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well one of the most established practices in the field of marketing and customer valuation is to summarize a
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customer using what's called rfm segmentation which means I take everything I know about my customer and
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I compute just three simple numbers how recently did they buy when's meaning
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when's the last time they bought how frequently do they buy meaning the number of time periods in which they
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bought and when they buy how much money do they spend that's called r FM segmentation it's the basis of the way
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in which most companies decide who are the valuable customers and who are the non-valuable customers and my research
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is very simple it basically says that's not a complete characterization of customers you have to add one more
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letter to rfm and I call that c which means clumpiness which means some customers do Buy in a regular pattern
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and historically if you bought orange juice if you bought diapers you bought things in a regular pattern but
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clumpiness refers to the fact that people buy and burst and those burst periods indicate something very
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different about the customer and that those customers could be extremely valuable the key takeaways of my
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research is very simple let's imagine you want to build a simple what I call a
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simple mathematical model you want to predict who are going to be the valuable customers in the future and you have
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four things you can use to predict it as I mentioned recency frequency monetary value and let let's say the marketing
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spend towards the customer those are the classic ways in which companies build what are called scoring models I'm
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claiming you need to add one more number and that's see how clumpy the customer
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is this is no more difficult to compute than rfm you can do it in Excel it's
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very quick to compute you can compute it for literally a 100 million customers in
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a second and the findings of my research suggest that higher clumpy customers are
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worth more out of sample meaning in their future value even after controlling for rfm and marketing
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expender which means we have found another variable that firm should track about customers and use it to predict
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their worth in the future two things surprised me about my conclusions one is um I just figured
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that this rfm based segmentation which had been around for so long and is used by so many firms had been validated in
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the sense that there wasn't any anything else simple out there that could help
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explain customer value I mean you can do all kinds of fancy web scraping and all
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kinds of other variable construction but clumpiness is so simple so first I was surprised that that had been missed that
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in other words hot and cold periods are indicative of something about the customer I think the second part that
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surprised me is that at least the data sets I've analyzed it's true for let's
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call it digital and online consumption Goods but it's not true for regular consumer package Goods in other words
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historical models I can see why they fit fine because you buy toilet paper in a regular pattern you buy orange juice in
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a regular pattern but you don't consume Hulu in a regular pattern you don't bid
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on auctions at eBay at a regular pattern you don't buy books at Amazon on a regular based pattern so I think the two
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things that surprised me is number one that people had missed what seemed to me to be something fairly obvious and
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secondly that it applied historically if you look at historically purchased Goods clumpiness really isn't
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there but if you look in the new wave the new economy clumpiness is pervasive in every data set I've
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analyzed I think of all the research I've done over my career which is now it's hard for me to believe but it's
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been a 20-year career I think this is probably the most practical thing I've
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done um the work I do tends to be what I call fancy complex statistical modeling
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and this is n about statistical modeling this is about a number clumpiness that firms can actually compute today they
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don't need to collect any additional data it's the same data they're using to
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compute R F&M and customer lifetime value and they can figure out how much value it adds to predicting customer
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value your rank ordering of customers will change your decisions about which customers are valuable to reactivate
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imagine customers have churned well which ones are valuable to reactivate my claim is the clumpy ones even though
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they've churned those are the ones to reactivate cuz if you reactivate them they'll come back and be clumpy again
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and do a lot of stuff in the future so I think it has huge practical value and the beauty of it is if you go to my
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website I have an Excel sheet there that has worked out examples it actually has
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an Excel sheet that you can just download and you can start using clumpiness today a lot of people have today talked
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about big data and I'm actually I love big data but I'll tell you what I love
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even more than big data I love data compression and what I mean by data compression is you can collect thousands
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and thousands of variables on people now you can track where they are you can track what they bought what web pages
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they looked at but that's not science that's a that's data collection now the
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question is which of that information is actually useful for the business problem
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at hand and that's what I call data compression so the way I view clumpiness
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as an addition to traditional variables like rfm marketing activity and stuff like that I Vis I view it as a form of
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let's call it increased data compression I'm just telling you you need to cover
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you need to keep a little bit more data you can't compress things down to three
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numbers you got to compress it down to four so I view This research that I'm
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doing as kind of I view it in the data compression world I love the problem of taking big data and compressing it down
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to small data and that's how I view clumpiness the part that's unknown to me right now
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which is I've done a lot of work on clumpiness I know it exists across Industries I know it exists I know it
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can be predict of predictive value here's what I don't know what causes it
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so what I do know is I've related marketing activity to clumpiness so firms can try to make you clumpy by
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sending you an email by sending you a catalog by targeting you Etc that much I know but I haven't really studied yet
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what's the optimal way in which firms should targeting you target you knowing
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that clumpiness exists I haven't looked at like for example do you consume more
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clumpy content if it's a series like imagine watching Breaking Bad or Mad Men
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or something like that or imagine you're a firm and you're trying to sell a sweet
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of products like you know a facial care line and a you know moisturizer line and
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all this other stuff should you package it together and make it seem like people
