|
Thanks for visiting the
original Drilling Down web site! Get
the book at Booklocker.com
Customers Speak Up on Book & Site Workshops,
Project Work: Retail Metrics & Reporting,
High ROI Fresh
Customer High
ROI Customer LifeTime
Value and Intro to
Customer Customer
Model: Measuring
Retention CRM
Analytics: Pre-CRM
Testing for See Customer
|
Drilling DownTurning Customer Data into Profitswith a SpreadsheetSite and Book topic: Maximizing marketing ROI with customer behavior analysis
Learn Methods, Metrics |
Drilling Down Newsletter - July 2001In this issue: Let's do some Drillin'! Note to web site visitors: These links may have expired by the time you read this. You can get these "must read" links e-mailed to you every 2 weeks before they expire by subscribing to the newsletter. How Much
Should Be Spent on CRM Three
Keys to Ensuring CRM Success Discerning
Distinctions in Buying Behavior Making
Customer Relationship So we're going to take a little tour though LifeCycle-based
marketing land the next few issues of the newsletter. If you're
new to our Drilling Down crew, or if you have not seen the articles on
the Drilling Down web site describing customer LifeCycles, you might
want to brush up. See the article Customer
LifeCycles and the tutorial Comparing
the Future Value of Customer Groups
Many approaches to customer marketing rely on customer behavior
"triggers". For example, a win-back program is
triggered when the customer defects. Have you switched
long distance or cellular providers lately? Did you get
inundated with win-back calls begging you to reconsider?
"Jim, we just wanted you to know we have lowered our rates".
Yea, well, thanks for telling me after over-charging me. But
could they have known I was about to switch? I would humbly submit marketing to the customer *after* they defect is a sub-optimal approach; the decision has already been made. If you can market to them when they appear *likely* to defect, you optimize your marketing resources by not applying them too soon or too late in the customer LifeCycle. An easy to implement and proven powerful LifeCycle trigger is called latency. Latency refers to the average time between customer activity events, for example, making a purchase, calling the help desk, or visiting a web site. All you have to do is calculate the average time elapsed (latency) between the two events, and use this metric as a guide for creating and timing anti-defection campaigns. When you see a particular customer's behavior diverge from the average customer behavior, you get a triggering event. Since the calculation of latency is very simple, and the diverging behavior is easy to spot, this type of anti-defection campaign is an ideal candidate for "lights-out" or automated rules-based customer retention campaigns. As an example, let's take purchase behavior in a retail scenario. If you were to examine your customers, and find the average time between the second purchase and the third purchase was 2 months, you have found "third purchase latency". Any customer who goes more than 2 months after the second purchase without making a third purchase is diverging from the norm, and a likely defection candidate. It's simple logic. If the average customer makes a third purchase within 2 months of the second purchase, and a particular customer breaks this pattern, they are not acting like the average customer. Something has changed. This particular customer's LifeCycle has become out of synch with the average customer LifeCycle, and this condition is a trigger point for high ROI customer marketing. On average, if you divert marketing resources away from customers who have made a 3rd purchase within 2 months after the second, and apply these resources to customers who are "crossing over" the 2 month LifeCycle trigger point without making a third purchase, you will end up spending less money and generating higher profits for any given marketing budget. You are applying your limited resources right at the time in the customer LifeCycle when they create the most powerful impact - at the point of likely customer defection. Now, will all these customers respond? No, of course not. But the ones that do become active, loyal customers again, and those that don't are probably not going to be good customers in the future. The behavior of the rest of your customers tells you so. These non-responding customers may not be worth spending money on to "win-back", and in fact, will have much lower response rates to a win-back campaign. They have already demonstrated their lack of interest with their behavior, and you could be better off financially by just letting them go and focusing on more responsive, more profitable long-term customers. The above example is a relatively crude approach to latency. As you might suspect, different customer segments will have different latencies, and the more you fine-tune a latency campaign, the more profitable it will become. For example, let's say you execute the latency campaign described above, and succeed in retaining 30% of the defecting customers, making a tidy profit. But you really have two major product lines, software and hardware, each 50% of sales. Could the latency be different between software and hardware customers? You betcha. Upon further analysis, you find third purchase latency for software is really one month, and for hardware it's three months. The *average* is 2 months. So you bust the two groups apart, and run separate latency-based campaigns, one for each product line. In your original third purchase latency campaign, you promoted to customers who did not make a third purchase within 2 months of the second purchase. This means you were "late" for software (because the average latency is really 1 month) and early for hardware (because the average latency is really 3 months). When you realign the timing based on the line of merchandise, you find instead of retaining 30% of customers, you retain 50% of the customers, because you have synched-up the marketing effort with the true customer LifeCycle more tightly. And that, folks, is what LifeCycle-based marketing is all about - using your own customer's behavior to telegraph to you the most important (and profitable) time to market to them. The customer, through their behavior, raises a hand and asks you to take action. If you synch up your marketing efforts with the natural customer LifeCycle, you can't help but being more successful. ------------------------- Practice What You Preach: Online Advertising Last month, we looked at a chart comparing the value of these visitors for my top 3 search terms (relationship marketing, customer retention, customer loyalty), broken out by visitor value by source - paid ad or "free" search. By the way, in many cases both paid and free links are displayed at
the same time (if I rank high enough for the search term involved).
