team to write three or four dream headlines that they would like to see come out of the research.  Our instructions were as follows:

1.  Focus on headlines that would be most useful to communicate to your audience

2.  Do not look at the survey or the data—forget about what you think it says

3.  Do not go back to any of your previous documents or thinking—do it top of mind, quickly

4.  Do not worry about making your headlines pretty or accurate—make it a brain dump of your dream headlines

Of course we cannot guarantee that the data will support every claim they want to make.  But we can guarantee that with so many data points and ways of linking data in even the smallest of data sets, there are compelling ways to make that data support a story that is on target and relevant.

How do you find the gold in your data mine?  By providing a frame of reference in which the data becomes gold.  Take three steps backward to consider the critical (and specific) questions you need to have answered and outline the relevant stories that would be useful to your audiences.  Then start mining your data for answers to those questions and for data points, contradictions, anomalies, and surprising patterns that relate to your stories.

—Joe Hopper, Ph.D.