0 APQC Provides 3 LTV Case Studies

One of the common criticisms of lifetime value is that it has no practical applications. You and I know this is false, but some people still need convincing. The APQC formerly American Productivity and Quality Council) recently published “Insights into Using Customer Valuation Strategies to Drive Growth and Increase Profits from Aon Risk Services, Sprint Nextel, and a Leading Brokerage Services Firm,” which provides three mini-case histories that may help.

Aon created profitability scorecards for 10,000 insurance customers. The key findings were variations in customer service costs, which had a major impact on profitability. The cost estimates were based on surveys of customer-facing personnel. Results were used for planning, pricing, and to change how clients were serviced, and have yielded substantial financial gains.

Sprint Nextel developed a lifetime value model for 45 million wireless customers, classified by segments and services and using “a combination of historical costs, costing assumptions, cost tracing techniques, and activity-based allocations”. The model is used to assess the financial impact of proposed marketing programs and for strategic planning.

The brokerage firm also built a lifetime value model for customer segments, which were defined by trading behaviors, asset levels, portfolio mix and demographics. Value is determined by the products and services used by each segment, and in particular by the costs associated with different service channels. The LTV model is used to evaluate the three-year impact of marketing decisions such as pricing and advertising.

The paper also identifies critical success factors at each company: senior management support, organizational buy-in and profitability analysis technology at Aon; model buy-in at Sprint Nextel; and the model, profitability analysis and customer data at the brokerage firm.

My own take is that this paper reinforces the point that lifetime value is useful only when looking at individual customers or customer segments: a single lifetime value figure for all customers is of little utility. It also reinforces the need to model that incremental impact of different marketing programs, or of any change in the customer experience. Although the Aon and brokerage models are not described in detail, it appears they take expected customer behaviors as inputs and then calculate the financial impact. This is less demanding than having a model forecast the behavior changes themselves. Since it clearly delivers considerable value on its own, it’s a good first step in a larger project towards a comprehensive lifetime value-based management approach.

0 Is Marketing ROI Important?

You may have noticed that my discussions of marketing performance measurement have not stressed Return on Marketing Investment as an important metric. Frankly, this surprises even me: ROMI appears every time I jot down a list of such measures, but it never quite fits into the final schemes. To use the categories I proposed yesterday, ROMI isn’t a measure of business value, of strategic alignment, or of marketing efficiency. I guess it comes closest to the efficiency category, but the efficiency measures tend to be more simple and specific, such as a cost per unit or time per activity. Although ROMI could be considered the ultimate measure of marketing efficiency, it is too abstract to fit easily into this group.

Still, my silence doesn’t mean I haven’t been giving ROMI much thought. (I am, after all, a man of many secrets.) In fact, I spent some time earlier this week revisiting what I assume is the standard work on the topic, James Lenskold’s excellent Marketing ROI. Lenksold takes a rigorous and honest view of the subject, which means he discusses the challenges as well as the advantages. I came away feeling ROMI faces two major issues: the practical one of identifying exactly which results are caused by a particular marketing investment, and the more conceptual one of how to deal with benefits that depend in part on future marketing activities.

The practical issue of linking results to investments has no simple solution: there’s no getting around the fact that life is complex. But any measure of marketing performance faces the same challenge, so I don’t see this as a flaw in ROMI itself. The only thing I would say is that ROMI may give a false illusion of precision that persists no matter how many caveats are presented along with the numbers.

How to treat future, contingent benefits is also a problem any methodology must face. Lenskold offers several options, from treating several investments into a single investment for analytical purposes, to reporting the future benefits separately from the immediate ROMI, to treating investments with long-term results (e.g. brand building) as overhead rather than marketing. Since he covers pretty much all the possibilities, one of them must be the right answer (or, more likely, different answers will be right in different situations). My own attitude is this isn’t something to agonize over: all marketing decisions (indeed, all business decisions) require assumptions about the future, so it’s not necessary to isolate future marketing programs as something to treat separate from, say, future product costs. Both will result in part from future business decisions. When I calculate lifetime value, I certainly include the results of future marketing efforts in the value stream. Were I to calculate ROMI, I’d do the same.

So here's what it comes down to. Even though I'm attracted to the idea of ROMI, I find it isn't concrete enough to replace specific marketing efficiency measures like cost per order, but is still too narrow to provide the strategic insight gained from lifetime value. (This applies unless you define ROMI to include the results of future marketing decisions, but then it's really the same as incremental LTV.)

Now you know why ROMI never makes my list of marketing performance measures.

0 Marketing Performance: Plan, Simulate, Measure

Let’s dig a bit deeper into the relationships I mentioned yesterday among systems for marketing performance measurement, marketing planning, and marketing simulation (e.g., marketing mix models, lifetime value models). You can think of marketing performance measures as falling into three broad categories:

- measures that show how marketing investments impact business value, such as profits or stock price

- measures that show how marketing investments align with business strategy

- measures that show how efficiently marketing is doing its job (both in terms of internal operations and of cost per unit – impression, response, revenue, etc.)

We can put aside the middle category, which is really a special case related to Balanced Scorecard concepts. Measures in this are traditional Balanced Scorecard measures of business results and performance drivers. By design, the Balanced Scorecard focuses on just a few of these measures, so it is not concerned with the details captured in the marketing planning system. (Balanced Scorecard proponents recognize the importance of such plans; they just want to manage them elsewhere). Also, as I’ve previously commented, Balanced Scorecard systems don’t attempt to precisely correlate performance drivers to results, even though they do use strategy maps to identify general causal relationships between them. So Balanced Scorecard systems also don’t need marketing simulation systems, which do attempt to define those correlations.

