Calculating customer lifetime value has been a critical part of the marketing process for years, but new technologies are changing consumer behavior, and marketing professionals need to catch up.
Ever have a weekend where you just can’t stop spending money? Maybe you’re using some free time to stock up, or you’re decorating a room or planning a party. Or maybe you’re not spending more but just consuming more — a good television or movie series or, for example, plowing through a good book on your Kindle. This kind of behavior is called “clumpiness.” And while it’s not necessarily new, new technologies — particularly in the world of digital content — have shed light on this type of behavior for all industries. The problem is that it hasn’t been included in the formula marketers have been using for decades.
Three very basic measurements have traditionally been used to evaluate the lifetime value of a customer and assess the risk of customer churn:
- Recency: When was the last time that a customer shopped with us?
- Frequency: How regularly does the customer shop with us?
- Monetary Value: How much does the customer regularly spend when they shop with us?
Evaluating customers across these observable traits (known as “RFM” for short) enabled retailers and other marketers to develop a reasonably accurate understanding of the financial implications of their relationship with each of their customers.
Yet rapid advances in consumer technologies have increased the complexity of the customer relationship and of predicting customer lifetime value. This complexity now calls into question the viability of relying solely on RFM as a rubric for driving business decisions that focus on shaping the customer experience, driving spend, and maintaining loyalty.
The classic consumer packaged goods case about razor blades famously asserts that a male consumer will replace his razor blade head every month on average. If we know that our customer bought a four-pack of razor blades just over three and a half months ago, we can apply the RFM measurements above to calculate that approximately now would be the right time to start marketing and promoting replacement razor heads to the customer. The straightforward RFM model appears to perform just fine.
With new or more complex scenarios, however, we start to see how the classic methodology for quantifying customer lifetime value starts to break down. This is especially true when it comes to consuming digital content. We’re all familiar with binge-watching a series on Netflix, where one doesn’t leave the couch or get dressed until they’ve seen every episode in the new season of Orange Is the new Black. But consumers have expanded this behavior beyond digital content, and we’re now seeing it everywhere — from sharing services like Uber and AirBnB to retail stores and Websites.
Clumpiness is defined as “the degree of nonconformity to equal spacing.” More loosely, we would define clumpy behavior as making multiple purchases or consuming an unusual amount of goods or services in a short period of time, and/or spending an unusual amount of money in a short period of time. A multitude of factors can drive clumpy behavior. In the Netflix case, the key driver is availability. Netflix releases a season of shows, and suddenly everyone wants to watch it very quickly. An Uber user may exhibit clumpy behavior when traveling, and a retail store may find clumpiness that stems from pregnancy, an influx of cash or income, dramatic weight loss, or even when certain items go on sale or are in stock after a period of being out of stock.
Regardless of what the behavior is or what’s driving it, Analytical Marketers at Wharton have started to examine these hot and cold periods of the consumer lifecycle to identify how, when, and where the classic RFM model begins to break down. Cofounder and Chief Research Officer at GBH Insights Eric Bradlow and his fellow researchers found that these hot and cold “binge-like” characteristics exist and are measurable across nearly all industries.
The Wharton study examined multiple retailers in specific product categories. Among the key findings, they found that Millennials are more clumpy than other generations and that women are more clumpy than men. This is helpful in particular because many marketers are struggling to figure out how to market to Millennials. So if businesses can identify clumpiness and factor it in with the standard RFM model, they could find the key to crack that code.
The overlap of these two insights yields the Holy Grail of communicating to high-value retail customers. In our most recent Webinar, we discuss the customer journey, and how it’s necessary to have visibility into and insights from that journey to better personalize every marketing touch point. By singling out clumpy behavior, knowing to look for it, and even analyzing the level of clumpiness, marketers, customer service reps, and other key decision makers gain a new metric for measuring and predicting CLV and choosing which customers to focus on and when. They can also gain a better understanding of customer satisfaction and react to it faster.
Let’s consider our Netflix example. Right before the company released Orange Is the New Black, it increased prices. While that sounds like a smart marketing move at first — if people want to see the anticipated show, they need to pony up the extra cash — it may have backfired. This fall, Netflix has seen a good deal of churn, most likely due to its price hike (though we should note that long-time users were “grandfathered in” at the lower rate for up to two years). Those of us who actually watch Orange Is the New Black, however, feel that perhaps it was a double-whammy — a lackluster season combined with the news of the price hike may have been enough to cause subscribers to cancel. However, since Netflix is no stranger to analytics or to the concept of binge-watching (aka “clumpiness”), it could have analyzed the level of binging for consumers and used that to its advantage. Watching the series over a few weeks instead of a few days, as a given consumer may have done in the past, could indicate lack of interest or enjoyment in the show. Netflix could have used that information to wage an appropriate response: Either get them hooked on a new show to prevent churn, waive the price hike, or give them a few free months until the next show they love is about to air.
The same could be said for varying types of retail environments and among service providers. Identifying clumpy customers helps companies engage more effectively with them and develop a non-transactional relationship with them through an assortment of one-to-one marketing techniques that span multiple channels and touch points. And in the Wharton article, “Are Your Customers Clumpy? What Binge-Buying Means for Marketers,” Eric Bradlow said:
“…I haven’t really studied yet what’s the optimal way in which firms should target you, knowing that clumpiness exists. I haven’t looked at, for example, do you consume more clumpy content if it’s a series? Imagine watching “Breaking Bad” or “Mad Men” or something like that. Or imagine you’re a firm and you’re trying to sell a suite of products like a facial care line and a moisturizer line, and all this other stuff. Should you package it together and make it seem like people are progressing towards a goal? … So … I know mathematically how to compute it. I know it’s trivial for firms to do. I know it’s predictive. But the part that’s left unknown to me is the psychology of why…”
But Opera Solutions has been looking at exactly this for retailers. And we’ve learned quite a bit. We have cracked the code on certain product categories to figure out whether they lend themselves to clumpy behavior, and if so, how to leverage this new insight. It’s all part of the service we offer alongside our Big Data analytics platform, Signal Hub. The models we build into Signal Hub can now tell businesses which customers possess clumpy behavior, which products drive clumpy behavior, when this behavior is likely to happen next, and how best to market to the customers most likely to exhibit it.
With all of our retail customers, we are contributing to substantial gains in the quality of their customers’ experiences, greater depth of retailers’ customer relationships, and significant boosts in customer lifetime value. Retailers need not completely abandon RFM, but incorporating clumpiness into their formula (now called “RFMC”) and using Big Data analytics to do so elevates the sophistication of their efforts to shape the customer journey and increases the likelihood that the journey will be both long and rewarding for retailer and customer alike.
Want to learn more about our work with retailers and how we’re incorporating clumpiness into our analytics platform? Click below to schedule a demo.
Michael Turon is a Vice President at Opera Solutions. He focuses extensively on advanced analytics solutions for the retail industry.
Sarah Anderson, Director, Marketing at Opera Solutions, contributed to this story.