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are progressing towards a goal so here's what I do know I know mathematically how
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to compute it I know it's trivial for firms to do I know it's predictive but
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the part that's left unknown to me is the psychology of why which is why I'm
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partnering right now with a lot of my more consumer psychology oriented colleagues we're going to start running
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a lot of Behavioral experiments in the lab to try to get to the under underlying psychological underpinnings
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of why people behave in a clumpy fashion I'm not sure I've seen much about clumpiness if except what you see
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is you see stories in the news all the time about people kind of binging on content and so I like the word
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clumpiness other people like the word binging um the reason I like clumpiness is that it refers to you know the
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opposite which is non- clumpy which is kind of equally spaced kind of arrivals or equally spaced purchases so what I
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would say is I don't think I've seen a story about clumpiness but anytime you
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see a story about people binging content people consuming things you know a student sat up for 18 hours watching
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this it applies and the concept is so pervasive and every time I talk to whether it's managers students academics
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about it everyone believe it exists the part as you mentioned in your earlier question that shocks people is that it's
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actually predictive of customer value I think it dispels the idea that in some sense um customers can just be
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categorized by a simple set of numbers um you need to go a little bit beyond that you need to go a little bit beyond
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what I would call Simple theories of how people behave and what clumpiness if you
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actually think about what clumpiness says is if you look at recency frequency monetary value which is kind of the
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historical basis of consumer Behavior it basically ignores what I call the inter
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Ral times it basically says I can take all the data like if a two-day P it was a two-day window and then a 4-day window
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then a a three-day window then a sixday window I can throw all of that away and all I need to know is when's the last
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time you came and how many times did you come but what this dispels is that the arrival pattern of people is
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uninformative it's very informative people that come and burst then go away and then come back and burst and then go
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away it's just I think those are just different types of people I think those
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people are fundamentally different I personally believe there are clumpy type people and non- clumpy type people
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however what we've also shown is it varies by product categories so we found
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for example that women tend to be more clumpy than men we found that younger people tend to be more clumpy in their
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consumption than older people so I think what's really going to dispel I think
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what the myth that we're going to dispel is that like not only are all people
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created equal but that there are simple ways to just categorize all people into a certain
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type I think what everyone's done is there's a whole class of mathematical
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models that have been popularized although they've been around for 50 years but have been popularized over the
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last 10 years called hidden Markov models the idea is very simple let's imagine there are two states of the
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world you're in a hot state or a cold State and you rotate back and forth between a hot and a cold state that
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mathematical model is clumpiness you're hot you do a lot of stuff you're cold
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you don't hot cold hot cold what TP what separates this work is the work I'm
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doing isn't Ivory Tower mathematics it's a simple number that someone can compute
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so I fit hidden Mark of models to data what I wanted to do was to bring to the practitioner a way that they could
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compute a simple number it's a statistic it's not a statistics paper it's a paper
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about a number a statistic as we call it you just compute the number and then do
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what you want with it you could try to use it to predict customer value you could use it to see are men more than
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women you could use it to segment people that's what typifies and separates this
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work is that it's a simple metric based approach that practitioners can use it's
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not a fancy modeling based approach but they're both trying to cover the same
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problem what we've studied so far with reaching clumpy customers is we've
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studied whether email cataloges different types of marketing channels are more effective and what we found not
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surprisingly is email has more of a short-term effect as you would expect catalog has more of a longer term effect
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what we've yet to really understand is are there certain words in a email or
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catalog or you know video campaign that will engage or you know if you'd like
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cause people to be more clumpy are there certain topics that are more clumpy more
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some product categories that will necessarily be more clumpy all we've done so far is I think I've established
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the phenomenon on exists I know it exists across lots of Industries I know certain types of people tend to be more
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clumpy the part that I haven't done which is shocking because I'm a professor of marketing is talk about the
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marketing implications of it yet that's going to require bigger and newer data
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sets that allow me to link things about marketing campaigns to people's clumpy
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Behavior I know how to do it it's just I need richer and better data to do it I'm thinking about three different
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streams to follow up this clumpy research first of all I'd be thrilled to just analyze more data sets and prove
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how pervasive the clumpiness measure is so I've analyzed data sets from Amazon
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from CD Now from eBay from Hulu from YouTube and also from some traditional consumer package Goods companies look if
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PNG wants to contact me tomorrow I'm happy to apply clumpiness to their work
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if Google wants to contact me tomorrow if Goldman Sachs wants to contact me tomorrow if fizer wants to contact me
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tomorrow I'm happy to work with their data and understand clumpiness and how
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it can predict customer value so that's one area I just want to apply it to new
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data sets the second is I want to understand the psychological processes why are people behaving in a clumpy
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fashion the third and final pieces I want to relate marketing activity to clumpiness now that's going to require
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not just people's behaviors like what did they do what websites did they visit
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what did they purchase but information about the marketing campaigns themselves possibly even the copy of the marketing
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campaign which channels they were sent through and that's going to allow me to
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come up with optimization ways for firms to optimize their marketing campaigns to
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activate clumpiness so those are the three Avenues I'm going to be working on
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