This leaves the high-level measures of business value and the low-level measures of efficiency. Clearly the low-level measures rely on detailed plans, since you can only measure efficiency by looking at performance of individual projects and then the project mix. (For example: measuring cost per order makes no sense unless you specify the product, channel, offer and other specifics. Only then can you determine whether results for a particular campaign were too high or too low, by comparing them with similar campaigns.)

But it turns out that even the high-level measures need to work from detailed plans. The problem here is that aggregate measures of marketing activity are too broad to correlate meaningfully with aggregate business results. Different marketing activities affect different customer segments, different business measures (revenue, margins, service costs, satisfaction, attrition), and different time periods (some have immediate effects, others are long-term investments). Past marketing investments also affect current period results. So a simple correlation of this period marketing costs vs. this period business results makes no sense. Instead, you need to look at the details of specific marketing efforts, past and present, to estimate how they each contribute to current business results. (And you need to be reasonably humble in recognizing that you’ll never really account for results precisely—which is why marketing mix models start with a base level of revenue that would occur even if you did nothing.) The logical place to capture those detailed marketing effort is the marketing planning system.

The role of simulation systems in high-level performance reporting is to convert these detailed marketing plans into estimates of business impact from each program. The program results can then be aggregated to show the impact of marketing as a whole.

Of course, if the simulation system is really evaluating individual projects, it can also provide measures for the low-level marketing efficiency reports. In fact, having those sorts of measures is the only way the low-level system can get beyond comparing programs only against other similar programs, to allow comparisons across different program types. This is absolutely essential if marketers are going to shift resources from low- to high-yield activities and therefore make sure they are optimizing return on the marketing budget as a whole. (Concretely: if I want to compare direct mail to email, then looking at response rate won’t do. But if I add a simulation system that calculates the lifetime value acquired from investments in both, I can decide which one to choose.)

So it turns out that planning and simulation systems are both necessary for both high-level and low-level marketing performance measurement. The obvious corollary is that the planning system must capture the data needed for the simulation system to work. This would include tags to identify the segments, time periods and outcomes the each program is intended to affect. Some of these will be part of the planning system already, but other items will be introduced only to make simulation work.

0 Using Lifetime Value to Measure the Value of Data Quality

As readers of this blog are aware, I’ve reluctantly backed away from arguing that lifetime value should be the central metric for business management. I still think it should, but haven’t found managers ready to agree.

But even if LTV isn’t the primary metric, it can still provide a powerful analytical tool. Consider, for example, data quality. One of the challenges facing a data quality initiative is how to justify the expense. Lifetime value provides a framework for doing just that.

The method is pretty straightforward: break lifetime value in its components and quantify the impact of a proposed change on whichever components will be affected. Roll this up to business value, and there you have it.

Specifically, such a breakdown would look like this:

Business value = sum of future cash flows = number of customers x lifetime value per customer

Number of customers would be further broken down into segments, with the number of customers in each segment. Many companies have a standard segmentation scheme that would apply to all analyses of this sort. Others would create custom segmentations depending on the nature of the project. Where a specific initiative such as data quality is concerned, it would make sense to isolate the customer segments affected by the initiative and just focus on them. (This may seem self-evident, but it’s easy for people to ignore the fact that only some customers will be affected, and apply estimated benefits to everybody. This gives nice big numbers but is often quite unrealistic.)

Lifetime value per customer can be calculated many ways, but a pretty common approach is to break it into three major factors:

- acquisition value, further divided into the marketing cost of acquiring a new customer, the revenue from that initial purchase, and the fulfillment costs (product, service, etc.) related to that purchase. All these values are calculated separately for each customer segment.

- future value, which is the number of active years per customers times the value per year. Years per customer can be derived from a retention rate or a more advanced approach such as a survivor curve (showing number of customers remaining at the end of each year). Value per year can be broken into the number of orders per year times the value per order , or the average mix of products times the value per product). Value per order or product can itself be broken into revenue, marketing cost and fulfillment cost.

Laid out more formally, this comes to nine key factors:

- number of customers

- acquisition marketing cost per customer
- acquisition revenue per customer
- acquisition fulfillment cost per customer

- number of years per customer
- orders per year
- revenue per order
- marketing cost per order
- fulfillment cost per order

This approach may seem a little too customer-centric: after all, many data quality initiatives relate to things like manufacturing and internal business processes (e.g., payroll processing). Well, as my grandmother would have said, feh! (Rhymes with ‘heh’, in case you’re wondering, and signifies disdain.) First of all, you can never be too customer-centric, and shame on you for even thinking otherwise. Second of all, if you need it: every business process ultimately affects a customer, even if all it does is impact overhead costs (which affect prices and profit margins). Such items are embedded in the revenue and fulfillment cost figures above.

I could easily list examples of data quality changes that would affect each of the nine factors, but, like the margin of Fermat’s book, this blog post is too small to contain them. What I will say is that many benefits come from being able to do more precise segmentation, which will impact revenue, marketing costs, and numbers of customers, years, and orders per customer. Other benefits, impacting primarily fulfillment costs (using my broad definition), will involve more efficient back-office processes such as manufacturing, service and administration.

One additional point worth noting is many of the benefits will be discontinuous. That is, data that's currently useless because of poor quality or total absence does not become slightly useful because it becomes slightly better or partially available. A major change like targeted offers based on demographics can only be justified if accurate demographic data is available for a large portion of the customer base. The value of the data therefore remains at zero until a sufficient volume is obtained: then, it suddenly jumps to something significant. Of course, there are other cases, such as avoidance of rework or duplicate mailings, where each incremental improvement in quality does bring a small but immediate reduction in cost.

