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Comparing the Potential 
Value of Customer Groups

Jim's Intro:  This short tutorial will provide you with all the information you need to implement a simple potential value scoring process.  Use these scores to compare the potential value of customers from various ad sources, buying certain products, or visiting specific areas of your site.  You will also learn how the concepts of Customer Lifecycles, LifeTime Value, and ROI fit into scoring the potential value of customer groups.  If you can compare potential value, you can allocate more spending to higher value customer groups.

This article assumes you have some background in customer marketing; you might want to read the short articles on customer profiles and customer models if you don't.  


Over the past five decades, a lot of research and testing has been carried out concerning the profiling of customer behavior based on transactional data.  The appearance of computers and "data-mining" have allowed even more extensive studies to be carried out.

The end result?  If you had to pick one variable to predict the likelihood of a customer to repeat an action, Recency, or the number of days that have gone by since a customer completed an action (purchase, log-in, download, etc.) is the most powerful predictor of the customer repeating this action.

As each day goes by after the customer completed the action, the customer gets less and less likely to repeat it.  Plain and simple.  You can run all the fancy data-mining scenarios on "likelihood to buy" or "likelihood to visit" you want to - Recency always comes up as the most important variable in predicting the likelihood of a customer to repeat an action.

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, log-ins, game plays, 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.  They know you are most likely to order again immediately after your first order.  Recency is the most powerful predictor of future behavior.

It should not surprise you that Recency is also the most powerful predictor of a customer to respond to a promotion - after all, the more likely a customer is to repeat an action, the more likely they are to respond to a promotion asking for this action (purchase, log-in, download, etc.).

If a Recent customer is more likely to repeat an action, and is more responsive to promotions for this action, it follows the more Recent a customer is, the higher their potential value, because Recent customers are the most likely to contribute to profits in the future by responding to your promotions (or simply just coming back by themselves).

Customers who are more Recent have higher potential value than customers who are less Recent, for any given activity.  Customers who made a purchase 15 days ago have higher potential value than customers who made a purchase 60 days ago.  Customers who logged in last week are much more likely to visit than customers who logged in 30 days ago, and so have higher potential value.

Make sense?  Great.  But how is Recency implemented, how do you actually do anything with this information?  Glad you asked.  Let's use Recency to compare the potential value of customers coming from two different ads (Ad #1 and Ad # 2) that ran at the same time, for the same duration.  The following example uses a spreadsheet, but if you know your way around databases, and can query your customer records, then have at it your way.  

1.  Identify the groups you want to compare for potential value.  In this example, it's the customers who clicked on either of two ads, Ad #1 or Ad #2 (two groups).

2.  Decide which activity is most important to you for these groups.  If you're a publisher,  probably log-ins or page views are most important.  If you are selling merchandise, you would use purchases.  For this example, we will use purchases.  An example using visits (or log-ins, if you don't track visits) is below.

3.  Import all the purchase records of people who clicked on Ad #1 or Ad #2 into separate spreadsheets.  These transactions need to have a date; most interactive activities are date-stamped so this should not be a problem.  If an activity you want to profile for potential value has no date stamp, start collecting the dates of activity.

4.  Pick a time frame to look at Recency.  For page views, it might be 1 week; for purchases, maybe 30 days.  The exact length is not very critical, because you are interested in comparing the activity between the Ad #1 and Ad #2 groups - you want to know which is "better."  As long as you use the same time frame for both groups, you are fine.  Pick something reasonable based on what you know about your customers.  Anywhere from 30 to 90 days would be reasonable for purchases; let's use 30 days.

5.  Sort the purchase records for Ad #1 from most Recent to least Recent and find out what percentage of the people who clicked on Ad #1 and made a purchase have made at least one more purchase in the past 30 days.  Count back 30 days using the transaction dates, total the number of customers making a purchase, and divide by the total people in the spreadsheet.  Perhaps it is 20% .  Note: The software that comes with the book will automatically aggregate multiple transactions by customer and sort customers by their most Recent transaction for you).

6.  Run the same analysis for people who clicked on Ad #2 and made a purchase.  Let's say only 15% of these people have made at least one purchase in the past 30 days.

7.  You're done, and you know the answer.  A higher percentage of people who clicked on Ad #1 are Recent - active and purchasing - when compared with Ad #2.  This means Ad #1 generates customers with higher potential value. You need to take this into account when analyzing the success of the ads.

Do you understand how powerful this idea is? 

If you go through this process for customers grouped by product they bought first, you can determine which products generate new customers with highest potential value.

Go through this process for customers grouped by which area of the site they visit most, and you will find which areas generate highest potential value customers.

If you go through this process for customers grouped by the demographics or the survey data they provide, you can determine which data points define customers with the highest potential value.

This is a simple example of  how companies with experience in managing remote shopping customers find ways to maximize sales and minimize expense.  The customers, through their actions, tell them which route is the most profitable to take.  The most Recent customers for any particular activity are always the ones most likely to repeat that activity, and so have a higher potential value.

You can track multiple activities for the same customer groups.  In the first example, you found customers who clicked on Ad #1 and made a purchase are more Recent on purchases, so they have a higher potential value on the activity "purchases."  But what about the Recency of people who clicked on the ads for visits?  If they keep coming back, they could be of some future value.  Let's see how this Recency study might look.

1.  Visits / log-ins example:  Import all the visits (or log-ins if you don't track visits) into two separate spreadsheets of people who clicked on Ad #1 or Ad #2 (need date stamp).

2.  Pick a Recency cut-off.  Again, we are interested in a comparison, so the number isn't critical.  Let's use 1 week.

3.  Sort each spreadsheet from most Recent to least Recent and find out what percentage of the people who clicked on Ad #1 have visited (logged-in) at least once in the past week, as was done above for purchases.  You might come up with 10%.

4.  Run the same analysis for people who clicked on Ad #2.  You might come up with 30% who have visited / logged-in at least once in the past week.

5.  You're done, and now you have an interesting situation.  It appears the customers who clicked on Ad #1 have a higher potential value on purchases, but people in general who clicked on Ad #2 have a higher potential value on visits.  Maybe they're just tire kickers, or maybe they're doing research.  We'll take a closer look at finding answers to this situation on the next page of the tutorial.

Note that this method is based on the actual facts of customer behavior - not speculation or "best guess" theories.  The behavior of the customer is the most accurate yardstick you will find for assessing potential value.

Once you complete studies like these, you can begin to organize all your business practices around the potential value of the customers they generate.  If you allocate money away from activities generating low potential value customers, and allocate this money to activities generating higher potential value customers, you will become more profitable over time.  It's really as simple as that.

Next in Tutorial:
Adding Customer LifeCycles to the Mix

Read advanced version of this model
Maximizing the value of customers using Recency is explained in the Drilling Down book.

 

 
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This is the original Drilling Down web site; the advice and discussion continue on the Marketing Productivity Blog and Twitter.

Download the first 9 chapters of the Drilling Down book here: PDF
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