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How is it the catalogs, and their distant cousins the TV Shopping Networks, are virtually the only profitable major B2C retailers on the Internet?   The Direct Marketing Association’s latest study of catalogs with web sites found 69% of them were making profits online.

Catalog and TV shopping marketers have understood for a very long time that it is much less expensive to retain customers than it is to acquire new ones.  They have been doing business with this philosophy for decades.  Customer acquisition is certainly important; if you don’t do it, the business eventually dies.  So why focus on customer retention?

Because by understanding customer retention behavior, catalogs lower their customer acquisition costs.  It’s all tied together.  Sure, there has to be a "first shot" somewhere, the initial push for customers.  But even these first efforts are based on what is known generally about customer retention in the catalog business.  So understanding customer retention is extremely important to the entire direct model of doing business with consumers.  The secret to good customer retention is to acquire the right customers in the first place.

So understanding customer retention is extremely important to the entire direct selling model of doing business with consumers, both for customer acquisition and retention.  Good retention marketers have two objectives with any kind of customer retention marketing:

1. Hold on to the most valuable customers

2. Try to make less valuable customers more valuable

To retain and increase the value of customers, you have to create marketing promotions and execute them.  To do this in the most efficient and effective way, you have to know the value of your customers and their likelihood to respond to a promotion, for these 2 reasons:

1.  You don't want to waste money on promoting to low value customers because you can't make a profit

2.  You don't want to waste money promoting to customers who won't respond because this is just throwing money away.

Customer Retention and Valuation Concepts

Have you ever heard somebody refer to his or her customer list as a "file"? If you have, you were probably listening to someone who has been around the catalog block a few times.   Before computers (huh?), catalog companies used to keep all their customer information on 3 x 5 cards.

They’d rifle through this deck of cards to select customers for each mailing, and when a customer placed an order, they would write it on the customer’s card.  These file cards as a group became known as "the customer file", and even after everything became computerized, the name stuck.

Who cares? It happens that while going through these cards by hand, and writing down orders, the catalog folks began to see patterns emerge.  There was an exchange taking place, and the data was speaking.  What the data said to them, and what they heard, were 3 things:

1.  Customers who purchased recently were more likely to buy again versus customers who had not purchased in a while

2.  Customers who purchased frequently were more likely to buy again versus customers who had made just one or two purchases

3.  Customers who had spent the most money in total were more likely to buy again.  The most valuable customers tended to continue to become even more valuable.

So the catalog folks tested this concept, the idea past purchase behavior could predict future results.  First, they ranked all their customers on these 3 attributes, sorting their customer records so that customers who had bought most Recently, most Frequently, and had spent the most Money were at the top.  These customers were labeled "best".   Customers who had not purchased for a while, had made few purchases, and had spent little money were at the bottom of the list, labeled "worst".

Then they mailed their catalogs to all the customers, just like they usually do, and tracked how the group of people who ranked highest in the 3 categories above (best) responded to their mailings, and compared this response to the group of people who ranked lowest (worst).  They found a huge difference in response and sales between best and worst customers.  Repeating this test over and over, they found it worked every time!

The group who ranked "best" in the 3 categories above always had higher response rates than the group who ranked "worst".  It worked so well they cut back on mailing to people who ranked worst, and spent the money saved on mailing more often to the group who ranked best.  And their sales exploded, while their costs remained the same or went down.  They were increasing their marketing efficiency and effectiveness by targeting to the most responsive, highest value customers.

The Recency, Frequency, Monetary value (RFM) model works everywhere, in virtually every high activity business.  And it works for just about any kind of "action-oriented" behavior you are trying to get a customer to repeat, whether it’s purchases, visits, sign-ups, surveys, games or anything else.  I’m going to use purchases and visits as examples.

A customer who has visited your site Recently (R) and Frequently (F) and created a lot of Monetary Value (M) through purchases is much more likely to visit and buy again.  And, a high Recency / Frequency / Monetary Value (RFM) customer who stops visiting is a customer who is finding alternatives to your site.  It makes sense, doesn’t it? 

Customers who have not visited or purchased in a while are less interested in you than customers who have done one of these things recently.  Put Recency, Frequency, and Monetary Value together and you have a pretty good indicator of interest in your site at the customer level.  This is valuable information to have.

Assuming the behavior being ranked (purchase, visit) using RFM has economic value, the higher the RFM score, the more profitable the customer is to the business now and in the future.  High RFM customers are most likely to continue to purchase and visit, AND they are most likely to respond to marketing promotions.  The opposite is true for low RFM customers; they are the least likely to purchase or visit again AND the least likely to respond to marketing promotions.

For these reasons, RFM is closely related to another customer direct marketing concept: LifeTime Value (LTV).  LTV is the expected net
profit a customer will contribute to your business as long as the customer remains a customer.  Because of the linkage to LTV, RFM techniques can be used as a proxy for the future profitability of a business.

High RFM customers represent future business potential, because the customers are willing and interested in doing business with you, and
have high LTV.  Low RFM customers represent dwindling business opportunity, low LTV, and are a flag something needs to be done with those customers to increase their value.

RFM scoring of individual customers is a catalog and TV shopping technique used to select which customers you can most profitably
promote to.  There is a more simplistic application of RFM online retailers can use to easily track the quality of overall customer retention, without going through the effort of RFM scoring individual customers.  We will consider this easier "group tracking" approach in the rest of this report.

If you'd like more information on the individual RFM scoring approach
or the validity and use of RFM scoring in general, see the link at the 
end of this report.