Once the business value of a particular data quality effort has been calculated, it’s easy to prepare a traditional return on investment calculation. All you need to add is the cost of improvement itself.

Naturally, the real challenge here is estimating the impact of a particular improvement. There’s no shortcut to make this easy: you simply have to work through the specifics of each case. But having a standard set of factors makes it easier to identify the possible benefits and to compare alternative projects. Perhaps more important, the framework makes it easy to show how improvements will affect conventional financial measurements. These will often make sense to managers who are unfamiliar with the details of the data and processes involved. Finally, the framework and related financial measurements provide benchmarks that can later be compared with actual results to show whether the expected benefits were realized. Although such accountability can be somewhat frightening, proof of success will ultimately build credibility. This, in turn, will help future projects gain easier approval.

0 Why Balanced Scorecards Haven't Succeeded at Marketing Measurement

All this thinking about the overwhelming number of business metrics has naturally led me consider balanced scorecards as a way to organize metrics effectively. I think it’s fair to say that balanced scorecards have had only modest success in the business world: the concept is widely understood, but far from universally employed.

Balanced scorecards make an immense amount of sense. A disciplined scorecard process begins with strategy definition followed by a strategy map, which identifies the measures most important to a business and how they are relate to each other and final results. Once the top-level scorecard is built, subsidiary scorecards report on components that contribute to the top-level measures, providing more focused information and targets for lower-level managers.

That’s all great. But my problem with scorecards, and I suspect the reason they haven’t been used more widely, is they don’t make a quantifiable link between scorecard measures and business results. Yes, something like on-time arrivals may be a critical success factor for an airline, and thus appear on its scorecard. That scorecard will even give a target value to compare with actual performance. But it won’t show the financial impact of missing the target—for example, every 1% shortfall vs. the target on-time arrival rate translates into $10 million in lost future value. Proponents would argue (a) this value is impossible to calculate because there are so many intervening factors and (b) so long as managers are rewarded for meeting targets (or punished for not meeting them), that’s incentive enough. But I believe senior managers are rightfully uncomfortable setting those sorts of targets and reward systems unless the relationships between the targets and financial results are known. Otherwise, they risk disproportionately rewarding the selected behaviors, thereby distorting management priorities and ultimately harming business results.

Loyal readers of this blog might expect me to propose lifetime value as a better alternative. It probably is, but the lukewarm response it elicits from most managers has left me cautious. Whether managers don’t trust LTV calculations because they’re too speculative, or (more likely) are simply focused on short-term results, it’s pretty clear that LTV will not be the primary measurement tool in most organizations. I haven’t quite given up hope that LTV will ultimately receive its due, but for now feel it makes more sense to work with other measures that managers find more compelling.

0 So Many Measures, So Little Time

I’ve been collating lists of marketing performance metrics from different sources, which is exactly as much fun as it sounds. One result that struck me was how little overlap I found: on two big lists of just over 100 metrics each, there were only 24 in common. These were fundamental concepts like market share, customer lifetime value, gross rating points, and clickthrough rate. Oddly enough, some metrics that I consider very basic were totally absent, such as number of campaigns and average campaign size. (These are used to measure staff productivity and degree of targeting.) I think the lesson here is that there is an infinite number of possible metrics, and what’s important is finding or inventing the right ones for each situation. A related lesson is that there is no agreed-upon standard set of metrics to start from.

I also found I could divide the metrics into three fundamental groups. Two were pretty much expected: corporate metrics related to financial results, customers and market position (i.e., brand value); and execution metrics related to advertising, retail, salesforce, Internet, dealers, etc. The third group, which took me a while to recognize, was product metrics: development cost, customer needs, number of SKUs, repair cost, revenue per unit, and so on. Most discussions of the topic don’t treat product metrics as a distinct category, but it’s clearly different from the other two. Of course, many product attributes are not controlled by marketing, particularly in the short term. But it’s still important to know about them since they can have a major impact on marketing results.

Incidentally, this brings up another dimension that I’ve found missing in most discussions, which often classify metrics in a sequence of increasing sophistication, such as activity measures, results measures and leading indicators. Such schemes have no place for metrics based on external factors such as competitor behavior, customer needs, or economic conditions--even though such metrics are present in the metrics lists. Such items are by definition beyond the control of the marketers being measured, so in a sense it’s wrong to consider them as marketing performance metrics. But they definitely impact marketing results, so, like product attributes, they are needed as explanatory factors in any analysis.

0 Dashboard Software: Finding More than Flash

I’ve been reading a lot about marketing performance metrics recently, which turns out to be a drier topic than I can easily tolerate—and I have a pretty high tolerance for dry. To give myself a bit of a break without moving too far afield, I decided to research marketing dashboard software. At least that let me look at some pretty pictures.

Sadly, the same problem that afflicts discussions of marketing metrics affects most dashboard systems: what they give you is a flood of disconnected information without any way to make sense of it. Most of the dashboard vendors stress their physical display capabilities—how many different types of displays they provide, how much data they can squeeze onto a page, how easily you can build things—and leave the rest to you. What this comes down to is: they let you make bigger, prettier mistakes faster.

Two exceptions did crop up that seem worth mentioning.

- ActiveStrategy builds scorecards that are specifically designed to link top-level business strategy with lower-level activities and results. They refer to this as “cascading” scorecards and that seems a good term to illustrate the relationship. I suppose this isn’t truly unique; I recollect the people at SAS showing me a similar hierarchy of key performance indicators, and there are probably other products with a cascading approach. Part of this may be the difference between dashboards and scorecards. Still, if nothing else, ActiveStrategy is doing a particularly good job of showing how to connect data with results.

