David Raab Article Archive

Some phrases have charisma and others simply don’t. Successful terms like “customer relationship management”, “knowledge management”, “data warehousing”, and “data mining” all somehow sound important, exciting and complicated enough to justify large sums of money and conferences in desirable locations. Other terms, like “cost-benefit analysis”, just don’t make the cut.

“Optimization” will never be a really hot buzzword: it sounds too dry, too limited to wringing the last bit of value from a well-worn set of options. This is emotionally unappealing: people want to blaze a new trail through the wilderness, not cut two minutes from their trip to the grocery store. It is also a dubious business strategy: with the rapid change and new opportunities of today’s environment, there truly are new wildernesses to explore. So fine-tuning an existing process just doesn’t seem all that important.

Still, while optimization will never attract stadiums of screaming fans, it does have its own followers–particularly among the analytically minded, and in industries that are relatively stable. In fact, the term is popping up with surprising frequency in vendor presentations these days. Unfortunately, different vendors use it in different ways–a common enough situation, but one that will further contribute to the term’s ultimate lack of utility.

In the hopes of salvaging some value from this soon-to-be-overused word, let’s take a closer look at what it can mean.

First stop, dictionary. My ancient one defines “optimize” as “to be optimistic”, but then gets around to today’s more common meaning of “to make as effective, perfect or useful as possible”. The key here is “as possible”: because what optimization systems truly do is manage sets of constraints. The focus on constraints is inherently pessimistic, and part of why “optimization” is psychologically unappealing. But, more important, it also gives hint of how to classify optimization systems: by looking at the type of constraints that they manage. The major distinction might be called tactical vs. strategic optimization.

Tactical optimization manages constraints related to a single decision. This kind of optimization has been around for a long time–it is as simple as finding the exact mailing quantity that will yield the highest profit on a list of names ranked by expected response rate. Today, any decent predictive modeling software provides this capability, usually in the form of a “gains chart” that shows the expected costs, revenues, profits, and response quantity from mailing to different depths in the ranked file. The better implementations–such as MarketSwitch Corporation’s Targeting Optimizer (www.marketswitch.com) and Group 1 Software/Unica Model 1 Campaign Optimizer (www.g1.com or www.unica-usa.com)–provide a slick graphical display that shows how these metrics change with different mail quantities, and even tell the user what quantity will meet specific constraints such as a fixed promotion budget or target number of new customers.

MarketSwitch’s Cross-Selling Optimizer takes this a step further including multiple offers subject to their own constraints–such as a maximum promotion quantity or minimum sales target per offer. This is in addition to customer-level constraints such as a maximum number of offers or minimum profit per name. The output is a plan that assigns treatments to each customer in a way that is expected to yield the best over-all result.

But whether the optimization involves one offer or many, what makes these approaches “tactical” is that they consider only the results of promotion at hand. The result is typically measured in immediate profit or return on investment, although it could also incorporate future values such as lifetime purchases from a new customer. While any sensible marketer realizes the future value is determined in part by future decisions, tactical optimization systems themselves do not attempt to measure or manage the future alternatives.

Strategic optimization does exactly this. That is, it looks at a sequence of future decisions and outcomes, and attempts to find policies that will yield the highest long-term value. This is a much more ambitious undertaking than tactical optimization, and probably needs a more exciting buzzword to capture its importance. Of course, one could argue that “customer relationship management” already does this quite nicely.

Semantics aside, the importance of strategic optimization is that it offers the ability to change the long-term value of an existing customer relationship. This involves two major tasks: figuring out what the optimal policies are, and finding ways to implement them. Today, these tasks are handled by separate systems–although there is no particular reason a single system to do both might not appear in the future.

Developing optimal policies is the greater challenge, because it involves true creativity: thinking up a new product, or type of offer, or service policy. Of course, no computer system can really do this today; the problem is simply too unstructured. (Some advocates of artificial intelligence may disagree, but that’s another discussion.) Still, a computer system can report on the results of past policies, predict what will happen if the same policies are applied in the future, and perhaps even estimate the results of combining them in new ways. This involves lots of model building and simulation, so if the number of options to consider or events to predict increases beyond a fairly limited point, the volume of work becomes overwhelming for even the largest computers. This is one reason that strategic optimization has so far been applied primarily in the credit card industry, where there are a limited number of key options (interest rate, credit limit, annual fee, grace period), relatively few key events (activation, balance maintenance, payment, renewal), and lots of customers to provide data and amplify the value of any improvements. Credit cards are also a fairly stable industry with lots of analytical people in control.

