Archive for the ‘Mktg – Market Research’ Category

Emotional profiling: I like you, but I don’t love you …

September 7, 2011

Punch line: Why do two identical-looking products that get the same score in acceptability tests, perform wildly differently in the marketplace?

“Emotional research ” tries to find out why, and create profiles of prime prospects.

Excerpted from CPGmatters “Kraft Foods Develops ‘Emotional Profiling’

Kraft Foods has been developing a sophisticated new science of “emotional profiling”.

Kraft has been working on emotional profiling for three years as part of its sensory and consumer-testing work.

“The theory behind emotional profiling is uncovering the difference between ‘liking’ something and ‘preferring’ it.”

“The idea is fairly basic.

Even if an individual likes two different products, they may still prefer one over the other.

We’re trying to figure out that difference or gap so that we can make the best possible products that consumers will truly prefer.”

Traditional research tools may not be enough to capture the implications of emotion on food shopping.

“We use emotional research to define unique points of difference and create a new hierarchy of attributes that go beyond ‘liking.’

Qualitative research usually includes in-depth interviews exploring sensory reactions with target customers who represent a variety of positions along the brand-loyalty scale.

So, tell me again why kids love artificial-looking, artificial-tasting Kraft mac & cheese …

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“It’s not the consumers’ job to know what they want.”

August 26, 2011

That was what Steve Jobs has has been saying for years.

The statement seems to be rippling through the marketing community now that Jobs has resigned.

NY Times, Without Its Master of Design, Apple Will Face Many Challenges

Mr. Jobs explained that his design decisions were shaped by his understanding of both technology and popular culture.

His own study and intuition, not focus groups, were his guide.

When a reporter asked what market research went into the iPad, Mr. Jobs replied: “None. It’s not the consumers’ job to know what they want.”

What’s the rub?

Jobs’ success flies in the face of marketers who spend  time and energy arguing for and doing extensive consumer research (surveys. focus groups, etc.).

And, it’s hard to argue with his success,


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The Evolution of Focus Groups

December 22, 2010

TakeAway:  Arguing that focus groups were never really all that effective in the first place, agencies and research facilities have introduced a variety of methods aimed at shaking up the traditional focus group approach. 

Young & Laramore, an Indianapolis-based agency, frequently runs what the company president calls “friendship groups.”

That’s when the company will tap one consumer and ask that individual to recruit two or three others from his/her social circle. The assumption is that one is more likely to be comfortable in an experimental setting when with others in one’s social network.

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Excerpted from Forbes, “From Focus Groups to ‘Friend’ Groups” By Elaine Wong, November 19, 2010

In these situations, researchers can tell when participants are sharing “secrets with each other, you can catch them winking their eyes or exchanging signals with each other, and you dig into that and find out what’s up.”

Contrast that with the conventional focus group model, in which the scenario in question usually runs something like this: A packaged goods company, retailer or marketer, let’s say, asks an agency or research partner to recruit a panel of consumers with whom to test new products, packaging or ideas. These groups, which can range anywhere from six to 12 or more in number, then gather in a “sterile” room, as many agency execs describe it. A moderator then runs through a list of questions and records participants’ responses while researchers in the back room watch. Such procedures are routine, boring, not to mention long—one session can last 90 minutes—and yield few, if any, new insights.

One catalyst driving the push is the proliferation of social media mining tools, which allow companies to test and tweak new go-to-market strategies in real time and without the need for an actual focus group.

To avoid the typical, ho-hum answers, one company, The New England Consulting Group, uses a methodology called Super Groups, which involves finding the extreme, “lunatic fringes” of a consumer set. Talking to those who are not your average consumer ensures that you get not-so-average—and in some cases, off the chart—results. Several agency executives also brought up the idea of “conflict groups,” when “you recruit and mix people who love something [with] others who hate it or [bring together] passionate lovers of two different brands,” explains an Arnold executive.

Efficiency aside, the historical focus group also posed other problems. One is the gap between what people think and how they later act. Consumers may rationalize their shopping or buying behaviors, but emotion, rather than reason, is often a big driver of these decisions.