- VisualAcuity doesn’t have the same strategic focus, but it does seek more effective alternatives to the normal dashboard display techniques. As their Web site puts it, “The ability to assimilate and make judgments about information quickly and efficiently is key to the definition of a dashboard. Dashboards aren’t intended for detailed analysis, or even great precision, but rather summary information, abbreviated in form and content, enough to highlight exceptions and initiate action.” VisualAcuity dashboards rely on many small displays and time-series graphs to do this.

Incidentally, if you’re just looking for something different, FYIVisual uses graphics rather than text or charts in a way that is probably very efficient at uncovering patterns and exceptions. It definitely doesn’t address the strategy issue and may or may not be more effective than more common display techniques. But at least it’s something new to look at.

0 Accenture Study Underlines Need to Measure Customer Service Technology Impact

Accenture released an intriguing study (registration required) earlier this week contrasting the views of high-tech executives and their customers regarding after-sales support.

Perhaps the most substantive finding was that while 74% of the executives who implemented new customer self-service systems believed they now had higher customer satisfaction, only 14% of their customers rated their experience as “much better”. Twenty two percent actually said service had gotten worse.

This is intriguing for two reasons. First, it shows that customers just don’t find service technology all that helpful. Specifically regarding online self-service, only 11% said it was a priority. (The highest priorities were solving problems completely [69%] and quickly [65%].) Maybe that isn’t really a surprise—plenty of people don’t like self-service tools, particularly for technical issues where a simple FAQ is unlikely to be helpful. I suspect most companies really know this, but implement them anyway to save money.

Which brings us to the second point. That companies think satisfaction has increased even when it hasn’t, suggests they aren’t bothering to measure it. I suppose this isn’t really a surprise either, but the optimist in me never quite wants to accept what a truly miserable job most firms do at customer management and how little they truly care.

The same issue appears in another gap uncovered by the study: 75% of executives feel they provide “above average” service while 78% of customers feel their service is “at or below average”. Yes, humans have a well-known tendency to overestimate themselves, but such delusions can only persist if they don’t bother to measure actual performance. Apparently, the great majority of executives aren’t bothering.

Taken together, these two factors (customer dislike of self-service, and company failure to measure results) hint that investment in self-service systems may actually be value-destroying. If the systems make customers feel service has gotten worse, they will be more likely to leave, and if companies don’t measure this, they’ll never know about it. In addition, self-service systems may not even save money, since people must eventually speak to a human to get their problems resolved anyway. (To the first point: the press release accompanying the study states that 81% of customers who rate their service satisfaction as “below average” plan to purchase from a different supplier in the future. To the second point, the study reports that 64% of customers had to access service channels two or more times to resolve their issue.)

All of this just reinforces the conventional wisdom that you have to measure the impact of a CRM project, and the only measure that matters is the impact on customer behavior (dare I mention…lifetime value?) But since so many people keep ignoring this most basic of principles, I guess it needs repeating.

0 Aberdeen Study Confirms Value of LTV Measures

I had truly intended to give lifetime value a rest, but then an email arrived from Aberdeen Group asking me to participate in one of their surveys on “customer value management”. You can fill it out too by clicking here and earn a free copy of the results. They’re asking all the right questions, although I wonder how many people can really answer them accurately.

Aberdeen’s “research preview” for the study certainly is pro-LTV. And I quote:

“Recent Aberdeen research indicates that Best-in-Class organizations utilize “customer lifetime value” metrics in modeling and predicting which mix of customers, products, sales, marketing or media channels will help them to best achieve revenue targets and goals. In contrast, average and lagging companies are apt to take a more short-term, transactional approach to marketing strategic planning. Specifically, the Best-in-Class are more than twice as likely to achieve a greater than 15% improvement in annual customer retention rates. The Best-in-Class also outperform average and laggard companies in annual increase in revenues and in achieving a return on marketing investment (ROMI).”

I guess that makes me feel better--although it doesn't really change the fact that most people don't want to listen.

0 If Lifetime Value Falls and Nobody Measures It, Has It Really Gone Down?

I’m starting to rethink my focus on lifetime value as the key to customer centricity. I’m still fully convinced of my position: LTV is the essential guide for customer level management. But that message just doesn’t seem to resonate, even among managers who have accepted customer centricity as their goal.

I haven’t quite figured out why this is. The specific objections—lack of data, no practical applications, need for short term results—all have responses that I find convincing. Others may not, but I sense the problem is less specific objections than a general sense that LTV is irrelevant to day-to-day needs. People get much more excited talking about a specific online marketing approach or new analytical tool. They don’t see lack of measurement systems as a problem, and therefore aren’t interested in LTV as a solution.

This makes me sad, since people who fail to address this fundamental issue can never fully succeed. But if people want to focus on immediate concerns, I can’t stop them. Perhaps the best I can do is to ensure any short-term solutions are compatible with LTV measurement, so it will be available when people decide they need it.

0 Still More Thoughts on Measurement for Product Managers

I think yesterday’s comments on lifetime value and product reporting need a bit of clarification. It’s important to distinguish measurements of customer acquisition efforts from measurements of other customer contacts. With acquisition, lifetime value in as a formal financial measure is very important and widely accepted, even though the actual calculation often does not include the full scope of future cross sales and other ancillary values. Here, the sort of attitude measures I was proposing as a more-accessible proxy for future value calculations are neither necessary nor appropriate.

