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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
conjoint-modern-market-research-In my last blog I referenced an article about design elements that no longer serve a purpose and I argued that techniques like Max-Diff and conjoint can help determine whether these elements are really necessary or not. Today I’d like to ask the question “What do we as researchers use that are still useless?”
 
For many years the answer would have been telephone interviewing. We continued to use telephone interviewing long after it became clear that web was a better answer. The common defense was “it is not representative”, which was true, but telephone data collection was no longer representative either. I’m not saying that we should abandon telephone interviewing…there are certainly times when it is a better option (for example, when talking to your clients customers and you don’t have email addresses). I’m just saying that the notion that we need to have a phone sample to make it representative is unfounded.
 
I think though we need to go further. We still routinely use cross tabs to ferret out interesting information. The fact that these interesting tidbits might be nothing more than noise doesn’t stop us from doing so. Further, the many “significant differences” we uncover are often not significant at all…they are statistically discernable, but not significant from a business decision making standpoint. Still the automatic sig testing makes us pause to think about them.
 
Wouldn’t it be better to dig into the data and see what it tells us about our starting hypothesis? Good design means we thought about the hypothesis and the direction we needed during the questionnaire development process so we know what questions to start with and then we can follow the data wherever it leads. While in the past this was impractical, we not live in a world where analysis packages are easy to use. So why are we wasting time looking through decks of tables?
 
There are of course times when having a deck of tables could be a time saver, but like telephone interviewing, I would argue we should limit their use to those times and not simply produce tables because “that’s the way we have always done it”.  
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new-product-research-car-grilleI read an interesting article about design elements that no longer serve a purpose, but continue to exist. One of the most interesting one is the presence of a grille on electric cars. 
 
Conventional internal combustion engine cars need a grille because the engine needs air to flow over the radiator which cools the engine. No grille would mean the car would eventually overheat and stop working. Electric cars, however, don’t have a conventional radiator and don’t need the air flow. The grille is there because designers fear that the car would look too weird without it.  It is not clear from the article if that is just a hunch or if it has been tested.
   
It would be easy enough to test this out. We could simply show some pictures of cars and ask people which design they like best. A Max-Diff approach or an agile product like Idea Magnet™ (which uses our proprietary Bracket™ prioritization tool) could handle such a task. If the top choices were all pictures that did not include a grille we might conclude that this is the design we should use. There is a risk in this conclusion.
 
To really understand preference, we need to use a discrete choice conjoint. The exercise I envision would combine the pictures with other key features of the car (price, gas mileage, color…). We might include several pictures taken from different angles that highlight other design features (being careful to not have pictures that contradict each other…for example, one showing a spoiler on the back and another not). By mixing up these features we can determine how important each is to the purchase decision.  
It is possible that the results of the conjoint would indicate that people prefer not having a grille AND that the most popular models always include a grille. How?
 
Imagine a situation in which 80% of people prefer “no grille” and 20% prefer “grille”. The “no grille” people prefer it, but it is not the most important thing in their decision. They are more interested in gas mileage and car color than anything else. The “grille” folks, however, are very strong in their belief. They simply won’t buy a car if it doesn’t have one. As such, cars without a grille start with 20% of the market off limits. Cars with a grille, however, attract a good number of “no grille” consumers as well as those for whom it is non-negotiable.
 
Conjoint might also find that the size of the grille or alternatives to it can overcome even hard core “grille” loving consumers. Also worth consideration that preferences will change over time. For example, it isn’t hard to imagine that early automobiles (horseless carriages as they were called originally) had a place to hold a buggy whip (common on horse drawn carriages), but over time, consumers determined they were not necessary (or perhaps that is how the cup holder was born :)).
 
In short, conjoint is a critical tool to insure that new technologies have a chance to take hold. 
 
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Brand PerceptionsIs the Mini Cooper seen as an environmentally friendly car? What about Tesla as a luxury car? The traditional approach to understanding these questions is to conduct a survey among Mini and Tesla buyers (and perhaps non-buyers too, if budget allows). Such studies have been conducted for decades and often involve ratings of multiple attributes and brands. While certainly feasible, they can be expensive, time consuming and can get outdated over time. Is there a better way to get at attribute perceptions of brands that can be fast, economical and automated?

Aron Culotta and Jennifer Cutler describe such an approach in a recent issue of the INFORMS journal Marketing Science, and it involves the use of social media data – Twitter, in this case. Their method is novel because it does not use conventional (if one can use that term here) approaches to mining textual data, such as sentiment analysis or associative analysis. Sentiment analysis (social media monitoring) provides reports on positive and negative sentiments expressed online about a brand. In associative analysis, clustering and semantic networks are used to discover how product features or brands are perceptually clustered by consumers, often using data from online forums.

Breaking away from these approaches the authors use an innovative method to understand brand perceptions from online data. The key insight (drawn from well-established social science findings) is that proximity in a social network can be indicative of similarity. That is, understanding how closely brands are connected to exemplar organizations of certain attributes, it is possible to devise an affinity score that shows how highly a brand scores on a specific attribute. For example, when a Twitter user follows both Smart Car and Greenpeace, it likely indicates that Smart Car is seen as eco-friendly by that person. This does not have to be true for every such user, but at “big data” levels there is likely to be a strong enough association to extract signal from the noise.   

What is unique about this approach to using social media data, is that it does not really depend on what people say online (as other approaches do). It only relies on who is following a brand while also following another (exemplar) organization. The strength of the social connection becomes a signal of the brand’s strength on a specific attribute. “Using social connections rather than text allows marketers to capture information from the silent majority of brand fans, who consume rather than create content,” says Jennifer Cutler, who teaches marketing at the Kellogg School of Management in Northwestern University.

Sounds great in theory, right? But how can we be sure that it produces meaningful results? By validating it with the trusted survey data that has been used for decades. When tested across 200+ brands in four sectors (Apparel, Cars, Food & Beverage, Personal Care) and three perceptual attributes (Eco-friendliness, Luxury, Nutrition), an average correlation of 0.72 shows that social connections can provide very good information on how brands are perceived. Unlike with survey data, this approach can be run continuously, at low cost with results being spit out in real time. And there is another advantage. “The use of social networks rather than text opens the door to measuring dimensions of brand image that are rarely discussed by consumers in online spaces,” says Professor Cutler.

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