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The Numbers Don’t Lie: Competing on Analytics

January 20, 2009

Excerpted from Knowledge@Emory, “The Value and Benefits of Competing on Analytics”, November 13, 2008

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A lot of companies collect data. But it’s the ability to analyze and strategically act on that data that matters, notes Thomas Davenport, the author of the best-seller Competing on Analytics: The Science of Winning.. Most companies use data in a supporting role—not as the strategic weapon it can be.

At a time when competing companies offer similar products and have access to the same technologies analytical customer-facing processes are some of the only areas where businesses can differentiate themselves. “Analytical competitors” mine their data for every sliver of information it offers and then utilize that information in strategic ways. Firms like Harrah’s, Marriott  and Google that use analytical tools in a strategic manner are raising the bar in their industries when it comes to things like customer service, supply chain management and marketing.

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Companies are taking their metrics—marketing metrics, sales metrics and service metrics—and using them, among other things, to segment, cross-sell, forecast, target and study drivers of satisfaction. Rather than looking at what happened, companies are able to predict more precisely what will happen and to better understand how and why it happened. Decisions are based on facts, not hunches or incomplete information.

Historically there’s been as much “art” in advertising and marketing as there has been “science.” Davenport argues that science is more likely to be correct than “art,” and adds that science enables a company to experiment on a small scale before committing a slew of resources to a huge marketing campaign or program.

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Example: Harrah’s

In order to attract and retain customers, Harrah’s uses analytical tools not only to identify profitable customers, but to pinpoint its customers most likely to be wooed by competitors. Through well-designed marketing campaigns—created around information the analytical tools delivered—Harrah’s marketing methods can be strategically aimed at keeping those customers.

Analytical tools allowed the entertainment company to create a well-defined profile of its best customer, developing a beefed-up customer relationship management system anchored by a centralized data warehouse filled with pertinent information about how Harrah’s customers interact with the company. And given this information, Harrah’s marketing efforts are tailored to attract its different constituencies.

For instance, Harrah’s customers who live within driving distance of a casino receive different offers than those who do not. Frequent visitors to the casino in New Orleans are likely to receive different offers than frequent customers in Las Vegas or St. Louis. Harrah’s only targets customers likely to respond to such offers and it has more than 80 different segments for each marketing campaign.

One of the reasons Harrah’s has been so successful, notes Davenport, is because they focused on “one thing early” when it came to analytics. In Harrah’s case that “one thing” was customer loyalty. “In terms of marketing, companies need to think about target areas and about what they’re trying to accomplish,” adds Davenport. According to information available on Harrah’s website, nearly 50 percent of the company’s revenue is driven by marketing and the company’s analytical efforts have helped boost the company’s bottom line.

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In studying companies that use analytics successfully, Davenport crafted what he calls “The Ladder of Analytical Marketing Targets”—essentially a best practice guide. First up, build a centralized customer database so that the company can get a comprehensive idea about its customers. The company can then treat “different customers differently” and respond appropriately to a customer’s activity. Companies can keep track of who got what and better manage their marketing campaigns—to the point of personalizing them. Via predictive modeling, companies can answer with much more certainty what customers are likely to do next given what they’ve already done. As a result, companies can make real time, customized offers that make sense to their customers.

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Consumer choice modeling … how people decide to buy

January 15, 2009

Excerpted from Strategy+Business, “Tracking the Elusive Consumer”, by John Jullens and Gregor Harter, Novermber 11, 2008

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Consumer choice modeling … offers a better understanding of consumer preferences:
  • What does the consumer want?
  • Why do individuals prefer one product or service over another?
  • How, precisely, do most consumers make their purchasing decisions?

Recent work on the art and science of consumer behavior has refined, updated, and strengthened an analytical tool known as consumer choice modeling, initially developed in the 1960s by Daniel McFadden, a winner of the 2000 Nobel Prize in economics.

Simply put, this model examines the personal reasons for individual choices and provides techniques researchers can use to measure and predict those choices. By exploring why individuals make specific trade-offs among various product options, consumer choice modeling can determine the features that people in different economic and demographic strata are looking for and how much they are willing to pay.

