OnDemand WTP Pricing Research

How to Turn Online Data into a Pricing Strategy That Works | Knowledge@Wharton

Knowledge@Wharton: Is there a clear indication of how companies should be playing with price based on how someone is monitoring price?

Moon: Those are exactly some of the issues that we wanted to explore in this research. One of the interesting things that we found is that, even in this very informationally rich space, sometimes very simple policies and very simple decisions can be very effective. You can capture most of the value as a firm.

To give an example, the retailer that we worked with follows a very simple pricing policy. For each product, you’re going to start at a certain price, a list price. Then at a certain point in time during a season, you drop the price down to its sale price, which is a very predictable percentage of that initial list price. Finally, you move to another predictable price, a clearance price, where you’re trying to just get the products off the shelves.

What’s interesting there is that the consumers understand what prices they’ll see. It’s very predictable. But all the retailer did was to make the timing of those markdowns unpredictable. By doing something very simple like that, it really exacerbated the informational asymmetry in terms of the cost of monitoring. Those customers who were price-insensitive, for whom it was very costly to be monitoring often, were the ones who couldn’t take advantage of a markdown when it happened. They understood that, so they would buy earlier. There is an interesting aspect there where this sort of pricing has an allocative role. You’re deciding who buys at what price.

Knowledge@Wharton: It seems like more companies have moved toward unpredictable pricing. There used to be the price, then it went on sale, then it went on clearance. Now it seems that items could be 50% off one day, 30% off the next day, or it could be full price and then 50% again. Is that unpredictable strategy hurting retailers?

Moon: I think it depends on the market. In this sort of setting, we find that being predictable, being simple but also having some degree of flexibility, is actually the right way to go. You are capturing, from the first perspective, most of that value. If you think about an industry where that sort of quickly changing pricing has been very successful, an example might be the airline industry. You might be on a plane, and you sit next to someone who’s paid a very different price for the same ticket. I’m pretty price-sensitive, so I might have bought a cheaper ticket.

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How to Turn Online Data into a Pricing Strategy That Works – Knowledge@Wharton.

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