Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)
Topic Overview
Hi again folks, Jim Novo here.
Often we spend a lot of time talking about analyzing “customer data”, and the implication is we are looking at marketing or sales related information. That may be true for companies just beginning to use customer data; this data often is the easiest to understand and access. But true data-driven organizations have analysts who reach across the silos for data, looking for customer service or operational customer data that can impact the current and potential value of the customer. We have one such example from a Driller today.
Sound good? Then let’s do some Drillin’!
Q: I work as a management consultant, currently working in a project where my client (Oil & Gas company) is trying to calculate and implement Lifetime Value into one of their businesses. One of their business units (Industrial Lubricants) sells different kinds of lubricants and services to corporate customers such as Ford, Toyota, BMW, etc. They have already done some customer profitability analysis and they are currently trying to calculate Lifetime Value.
A: That’s a pretty interesting place to find a concern for analyzing LTV…
Q: My questions:
1. What’s the best way to forecast future cash flows in a B2B scenario where models such as RFM are not relevant (Recency and Frequency do not really apply given that their customers have been with them for ages and are often in long-term contracts). How can I project customer profit over time and how can I estimate the “lifetime” of individual customers?
A: Well, it’s not that Recency and Frequency don’t apply, they probably apply in a different way. In most businesses driven by contracts, service is the issue. So you need to look for Recency and Frequency of “problems”, whatever that might mean in the industry. I imagine “logistics” is an issue for these businesses – on time delivery, quality, “ease of use” (which could cover many factory / service issues), packaging, and so forth. This can take a lot of research, particularly if there are no “systems” capturing this kind of data. But usually, even in very old line companies, there is some place where this data resides. You just have to find it and get access to it.
Often in an environment like this it is easier to work backwards – first, identify defectors, then look for service issues or changes in behavior that imply service issues – declining order size / Frequency, expanding order Latency (weeks between orders) and so forth.
Q: 2. How can I make Lifetime Value analysis attractive in a Key Account Management setting? My client might be saying: “This is a nice idea BUT looks like a lot of work and besides we currently do account planning.” How can I make the case or show that by implementing Lifetime Value they will be extracting more profit from key accounts?
A: Yes, that’s quite a different story, isn’t it? The problem, if I am imagining the situation correctly, is the Key Account Manager is not going to be particularly fond of you dredging through all this service data, identifying defectors, and coming up with potential defection reasons. Not going to like it one bit.
But here’s the thing – why would a company like this be concerned with LTV in the first place, if everything is so rosy and the contracts take care of everything? What is the motive? Someone has the feeling they could be better allocating resources by customer, losing fewer customers, or acquiring more profitable customers.
I find there is nothing like the presentation of hard data on defection to make believers out of doubters. The first time you *prove* to someone “20% of your best customers last year decreased their volume this year” or the equivalent in your business will be the last time you need to convince them of the value of this kind of work. Don’t position the analysis as a “substitute” for account management but as “assistance” to account management. No matter how good an account manager is, they can’t be everywhere all the time, and your analysis will provide an additional “window”.
In a business I know so little about it is hard to be more specific. But you know the business, so you should be able to understand where the “levers” are and look for evidence in and around those levers. For example, here is a pure fabrication but one I can imagine might occur in your business:
Most if not all of these customers probably use 2 or more vendors for the same product to preserve competitive pricing; the products might not be identical but can be substituted. “Share” is then the issue, not whether there is a contract. Let’s say a customer develops a preference for a competitor’s product and the “share” of your client’s product used begins to fall. In reaction, the Key Account Manager promises special “just in time” delivery changes to maintain share, but these concessions decrease the profitability of the account. In addition, these costs are largely “hidden” because they are not allocated to the product but to some sort of overhead.
What would happen is the division would start to see margins fall due to the expense of this special service, but might not be able to figure out why. That would perhaps make someone wonder about LTV analysis and whether certain customers were not really worth keeping or should be kept at a smaller share – the cost of maintaining share is not worth the hit to the margins. In other words, maybe it’s not really about customer defection, it’s about profitability. More like a forensic accounting exercise or Activity-Based Costing.
For example, I’m reminded of a manufacturing company that had a customer who was particular about the quality of packaging. This customer rejected an above average amount of product which caused the customer to increase order quantity to make up for the rejects. For some reason, as the order quantity increased, the customer got higher and higher “discounts” based on volume. Part of this was automation based on quantity in the billing system and part of it was people “looking the other way” because the customer was very important.
The problem: rejected product was sent directly to a 3rd party refurbisher, who shipped directly back to the customer as part of the customer orders. This refurb product was routinely rejected at higher rates than factory product and you had an endless cycle in the making. The customer paid less and less for product but never actually “bought” any more product. This spiraled out of control until someone in accounting came up with a theory.
I was not able to access accounting data directly but made in a contact in Finance who had an interest in this kind of work. I told him to have his people go back 6 months and look for any “weird changes” in major accounts over time – order size, time between orders, excessive credits relative to norm, etc.
An accountant noticed that the Frequency of ordering with this customer was going “parabolic”; the time between orders (Latency) was getting shorter and shorter. Working on a hunch, accounting went to the 3rd party refurb place and found product all over the place – in offices, outside in sheds, and so forth. This meant, of course, that the quality of the refurbs was dropping as the volume was increasing. The refurb folks were hopelessly backlogged and soon went out of business. The customer defected.
Based on volume, this company was the manufacturer’s “best customer”. Indeed. In the end, it was found that each unit was “sold” 5.36 times to the customer, meaning some must have been refurbed 7 – 10 times.
So yes, you have a situation that is quite complex and you have to expand the vision of customer behavior modeling to fit what you have on the plate. The same metrics can help you do this; after all, Recency, Frequency, and Latency are just ways to better organize data so that it can be analyzed behaviorally.
Q: By the way, I have read most of your book and I like it – it’s practical, easy to read and straight to the point.
Well thanks for that, and as you get deeper into it, if you have any more questions, just let me know. LTV is where marketing and finance intersect, and sometimes, it’s really more about finance than it is marketing! Perhaps you should seek out a partner in Finance.
After all, “Management Consulting” is (in my mind) about cutting across the silos and seeing what the company itself is unable to see. Customer data provides a unique and actionable way to do this; analysts must begin to realize customer retention and LTV are not always about “marketing” but about the entire relationship the customer has with the company. The key behavioral insight often lies outside marketing / sales / contracts data.
Jim
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