eCommerce Analytics

I am a bit discouraged today. I am surprised that we have not had more takers on our free customer scorecard application. Granted we have not unleashed the Social Media channel on the tool but I figured that after a week of availability we would have 10% more customers. The customers that have used it are overwhelmingly surprised of the results but I still wonder if the tool’s purpose is clear.

One of the leading, and might I say insightful, retail marketing professionals, Kelly Mooney, recently posted a blog titled: “Myths about online Retail Marketing”. In the blog she does a great job of providing an analysis of several marketing myths and there are a couple of takeaways that directly support how we perceive our tools to add value.

Read How Predictive Analytics Optimizes Frugal Environments »

Kevin Hillstrom has an excellent post today entitled Customer Retention: The Myth where he says improving customer retention might sound like a good idea but is actually quite difficult to do. I agree with his assertion that retention rate, average order value, and orders per buyer are difficult to improve in the short term, but I, perhaps unlike Kevin, I think that there can be real immediate gains to be had by trying.

Read Customer Retention: a Tough Needle to Move? »

As marketers, we only have a few levers to really impact the bottom line. We can acquire customers more cheaply, acquire more valuable customers, increase customer purchasing amount and frequency, and improve retention rate. We only have so many resources to devote to moving these levers.  Which one should get attention first?

Read Which Marketing Lever is Biggest? »

As the economy turns ever uglier, it might be a good time to take a long hard look at your business to accurately assess the health of your customer base. Businesses with strong healthy customers are in prime position to weather the economic storm and take market share from flailing compeititors. But if you work for a company with a growing share of weak, low value, high cost customers, it might be time to start getting that resume togther.

Here are the three signposts that indicate your customer base is weakening:

Read How Healthy is Your Customer Base? Here are 3 Metrics to Find Out »

With today’s news that the retail sector is experiencing a slowdown, now is a better time than ever for multi-channel retailers to do two things: turn to cheaper forms of advertising (email) and use quick-return customer analytics to compete with gargantuan discounters like Wal-Mart that threaten to swallow retail whole. The truth is that Wal-Mart will continue to invest in analytics during the tough economy because they will see immediate ROI from understanding which customers are poised to buy, which items they want, and how much those customers are willing to spend. I can think of two, good reasons for smaller multi-channel retailers to follow suit.

Harvest your current customers
Most would say that the thick of a poor economy is a poor time to invest in new marketing projects. If these projects are tied to new customer acquisition, I might agree. It’s damned expensive to acquire customers and you tend to forget what you already have while you’re out prospecting, buying lists, etc. Sometimes, the answer is in front of you. In a poor economy, isn’t it imperative that you retreat to your base? Multi-channel retailers need to figure out ways to:

A. Not lose your current customers to competition (like Wal-Mart)
B. Harvest your existing customers by making them feel as though you understand them

Really, achieving B is the answer to question A. A redoubling of your customer service effort will always make your customers more loyal and less likely to jump ship. But we have to remember that larger players can always offer deeper discounts in an effort to combat your superior customer understanding. One way around this is to deepen your customer understanding on the marketing front with timely, personalized emails to your customer base. Ultimately, if you can address your customers’ needs first - make your customers offers at the cusp of when they need those products - then you are likely to win their business. This is the advantage that predictive models based on your customers behaviors provide you: the ability to beat your larger competition on timing as opposed to discounting.

Quick ROI
Customer analytics like those that Istobe proposes are great because the analysis takes advantage of data that you, as a multi-channel retailer, already possess. You’ve already got a record of your cusotmers’ purchases. In other words, there is no up-front infrastructure or talent investment. What this ultimately means is that your ROI emerges quickly. How quick? Well, let’s just say that you’re in the black (or, green) around month two. This is especially true if you’re already used to sending your customer data to a co-op database (like Abacus or NextAction); you’ve already made your data collection and transfer investment. Now it’s simply about turning those investments to a different use - customer development not acquisition - by focusing how that data helps you pull in the monetary margins in your current customer base.

