"Use Recency Metrics to Make More Money on Your
Jim Novo, Author and Customer Retention Guru
http://www.jimnovo.com July 25, 2001
Recency and Response
Recency is defined as the time elapsed since a customer has engaged in a specific activity with you. The more time elapsing since the customer engaged in an activity, the less likely it becomes the customer will repeat the activity. This activity could be a purchase, visit, download, or log-in - almost anything that requires an "action" of the part of a customer. Customers engaging in multiple actions can have multiple Recency metrics simultaneously; for example, the customer is very Recent on page views but not very Recent on purchases.
You will generally see response rates to a promotion asking for a specific action (purchase, visit, click a link) fall as a function of Recency - the number of weeks or months since the customer last engaged in the activity. This relationship is a very smooth curve and quite predictable once you establish the "slope" of it for your business. Response rate by Recency might look like this:
Customer inactive for 1 month, Response rate = 20%
Customer inactive for 2 months, Response rate = 10%
Customer inactive for 3 months, Response rate = 4%
Customer inactive for 4 months, Response rate = 1%
The absolute response rates will be different depending on the business, media used, and offer, but the relative response rates will follow a decelerating curve as shown above, that is, the less Recent the customer, the more dramatic a drop in response rate you will get to your request for an action.
In terms of using this information for promotions, you will find some point along the curve where you will get "breakeven", meaning the cost of the campaign will equal the profits or benefit generated. For example, let's say you offer a discount, gift, or other incentive in your retention / lapsed customer campaign and need a response rate of at least 4% to pay back the campaign cost. This is your breakeven point.
The implication for this campaign in the Recency information above is this: don't bother to promote to any customer who hasn't engaged in the activity you are trying to encourage for over 3 months, because you're wasting your money; response will be too low to pay back the cost of the campaign with any customer who has been inactive for over 3 months.
How do you use this information on Recency, how is it implemented? Take any promotion you have done and look at the response rates by the Recency of the customer prior to the promotion, or set up a new promotion and look at your response rate by the Recency of the customers involved.
For example, classify customers in 30 day “buckets” the last activity of the customer was in the past 30 days, last activity 31 60 days ago, last activity 61 90 days ago, last activity 91 120 days ago, last activity 121 150 days ago, last activity 151 180 days ago, and last activity 181+ days ago . Look at the average response rate by these 30-day buckets. You will find response falls off significantly as you look at Recency buckets further back in time.
This Recency effect is very stable over time, allowing you to predict in advance what response to a campaign will be, once you do this "establishing" campaign to see what your response rate is for any particular offer in each Recency bucket. Recency will predict average response rate for any specific combination of offer and media used. You can save a tremendous amount of money by forecasting your response for each bucket, and not promoting to any bucket where you will lose money on the promotion.
Recency and Offers
What many people don't know is if you "ladder" the discount, gift, or incentive value according to Recency, you will boost overall response while cutting expenses by minimizing discount or other incentive costs.
Let's use purchases as an example, and say you usually e-mail all your customers a 10% discount when you do a promotion. If you were using a Recency ladder approach for this purchase incentive, you might apply your discount strategy this way:
Using this approach, you are allocating the most "bang for the buck" discount-wise where you need it most - the least Recent, lowest response customers, and pulling back on some discounting where you don't need it as much - the most Recent, highest response customers.
Since your most Recent customers are most likely to respond, you can back off on their discount and you reduce the cost of giving discounts to customers who “may have bought anyway without a discount”. You then reallocate this discount money to where it is needed most boosting the response rates of those much less likely to respond - the less Recent customers.
Now, as I said above, your response rates will vary depending on the offer, media used, and your business. You have to test these ladders with different
combinations of offer and media to find the optimum profitability for each Recency bucket. The interesting and quite useful benefit of this approach is the "automatic" overall customer retention effect discount ladders have.
Using a ladder of this type means your promotional discount budget is automatically working harder and harder to keep a customer active with you as they drift further and further away from you. The less Recent a customer is, the less likely they are to buy or visit again, and by using a discount ladder you are counteracting the customer LifeCycle (the tendency of customers to leave you over time) with stronger discounts as the defecting customer behavior plays out.
If a most Recent customer does not respond to the 5% offer, as they get less Recent, they automatically get offers rising in value, and at some point, many will take advantage of an offer. The customers who run through this system without taking any offers were likely lost to you as a customer already, and not worth the extra expense to try and keep promoting to them.
This approach to creating a customer retention program is clean, simple, and easy to implement. And if you don't have any formal customer retention program in place, much better than what you're using now!
Jim Novo is an interactive retailing expert with over 15 years of experience generating high ROI customer marketing programs for Home Shopping Network, CBS/SportsLine, MBNA, and many others. Jim writes for national magazines, is a speaker at industry conferences, and has written a book on using customer data to create high ROI customer marketing programs. More "How To" customer marketing articles can be found on his website: