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Dynamic Pricing and Incomplete People Information

One of the main problems in analytics is the lack of people information (e.g., demographics, interests). It is controlled by superplatforms like Google and Facebook, but as soon as you have transition from the channel to the website, you lose this information.

So, I was thinking this in context of dynamic pricing. There’s no problem for determining an average solution, i.e. a price point that sets the price so that conversion is maximized on average. But that’s pretty useless, because as you know averages are bad for optimization – too much waste of efficiency. Consider dynamic pricing: the willingness to pay is what matters for setting the price, but it’s impossible to know the WTP function of individual visitors. That’s why aggregate measures *are* needed, but we can go beyond a general aggregate (average) to segmentation, and then use segment information as a predictor for conversion at different price points (by the way, determining the testing interval for price points is also an interesting issue, i.e. how big or small increments should you do —  but that’s not the topic here).

Going back to the people problem — you could tackle this with URL tagging: 1) include the targeting info into your landing URL, and you’re able to do personalization like dynamic pricing or tailored content by retrieving the targeting information from the URL and rendering the page accordingly. A smart system would not only do this, but 2) record the interactions of different targeting groups (e.g., men & women) and use this information to optimize for a goal (e.g., determining optimal price point per user group).

These are some necessary features for a dynamic pricing system. Of course then there’s the aforementioned interval problem; segmentation means you’re playing with less data per group, so you have less “trials” for effective tests. So, intuitively you can have this rule: the less the website has traffic, the larger the increments (+/-) should be for finding the optimal price point. However, if the increments become too large you’re likely to miss the optimal (it gets lost somewhere in between the intervals). I think here are some eloquent algorithmic solutions to that in the multi-armed bandits.

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