Visitors from paid ads are clearly of better quality - higher rates of
downloading, bookmarking, and newsletter subscription. Paid ad
visitors also stay twice as long on the web site. The implication of the above shift: there is variability in the quality of visitor generated according to the *search phrase*, and this may account for some or all the difference between the quality of a pay versus free visitor. Intuitively, this makes sense to me, because I only pay for relevant search terms, and "free visitors" may be arriving as a result of a non-relevant search. This is tremendously important to know, especially in light of the general industry commentary that paid search listings result in poorer search quality for users. Hmm... So, let's take a closer look at search term quality by busting up
the aggregate "paid" search results above by search term,
and see what we get. The following table compares each search
term individually with the total site statistics, where RM =
Relationship Marketing, CR = Customer Retention, CL = Customer
Loyalty, and TS = Total Site statistics. So find out we will, by Drilling Down yet another level in the next newsletter. --------------------------- A: Well, that's a big topic. The vendors pretty much use the same algorithms - there are only so many approaches, and once they're coded, anybody can use them. They may have minor differences in the way they are implemented, but particularly among the "data mining" type algorithms, they are all very closely related. New ones come along once in awhile (the latest is called "genetic modeling") but they are all parts of the same family; each has strengths and weaknesses, depending on the data and ultimate goal. There are really two big camps - so called "top down" modeling, where a human creates a hypothesis and tests it by building a model, and "bottoms up", which is data mining or machine learning, where the machine looks for patterns and tries to make sense of them. Top down models include all the pure statistical approaches, like nearest neighbor, clustering, regression, and so on. Machine learning includes neural networks, fuzzy logic, case-based reasoning, genetic modeling, and so forth. Algorithms like CHAID and CART are something in between; they evolved out of statistics but are also the basis for machine learning models. If you are really interested in descriptions of what all these
things are and how they work, try these two books:
Building Data Mining Applications for CRM Generally, to be *incrementally* more successful than a traditional statistical approach, data mining requires very clean data, a long period to "train" the application, customer records with 100's of variables, and a few Ph.D. stats people hanging around to interpret the machine language. The training thing consists of the machine spitting out improbable answers over and over until you train it to spit out the right ones. How do you know which are the right ones? Frequently, from "top down" analysis done by humans. So there is a somewhat circular argument for data mining, and it is really best used when you have gone though all the statistical top down work first, and are looking for the "next level" of an answer. Otherwise, you don't know if what you got out of the miner makes any sense, unless you know your business very well from a stats standpoint. Of course, the reason data mining became so popular as a concept in the past couple of years is the software was going to tell you everything and like magic run your business for you - no humans needed. Turns out not to be the case, it seems. Good top down modelers have a number of different modeling approaches at hand, and will "test" statistically to see which modeling approach provides the best "fit", that is, is the most stable over time and doesn't under or over predict an outcome. The most common stats packages, some of which have evolved to the point where you can pretty much run them out yourself without a stats background, are from SPSS and SAS, which have naturally made forays into data mining as well. In fact, many of the CRM packages that "deliver" data mining capabilities really deliver SPSS or SAS. Which brings us to RFM, the original behavioral model and the topic of my site and book. When you run all this stuff above looking for response or future value models, Recency and Frequency will always factor highly into the result, no matter whether you use top down stats or bottoms up machine analysis to derive your forumlas. The Recency and Frequency variables are so embedded into human nature they always end up in any model predicting behavior. What you get with all these other models, top down or bottom up, is the 3rd, 4th, 5th etc. most powerful predictors, with diminishing predictive power at each level. Said another way, Recency and Frequency will give you an 80% accurate model. Add a 3rd variable and you get 85%. Add a 4th and you get 88%. Add a 5th and you get 90%, and so on. That's why I tell people who have never done any customer modeling before this data mining stuff is like trying to get a Ph.D. without ever going to high school. It's overkill in this situation, and is most useful only if you have gone through basic stat models first. In fact, the result of a regression model or other statistical approach is one of the best data sets you can feed a mining engine. Likewise, any good human stat person, before they build you a regression model, will ask for RFM testing results to help build a model. It's like a pyramid, with RFM at the bottom, statistics in the middle level and data mining on the top. The most powerful, most broadly applicable models providing the highest immediate impact, the so-called "low hanging fruit", are RFM-based. Statistics adds further refinement, targeting even finer shades of behavior. Data Mining figures out if there is anything left to predict; it can produce segments you would never have found otherwise, although there may only be 1/8 of 1% of your customers in these segments, **particularly** if you have gone through RFM and stats first. The original RFM is a static model, predicting behavior at a point in time. In my book, you learn how to convert the model to look at behavior over time, a much more powerful, LifeCycle-oriented tool. Any company considering adding customer analytics, whether CRM-related or not, should go through the process of scoring customers using simple behavior-based models and trying their hand at high ROI marketing programs first. If you do this before shelling out the money for a data mining package, you'll be more likely able to justify the ROI and figure out if data mining will really help you. Want to do a quick test for potential ROI on customer analytics? Read this. Will any CRM consultant or software vendor tell you all this? Nah. That's why I wrote the book, don't you know... --------------------------- That's it for this month's edition of the Drilling Down newsletter. If you like the newsletter, please forward it to a friend! Subscription instructions are at the top and bottom.
Any comments on the newsletter (it's too long, too short, topic
suggestions, etc.) please send them right along to me, along with
any other questions on customer Valuation, Retention, Loyalty,
and Defection, to me.
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) |
The Drilling Down Project. All rights reserved.