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Originally, this technique suffered from a lack of sophistication. A typical implementation involved asking respondents to react to lengthy paper-and-pencil surveys offering a series of preconfigured and static product or service possibilities. Although some insight about consumer preferences was typically evinced, it was often shallow, limited by researchers’ inability to dynamically change the direction of the questioning on the basis of the responses.

However, advances in experimental designs and information technology now allow researchers to better approximate the shopping experience when asking questions by adjusting product choices in reaction to a person’s answers. By analyzing the responses from a representative sample of consumers (or potential future customers), researchers can produce econometric models that depict the relative weighting of specific product features and price points.

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Early in 2007, Booz & Co.applied consumer choice modeling to identify and measure the drivers of demand for mobile phones. One example::  Apple’s iPhone.

Long before the iPhone’s launch,  the Booz  model correctly predicted that it would be the most attractive overall offering to consumers despite its high price tag. 

Booz  surveyed more than 1,800 consumers by simulating the actual mobile phone purchasing process and asking people to compare their existing package with alternatives.

For example, owners of low-cost Sharp handsets running on pay-as-you-go carriers such as Virgin Mobile or Boost Mobile were offered a U$100-plus Samsung phone with Nextel service and a $250-plus LG phone with Verizon’s network. Respondents were asked, “If these two packages were your only alternatives, which one would you choose: Samsung/Nextel, LG/ Verizon, or neither?” and “If Samsung/Nextel were your only option, would you purchase it or continue to use your current package?”

The majority of the low-end and midlevel consumers were highly commodity driven. Other than by offering an attractive handset price, it is almost impossible to convince an individual to change his or her current mobile phone package. In fact, further analyses revealed that one-third of U.S. consumers are unwilling to change their wireless package, no matter how much the handset price is lowered.

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Of all phone users, owners of low-end handsets made by the Nokia  value their phone package the least. Consequently, these consumers are the most willing to switch to another carrier and handset — an opportunity for competitors to attack Nokia’s base by producing a low-cost package with a function or two that outpaces the relatively plain Nokia product.

The consumer choice model also revealed that owners of handsets made by Sony Ericsson , which tend to be highly designed, full-featured products, care much more than Nokia users about functionality, usage range, and purchase location (they prefer to buy their packages at stores that offer personal attention, rather than at Costco or Circuit City, for example). And although these customers, too, are price conscious, they’re willing to pay a premium to have their preferences met. A service provider could use these findings to target Sony Ericsson owners with a slightly less expensive offering that in all other ways matches their current package.

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Consumer choice modeling also has the ability to predict the impact of future products and services on the market. Booz  simulated the characteristics of “the ideal high-end phone” as consumers viewed it. From this, the survey gleaned that three primary factors — feature, design, and brand — are of paramount value to consumers considering a higher-priced model. These factors, of course, were exactly what Apple focused on in developing its blockbuster iPhone, launched in July 2007.

Significantly, as the model predicted, Apple stumbled when it came to price, which the survey showed matters at all levels of cell phone purchases.

At a price point of $599 for an eight-gigabyte phone, the research forecasted that Apple would have difficulty reaching a significant portion of the high-end market. But the same research suggested that performance would improve quickly as soon as Apple cut prices. In fact, that is precisely what happened: In September 2007, Apple discounted the phone by $200, and sales rose well over 1,000 percent in the succeeding quarter from sales in the prior three-month period. And in June 2008, CEO Steve Jobs announced a much faster eight-gigabyte iPhone — using AT&T’s state-of-the-art 3G network — for only $199, a move that further aligned Apple’s pricing with that of its peers and that will almost certainly improve the product’s market share.

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Consumer choice modeling yields valuable insights for demand-driven strategy development by providing customer value segmentation maps, measuring market share impact of new product–service combinations, and assessing overall brand equity. Perhaps most important, choice modeling can reveal sa­lient differences between managers’ beliefs about customers’ needs and preferences and customers’ actual needs and preferences. For managers seeking reliable feedback on how customers view their products and services, consumer choice modeling provides a rigorous way to turn customer-driven feedback into profitable and sustainable tactics for retaining or capturing market share.

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