0 James Taylor on His New Book

A few months ago, James Taylor of Fair Isaac asked me to look over a proof of Smart (Enough) Systems, a book he has co-written with industry guru Neil Raden of Hired Brains. The topic, of course, is enterprise decision management, which the book explains in great detail. It has now been released (you can order through Amazon or James or Neil), so I asked James for a few comments to share.

What did you hope to accomplish with this book? Fame and fortune. Seriously, what I wanted to do was bring a whole bunch of threads and thoughts together in one place with enough space to develop ideas more fully. I have been writing about this topic a lot for several years and seen lots of great examples. The trouble is that a blog (www.edmblog.com) and articles only give you so much room – you tend to skim each topic. A book really let me and Neil delve deeper into the whys and hows of the topic. Hopefully the book will let people see how unnecessarily stupid their systems are and how a focus on the decisions within those systems can make them more useful.

What are the biggest obstacles to EDM and how can people overcome them?
- One is the belief that they need to develop “smart” systems and that this requires to-be-developed technology from the minds of researchers and science-fiction writers. Nothing could be further from the truth – the technology and approach to make systems be smart enough are well established and proven.

- Another is the failure to focus on decisions as critical aspects of their systems. Historically many decisions were taken manually or were not noticed at all. For instance, a call center manager might be put on the line to approve a fee refund for a good customer when the decision could have been taken by the system the call center representative was using without the need for a referral. That’s a unnecessarily manual decision. A hidden decision might be something like the options on an IVR system. Most companies make them the same for everyone yet once you know who is calling you could decide to give them a personalized set of options. Most companies don’t even notice this kind of decision and so take it poorly.

- Many companies have a hard time with “trusting” software and so like to have people make decisions. Yet the evidence is that the judicious use of automation for decisions can free up people to make the kinds of decisions they are really good at and let machines take the rest.

- Companies have become convinced that business intelligence means BI software and so they don’t think about using that data to make predictions of the future or the use of those predictions to improve production systems. This is changing slowly as people realize how little value they are getting out of looking backwards with their data instead of looking forwards.

Can EDM be deployed piecemeal (individual decisions) or does it need some overarching framework to understand each decision's long-term impact?
It can and should be deployed piecemeal. Like any approach it becomes easier once a framework is in place and part of an organizations standard methodology but local success with the automation and management of an individual decision is both possible and recommended for getting started.

The more of the basic building blocks of a modern enterprise architecture you have the better. Automated decisions are easier to embed if you are adopting SOA/BPM, easier to monitor if you have BI/Performance Management working and more accurate if your data is integrated and managed. None of these are pre-requisites for initial success though.

The book is very long. What did you leave out? Well, I think it is a perfect length! What we left out were detailed how-tos on the technology and a formal methodology/project plans for individual activities. The book pulls together various themes and technologies and shows how they work together but it does not replace the kind of detail you would get in a book on business rules or analytics nor does it replace the need for analytic and systems development methods be they agile or Unified Process or CRISP-DM.

0 Free Data as in Free Beer

I found myself wandering the aisles at the American Library Association national conference over the weekend. Plenty of publishers, library management systems and book shelf builders, none of which are particularly relevant to this blog (although there was at least one “loyalty” system for library patrons). There was some search technology but nothing particularly noteworthy.

The only exhibitor that did catch my eye was Data-Planet, which aggregates data on many topics (think census, economic time series, stocks, weather, etc.) and makes it accessible over the Web through a convenient point-and-click interface. The demo system was incredibly fast for Web access, although I don’t know whether the show set-up was typical. The underlying database is nothing special (SQL Server), but apparently the tables have been formatted for quick and easy access.

None of this would have really impressed me until I heard the price: $495 per user per year. (Also available: a 30 day free trial and $49.95 month-to-month subscription). Let me make clear that we’re talking about LOTS of data: “hundreds of public and price industry sources” as the company brochure puts it. Knowing how much people often pay for much smaller data sets, this strikes me as one of those bargains that are too good to pass up even if you don’t know what you’ll do with it.

As I was pondering this, I recalled a post by Adelino de Almeida about some free data aggregation sites, Swivel and Data360 . This made me a bit sad: I was pretty enthused about Data-Planet but don’t see how they can survive when others are giving away similar data for free. I’ve only played briefly with Swivel and Data360 but suspect they aren’t quite as powerful as Data-Planet, so perhaps there is room for both free and paid services.

Incidentally, Adelino has been posting recently about lifetime value. He takes a different approach to the topic than I do.

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 Unica Paper Gives Marketing Measurement Tips

If the wisdom of Plato can’t solve our marketing measurement problems, perhaps we can look to industry veteran Fred Chapman, currently with enterprise marketing software developer Unica. Fred recently gave a Webinar on Marketing Effectively on Your Terms and Your Time which did an excellent job laying out issues and solutions for today’s marketers. Follow-up materials included a white paper Building a Performance Measurement Culture in Marketing laying out ten steps toward improved marketing measurement.

The advice in the paper is reasonable, if fairly conventional: ensure sponsorship, articulate goals, identify important metrics, and so on. The paper also stresses the importance of having an underlying enterprise marketing system like, say, the one sold by Unica.

This is useful, so far as it goes. But it doesn't help with the real challenge of measurement projects, which is choosing metrics that support corporate strategies. So far, I haven’t come across a specific methodology for doing this. Most gurus seem to assume a flash of enlightenment will show each organization its own path. Perhaps organizations and strategies are all too different for any methodology to be more specific.

Relying on such insights suggests we have veered from Western rationalism to Eastern mysticism. I haven't yet seen a book "The Zen of Marketing Performance Measurement", but perhaps that's where we're headed.

0 Plato's View of Marketing Performance Measurement

I reread Plato’s Protagoras over the weekend for a change of pace. What makes that relevant here is Socrates’ contention that virtue is the ability to measure accurately—in particular, the ability to measure the amount of good or evil produced by an activity. Socrates’ logic is that people always seek the greatest amount of good (which he equates with pleasure), so different choices simply result from different judgments about which action will produce the most good.

I don’t find this argument terribly convincing, for reasons I’ll get to shortly. But it certainly resembles the case I’ve made here about the importance of measuring lifetime value as a way to make good business decisions. So, to a certain degree, I share Socrates' apparent frustration that so many people fail to accept the logic of this position—that they should devote themselves to learning to measure the consequences of their decisions.

Of course, the flaw in both Plato’s and my own vision is that people are not purely rational. I’ll leave the philosophical consequences to others, but the implication for business management is you can’t expect people to make decisions solely on the basis of lifetime value: they have too many other, non-rational factors to take into consideration.

It was none other than Protagoras who said “Man is the measure of all things”—and I think it’s fair to assume he would be unlikely to accept the Platonic ideal of marketing measurement, which makes lifetime value the measure of all things instead.