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We're here today with Ken Moon, a Wharton professor of operations, information, and decision, and he's here
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to talk to us about some of his recent research. Ken, thanks for being with us. Oh, thank you so much. So, first of all,
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could you give us kind of a short summary of what you looked at, what you're trying to find out? Um, so I I do
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research in empirical operations. So, um, basically that means two things. One, I I work with data. Um, typically
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sometimes in collaboration with uh companies, hospitals, marketplaces. Um, and second, I'm really trying to be uh
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prescriptive about decision- making in my research. Um, in this particular project, uh, we work with an a retailer
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that's active online. Um, it was a very, uh, detailed customer level data set.
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Uh, what's kind of interesting about it is that you can track a single customer
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um, both online and offline. So, for instance, if the customer today were to um go on their phone, look at a product,
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go to their computer tomorrow, look at that, and then walk into a store on another day and actually buy it, we
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would be able to track all of those things. Um, so it really opened up a lot of avenues to explore there. And um I
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think uh one thing that's very interesting about uh this particular project was that we were able to look at
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a very information-rich environment in a way that I think um we see in our everyday lives. So, not only can we
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browse in that way and have a lot more information, say we think a product that we're interested in might drop in price,
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we can check our smartphone. Um, it's also the case now that uh these companies can actually track all of this
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information um at a very individual level. Um, so for both sides, it's a very information-rich environment. And
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so it's very interesting um to think about how does that affect uh the decisions of say companies that are
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active in this sort of space but also um how does that affect outcomes for for customers and consumers right I mean
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price monitoring has really become kind of a daily part at least of my life I know so when you were looking at this
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data set what did you find about price monitoring um I think one uh sort of broad takeaway
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is that information seems to matter um so you do have customers who are very intensive in their monitoring. Um
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they're typically the more price sensitive customers actually, but also their opportunity cost to be doing this
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sort of monitoring is very low. So they're going to be checking very often. Um and your most price insensitive
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customers actually uh they're not checking very often. It's about every 20
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days on average between uh visits. Um so it's a very big difference in terms of
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how these uh consumers are able to access information even from this very ubiquitous channel. Um, and it makes a
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big difference in terms of outcomes as well. Now, is there a clear indication of how companies should be doing this?
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Like how they should be playing with price based on how someone's looking at
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and monitoring price? Should we be doing one thing or does it depend on the customer or No, that's it's it's very
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interesting. It was it was that's exactly uh those are exactly some of the issues that we wanted to explore in this
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research. And um uh one of the interesting things that we found is that even in this very uh rich
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informationally rich space um sometimes very simple uh policies and very simple decisions can be very
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effective. You can capture most of the value as a firm. Um so to give you two examples um the retailer that we worked
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with follows a very simple pricing policy for each product. You're going to start at a certain price, a list price,
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and then at a certain point in time during its season, you drop uh the price down to its sale price, which is a very
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predictable percentage of that initial list price. And then finally, you move to another predictable price, a
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clearance price, where you're trying to just get the products out of the off the
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shelves. And um what's interesting there is that the consumers understand what
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prices they'll see. Um it's very predictable. Um but all that the retailer did is to make the timing of
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those markdowns unpredictable. And by doing something very simple like that um it really uh uh uh exacerbated
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theformational uh asymmetry in terms of the cost of monitoring. So those customers who were
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price insensitive who um it was very costly for them to be monitoring often they were the ones who couldn't take
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advantage of a markdown when it happened and they understood that. So then um they would buy earlier. So there's an
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interesting aspect there where um this sort of pricing has an allocative role. You're deciding who buys at what price
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because it seems like I mean it seems like more companies and maybe correct me if I'm wrong that more companies have
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moved towards more unpredictable pricing. I mean, it used to be it seems like that, you know, there was there was
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the price, then it went on sale, then it went on clearance, and now it seems to be like, you know, one day it could be
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50% off, one day it could be 30, the next day it could be full price, and then it could be 50 again. I mean, it
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seems like companies are actually moving towards that as opposed to predictability. And is that hurting
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them? I think it depends on the market. And an interesting thing is that um in this sort of setting, we find that being
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predictable, being simple, but also having this um uh some degree of flexibility is actually the right way to
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go. So um you are capturing from the firm's perspective uh most of that value. An interesting thing there I
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think that you're sort of mentioning is that um uh if you think about an industry where that sort of uh quickly
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changing pricing has been very successful. An example would be the airline industry where you might be on a
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plane and you sit next to someone who's paid a very different price for for the
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same ticket. U for in I'm pretty price sensitive so I might have bought a cheaper ticket. Um and the other thing
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in that setting is when they do that very successfully um the plane tends to be full. So they tend to be able to
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allocate all of um the seats that they have. So that's sort of the price I think you pay for having that cheaper
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ticket. But the same message carries over into this setting. We find that when you do this sort of pricing
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correctly um with these um simple sort of policies, you're actually able to sell a lot more units. you're actually
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able to put more products profitably in the hands of more people who want uh those products and also um with these
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simple uh policies you're actually able to get more of those products into the
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hands of uh the consumers who want them the most. Um so there's an allocative uh
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role there. So uh I think an important message here is that um from a consumer welfare standpoint uh this sort of uh
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pricing um can have um ripple effects that have positive implications. So if I'm a retailer and looking at this
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research, what are some ways that practical ways that retailers could kind of apply this or some sort of advice or
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takeaways that they could have from it that they could use in their business? Uh I I think one is um to uh to be able
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to understand why certain price uh policies might work including ones that you're using already. So in this case um
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our retailer one thing that was interesting is we asked them why are you using this type of policy and they
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almost think of the customer sort of like a pet or a dog where if you train them the wrong way they'll just start
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expecting to wait for a markdown. Um so this was their way actually um heristically of uh sort of uh not
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training the customer by introducing this uncertainty making them unsure but actually what we found is you have
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different types of customers who have these different costs of monitoring this channel and that's really what's driving
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um what was good about this way of pricing. Um so one is to um if you have a lot of data be able to understand even
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with a simple policy why is it effective. Um and the second message there was that um again going to the
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sort of uh simplicity um that simple the message that simple works um in this setting where you were
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trying to say give coupons to your most price sensitive uh customers identify who they are and you have this mountain
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of data recording all of their behavior online. Um what we find is that uh there's some very strong signals from
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that data. So you only need a few things. Um if you look at how people monitor online the frequency with with
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which they monitor that's a very strong signal of their price elasticity. So um
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you actually don't need to take you don't need to always be using all of
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that information. Tracking something very simple like the ratio of purchases to visits online is actually a very
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strong signal and captures almost all of that value um that you would have from uh sophist a sophisticated analysis of
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all the data. And so what's next for this research or what are you planning on looking at
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next? Um I I think directly um there uh the most related thing would be looking at these sort of informationational
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costs, these frictions um in a number of other settings. Um uh I'm I'm doing some
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work in online marketplaces and and other places where you can get very interesting data um at the sort of
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granular level. Um but more broadly I think there are a lot of settings that are becoming much more informationally
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rich whether it's um firms that are able to track you online or um uh as a patient or in a marketplace or or even
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in the workplace. And I think um an important aspect of these changes is to be able to understand how does it affect
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um firms who are sort of experimenting to see what can they do with this sort of data but also how should consumers
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and workers feel about um uh how comfortable should they feel about these changes. So I think it's a very
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interesting space from a research standpoint. You get lots of data so it's very interesting as well and uh um I'm
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I'm excited about it. Great. Thanks Ken. Thanks so much for being here. Oh, thank
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you. [Music]