eCommerce Analytics

As a follow-up to Doug’s post about customer lifetime value yesterday, and the advent of our online customer lifetime value calculator, I wanted to revisit the most famous use of customer lifetime value in recent business strategy and practice: Gary Loveman and Harrah’s. Harrah’s developed sophisticated customer lifetime value models to predict the ultimate value Harrah’s could aspire to for each individual customer. Then Harrah’s used its well-known customer loyalty program to try to reach that value.

Read The Strategic Impact of Customer Lifetime Value: The Harrah’s Story »

Internal acceptance of customer lifetime value as the primary metric to use in marketing decisions is an important milestone in the growth of any marketing organization. Using an online customer lifetime value calculator is a good first step in understanding how much an average customer is worth and how much should be spent in acquiring the next customer. The real value in using CLV will be realized, however, by organizations that calculate customer lifetime value across customers, segments, and marketing campaigns.

Marketers are consistently faced with questions that one average CLV alone cannot answer:

• Which customer acquisition campaigns should we spend more money on and which less?
• What are the demographic and behavioral attributes that define my best customers?
• How much should we spend to retain a specific customer? A customer in a given segment?

Imagine, though, if you could slice and dice customer lifetime values to answer these questions. You would be able to decide with confidence exactly how much budget to allocate various customer acquisition and retention activities. Even better, you could look at your sales pipeline and predict which leads are likely to become valuable customers and which are a waste of time based on attributes like location, industry, size, title of contact, etc.

Unfortunately, calculating customer lifetime value on anything but an average basis can get tricky. It requires a good understanding of SQL and dedication to hammering out many little details. How should we calculate a customer who bought for the first time within the last few months? Has a customer who hasn’t bought in two years expired or just between purchases? How do we find the statistically significant attributes that predict customer lifetime value?

We understand that dealing with these issues is not easy so we’re helping solve them by offering a Premium Customer Lifetime Value Analysis. Our goal with this service to provide a jumpstart to organizations that want to move to a more analytical marketing approach. By keeping the price point low at $495, we hope to remove price as a barrier to what we think is the most important first step in understanding customers.

We’re excited about offering this service and hope that it helps companies solve the CLV problem in a way that is affordable and easy. We’ll keep you posted on the results.

Tim Ferriss makes some excellent points in his post The Margin Manifesto: 11 Tenets for Reaching (or Doubling) Profitability in 3 Months which it got me thinking about how margins are changing in the software business and why enterprise software companies must start “firing” their high maintenance customers.

The software industry for some time has been forgiving of poor fiscal discipline. With 90% margins, it is possible to blow lots of cash on unprofitable sales and marketing campaigns and still make a mint. Furthermore, Wall Street has always rewarded new license revenue growth over cost control. In this kind of environment any new revenue is good revenue, regardless of its ultimate price.

Sadly, the days of inflated margins are nearing an end. The price of software is crashing, and SaaS along and the consumerization of IT is turning software into a commodity. Enterprise software companies doing $500k deals on six month sales cycles will have to reduce their cost structures quickly to survive this disruption to their model.

With these changes afoot, plenty of blog space has been devoted to exploring how software companies can cut sales and marketing costs through search engine optimization, pay-per-click advertising, and viral marketing. Comparatively little has been written about Tim’s #10 point, however: firing high maintenance customers.

Despite the huge improvement in margins that result from firing poor customers, there are three reasons that the idea rarely gains traction in an organization:
1) Cultural resistance due to short-sighted metrics

Most companies are reluctant to fire customers. After all, no sales or marketing organization can be convinced that it’s a good idea to forgo revenue in pursuit of improved profitability down the line. This is especially true when they are measured on how much they drive top line growth, as they almost always are.
2) Data integration challenges

Even if sales and marketing can be convinced of the value in firing poor customers, there are still huge technical barriers to integrating the data required to for analysis. Challenges abound in getting CRM data to merge neatly with support and billing databases. Unless IT has a lot of spare capacity (an occurence as common as a Bigfoot sighting), significant budget will have to be allocated for data integration.
3) Inability to make sense of the results

Finally, once all the data is integrated, a healthy dose of marketing analytics know-how is required to make sense of it all. Without highly trained business analysts on staff, it is very difficult understand which customers are profitable and which aren’t. Furthermore, unless you want to keep spending money acquiring bad customers, statisticians and data miners will need to be called in to help build attribute profiles of unprofitable segments.

While firing unprofitable customers is a powerful way to improve margins and profitability, these three barriers ensure that it rarely gets done.  Unfortunately for most enterprise software companies, it will be too late by the time they realize how criticial it is to shed themselves of poor customers.   Those with the foresight and fortitude to make it happen sooner than later, however, can expect great rewards.