Once a customer is acquired, it becomes much more plausible to consider each sale as independent. This is where product- and promotion-specific metrics are often used without consideration of their future value impact. It’s also difficult to measure the true incremental impact on future value of any one promotion or purchase (or other contact, such as product use or customer service.) Since future value is less obviously needed and more difficult to calculate, there’s little wonder it is used so rarely in these situations.

I haven’t changed my position: understanding the future value impact of each contact is still the only way to truly optimize business results. But this distinction does suggest that companies might start by improving the accuracy of their acquisition LTV measurements, for example by ensuring they include results across all product lines. This will be easier for managers to understand and accept, while laying the data and analytical foundation needed for the later, more challenging task of measuring incremental value changes from post-acquisition contacts.

0 More Thoughts On Measurements for Product Managers

I’m still thinking about how to measure product performance a customer-based world. Where I ended up yesterday was pretty much that there’s no alternative to using lifetime value, which specifically means calculating the incremental impact of each product sale on a customer’s future value. The main objection to this is the numbers will contain a great many estimates that will probably seem arbitrary, political or downright incomprehensible to most product managers. This violates one of the fundamental rules of management metrics, which is that managers should be judged on measures they can understand.

It also violates the rule that managers should be held accountable for things they can control. This is because the future value of the customer is affected by many factors other than that one product purchase. Managers would rightly feel they were being treated unfairly if the value assigned to their work was based largely on external elements.

One shouldn’t make too much of these issues. Although revenue is pretty easy to measure directly, any profit statement includes a fair number of allocated costs that are somewhat questionable. And even revenue figures will include estimated reserves for returns, bad debt and similar future losses. I suspect that few product managers could really explain how those calculations are made. Unless they suspect a major error (and that this error undervalues their performance), they are likely to just accept the figures provided. Lifetime value would ultimately work that way as well.

The “dependence on others” objection can also be overstated. In any large organization, many major revenue and cost drivers will be outside the product manager’s control. So they are used to that as well.

But, realistically, there is a big difference between being held responsible for profits on your own product’s sales—however those profits are measured—and profits on subsequent sales of other products. Both the fact that these are sales of other products and that they occur after the customer completes her experience with your product are problematic.

The best I can do right now is to suggest estimating the customer’s future behavior at the end of the product experience. This at least captures the customer’s state when they “left your hands”, so to speak, and before their intentions were affected by other activities. Even better, you could compare their expected behavior after the purchase with their expected behavior before the purchase, since any change is presumably due to their experience in between.

Of course, this immediately raises the question of when the product experience ends. Assuming they use the product after they buy it, its performance will affect their behavior at least as much as the purchase experience itself. I don’t have a specific answer for this; maybe you measure expected behavior in several places.

The other question this raises is where you get the estimates. In a rigorously tested environment, they could be based on firm data. When you find that environment, let me know. Here in the real world, you’ll probably be stuck measuring customer intentions with something like a net promoter score. Yes, I’m fully aware of the problems with such surveys, and still stand by my earlier criticisms. But if net promoter score is the best measure available, then that’s the one you use.

The point of measuring customer intentions after the purchase is simply to get product managers to think of affecting future behavior as part of their job. That’s what has to happen if they are going to help maximize lifetime value. Our real goal is to convert the net promoter scores into an expected future value stream, and thus report the change in expected lifetime value directly. But net promoter scores might actually be easier for them to grasp.

I’m still not thrilled with this solution, but it’s progress of a sort. At least it lets product managers manage something that is largely under their control, yet still orients them toward long term customer value. Those are the basic objectives.

0 Can LTV Really Replace Product-Based Metrics?

For some time now, I’ve been touting lifetime value as the magic cure-all for customer management programs. I still believe LTV is the essential foundation but recognize that product-oriented measurement will not simply vanish once LTV appears. As long products are what people purchase, companies will need product managers to nurture them and will create product-level profit statements to judge their performance.

I’m tempted to wave my LTV magic wand and argue that products themselves could be measured on their LTV contribution rather than traditional profit-and-loss. There’s considerable logic to this: if you had a product that was itself profitable but so annoyed customers that they never purchased from you again, wouldn't you want to know about this? The problem is that LTV measures inherently rely on projections, which feel less reliable to many people than numbers that simply record actual transactions. And, let’s face it, identifying the incremental impact of a particular product on a customer’s LTV is quite a challenge—at least one order of magnitude more difficult than measuring the LTV as a whole.

I don’t have a solution. One option is to double-down on LTV, working to develop the incremental impact measures and to make them credible throughout the organization. You would then essentially banish product profitability as a measure—not literally, but by deemphasizing it in reports and compensation programs. This could probably be done with strong management leadership but it would definitely be a strain.

The other choice is to find a way to use product profitability measures so they reinforce rather than conflict with LTV results. The traditional approach to this is transfer pricing, but nobody ever finds that satisfactory. Nor do I see exactly how transfer pricing fits in here. Going back to first principles, the reason we need to look at product performance is we want to incent product managers to build the best possible products and to ensure they are being sold as broadly as possible. Products do, after all, represent a major investment of corporate resources and we want to maximize return on that investment.

But is it possible to define the “best possible products” in any terms other than their impact on lifetime value? I think not. We’ve already seen why looking at product profitability in isolation is likely to be misleading. So it seems committing fully to LTV is the only real choice.

This is a classic dilemma. On the one hand, “failure is not an option”. On the other hand, no matter how hard we try, failure is a real possibility. So while trying very hard to make LTV work, we have to consider alternatives if we can’t come up with an effective approach. This presumably means some modified version of product profitability that incorporates certain LTV concepts. I don't know what that would look like, but will let you know if I come up with any ideas.

