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Drilling Down Newsletter - May 2001In this issue: # Best of the Best Customer Retention Links On
Average Thinking
Small New Recency-based Tutorial on the Web Site Optimizing Discounts Using Recency You will generally see response rates fall as a function of Recency - the number of weeks or months since the last interaction with the customer. This relationship is a very smooth curve and quite predictable once you establish the "slope" of it for your business. The response rate by Recency will look something like this: Time since contact = 1 month, Again, 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. 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 benefit generated. For example, let's say you offer a discount or gift in your retention / lapsed customer campaign and need a response rate of at least 4% to pay back the campaign cost. The implication for your campaign in the Recency information above is this: don't bother to promote to any customer who hasn't made contact in over 3 months, because you're wasting your money; response will be too low to pay back the cost of the campaign in the "over 3 month Recency" group of customers. This Recency effect is very stable over time, allowing you to predict in advance what response to a campaign will be, once you do an "establishing" campaign to see what your response rate curve is for any particular offer at each Recency level. With me so far? Recency will predict response rate. What many people don't know is if you "ladder" discounts according to Recency, you will boost overall response while cutting costs. Let's say you usually e-mail everybody a 10% discount. If you were using a ladder approach for a test, it might look like this: Time elapsed = 1 month, Time elapsed = 2 months, Time elapsed = 3 months, Time elapsed = 4 months, Using this approach, you are allocating the most "bang for the buck" discount-wise where you need it most - the least Recent customers, and pulling back on some discounting where you don't need it as much - the most Recent customers. Now, as I said above, your response rates will vary depending on the offer and your business, and you have to test these ladders to find the optimum profitability for each level. 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 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 this LifeCycle process 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 it. 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. Clean, simple, easy to implement. And if you don't have any formal customer retention program, much better than what you're using! Practice What You Preach: Take my current little pet peeve - I'm getting ripped off on advertising, it would seem. Or am I? Oh, not on the response rate side, I get great response rates with Google AdWords and GoTo. I'm talking about the quality of the visitors generated. It seems that visitors coming from my ads might be of a lower quality than free visitors coming directly through the search engines. Check out this little chart, all based on visitor sessions: Q: I have a large client with an opt-in e-mail campaign. We found that the majority of customers who subscribe to these e-mails have never clicked on the offers they contain, and about one third have never even opened an e-mail. Can you direct me to any industry best practices/tactics of dealing with these inactive populations? A: Well, I don't know about any "industry best practices" on this topic for e-mail, but I can tell you what is done offline - threaten 'em and then purge 'em. There is evidence for this working online, as it has been done successfully by people online wanting to maintain high list quality and responsiveness (the same reasons it is done offline). There also seems to be some evidence that a "hey, if you're not going to read the e-mail, we're going to stop sending it" threat in the subject line, when applied with a "click here to confirm you want to keep getting this e-mail" in the body, actually converts people who were previously inactive to active openers, readers, and responders. Again, this is what is seen offline. That's why you get catalogs with the similar "This is your last catalog if you don't buy" threat. It's a winner either way for the marketer; you make some additional sales from dormant targets and you stop spending effort on the ones who really don't care about your offers in the first place. More revenue, less cost. The Drilling Down mantra, for sure. Q: You seem to provide practical methods for turning data into useful customer information. Do you think the lack of this expertise is the biggest reason why data-driven marketing is not more widely used? A: Well, I don't know about not widely used. The $110 billion
catalog industry is built on data-driven marketing. There are
some people on the web doing a good job with it, though most of those
are catalog operations. But yes, in general people are confused
about which data is important and how to use it. There just are
not many people around with practical hands-on experience.
Dealing with data is also highly math-oriented, and a lot of people
don't feel comfortable with running numbers. These are the
reasons I wrote the book. Techniques like mine are explained in
some books, but either at too high of a level to be of practical use
or with such mind-numbing detail and math you go crazy reading
it. One day I thought to myself, Hey, you can strip this
all down into a very basic approach, and really show the average
person how to make a lot more money in customer marketing - by giving
them the "dummies version" of advanced database marketing
techniques. And to get rid of a lot of the math, I use graphical
displays of behavior so "getting the results" is much
easier. In a nutshell, that's the book. Any comments on the newsletter (it's too long, too short, topic suggestions, etc.) please send them right along, with any other questions on customer Valuation, Retention, Loyalty, and Defection to me. 'Til next time, keep Drilling Down! Get the book with Free customer scoring software at: Booklocker.com Amazon.com Barnes & Noble.com Find Out Specifically What is in the Book Learn Customer Marketing Models and Metrics (site article list) |
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