Measuring Customer Retention – Basic (Recent Repeater Model)

Jim’s Intro: Here’s an example of a simple yet very effective customer model for assessing the future profitability of your customers, explained in 2 parts – Frequency & Recency.

Part 1 (Frequency)

In the ongoing search for developing ways to measure the success of websites, stickiness, or the amount of time a person spends at a web site, has been put forth as an important concept to consider. 

Those in favor of using stickiness to measure success say the longer a person stays at a web site during a visit, the more interested and satisfied (loyal?) they are with what the site is offering.  Is this always true?

Let’s think about that for a minute.  Why would a visitor stay at a site for a long time if they were not satisfied?  Perhaps they’re confused.  The navigation design is not clear.  The content is poorly presented.  They went to make a ham sandwich in the kitchen.

So maybe stickiness (length of visit) is a good measurement; maybe it’s not. After all, if it seems like most people stay for a while, they can’t all be out in the kitchen. But this measurement works best for very large sites, where you can average the behavior over many visitors, like e-Bay, one of the current stickiness champions.

There’s another way to look at stickiness, one more important than looking at the time spent at a site (and more accurate). It’s the stickiness of your site to visitors or buyers over time, and involves looking at repeat visitors or buyers by customer segment. After all, someone who comes to your site and spends a half hour there the first time but never comes back isn’t contributing very much to your success, particularly if you paid money to attract him or her the first time.

Most sites track repeat visitors in the aggregate, meaning a visit to any part of the site is a “repeat”. Some track repeat buyers. This type of reporting doesn’t provide any real information; it’s too generic. You have to track the repeat behavior of customers sharing some other variable, some characteristic that allows you to make judgments about the value of the different customer groups and take some kind of marketing or design action based on this judgment.

For example, let’s say you run a pet site.  What if you found out the cat section was pretty sticky over time (lots of repeat visitors) but the dog section wasn’t. What would that mean?  Perhaps you need to pay more attention to your dog content, and handle it like the cat content to encourage repeat visiting.  Or perhaps you shouldn’t be a pet site at all; maybe you should focus on being a cat site because you do cats better than anybody else.  Your visitors think so, because they repeat at a higher rate for the cat section than the dog section.

Why does all this matter?  Because it has been shown over and over that past consumer behavior is the best predictor of future behavior.  Past behavior is a much better predictor of future behavior than demographics ever will be.  A visitor or buyer who repeats their behavior is more likely to continue repeating it, meaning their future value to the business is high.  So when you look at a particular segment of customers, if repeating visitors or buyers are rising, then your future business with this segment of customer will be stronger than it is today.  If repeaters are falling, business from this customer segment will be weaker in the future.

When you look at the repeating behavior of different groups of customers, you can make judgments about which customer groups will be most valuable in the future.  You want to do everything you can to attract customers with high repeat behavior, and reduce or eliminate any spending or other efforts on attracting customers with low repeat behavior.

Repeat behavior is a strong indicator of customer loyalty to your site – it’s a “likelihood to return” or “likelihood to buy again” indicator. So it’s very important to understand which groups of customers have high repeat behavior and which don’t.

Here’s a good way to track repeat behavior. Find the percentage of your customer base with a certain characteristic that has visited or purchased more than once. For visits, you might want to set a higher cut-off, maybe the percentage visited at least 3 times.? Then watch this percentage over time. If the percentage of a certain type of repeating visitor or buyer is rising, then your future business with this particular type of customer will be stronger than it is today. If the percentage of repeaters in a customer segment is falling, this part of your business will be weaker in the future, and you need to take some kind of action to get these people to come back or make additional purchases.

Changes in repeat percentage can be graphed and displayed visually. You can mix and match different behaviors and types of customers according to what is important to your business model. Rising and falling repeat rates act like an “early warning system”, providing important information about the future of your business with different customer segments.

How should you divide up or “group” your customers to analyze repeat rates? There are certain variables that affect the future value of a customer more than others. Here are some of the most important groups to look at:

  • Repeat rate by media source of the customer – search engines versus banner ads, or compare the repeat rate of customers generated by different banner ads.  There will be huge differences, and this metric can help you improve the long term ROI of ad campaigns.
  • Repeat rate by category or item of customer’s first purchase, and category of ongoing product preference, is another huge differentiator of customer behavior.  This will tell you which products are most profitable long term to feature or promote to new and current customers.
  • Repeat rate by price of first purchase, a similar idea to the one above.  This will tell you what price range is most profitable to feature to new customers, because customers buying in this range tend to repeat.
  • Repeat rate by content area favored by the customer during the first visit, and repeat rate by ongoing preference to a content area. This is the cats versus dogs example from the previous section.  Which areas of your site create the most loyal (highest repeat) customers?  You should feature those areas and not feature or eliminate areas that produce low repeat customers, particularly when looking at first visit behavior.
  • Repeat rate by demographics or other self-reported data.  Grouping by non-behavioral data can sometimes be effective, depending on how accurate the data really is.  Of all the groupings mentioned here, this is usually the weakest in predicting future value.