0 Customer Experience Management Isn't Enough

Ron Shevlin’s comment on yesterday’s post concludes that “without more disruptive changes (re-org and fundamental change in strategy) -- even starting with a focus on the "customer experience" won't guarantee a sustained change.”

Ron is absolutely right and the implications are worth considering. “Customer experience” and “customer experience management” are not goals in themselves. They are ways to understand and run a business. People adopt them because they make the business more profitable than alternative techniques. If you don’t think that’s the justification, consider the opposite: would anyone advocate “customer experience management” if they thought it would makes companies less profitable?

This is precisely why I’ve been so focused on Lifetime Value: I see it as the link between customer experience management and financial performance. My view, perhaps naively, is that companies will adopt customer experience approaches once LTV has shown what customer experience management is worth.

But Ron’s suggestion of “re-org and fundamental change in strategy” raises the question of whether LTV is enough. We all know that organizations act in ways that are not purely rational. Certainly the personal and political interests that favor existing, product-based organization structures will not vanish simply because LTV analysis shows they yield suboptimal results.

But if financial measures won’t lead to change, what can? This leads straight to leadership. Top management must first believe that customer experience management is a superior strategy. Then they can take steps to make it happen.

It’s impossible to discuss this in any other than religious terms: it’s a matter of faith and vision. As with any religious belief, it’s hard to predict who will feel the call at any particular moment. But spreading the faith does require continued evangelism and support systems to help new converts sustain and deeper their engagement.

LTV is one of those support systems. CEM consultants, marketing agencies like Ron’s Epsilon and technology companies like James Taylor’s Fair Isaac (with its Enterprise Decision Management concepts) are others. I guess it’s not terribly flattering to think of oneself as a support system. But it’s an important role and helps many companies to move ahead. So it’s certainly worth the effort.

0 BAI Banking Strategies Article Shows Importance of Managing Complexity

Last Thursday’s post on ad hoc analytical systems prompted an interesting set of comments about overcoming product-based organization at banks. As it happens, the BAI’s online Banking Strategies magazine published a related article last November, called Uncovering the Hidden Cost of Complexity.

The article starts by suggesting that customer focus causes the product proliferation that makes good customer experiences so difficult to create: “As banks became more customer-focused over the last decade, they expanded their product set rapidly.”

This is an intriguing thought although it seems like blaming the victim for the crime. But after describing the problems caused by complexity, the authors move in the opposite direction. “The implication is not that banks should limit the variety of options to their customers, but that they should do the following” to ensure complexity adds value:

- Understand what complexity is valued by customers
- Quantify the hidden costs
- Identify the drivers and impacts of complexity
- Design processes to handle variety
- Innovate around advantages

You can read the article for the details. I’m not sure whether complexity is a problem in its own right or just a symptom of other issues. But I do agree with the authors' fundamental point that systems and processes must be designed to deal with complexity effectively. And I definitely agree with the authors’ focus on the quality of customer experience as the primary goal of each business. Any approach that starts from that premise can only lead in a good direction.

0 Deltalytics' Lloyd Merriam Comments on LTV

My friend Lloyd Merriam has left a thoughtful comment on last week's post about Lifetime Value. It's worth treating as a post of its own. Here's Lloyd:

I completely agree that customer lifetime value (LTV) is the single metric against which all strategic business decisions should be evaluated. Although non-trivial, determining the current value of a customer isn’t particularly challenging. Calculating future LTV – which, as you know, is what really matters – is neither simple nor straight forward. LTV is driven by lifetime duration (LTD) and future purchases. How much a customer is likely to spend (on average per purchase), how often they’ll do so, and for how long will together determine their LTV. To the extent that these may have been poorly estimated, the accuracy of any subsequent analyses will be compromised.

That huge challenge aside, what’s even more difficult is to qualify and quantify the relationship between discreet business decisions (primarily strategic but sometimes tactical) and their back-end results. In other words, assessing which business drivers had what direct and specific impact on LTV. For example, even if we can reasonably estimate that LTV has increased, say, 15% overall, how do we tie this increase back to a particular driver when, in fact, many may be at work? Was it our redesigned website, product line expansion, more restrictive (or liberal) returns policies, or new factory that is primarily responsible? Whether a particular strategic driver had a positive or negative affect is difficult enough to discern. Assigning its quantitative score is typically next to impossible.

Therefore, while it’s perfectly valid to assert that it’s the impact of a given business driver on customer lifetime value that’s most important, it’s just as important to recognize that leveraging this principle is exceedingly difficult due to the sheer complexity of the numerous interactions taking place – especially internal, but also external as well (e.g. the actions of competitors).

Our approach (which we call “Deltalytics”) is to periodically estimate the average customer lifetime duration, the average customer spend, and subsequently track their change over time to expose trends that will ultimately govern future business performance. If we know that both are increasing, for example, it’s safe to say that the business is trending upwards. The rate at which this is occurring can, of course, be used to make specific predictions about future growth (or decline, as the case may be).

But these two metrics are just the tip of the iceberg. Others that can and should be used to gauge business performance include:

(1) Rate of new customer acquisition (and, conversely, attrition)

(2) Customer distribution by recency (the greater the proportion of recent buyers, the better the business will perform)

(3) Average latency (the sooner customers place subsequent orders the better)

(4) Customer distribution by frequency (the higher the better, although not nearly as predictive as recency)

(5) Multi-buyer conversion rate (the percentage of 1X buyers who become multi-buyers)

(6) Customer re-order rate by recency (the ratio of repeat buyers as a function of their recency segment, e.g. <30 days, 30-60 days, etc.)

