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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. 
 

Start out with Incentive Alignment 

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. 
 

Go beyond Simple Ratings, Employ a Choice Method

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.  
 

Find a Combination of Flavors that Would Please the Most People 

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.   
 

Find the Best Position on the Shelf with Discrete Choice Conjoint 

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….
 
Tagged in: Conjoint
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Half life-Market-Research

I heard a great episode of the “You Are Not so Smart” podcast in which Sam Arbesman talked about his book called “The Half Life of Facts”. This book has nothing to do with “truthiness”, “fake news” or any accusation that someone is or is not a liar, but it does provide some context for the world we live in.
 
The book’s title is taken from a scientific term (the time it takes an isotope to lose half of its radioactivity) and the notion that as we learn more, some things we took as “fact” will turn out to be wrong. Newton’s laws, for example, were supplanted by Einstein. The point of the book is not that we shouldn’t bother learning facts, but rather that we should be open to the possibility that they might be wrong. Modern medicine acknowledges that they don’t know everything and that some things they “know” will prove to be false. At the same time, they must treat patients based on what is known or thought to be known.
 
It got me thinking about our business. What is the half-life of facts here? You might be tempted to take comfort in the fact that things like margin of error have not changed. While technically true, this ignores that academia is facing a crisis of confidence over statistically significant findings that don’t hold up in subsequent studies. One cause for this is they run lots of cuts of the data and look for anything statistically significant and then build a rationale for that finding. They ignore that with so many cuts of the data they are likely to find some statistical noise. Don’t we run the same risk with each additional banner we run?
 
There is a known problem with Discrete Choice Conjoint that is often ignored. If you have a product made up of say 8 features each with three levels and 1 with 150 the importance of the feature with 150 levels will be overstated by the model. Still, the model will run, utilities will be calculated and a simulator can be constructed…all of which provide a sense of precision that is not warranted. A researcher who knows about it will guide the client either by changing the design OR by putting the results into their proper perspective. There are many other ways that a complex model like this can produce skewed results and I have little doubt more will be found in the future. 
 
This is not to say that we can’t trust results. Doctors have to treat patients based on what is known today and we must do the same for our clients. The important thing is that we have to acknowledge we have things to learn. As researchers that should be easy for us…
 
Tagged in: Conjoint
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hp-trivia-pricing-research-monetizing
In my previous blog about HQ Trivia I pondered how the creators of HQ were planning to make money.  Right now there is no advertising; venture capital funds the app and the jackpots. Apart from occasional sponsorships, there appears to be no immediate source of additional funding.
 
HQ could do many different things to achieve financial success – content sponsorships, jackpot sponsorships, advertising, product placement, buying ‘lives’ by watching a 15-second spot  – even sponsor logos on host apparel. In fact, there are probably different ways to monetize HQ Trivia that we haven’t even thought of yet – making this a perfect research case for TRC’s Idea Mill™.
 
Idea Mill™ is our method that employs Smart Incentives™ – harnessing the principles of crowd-sourcing to ask respondents for their best idea, and the ideas are then voted on by other respondents within the same research survey. The respondents with the best ideas as judged by their peers are rewarded with prizes. This is a great technique to use when you’re in the idea generation phase of product development.  
 
Once we get a list of potential ways to monetize HQ, we could then winnow the list to the ones that would be feasible to implement, and narrow the list using a prioritization-based research method such as Idea Magnet™. Results can be generated quickly.  
 
Before implementing the winning ideas, we could further explore options by building various scenarios of the sponsored game, and asking HQers to weigh in on which one would be most acceptable to them. Through a choice-based research tool such as discrete choice conjoint, we could vary HQ’s potential features, such as:
 
      • •  Number of ads or sponsorships per game
      • •  Where the ads appear (between rounds, upon game entry) 
      • •  Prize pool
      • •  Having sponsor-related questions
      • •  Getting bonus ‘lives’ for watching sponsor videos
 
All of these techniques employ strategies we use in pricing and product development research to include the consumer in the decision-making process. HQ’s creators are good at asking questions – I hope they do the same in further developing their product.
 
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GRIT-TOP-50-report

I appreciate that we are once again in the GRIT 50 Most Innovative Research Agencies. Innovation has always been important to me and so I am quite gratified when I see our efforts being recognized. What I don't know is how people are defining innovation.

I think as an industry we sometimes label things as innovative that are not while failing to recognize some things that are genuinely innovative. In my view, innovation requires that we provide something of value that wasn't available before. Anything short of that may be 'interesting' but not 'innovative'.

I would put things like neuroscience or most AI into the "interesting" category. There is a lot of potential but so far little so show in terms of tangible benefits. Over the years at TRC we've had many ideas that showed promise, but ultimately didn't prove out (my favorite being "Conjoint Poker"). Ultimately it is the nature of innovation that some things will never leave the drawing board or 'laboratory', but without them there would be no innovation.

On the other side, I think ideas that save time and money are often not viewed as innovative unless they involve something totally new. I disagree. If I can figure out a way to do the same process faster and/or cheaper then I'm innovating. It may not look flashy, but if it allows clients to do something they couldn't otherwise do it is innovation.

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Tagged in: Pricing Research
hq-pricing-research
 A bunch of us here at TRC enjoy trivia, so we’ve been playing HQ Trivia using their online app for the past few months. HQ is a 12-question multiple choice quiz that requires a correct answer to move on to the next question. As a group, we have yet to get through all 12 questions and win our share of the prize pool. But it’s a nice team-building exercise and we like learning new things (who knew that 2 US Presidents were born in Vermont).  
 
Given the fun we have playing it, I can understand HQ’s success from the player perspective. Where I am a bit confused is the value proposition for its creators. Venture capital funding provides the prize money.  But there are no ads, so I’m not sure how anybody’s actually making money. There are occasional tie-in partnerships (The awesome Dwayne Johnson hosted one of the gaming sessions to promote his newest movie release, “Rampage”.)  But I suppose the biggest question is, will interest in HQ still be there when they’ve finally signed on enough sponsors to be profitable?  
 
We do a lot of pricing research at TRC, and can model on a variety of variables. But predicting the direction of demand is nearly impossible for certain products. For consumables and many services, product demand is predictable. How your product fares compared to the competition may have its ups and downs, but you can assume that people who bought toilet paper 2 weeks ago will be in the market for toilet paper again soon.
 
But with something like HQ Trivia, product demand is much more difficult to determine in advance, especially more than a few weeks from now. Right now it’s still hot – routinely attracting 700,000 – 1,000,000+ players (HQers) in a given game. How do the creators – and investors and potential sponsors – know whether it’s a good investment?  What if interest suddenly declines, either because the novelty has worn off or because something better comes along?  
 
One way to find out is through longitudinal research. Routinely check in with HQers over time to determine their likelihood to play the next week, their likelihood to recommend to their friends, and their attitudes toward the game itself. This information can be overlaid with the raw data HQ collects through game play every day – number of players, number of referrals, and number of first-time players. This information can not only help shed light on player interest, but players could also weigh in on changes the creators are considering to keep the game fresh.
 
HQers are engaging in a free activity which gives them the opportunity to win cash prizes.  But just because it’s free to play doesn’t mean the HQ powers-that-be couldn’t do pricing research (more on that in a future blog).  
 
For now, I’ll keep on playing HQ hoping I can answer all the questions, not the least of which is: when will I – and the other million HQers – no longer care? 
 
 
Tagged in: Pricing Resarch
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