Segmentation by LTD & LifeCycle
Newsletter #111: 7/2010
Drilling Down - Turning Customer
Data into Profits with a Spreadsheet
Customer Valuation, Retention,
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Hi again folks, Jim Novo here.
Let's say you sell products online, and have a choice for the
"hero shot" on your homepage:
1. Random rotation of "best sellers"
2. Products known to generate long-term, highest value, most
Which would you choose?
Now, is your answer the same if you are the CFO? If you are the
One of the most powerful types of customer analysis you can do is
LifeCycle analysis. The information revealed can not only make
Marketing a lot more productive, but often has implications for many
other areas of the company. For those of you looking for a
"seat at the table", LifeCycle analysis enables the idea of
Marketing playing a greater role in Strategic decision making, as
opposed to being relegated to just Advertising and "MarCom".
What if you could not only predict which new customers would become
best customers, but tell decision makers which products, service
teams, marketing methods, media, industries, geographies, and
salespeople create best customers? That's a recipe for a seat at
the big table, helping decision-makers allocate resources across
the company to the highest and best use possible.
So, you up for some Drillin'?
Questions from Fellow Drillers
Segmentation by LTD & LifeCycle
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,
number 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
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 LifeCycle events creating this LTV.
A: Yes, but to be precise, LTV is typically a forecast when working with current customers;
LTV is 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.
In B2B, 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 team
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,
when compared with other teams doing installations.
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 benefits and increase in profitability.
The secret to creating meaningful customer analysis which 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
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!
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'Til next time, keep Drilling Down!
- Jim Novo
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