Segmentation by LTD & LifeCycle

The following is from the July 2010 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: One of the first things I am doing in my new job is to identify the Customer Lifecycle pattern – how many periods (month, year) will it be before a customer is likely buy again.  In enterprise software industry, where software cost easily 6 figures, # of years is a reasonable time frame.

A: Yes, one would assume this.  But these notions would most likely be based on a feeling of the “average” behavior, and on average, it probably does take a long time.

What is not known is this:  if the “average” is composed of short-cycle and long-cycle buyers, who are the short cycle buyers, and what are they like?  What industry SIC code, for example?  And can we get more of them, or at least focus more resources on them, if they are the most profitable?  So the challenge is not only to look for the “average”, but then understand how this average is composed.  If you can break down the average by industry, or by salesperson, for example, this might be highly directional information.

Q: From my internal analysis, however, I discerned from the sales figures something quite counterintuitive – the period between first and next sale is much shorter than I would have thought for the SW industry in general.

A: Pleasant surprise, eh?  I don’t know what kind of figures you are looking at, but make sure the data is in fact what you think it is.  For example, if you want to study software purchase itself, do the “sales” figures you are looking at include transactions involving not the sale of software, but also service, like installation or modification fees?  It would make sense that a “software sale” would be followed pretty quickly by an “installation” sale, for example.  You need to know this to properly segment.

Q: Would you be able to point me to some studies on how often customers wait after the first purchase before contemplating an upgrade of software or something you personally have done in consulting projects for SW companies?  This industry benchmark will then shed some light on whether this trend is something peculiar to our company or not…

A: I am not aware of any published study of this type.  And as you might imagine, these numbers would vary quite widely in the industry and the nature of the information would be a highly guarded corporate secret.  So I don’t think you will find any “benchmark” studies of this type, and sorry, I can’t share client data with you!

Q: The next step for me then is to map out the drivers for this behaviour and then calculate the LTV (LifeTime Value) and take a look at the actual LifeCycle events creating this LTV.

A: Yes, but to be precise, LTV is typically a forecast when working with current customers; it’s not known until the customer actually defects, marking end of  “Life”.  What you are probably looking for is more accurately called “Life to Date” (LTD), the actual sales of a customer from start of relationship until present.

Also, when segmenting customers by LTD, of course look for temporal bias – a 10-year customer is likely to have higher LTD than a 2-year customer.  If there are enough customers, it might be a good idea to first segment by start year, then LTD.  This way you have cohorts of customers who are going through the same experiences together and differences in LTD will be more significant in terms of predictive power – you don’t have to hunt around for external bias (e.g. competitive changes) that might affect LTD.

Think about what I said above about breaking the “average” down into different groups, because this will likely provide the Eureaka! moment and turn the data you are looking at into information.  For example, if you find the LTD of the “average customer”, this is very interesting information indeed, but not highly actionable – what “action” do you take knowing this information?  Can you point to or predict which customers are “average”?

However, if you were to find out the LTD differed dramatically by industry, by salesperson, by country, by time of year, by type of software module installed first – this is highly actionable information, because it provides very direct instruction on where the most profitable areas of business are.

If you lack thoughts in this area, try segmenting by variables that directly affect the experience of becoming a new customer.  At least one of these is most always predictive of LTD, when directly tied to the acquisition of a new customer:

1.  Campaign media (e.g. trade show versus magazine, online versus offline)

2.  Campaign content / offer

3.  Salesperson / Service teams

4.  Product or Category of first purchase

5.  SIC code / Industry (proxy for Product suitability to customer needs)

For example, if you find LTD differs by salesperson, you will find salespeople who create high LTD customers and salespeople who create low LTD customers; the company should study how each salesperson sells and teach the others based on results.

Or perhaps likelihood to purchase again is determined by which customer interface team installed the software – one team does such a good job the customer re-orders the next module very quickly, as compared with other teams.

Knowledge of this type would be extremely valuable to the company – you can use LTD to discover “best practices” hidden within the “average” data, and by spreading those best practices throughout the company, create enormous benefit and increase in profitability.

The secret to creating meaningful customer analysis that delivers high impact is this: always think about what you would **do** with the information you uncover.  If you can’t **do something** with the numbers you evolve, you probably need to drill down a little further and uncover the true meaning of the underlying data.

Hope this helps.  My personal guess based on what I know about behavior would be this: the type of software installed first and the sales / installation / after the sale care team are the two most likely variables predicting speed to next purchase, followed by the industry the buyer comes from.

This is a very interesting project; please keep me informed of your progress and I will help you in any way I can.


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4 thoughts on “Segmentation by LTD & LifeCycle

  1. Great post. I would strongly recommend the questioner to look at Survival Analysis for this type of time to event work. The idea of ‘censoring’ is very important and an ‘average’ is not a suitable measure in this case. You will want to develop a model where the drivers of the time to event can be examined and quantified (via the ‘hazard ratio’). Depending on the type of data (if time can be thought of as continuous or if it is discretely measured) Cox regression (continuous) or logistic regression (discrete) is the ‘normal’ way to go. The former is written about in many places and the latter is well explained in a book by Singer and Willett. Both are available in SAS, R etc.

  2. Hi Jeff, thanks for the astute comment, and I agree, though I’m not sure this particular person / company is quite ready for that level yet!

    What I often like to do for Marketing people is take the start date and an event as a segment and look at the fallout over time. For example, with a new customer campaign, time since last event as a definition of whether the new customer is still “engaged”.

    These “pipeline curves” are simple enough that most folks “get it” right away and by comparing different campaigns, they can clearly see the longer-term impact of their choices. Not as sophisticated as your suggestion and clearly suboptimal in terms of identifying all the drivers, but sometimes you have to work with the resources available to you.

    Thanks again for bringing up the best way to approach this idea!

  3. Great questions, great answer:

    My original contribution to the discussion is:

    The problem with shop-till-you-drop models in Customer LifeCycle Analysis is that they fail to predict revival.

    I prefer to think in terms of ‘hot’ and ‘cold’ states, and run the models forward on that basis.

    The ROE isn’t all that trivial in certain instances. But, for all pragmatic purposes, shop-till-you-drop will get you 80% of the way there, 95% of the time.

  4. Christopher, I agree with you if proactive retention activity is ignored, which many people obviously do. Why else would they keep emailing “the dead”, people who have not clicked or even opened in years.

    But there is huge financial leverage in understanding LifeCycles, and taking action to cream off and re-activate those folks out of the stream who are disposed to revive. This essentially leaves the “hard luck” folks who are not going to revive as they cycle out. I don’t need to predict revival because I’m not letting them die in the first place – unless they really want to.

    I believe we are saying the same thing, just approaching it in different ways. I would be actively screening for and taking action with customers *before* they get cold, and for those who get cold, stop marketing to them at all (waste of resources).

    If they come back, they come back as a new customer / new LifeCycle for Marketing purposes. Indeed, they often act quite differently when they come back (new interests, new needs) – and that’s probably one of the reasons they left in the first place!

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