I recently had the opportunity to sit down with one of the marketing professors at MIT’s Sloan School of Management to discuss the current state of marketing analytics. One of the many topics that came up during our discussion was why so many companies are still basing their marketing strategy on RFM. For those of you unfamiliar with the term, RFM stands for recency, frequency and monetary value. It has been used in direct marketing for the last 30 years and continues to be the basis of many “rule” based email and direct mail campaigns. Why? Well, for starters, it’s easy for many marketers to understand: customers who bought recently and have a history of buying large amounts often are more likely to purchase again in the future. Unfortunately, RFM fails to consider many of the factors necessary to truly evaluate the profitable of any one customer - mainly, the cost of acquisition and the cost of customer service and retention.

Does it matter? Absolutely. Way back in 2003, Rajkumar Venkatesan and V. Kumar published a paper highlighting the benefits of using CLV over RFM. They showed that the net profits from the top 5% of CLV-ranked customers were 1.6 times the net profits of the top 5% of RFMers. Additionally, they found that using CLV to better target customers increased profits by almost 67%.

So with increases like that, why are so many companies still using RFM? Well, to be honest, most companies still don’t know any better. However, as the global community gets more competitive, savvy marketers are beginning to look past RFM to make better use of their data through more advanced data analytics. And the good news is that the field of marketing analytics continues to provide us with better ways to analyze data every year. While CLV continues to be one of the best ways to target customers, research by professors like Pete Fader at Wharton and Dipak Jain at Kellogg have given us models that more accurately forecast number of purchases and retention rates of customers for non-contractual businesses. Recent papers have also focused on enhanced forecasting of the migration of customers from direct mail marketing to email blasts (check out Customer Channel Migration by Ansari, Mela, and Neslin).

With the rising cost of direct mail and the waning interest of customers through email blasting, it is clearly the time to improve the effectiveness of marketing to improve the overall profitability of the business. While I’ll admit that RFM was a great marketing tool in the past, there have been so many advances in marketing analytics since then that it’s time to move on.

Ask any direct mail marketer what the most disruptive force in marketing has been in the past ten years and the answer will undoubtedly be the rise of email. Email has many advantages over direct mail: it’s cheap, easy to send, and allows marketers to easily track results to learn what works and what doesn’t.  Despite that, email marketers would do well not to be seduced by the lure of “free” email.

Unlike direct mail where the postage costs are front and center, blasting 100,000 customers with the same message doesn’t cost much more than blasting 10,000. As a result, it can be tough to resist the temptation to add just one more customer segment to the blast list.  Here’s where it’s important to recognize the real hidden cost of email: the opportunity cost of a customer becoming blind to (or opting out of) your email because the messaging is too frequent or not relevant.

Put another way, consider that the marketers job is to nuture the full potential lifetime value out of each customer. A thoughful, well targeted, and patient email campaign can nudge a customer toward realizing that potential. Get too greedy, however, and some customers will tune out forever. If one in 10000 customers tune out, and the average unrealized lifetime value of those customers is $500, then each email actually costs $0.05 plus about $0.01 for actually sending the email. That’s still cheaper than a catalog, but it’s far from free.

To keep the real cost of email marketing down, ensure that you are sending targeted and relevant messages to each customer segment. You can do this by analyzing the buying affinities of each segment and making sure the offers you send are for products that will interest the reader. If you don’t have the time or resources to crunch the data, don’t be afraid to rely on your intuition. The important thing is to remain disciplined about shielding customers from irrelevant communications.

Do you sell recreational, hobbyist, or athletic gear? Are you looking to acquire high quality customers? Well, as much as we don’t like to admit it, not everything has to be analytics and data mining. Sometimes intuition is enough to achieve our goals.

Consider that most recreational activities have high and low seasons. For example, recreational boaters and pilots are most active during the summer months. Skiiers and hockey players are most likely to be thinking about picking up new gear during the winter (or shortly before).

Our intuition tells us that if someone, say, buys a new pair of skis in the summer, they are either a) well off enough to take a summer ski trip to South America, or b) are incredibly devoted to the sport. Either way, he or she is a likely highly desirable customer.

As a result, we should be willing to spend more to acquire customers in the low season. The low season is the time to crank up the minimum cost-per-click bids on Google or spend a little extra to rent a list. Let your competitors slug it out in the peak activity months, fighting tooth and nail for the dabblers and tightwads. You’ll be sitting back collecting money from your stable of devoted high spending enthusiasts.