(7) Customer reactivation rate (customers flagged as having lapsed but eventually reordered)

(8) RF Delta (the change in population density over time at the intersection of recency and frequency)

Although quite useful in themselves, the greater utility in each of the above metrics lies in evaluating and forecasting their deltas over time. Change, and the rate thereof, is far more meaningful and insightful in this context than the more common “static” approaches to predictive analytics.

Getting back to business drivers (and measuring their impact on the bottom line, viz. LTV) one must concede that no single solution or approach can effectively gauge them all. At some point, a seat-of-the-pants determination must be made based upon relevant, albeit inherently incomplete, data. Tests can be conducted to measure the impact of, say, introducing a new product line or instituting wide scale changes in pricing. But even then, other contributing factors that cannot be controlled for, and are likely to cloud the results, must be acknowledged (such as a new website, outsourcing the call center, and so on).

In a perfect world, strategic changes would be implemented in a linear and mostly piecemeal fashion to ensure that consistent and reliable analyses can be made. Because this is so rarely possible, however, some compromises must be made in terms of measuring and forecasting the impact of such changes.

It is our position that an optimal way to approach the problem is to analyze trends amongst the aforementioned business performance measures – more specifically, their change (and rate thereof) over time, and subsequently tying these back, as best we can, to their underlying business drivers. This, unfortunately, is much easier said than done.

Lloyd Merriam
lmerriam@deltalytics.com

0 Lifetime Value is More than Another Way to Spell ROI

One of our central propositions at Client X Client is that every business decision should be measured by its impact on customer lifetime value. This is because lifetime value provides a common denominator to compare decisions that are otherwise utterly dissimilar. How else do I choose whether to invest in a new factory or improve customer service?

I was presenting this argument yesterday when I realized that you could say the same for Return on Investment. That brought me up short. Is it possible that we’re really not adding anything beyond traditional ROI analysis? Have we deluded ourselves into thinking this is something new and useful?

But remember what most ROI analyses actually look like: they isolate whatever cost and revenue elements are needed to prove a particular business case. The new factory is justified by lower product costs; better customer service is justified by higher retention rates. But each of those are just portions of the actual business impact of the investments. If the new factory produces poor quality products, it may have a negative impact on lifetime value. If better customer service only retains less profitable customers, it may also be a poor investment.

This is the reason you need to measure lifetime value: because lifetime value inherently forces you to consider all the factors that might be impacted by a decision. As my previous posts have discussed, these can be summarized along two dimensions, with three elements each: order type (new, renewal and cross sell) and financial value (revenue, promotion cost, fulfillment cost). Those combine to form a convenient 3x3 matrix that can serve as a simple checklist for assessing any business analysis: have you considered the estimated impact of the proposed decision on each cell? There’s no guarantee your answers will be correct, but at least you’ll have asked the right questions. That alone makes lifetime value more useful than conventional ROI evaluations.

0 Proving the Value of Site Optimization

Eric’s comment on yesterday’s post, to the effect that “There shouldn’t be much debate here. Both full and fractional designs have their place in the testing cycle” is a useful reminder that it’s easy to get distracted by technical details and miss the larger perspective of the value provided by testing systems. This in turn raises the question posed implicitly by Friday’s post and Demi’s comment, of why so few companies have actually adopted these systems despite the proven benefits.

My personal theory is it has less to do with a reluctance to be measured than a lack of time and skills to conduct the testing itself. You can outsource the skills part: most if not all of the site testing vendors have staff to do this for you. But time is harder to come by. I suspect that most Web teams are struggling to keep up with demands for operational changes, such as accommodating new features, products and promotions. Optimization simply takes a lower priority.

(I’m tempted to add that optimization implies a relatively stable platform, whereas things are constantly changing on most sites. But plenty of areas, such as landing pages and check out processes, are usually stable enough that optimization is possible.)

Time can be expanded by adding more staff, either in-house or outsourced. This comes down to a question of money. Measuring the financial value of optimization comes back to last Wednesday's post on the credibility of marketing metrics.

Most optimization tests seem to focus on simple goals such as conversion rates, which have the advantage of being easy to measure but don’t capture the full value of an improvement. As I’ve argued many times in this blog, that value is properly defined as change in lifetime value. Calculating this is difficult and convincing others to accept the result is harder still. Marketing analysts therefore shy away from the problem unless pushed to engage it by senior management. The senior managers themselves will not be willing to invest the necessary resources unless they believe there is some benefit.

This is a chicken-and-egg problem, since the benefit from lifetime value analysis comes from shifting resources into more productive investments, but the only way to demonstrate this is possible is to do the lifetime value calculations in the first place. The obstacle is not insurmountable, however. One-off projects can illustrate the scope of the opportunity without investing in a permanent, all-encompassing LTV system. The series of “One Big Button” posts culminating last Monday described some approaches to this sort of analysis.

Which brings us back to Web site testing. Short term value measures will at best understate the benefits of an optimization project, and at worst lead to changes that destroy rather than increase long term value. So it makes considerable sense for a site testing trial project to include a pilot LTV estimate. It’s almost certain that the estimated value of the test benefit will be higher when based on LTV than when based on immediate results alone. This higher value can then justify expanded resources for both site testing and LTV.

And you thought last week’s posts were disconnected.

0 One Big Button is Built

I did go ahead and implement the “One Big Button” opportunity analysis in my sample LTV system (see last week's posts for details). As expected, it took about a day’s work, mostly checking that the calculations were correct. That still left the challenge of finding report layouts that lead users through the results. There is no one right way to do that, of course. QlikTech makes it easy to experiment with alternatives, which is a mixed blessing since it’s perhaps too much fun to play with.

