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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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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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You may have heard about the spat between Apple and Samsung. Apple is suing Samsung for alleged patent infringements that relate to features of the iphone and ipad. The damages claimed by Apple? North of 2 billion dollars. The obvious question is how Apple came up with those numbers? The non-obvious answer is, partly by using conjoint analysis – the tried and tested approach we often use for product development work at TRC.    

Apple hired John Hauser, Professor of Marketing at MIT’s Sloan School of Management to conduct the research. Prof. Hauser is a very well known expert in the area of product management. He has mentored and coauthored several conjoint related articles with my colleague Olivier Toubia at Columbia University. For this case, Prof. Hauser conducted two online studies (n=507 for phones and n=459 for tablets) to establish that consumers indeed valued the features that Apple was arguing about. Details about the conjoint studies are hard to get, but it appears that he has used Sawtooth Software (which we use at TRC) and used the advanced statistical estimation procedure known as Hierarchical Bayes (HB) (which we also use at TRC) to get the best possible results. It also appears that he may have run a conjoint with seven features, incorporating graphical representations to enhance respondent understanding.

There are several lessons to be learnt here for those interested in conducting a conjoint study. First, conjoint sample sizes do not have to be huge. I suspect they are larger than absolutely necessary here because the studies are being used in litigation. Second, he has wisely confined the studies to just seven attributes. We repeatedly recommend to clients that conjoint studies should not be overloaded with attributes. Conjoint tasks can be taxing for survey respondents, and the more difficult they are, the less attention will be paid. Third, he is using HB estimation to obtain preferences at the individual level, which is the state of the science approach. Last, he is incorporating graphics wherever possible to ensure that respondents clearly understand the features. When designing conjoint studies it is good to take these (and other) lessons into consideration to ensure that we get robust results.

So, what was the outcome?

As a result of the conjoint study, Prof. Hauser was able to determine that consumers would be willing to spend an additional $32 to $102 for features like sliding to unlock, universal search and automatic word correction. Under cross examination he acknowledged that this was stated preference in a survey and not necessarily what Apple could charge in a competitive marketplace. This is another point that we often make to clients both in conjoint and other contexts. There is a big difference between answering a survey and actual real world behavior (where several other factors come into play). While survey results (including conjoint) can be very good comparatively, they may not be especially good absolutely. Apple used the help of another MIT trained economist to bring in outside information and finally ended up with a damage estimate of slightly more than $2 billion.

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Recent comment in this post - Show all comments
  • Ed Olesky
    Ed Olesky says #
    how interesting! thanks for sharing this, Dr. Sambandam. i wonder how many price points they tested. and was it subsidized price,

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