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advanced-market-research-methods-and-candyAt TRC, the most popular spot in the office is our snack shelf. It features an array of sugary, salty and carb heavy treats. The contents vary and are determined by one person (Ruth, who stocks the shelf) with influence from the rest of us (based on past usage and suggestions). Sometimes the shelf has exactly what you’re looking for. Other times, not so much. But what if instead of relying on Ruth’s powers of deduction we were to use research to figure out the optimal shelf configuration?  We’re researchers, after all. 
 
We would start out by using our Idea Mill™ product to generate ideas on which snacks people want to have. It uses incentive alignment and gamification to bring out the most creative ideas and provide direction on the favorites. It is likely that this will create too long a list of ideas (the candy shelf is only so large) and while we can toss out ideas that are not feasible, we believe it is best not to toss out ideas just because you personally don’t like them (I’m looking at you Mr. Goodbar). Far better to get more consumer input…this time to narrow the list. 
 
We could ask our folks to rate all the suggested snacks and then use that to figure out which ones should make the cut. Ratings might be good enough to eliminate some things (my guess is that despite what people claim, healthy snacks would bite the dust), but among popular snacks (like different types of pretzels) we are not likely to see clear differentiation.
 
A choice method like Max-Diff could help but if the list was long it would require a lot of work on the part of our employee respondents. A method like our proprietary Bracket™  would do the job in a faster and more engaging fashion while still finding clear winners and losers.  
 
Stocking the winners would therefore make the most sense…but would it please the most people?
Currently the shelf features five types of M&M’s (original, almond, caramel, dark and strawberry nut). If dark chocolate was the least preferred it might get cut. But what if those who like almond, caramel and strawberry nut also liked original, but those who like dark only liked it. For situations like this we can take the results of the Bracket™ (or Max-Diff) and use TURF  to find the combination that would please the most people.   
 
Of course, another factor is positioning. The shelf is only so large. M&M’s can be dispensed from any size canister (in fact Ruth has one that spins so that it can dispense three types) while Pretzels tend to come in large bins that take up a lot of room. In addition, not all of the snacks cost the same. In an effort to keep our expenses and waistline under control we follow a strict budget. Might I trade off having greater quantity of a lesser snack in exchange for an expensive favorite? 
 
For these kinds of questions a discrete choice conjoint is the answer. We can include a variety of candy types and constraints related to the room they take up as well as cost. Simulations can then optimize how to spend our candy budget.  
Despite our love of research and wide array of tools though, I think in this case they would be overkill (we have a very small population of around 40 employees). So I think we’ll stick with Ruth’s instincts. I never go wanting….
 
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Lilly Allen Market Research representativenessSome months ago, Lily Allen mistakenly received an email containing harsh test group feedback regarding her new album. Select audience members believed the singer to be retired and threw in some comments that I won’t quote. If you are curious, the link to her Popjustice interview will let you see them in a more raw form. Allen returned the favor with some criticism on market research itself:

“The thing is, people who take part in market research: are they really representative of the marketplace? Probably not.” –Lily Allen

The singer brings up a valid concern. One of the many questions I pondered five months ago when I first took my current researcher-in-training position with TRC. Researchers are responsible for engaging a representative sample and delivering insights. How do we uphold those standards to ensure quality? Now that I have put in some time and have a few projects under my belt, I have assembled a starter list to address those concerns:

Communicate: All Hands on Deck

In order to complete any research project, there needs to be a clear objective. What are we measuring? Are we using one of our streamlined products, such a Message Test Express™, or will there be a conjoint involved? This may seem obvious, but it is also critical. A team of people is behind each project at TRC; including account executives, research managers, project directors, and various data experts. More importantly, the client should also be on the same page and kept in the loop. Was the artist the main client for the research done? My best guess is no, the feedback given was not meant to be a tool to rework the album.

Purpose

Was the research done on Lily Allen’s album even meant to be representative? Qualitative interviews can produce deep insights among a small, non-representative, group of people. This can be done as a starting point or a follow-up to a project, or even stand alone, depending on the project objectives.

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UFO sighting causation correlation market researchSmallI read a blurb in The Economist about UFO sightings. They charted some 90,000 reports and found that UFO's are, as they put it, "considerate". They tend not to interrupt the work day or sleep. Rather, they tend to be seen far more often in the evening (peaking around 10PM) and more on Friday nights than other nights.
The Economist dubbed the hours of maximum UFO activity to be "drinking hours" and implied that in fact that drinking was the cause of all those sightings.
As researchers, we know that correlation does not mean causation. Of course their analysis is interesting and possibly correct, but it is superficial. One could argue (and I'm sure certain "experts" on the History Channel would) that it is in fact the UFO activity that causes people to want to drink, but by limiting their analysis to two factors (time of day/number of sightings), The Economist ignore other explanations.
For example, the low number of sightings during sleeping hours would make perfect sense (most of us sleep indoors with our eyes closed). The same might be true for the lower number during work hours (many people don't have ready access to a window and those who do are often focused on their computer screen and not the little green men taking soil samples out the window).
As researchers, we need to consider all the possibilities. Questionnaires should be constructed to include questions that help us understand all the factors that drive decision making. Analysis should, where possible, use multivariate techniques so that we can truly measure the impact of one factor over another. Of course, constructing questions that allow respondents to express their thinking is also key...while a long attribute rating battery might seem like it is being "comprehensive" it is more likely mind numbing for the respondent. We of course prefer to use techniques like Max-Diff, Bracket™ or Discrete Choice to figure out what drives behavior.
Hopefully I've given you something to think about tonight when you are sitting on the porch, having a drink and watching the skies.

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what increases attention paid to adsAdvertisers and researchers do a lot of testing to determine how effective their advertising is prior to launching a campaign or message. We look for ways to get inside consumers’ heads, and as technology improves, we are afforded interesting glimpses into how consumers process information and make decisions. As my colleague Rajan pointed out in his blog different areas of the brain lead to different types of decision-making. Nobel Prize winner Daniel Kahneman posits that human thinking can be classified into two forms, System 1, which operates automatically, and System 2, which requires mental effort (I paraphrase). Jonah Lehrer, author of How We Decide asserts in his blog “Our best decisions are a finely tuned blend of both feeling and reason and the precise mix depends on the situation. When buying a house, for example, it’s best to let our unconscious mull over the many variables. But when we’re picking a stock, intuition often leads us astray. The trick is to determine when to use the different parts of the brain, and to do this, we need to think harder (and smarter) about how we think.”

With all of this exciting work being done in the field of neuroscience and behavioral economics, I wondered what kinds of answers we would get if we simply asked consumers directly what they think motivates them in considering advertising. Do they believe they respond to characters like the Geico gecko? Or is it really just a function of what they need at the time?

Segmenting Movie Goers

Posted by on in New Product Research

A few months ago I posted that we researched 18 factors in deciding which movie to see and where to see it. We reported that “It’s in 3D” was at the bottom of the list, and concluded that 3-D was unlikely to save the American movie box office.  

What made the top of the list was “I like the plot or story,” followed by “It is in my favorite movie genre” and “It has my favorite stars.”  

But surely the plot isn’t the critical decision-maker for every movie-goer; there must be groups of viewers whose decisions revolve around some of the other items on that list. We took their ratings and ran a segmentation analysis. While this type of analysis is done on a much grander scale by researchers in the movie industry, we thought it would be interesting to do some analysis of our own.

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