Measuring Overall Customer Retention

A simplified application of RFM is Hurdle Rate Analysis, where "hurdles" are selected for Recency, Frequency, and Monetary Value, and the entire customer base is evaluated against these hurdles as a group.

A Hurdle Rate is simply the percentage of your customers who have at least a certain activity level for Recency, Frequency, and Monetary Value.  It’s the percentage of customers who have engaged in a behavior since a certain date (Recency), engaged in a behavior a certain number of times (Frequency), or have purchased a certain amount (Monetary Value).

Because of the link between RFM and Lifetime Value, it can be concluded:

If the percentage of customers over each hurdle (Recency, Frequency, Monetary Value) is growing, the business is healthy and thriving.  Customers are responding positively to the experience they receive, and as a group are more likely to engage in profit generating behavior in the future.

If the opposite is true, and the percentage of customers over each hurdle (Recency, Frequency, Monetary Value) is falling over time, high value customers are defecting and the future value of your business is falling.   Customers as a group are responding negatively to the overall service they are receiving.

If the business has an understanding of customer LifeCycles, the logical Hurdle Rates to set for Recency, Frequency, and Monetary value would equate to customer behavior at primary changes in the customer LifeCycle.

If the business is very new or has never studied the customer LifeCycle, then a good default position to use is based on the 20/80 rule (20% of customers generally generate 80% of the behavior, be it sales, visits, etc.)  The analysis would default to a "starting Hurdle Rate" of 20% for each behavior (purchases, visits), and examine the customer base to determine RFM values corresponding to the 20% hurdle.

In this case, the business would look at the top 20% of their customers for each of the Recency, Frequency, and Monetary value parameters, and examine the "tail end" customers – the bottom customers of the top 20%.  These values would become the hurdles the customer base is judged against.  Customers would have to have at least the activity of these tail end customers to be considered "over the Hurdle".

For example, in a database of 10,000 customers, to determine the Recency hurdle using the 20/80 rule:

1.  Select the behavior to be profiled – purchases, visits, etc.

2.  Sort customers by most Recent date of the behavior

3.  Starting at the most Recent customer, count down to customer
      number 2,000 (20% of 10,000) in this sorted database.  Examine
      the group of customers near this target level, perhaps from 
      customer 1,950 to customer 2,050.

4.  Determine how long ago these customers, on average, 
     engaged in the behavior you are profiling

5.  You find these customers last purchased an average of 60 days ago

6.  The Recency hurdle becomes 60 days for the "today" or 
      starting Hurdle Rate of 20%

Regardless of whether the Hurdle Rate is set using the customer Lifecycle or the 20/80 rule, the operational implementation is the same.  Each week or month, sweep the database and determine the percentage of customers who have engaged in the behavior within the hurdle definition.  For a 60-day hurdle, it would be the percentage of customers engaging in the behavior in the past 60 days.

If the percentage of customers "over the hurdle" (engaging in the behavior less than 60 days ago) grows over time, the Recency Hurdle rate is rising, and the future value of the customer base (LTV) is rising.  If the percentage of customers "over the hurdle" is falling, the Recency Hurdle Rate is falling and future value is falling as well.

For example, if you started with 20% of customers having 60 day Recency for purchases, you would like to continue seeing 20% of your customer base purchase in the past 60 days.  Ideally, you would see 21%, then 22%, then 23%, and so on, purchase in the past 60 days.  If this percentage is rising, this means the future value of your customer base is growing, your high value customers are sticking with you, and your promotions will have increasing response rates.

This calculation can be completed on the same behavior (purchases, visits) for Frequency, and  if there is a transactional value to the behavior (a purchase),  Monetary Value as well. The only difference from the Recency example above would be in Step 2, where you would sort by total activity (units or dollars).  

Additional behaviors can also be monitored simultaneously; on the web, tracking purchases and visits together would make sense.  Unless the business has a very clear understanding of revenue per visit across different areas of the site, it is unlikely tracking the Monetary Value of visits would be very useful but Recency and Frequency would still be important.

The Hurdle Rate percentages can be graphed over time, and trends established.  Clearly there will be fluctuations up and down, and seasonality in retail or event oriented businesses.  But if solid trends in Hurdle Rates develop in either direction, or year over year comparisons are dramatically different for a seasonal business, the measurement should be judged to be significant and actionable.  Graphing Hurdle Rates over time provides an easy way to present a somewhat complex subject to management or investors: line up = good, line down = bad.

Hurdle Rates in Action

The lines in the chart show the percentage of customers over each Hurdle tends to be rising over time.  This particular chart is a combination of behaviors with differing RFM parameters – Recency of Visit, Frequency of Purchase, and Monetary Value of Purchase.

The percent of customers who have visited in the last 30 days (Recency, broken heavy line) is rising.  The percent of customers who have purchased over 10 items (Frequency, heavy solid line) is rising.  The percent of customers who have spent over $500 in total (Monetary Value, light solid line) is also rising.  A business can mix and match tracking of behaviors and Hurdle Rates according to priorities in the business model.

This is the picture of "growing the share" of best customers in your customer database.  Your best customers are remaining with you, and other customers are "growing into" becoming best customers.

If you don’t see this effect, the future value of your business is shrinking.  Higher customer activity levels among best customers are just not happening; this trend should be researched further and action taken to counteract the trend.

A business can mix and match tracking of behaviors and Hurdle Rates across the RFM metrics according to priorities in the business model.  Any important activity at your site can be assessed using RFM Hurdle Rates.

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