Optimizing Discounts with Data Mining

The New York Times has an article today about tailoring discounts to individuals.    They concentrated on Sam’s Club, a warehouse chain.  Sam’s Club is a good place for this sort of individual discounting since you have to be a member to shop there, and your membership is associated with every purchase you make.  So Sam’s Club has a very good record of what you are buying there.  (In fact, as a division of Walmart Stores, perhaps Sam’s has an even better picture based on the other stores in the chain, but no membership card is shown at Walmart, so it would have to be done through credit card or other information.)

The article stressed how predictive analytics could predict what an individual consumer might be interested in, and could then offer discounts or other messages to encourage buying.

Given how many loyalty cards I have, it is surprising how few really take advantage of the data they get.  Once in a while, my local supermarket seems to offer individualized coupons.   Barnes and Noble and Borders seem to offer nothing beyond “Take 20% of one item” coupons, even though everything in my buying behavior says “If you hook me on a mystery or science fiction series, I will buy each and every one of the series, including those that are only in hardcover”.

Amazon does market to me individually, seeming to offer discounts that may be designed for me alone (online retailers can hide individual versus group discounts very well:  it is hard to know what others are seeing).

For both Sam’s and Amazon, though, I would be worried that the companies would be using my data against me.  If the goal is to optimize net revenue, any optimal discounting scheme would have the following property:  if I am sufficiently likely to buy a product without a discount, then no discount should be given.  The NY Times article had two quotes from customers:

“There’s a dollar off Bounce. I use that all the time.”


“[A customer]  said the best eValues deal yet was $300 off a $1,200 television.

“I remember that day,” he said later. “I came to buy food, and I bought two TVs.”

The second story is a success for data mining (assuming the company made a profit off of a $900 TV):  the customer would not have purchased without it.

In the first first story, the evaluation is more complicated:  if she really was going to buy Bounce anyway, then the $1 coupon was a $1 loss for Sam’s.  But consumer behavior is complicated:  by offering small discounts on many items, Sam’s encourages customers to buy all of their items there, not just the ones on discount.  So the overall effect may be positive.  But optimal discounting for these sorts of interrelated items with a lifetime environment is pretty complicated.

But here is a hypothetical situation (presumably):  it turns out that 25 year olds (say) are at a critical point in purchasing behavior when they decide exactly what brands they will purchase for the rest of their lifetime;  50 year olds are set in their ways (“I always buy Colgate, I never buy Crest”).  A 25 year old goes into Sam’s, hits the kiosk and walks away with 10 pages of coupons;  a 50 year old gets nothing.  Is this a success for data mining?  Perhaps the answer depends on whether you are 25 or 50!

And, more importantly for me, does Amazon not give me discounts once it is sufficiently certain I am going to want a book?

4 thoughts on “Optimizing Discounts with Data Mining”

  1. Amazon tried differential pricing a while back, ostensibly to assess customer demand at various price points. It started a bit of a flap. http://www.jerrydwyer.com/digital/perspri.html describes it. IIRC, friends would compare notes and discover they’d bought the same book at different prices, which pretty much guarantees that someone will be unhappy.

    Coupons or bundle discounts may be safer routes to individual pricing. As far as stiffing us due to advancing age or certainty of purchase intent, I’m not too worried — if we see other (younger? less loyal?) customers getting discounts we’re not getting, we’re liable to start looking at competing outlets, and Amazon knows that.

  2. I have a question. If I buy one TV now, am I more likely to buy another TV in the future or am I less likely to do so? This can be an interesting OR project. A recommendation systems for perishable and fashion items is easy but for something like TV it can be very challenging and interesting.

    Is there any dataset available for research from retailers? I seriously looked for a good dataset but couldn’t find one.

  3. I await Amazon’s marketing emails with great anticipation. As a migrant I get courted by both .ca and .co.uk (which strikes me as a bit of an IT failure), so I get double the action. It’s fascinating to see what they make of my purchase history. When I get recommendations like the chance to pre-order the MUCH anticipated “Roadside Geology of Southern British Columbia”, it makes me chuckle.

    Sometimes I get really unexpected recommendations and that’s when I realize that I have purchased or have been shopping for gifts for others. In some ways outlier purchases must pose a difficult challenge for the algorithms, but then again we’re not looking for Aleksey’s taste profile, we’re looking for his purchasing profile and gifts for Mom will come up over and over again.

    I suspect I witnessed some sophisticated targeting from booking.com the other day. I had ads from them on at least two websites that were pushing hotels in Istanbul, just after I had been on their site searching. Suspicious! The internet is always an interesting place for targeted marketing, perhaps this same method could be adopted to offer individual discounts. As usual excellent data could be captured.

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