One of the toughest problems facing organizations with many customers and large numbers of transactions is determining when a customer has left the company for good. Unlike contractual businesses where the customer is locked in for a given period of time, most companies have to make educated guesses about whether or not a customer is worth spending their marketing dollars pursuing.  While there are complex data analysis models that can be more than 80% accurate in predicting when a customer will buy next, many times a quick and dirty calculation can give your marketing executive a better indication of who to contact in the future.

We sometimes use the following metric to give an initial suggestion of whether or not a customer is still active in a given period. To clarify, period here can mean any observable time length - days, weeks, months, even years can work depending on your type of business and how long your typical customer stays engaged:

Probability (Active) = (T/N)ⁿ

where:

T = last period when the customer made a purchase

N = total periods in your observation time

n = total number of purchases during observation time

To give you an example of how it’s used, let me take the following example out of the book Managing Customers for Profit: Strategies to Increase Profits and Build Loyalty by V. Kumar (highly recommended) to illustrate the power of its simplicity. Let’s say I have two customers with the following purchasing pattern over a twelve month time horizon (X’s indicate when a customer made a purchase):

The probability for each customer to purchase in time period 12 is calculated as follows:

Customer 1 = (7/12)⁴ = 11.58%

Customer 2 = (8/12)² = 44.44%

The initial results may seem counter-intuitive - why does Customer 1 have a lower chance of purchasing again when he/she has 4 purchases in the observation time while Customer 2 only has 2 purchases? Well, as you can see from the table, Customer 2 appears to buy every 4 months and is poised for another purchase in month 12 while Customer 1 was purchasing every 2 months, but hasn’t made a buy in over 5 months so is unlikely to buy again in month 12.  Nice, huh?

While the above metric doesn’t take into account all important information, it does provide a fast and easy way for marketers to get a better indication of whether a customer should be put into the active marketing group or the re-activation group.

Many of the companies we have talked with recently have expressed concerns over the coming recession. Consumer spending will continue to decrease while costs for everything from power to supplies are expected to rise. While the road ahead may look tough for many businesses, I believe the recession offers an opportunity to refocus on getting more value out of your existing customers.

A new report released by the CMO Council (“Business Gain From How You Retain: Addressing the Challenge of Customer Churn and Marketing Burn”) supports these thoughts. The CMO Council in conjunction with IBM, Dun and Bradstreet, and CSC surveyed over 450 marketing professionals about how they “‘operationalize’ customer intelligence” to make better marketing decisions and increase customer retention.

How important is this? Well, they point out some well-known facts:

• The average company loses more than 10 percent of its customers each year.
• Acquiring new customers can cost five times more than satisfying and retaining current customers.
• A two percent increase in customer retention has the same effect on profits as cutting costs by 10 percent.
• Loyal customers are 15 times more likely to increase spend than high-risk, intermittent customers.

Unfortunately, knowing that retention importance is increasing is not enough.  Most marketers do not know their customers well enough to increase customer retention and take advantage of cross-sell and up-sell opportunities. Only 7% of marketers in the survey said they had excellent knowledge of their customers; only 8% said their companies are doing a good job of making customer data easily accessible. More importantly, over 75% of respondents had not implemented some kind of customer intelligence or data integration system to make use of their information.

So what’s the solution to reducing churn? Well, many BI and customer intelligence providers would have you believe that implementing a full scale package will provide you with the reports you need to make informed decisions about your customers. Reports and dashboards are nice, but they provide little insight into the reasons behind why customers are churning. As the CMO article points out: “the reasons for churn are wrapped around the customer experience” and it’s hard to understand this from a bunch of reports.

I believe that the key to customer retention is effective and sustained one on one interaction with customers and to find the best ways to reach out and engage the existing customer base. The CMO Council report makes it clear that understanding your customers is one part of solving the “churn” problem, but I believe the second part of that puzzle is figuring out how to use that information successfully. In the months to come, I hope to offer some tips to make your customer data more understandable, and more important, actionable.