There will be huge differences in repeat rate when examining the first purchase, media source, and content experience the customer has with your site.  You will no doubt find others particular to your business.  These differences can be used to provide clues optimizing site design; you should increase the opportunity for any experience which leads to a high repeat rate, and decrease the opportunity for any experience leading to a low repeat rate.

Repeat rate is a strong indicator of future activity.  Looking at repeat rate by customer segment is the first step to building an effective customer model  (profile of customer behavior over time) you can use to drive higher profitability in your business.

Part 2 (Recency)

While you’ve got your hands dirty down there looking at customer segments and repeat rates, it might be a good time to also take a look at customer Recency by segment. Repeat rate is a Frequency Model, the 2nd most powerful predictor of future behavior, but one of the easiest to track and measure. If you want to be able to predict the future actions and value of customers even more accurately, you should also track Recency, the number of days or weeks since a customer last engaged in a behavior.

Recency is the number one most powerful predictor of future behavior. The more recently a customer has done something, the more likely they are to do it again. Recency can predict the likelihood of purchases, visits, game plays, or just about any “action-oriented” customer behavior. Recency is why you receive another catalog from the same company shortly after you make your first purchase from them. Recency is the most powerful predictor of future behavior.

Think about it this way. What good is it to have 10,000 people who have bought or visited at least 5 times when 80% have not repeated in the past 6 months? Repeaters who haven’t repeated Recently are former best customers.

Here’s an example of why the idea of Recency is so important. Let’s say you’re looking at your repeater segments, and noticing the percentages are kind of flat to slightly down over time, but still pretty good. Everything seems OK. But what if most of the repeaters in the segment were from 6 months ago, and new customers coming in, for whatever reason, were repeating less than customers used to? The sheer number of repeaters in your database might mask this decline in repeat rate. The lower quality (lower repeat rate) new customer behavior is overwhelmed by the behavior of older customers, and you get a false read on the future health and profitability of your business.

I have seen this happen; and it’s not pretty. Everything seems to be going smoothly, and new customers are ramping, but all of a sudden, sales or visits get soft. This happens at the point where the new, lower quality customers finally overpower the old, higher quality customers in the database. The future value of your customers has shifted and you didn’t know it until it was too late.

Recency measurement solves this problem, because you are always looking at what’s happening now with Recency. Using Recency as an additional filter in your segment tracking clears away the past, and provides you with a head’s up view of the future. Tracking repeats (or Frequency) by itself is a rear view mirror, because you never know how many of these people are really current customers without looking at Recency. The longer a customer has stopped engaging in a behavior, the less likely they are to repeat the behavior – they become defected customers.

So how does Recency fit into the tracking of repeaters, how is it implemented? The easiest way to do it is to set a cut-off, say 30 days. You want to track the percentage of customers in a segment who are repeaters, where the last repeat action (purchase, visit, page view, download) was in the prior 30 days. Watch to see if the percentage is rising or falling, just as when you were tracking repeat rate only. These Recent repeat customers are your future, they are the strongest, most powerful, most valuable customers, now and in the future. They are the most likely to repeat whatever behavior you are tracking. If these customers begin to decline in percentage, you will feel it down the road. A decline in Recent repeaters means you have to readjust your customer acquisition plans, because just to stay even, you’re going to have to start replacing these best customers who are in the process of defecting from the business.

Track the same segments discussed earlier – first purchase, favorite purchase category, media source, first time and ongoing content experience, and maybe demographics and other self-reported characteristics. Look at Recent repeat rate by any customer variable you have in the database! You will begin to see concrete patterns in repeater Recency, patterns which will help you make profitable decisions based on future customer value. You want to do more of anything that creates high Recency repeaters, and cut back on anything that doesn’t create these high future value customer segments. For a complete example of how to apply this model to ad spending, see the tutorial Comparing the Potential Value of Customer Groups.

The Recent Repeater customer behavior model is incredibly simple, yet very effective in predicting the relative future value and loyalty of customer segments.  How to use this information to create high ROI marketing promotions and site designs is explained step- by-step in the Drilling Down book.

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