The simulation inherent in strategic optimization also lets users examine the risk posed by different sets of policies–say if interest rates rise or bankruptcies increase. While this simulation could also be run without optimization, it’s nice to have both in the same system.

But even in the credit card industry, compromises are necessary to make strategic optimization practical. Trajecta (www.trajecta.com), which seems to have the most complete approach to this problem, limits its analysis to a handful of key variables and combines detailed modeling of near-term events with simpler forecasts of long-term behavior. Both shortcuts are justifiable: a few variables do account for most differences in behavior, and detailed long-term simulations are unlikely to be more accurate than simpler forecasts. But the shortcuts also mean that other tools would be needed to deal with more complicated industries or to make optimal decisions about non-key variables.

This last point is particularly sticky. It’s easy enough to argue that a handful of key decisions account for most of your business profit, and maybe you can even prove it with statistics. But try explaining this to the CEO who just spent $20 million for a new call center precisely because it was able to personalize every customer interaction. Chances are pretty good that she’ll want to treat different people differently, whether or not the optimization system can tell her how.

In fact, the call center rules will probably be defined the old fashioned way: by human beings making their best guess about what policies make sense, and then (hopefully) watching the results to improve the rules over time. This is the realm of the other strategic optimization systems, which do implementation.

The classic rule-implementing optimization systems also originated in the credit card industry: venerable products like Fair-Isaac TRIAD (www.fairisaac.com) and AMS Strata (www.amsinc.com), and the more recent HNC Capstone Strategy Manager (www.hnc.com) and Trajecta Decision Optimizer. All let managers define strategies comprising rules for key decision points, assign customers to different strategies, execute the strategies and evaluate the results. TRIAD and Strata, with roots stretching back more than a decade, have also been adopted in other financial services and telecommunications. These systems are usually integrated with operational processes such as billing so the appropriate decisions can be made and executed during the normal course of business. Optimization evolves over time as managers set up champion/challenger tests that assign customers to alternative strategies, compare the results and pick the winners. Although these systems could also be adapted to selecting names for outbound communications, like a conventional direct mail campaign manager, this is not the usual application.

Recently, however, there has been some movement toward outbound optimization. Recognition Systems Protagona (previously ideas Solution; www.recsys.com) and NCR Relationship Optimizer (www.ncr.com) includes extensive features to manage constraints such as maximum number of contacts or promotion expenses per customer over a time period. Protagona even takes a stab at balancing revenue received from a customer with value provided to the customer–a particularly knotty problem that most vendors more or less ignore by assuming the user will develop a long-term measure of value that encompasses both. Both systems also accommodate limits on marketing resources such as call center capacity. Relationship Optimizer can automatically track the load on marketing resources as responses come in, and shift lower-priority messages to alternate channels when necessary. Although lead management and call center systems have provided similar cascading functions for years, they are unusual in a campaign management system.

Or is there really a distinction between “outbound optimization” systems like Relationship Optimizer and an advanced front office system like a Siebel call center? True, both can implement customer-tailored business policies. But the ability to embed and analyze policies in campaigns and strategies is very limited in standard front office systems: anyone who wanted to develop true optimization would find it difficult at best. This may change over time as the front office vendors strive to make their products live up to the optimization claims inherent in the concept of customer relationship management. On the other hand, tools like Protagona and Relationship Optimizer most definitely do not provide the operational functions of a call center, sales automation or Internet response management product. That is, they don’t capture customer data or execute transactions. Like all strategy implementation systems, they are decision engines that tell other systems what to do–whether it is a batch process processing credit card statements, an on-line queue of messages to display at a bank teller station, or a real-time response to a customer action. Even if the front office vendors were to expand their strategy management capabilities, it seems unlikely that they would extend beyond messages delivered through their own customer interaction tools. So independent strategy implementation tools will probably remain necessary to truly coordinate–and optimize–all decisions regarding each customer.

But I still don’t think they’ll call it optimization.

David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.