My final (?) version shows a one line summary plus details for three types of changes (acquisition, renewal/retention, and cross sell), each split into recommendations for increased vs. decreased investment. Users can drill down to see details on the individual products and sources. That should tell them pretty much what they need to know.

I was eager to see the results of the calculations—remember, I’m working with live data—and was pleased to see they were reasonable: the system proposed changes to fewer than half the total products and estimated a 10% increase in value. Claims of huge potential improvement would have been less credible.

That left just one question: what should be on the One Big Button itself? The color choice was easy—a nice monetary green. But “Make more money!” seems a bit crass, while “Recommendations” sounds so bland. Since the button label can be a formula, I ended up calculating the estimated value of the opportunities and displaying “How can I add $2,620,707 more profit?” If that doesn’t get their attention, I don’t know what will.

0 Convincing Managers to Care about Customer Value Measures

I spoke earlier this week at the DAMA International Symposium and Wilshire Meta-Data Conference, which serves a primarily technical audience of data modelers and architects. My own talk was about applications for customer value metrics, which boiled down to lifetime value applications and building them with the Customer Experience Matrix. (In fact, preparation for this talk is what inspired my earlier series of posts on that topic.)

One of the questions that came up was how to convince business managers that this sort of framework is needed. I’m not sure I gave a particularly coherent answer at the time, but this is in fact something that Client X Client has given a fair amount of thought. The correct (if cliched) response is that different managers have different needs, so you have to address each person appropriately.

CEOs, COOs and other top managers are looking at company-wide issues. Benefits that matter to them include:

- understanding how customers are being treated across different parts of the organization. Of course, this “customer eye view” is the central proposition of both the Customer Experience Matrix and customer experience management in general. But in practice it’s still very hard to come by, and good CEOs and COOs recognize how desperately they need it.

- gaining metrics for customer experience management. I’ve made this point many times in this blog but I’ll say it again: the only way to focus an organization on customer experience is to measure the financial impact of that experience. Top managers understand this intuitively. If they really believe customer experience is important, they’ll eagerly adopt a solution that provides such measures.

- identify opportunities for improvement. Measuring results is essential, but managers want even more to know where they can do better. This comes back to the One Big Button I’ve been writing about all week. The Customer Experience Matrix and other customer value approaches offer specific techniques to surface experience improvement opportunities and estimate their value.

- optimize resource allocation. Choosing where to direct limited resources is arguably the central job of senior management. Impact on customer value is the one criterion that can meaningfully compare investments throughout the company. It offers senior managers both a tool for their own use and a communication mechanism to get others in the company thinking the same way.

Chief Financial Officers share the CEO’s a company-wide perspective but look at things from a financial viewpoint. For them, customer value approaches offer:

- new business insights from new metrics. Although the CFO’s job is to understand what’s happening in the business from financial data, the information from traditional financial systems is really quite limited. Customer value measures organize information in ways that reveal patterns and trends in customer behavior which traditional measures do not.

- better forecasting. Forecasts based on individual customers or customer segments can be significantly more accurate than simple projections based on aggregate trends or percentage changes. Forecast quality has always been important but it’s even more of a hot button because of Sarbanes-Oxley and other corporate governance requirements.

- cross-function Return on Investment measures. CFOs are ultimately responsible for ensuring that ROI estimates are accurate. Customer value metrics help them to identify the impact of investments across departments and over time. These effects are often hidden from departmental managers who would otherwise prepare estimates based only on the impact within their own area.

Marketing departments gain substantial operational benefits from customer value measurements and the Customer Experience Matrix. These include:

- better ways to visualize, control and coordinate customer treatments. Different departments and systems execute treatments in different channels and stages of the product life cycle. Bringing information about these together in one place is a major challenge that the Customer Experience Matrix in particular helps to meet. Applications range from setting general experience strategies to managing interactions with individual customers.

- monitor customer behavior for trends and opportunities. A rich set of customer value measures will highlight important changes as quickly as they occur. On a strategic level, the Customer Experience Matrix identifies the value (actual and potential) of every step in the purchase cycle to ensure companies get the greatest possible return from every customer-facing event.

- measure return on marketing investments. Customer value measurements give marketers the tools they need to prove the value of their expenditures. This improves the productivity of their spending while ensuring they can justify their budgets to the rest of the company.

Customer Service managers, like marketers, deal directly with customers and need tools to measure the effectiveness of their efforts. Benefits for them include:

- visualization of standard contact policies and of individual contact histories. The Customer Experience Matrix provides tools to track the flow of customers through product purchase and use stages, to see the specific treatments they receive, and to display individual event histories to an agent or self-service system as an interaction occurs. All this helps managers to understand and improve how customers are treated.

- identify best treatment rules based on long-term results. Customer value measurements can show the impact of each treatment on long-term value. Without them, managers are often stuck looking only at immediate results or have no result information at all. Having a good measurement system in place makes it easy for managers to continually test, evaluate and refine alternative treatments.

- recommend treatments during interactions. The optimal business rules discovered by customer value analysis can be deployed to operational systems for execution. A strong customer value framework will support on-the-fly calculations that can adjust treatment recommendations based on information gathered during the interaction itself.

If there’s a common theme to all this, it’s that customer value measurement gives managers at all levels a new and powerful tool to quantify the impact of business decisions on long-term value. Let me try that again: in plain English, it helps them make more money. If that’s not a compelling benefit, I don’t know what is.