Are Your Customers Clumpy?

If you’ve ever spent a weekend binge-watching a season of “Breaking Bad” or some other series on streaming video, you can take comfort in the fact that you’re certainly not alone. Whole sections of the population are consuming digital products and services in a “clumping” pattern that features extended periods of inactivity punctuated by short, intense buying bursts. Once marketers realize this — and they already have the data in hand to track it — they could mine a rich, new digital behavior vein, with implications for extending the RFM model to include C for "Clumpiness"--RFMC.


If you’ve ever spent a weekend binge-watching a season of “Breaking Bad” or some other series on streaming video, you can take comfort in the fact that you’re certainly not alone. Whole sections of the population are consuming digital products and services in a “clumping” pattern that features extended periods of inactivity punctuated by short, intense buying bursts.

Once marketers realize this — and they already have the data in hand to track it — they could mine a rich, new digital behavior vein, says Wharton marketing professor Eric Bradlow. He notes that a basic customer value measure for decades has been RFM segmentation (recency, frequency and monetary value). “My research … says that’s not a complete characterization…,” he adds. One more letter is needed: “I call that ‘C,’ which [stands for] clumpiness.” Bradlow explains that concept in this Knowledge@Wharton interview, based on a research paper titled “New Measures of Clumpiness for Incidence Data,” which he co-authored with Yao Zhang, an associate at Credit Suisse, and Dylan Small, a Wharton statistics professor.

Burst periods indicate something very different about the customer and that those customers could be extremely valuable.

What is “clumpiness” in customer data, and why it matters:

One of the most established practices in the field of marketing and customer valuation is to summarize a customer using what’s called RFM segmentation — recency, frequency, monetary value — which means I take everything I know about my customer and I compute just three simple numbers: How recently did they buy? How frequently do they buy? And when they buy, how much money do they spend? … It’s the basis on which most companies decide who are the valuable customers and who are the non-valuable customers.

My research … says that’s not a complete characterization of customers. You have to add one more letter to RFM, and I call that “C,” which [stands for] clumpiness Twitter , which means some customers do buy in a regular pattern. Historically, if you bought orange juice, if you bought diapers, you bought things in a regular pattern. But clumpiness refers to the fact that people buy in bursts. And those burst periods indicate something very different about the customer and that those customers could be extremely valuable.

On the key takeaways:

The key takeaways of my research are very simple. Let’s imagine you want … to predict who are going to be the valuable customers in the future. And you have four things you can use to predict it. As I mentioned: recency, frequency, monetary value and let’s say the marketing spend towards the customer. Those are the classic ways in which companies build what are called scoring models. I’m claiming you need to add one more number, and that’s C — how clumpy the customer is. This is no more difficult to compute than R, F and M. You can do it in Excel. It’s very quick to compute. You can compute it for literally 100 million customers in a second.

Burst periods indicate something very different about the customer and that those customers could be extremely valuable.

And the findings of my research suggest that higher clumpy customers are worth more out of sample, meaning in their future value, even after controlling for RFM and marketing expenditure — which means we have found another variable that firms should track [concerning] our customers and use it to predict their worth in the future.

Read the Knowledge@Wharton interview for more on the practical implications of clumpy behavior and some surprising conclusions from the research. View the full story on the Knowedge@Wharton site

top