Some businesses look on the surface like they many not be suitable for using behavioral profiling. Take the electric power business. The customer bill is just about the same every month, with seasonal variations, and it’s not like the customer has a choice as to whether to buy power each month. Yet they can defect to an alternative provider, and before you know it – it’s too late. The cellular biz has similar attributes, particularly with the growth of annual contracts and bulk rate minutes. How do you know when a telecommunications customer is about to defect?
Or take the insurance business. A very long cycle business with very little transactional activity. You sign up for insurance, get billed once or twice a year, and that’s it. How do you profile behavior in a business like this, where the customer may be around for 5 years and then all of a sudden, just defects?
The answer is you profile activity other than the revenue related activity, for example, calls to a phone center or visits to a web site. When you look at “best” customers vs. “worst,” are there calling or visiting patterns which stand out? In some service-oriented cases like this, you have to “flip over” the behavioral approach – if the customer is Recent and Frequent on phone calls, this may be a “bad” thing and a pattern people engage in just before they defect.
The key is to group customers by “best” and “worst” and look for any pattern that separates the two, then use behavioral profiling methods to detect the likelihood of defection. You can also do some limited testing on your customer base to find out if behavioral profiling is useful on your particular customers. If you haven’t already, see the following article, and substitute “calls to call center” or “visits to web site” for purchases:
If you have a website or telephone “self service” interface, falling use of it might mean customers are getting ready to defect, or it might mean they are satisfied and are going to stay long term.
For example, if a pattern of increasing Recency and Frequency of visits to the “help” section of the web site tends to predict customer defection, you could trip an intervention call from the call center to “just check in.”
In a long cycle, low transaction frequency business like insurance, you may have to extend your time horizon to pick up enough meaningful transactions. Instead of looking at behavior on an annual basis as suggested in the above article, you might have to look over a 3 year or 5 year period.
There’s no way to tell in advance what these metrics will be, but the customer behavior will “speak” and tell you which data points matter. Here’s what you should do:
1. Make sure you understand all the internal data points available to you – what exactly they are, where they come from, how they are derived. Billing records, service records, installation records, and so on. Customer source is always very important, if you can get at that through the marketing area.
2. Isolate “best customers” – those who signed up and stayed signed up for the longest time, with the least cost (variable cost to you – installation, marketing etc, not in terms of total calls to the center).
3. Run profiles over time (LifeCycle) on each piece of “action-oriented” data available to you, and determine which provides the highest correlation to best customer behavior.
For example, high Recency and Frequency of calls to the center might be highly positively linked (good service leads to better customer) or might be highly negatively linked (billing problems create repeated calls = mad customers who disconnect).
In service businesses, you generally look for sharp changes in behavior – a drop of 30% in
usage, and increase of 50% in calls. These are good targets for automation since they’re quite clear cut.
And finally, source of customer is absolutely critical in this kind of business, especially since your “markets” may be geographically constrained. Good customer retention starts with proper customer acquisition, and it should be relatively easy to look at LifeTime Value by customer source. Here’s what I mean.
Pick a start date, say one year ago (3 or 5 years for long cycle businesses), and take a quick look at your highest value customers (gross billings?) over this time and see where (what campaign, geography, etc.) they came from. Then look at lowest value (high churn, disconnected) customers from the same start point, and see where they came from. If there are differences, you’re on your way to finding the answer you’re looking for. In addition, once you determine there is a difference, survey a subset of each group and try to find the commonality in the groups and differences between the groups. This links the data to the emotions and provides a backdrop for improving acquisition technique.
Don’t try to do this starting from a “micro” level and looking up. Start with macro ideas (geography?) then “drill down” (couldn’t resist) a layer, then another. When you get down to
the level where their appear to be no sizable differences between best and worst groups anymore, you’re done. Going any lower is just “noise” and is not generally helpful.
Something else that works in cycle billing environments is Latency of payment. If a customer averages payment 20 days after billing, and that slips to 30 or 40 days, it can be a good indicator, especially when combined with the “seeking help” activity above.
For more on Latency, see:
You really have to just sit down with a customer record, look at all the data in it, and say to yourself, “If there was a change in this data point, could it mean something?.” Then compare best (long life) and worst (fast churn) customers and see if there is a difference.
Perhaps the Latency indicator you are looking for is outside of billing. Look at Latency of initial service – the number of days between asking for service and getting it. Or the number of days between getting service and the first “trouble call” or billing dispute. There is something in there – always is. The “it” may differ by geography, which can lead to the discovery of other more operationally oriented problems causing customer defection.
You may not be tracking any of these kind of metrics now, and you may have to “make up” some using the raw data. My suggestion would be to just grab a couple of these ideas, and see if you can make a dent. Find a couple of best and a couple of worst customers, and really take a calculator to some of these ideas. If you get a Eureka! moment (it always happens that way), then ask IT to run a broader cross section of customers on the same parameters to prove it out. There is always something, a tip-off by the customer, as to what they’re thinking.
Here’s a personal example. I switched long distance providers and accepted an offer of a personal 800 number. I never used it or gave it to anybody. When I got my first bill, I was billed for a bunch of 800 number traffic. Since they supplied the “origin” numbers, I called them. Every one of them was some kind of internal engineering number, the kind that spit numbers back at you, like “System 4820, online” and the like.
When it happened again the second month, I just switched everything to the local phone company. If the offending company looked at my billing record, saw I was a new residential 800 customer, profiled all new 800 customers who defected, and compared results with those who didn’t, they might see a series of very short 800 number calls from internal engineering numbers on a decent amount of the defected customer bills – and none on those that did not defect. Or maybe they could have just looked at average call length on new 800 numbers, which was very short on my bill compared to what you would expect. They could have seen a pattern – defection potential detected, and taken a corrective action before the defection.
A note on win-back – it’s very tough in a commodity service-oriented business. The customer is facing substantial potential switching costs – tangible and intangible. Once they go, they’re gone, and calling them to say “did you know we offered a cheaper rate” which is to say “did you know we have been screwing you all this time” is not helpful. The flip side of this situation is the ROI can be very, very high when you can predict defection and save the customer – and very trackable too.
What’s the point of all this? Just because you don’t have a lot of customer-controlled purchase activity going on doesn’t mean you can’t use behavioral profiling to predict customer defection. Once you find your predictive data points using the process described above (or the much more expensive data mining route), then you can use the concepts on this site and in the book to organize and track your customer retention program. Instead of purchases, you track phone calls, web visits, or some other indicator using the Drilling Down method to create your early warning system defection indicators.
Many companies offering long purchase cycle products actively shorten the cycle by employing an inter-purchase contact strategy. By actively contacting the customer between purchases, these companies try to “bridge” the purchase cycle and maintain Recency of contact. This approach can lead to an increase in repeat purchase rate, if handled correctly.
In fact, this approach is not new and has nothing to do with the Internet. State Farm Insurance has for a long time pursued this contact strategy using mail. Weber-Stephen Products Co., the manufacturer of Weber Barbecue Grills, sends a quarterly magazine full of seasonal cooking tips and accessories to customers who buy high-end grills.
If the above makes sense to you, then you are on your way to designing the highest ROI customer marketing campaigns of your career. The Drilling Down book teaches you all of the proven LifeCycle-based marketing techniques step-by-step, building up from simple ideas like Latency to full-blown visual customer LifeCycle mapping techniques.
If you want to start returning profits of 2 – 5 times the money you spend on a customer marketing campaign, you need this book!
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