“Optimization” is one of the few successful buzzwords in marketing technology today. It captures the desire to make the most of existing resources in an era when new resources are hard to come by.
More subtly, it also implies a degree of control many people would love to believe they have, though they know they don’t. It’s nice to imagine yourself fine-tuning the dials on a marketing control panel, especially when your real job is more like riding a bronco in a rodeo.
But success has its price, and for a buzzword the price is clarity. Optimization has a specific meaning: It is a mathematical technique that identifies the set of rules that best achieve an objective within a given set of constraints. But the term is now used more loosely to describe any method for assessing multiple options.
PILOT (Paramark, 408/830-5993, www.paramark.com) is a good case in point. It stands for Paramark’s Interactive Learning and Optimization Technology and is described by the company as a real-time optimization platform. So optimization is at the heart of the product’s market position. Yet what the system really does is automate the champion/challenger testing process. Though this is optimization in the loose sense of finding the best choice, it lacks the constraint-balancing that characterizes true optimization.
Of course, what PILOT does is more important than what it calls itself. Its core function is to select offers: an external system such as a Web site presents PILOT with a customer and PILOT returns an offer ID. Later, PILOT receives information on the customer’s response. What’s special is that PILOT automatically identifies and recommends the offers that are most successful.
This isn’t as simple as it sounds. The system starts by conducting random tests of all offers and evaluates results until it can declare a winner. PILOT calculates both the probability that a customer will accept each offer and the statistical confidence interval of the probability estimate. This ensures that the winner is declared only when it has truly been proven superior. PILOT also will restart testing if the winner’s results fall to a level where it appears another offer might be competitive. This lets the system automatically adjust to changes in customer behavior. Calculations and decisions are updated immediately each time a new interaction is processed.
PILOT can speed the learning process by pooling results from separate customer sources, such as different Web sites. The system reduces the weight assigned to this data as it builds its base of information within each source.
The definition of success is also sophisticated. PILOT can target a discrete measure, such as orders placed, or a variable measure such as order value. Users also can assign weights to different measures and have the system maximize the combined result.
Another advanced function lets the system automatically identify customer segments that are most responsive to different offers. The segments are based on customer attributes, which can be presented to PILOT with each selection request or be loaded in advance to a separate customer database. Either way, PILOT will automatically assess which attributes correlate with which offers and will create new customer segments based on these attributes when appropriate.
Other features are less exotic than its automated selection engine but still important. Users can set up multiple advertising campaigns, each with separate subcampaigns that might represent different advertising buys, areas on a Web site or types of e-mails. Campaigns and subcampaigns can be assigned start and stop dates, budgets, maximum numbers of impressions and system-specific parameters such as how long to wait before changing the winning offer.
Users also specify the set of offers available in each subcampaign. This lets them limit certain offers to specific customer groups. The offers themselves are assigned attributes defined by the user, such as color, positioning and product. System reports show which offer attributes are most strongly related to customer response, providing insight into customer behavior.
PILOT does not use the attributes directly within offer selection, however. Instead, it tests each offer independently. This limits the number of offers that can be tested simultaneously: If there are too many, the system will never run enough tests to identify a clear winner. The limit depends on the number of interactions and differences in offer performance. PILOT has optimized up to 100 offers in a campaign, but probably could handle many more.
Other reports provide logs of user- and system-generated events, show where and how often each offer was used and display performance by campaign, subcampaign and offer. An “optimization” report estimates what results would have been had all offers been used equally. It compares this with actual results to show what the vendor interprets as the improvement due to optimization. All reports are delivered as Web pages and let the user select options such as date ranges and metrics. Many provide graphs as well as tabular formats.
The current version of PILOT is run as a service hosted by Paramark. Users set up campaigns and offers via a Web browser. Integration with external systems varies slightly by application, but generally comes down to having the external system send transactions to PILOT and receive the Web address of the selected offer in return.
PILOT originally was developed to optimize online advertising campaigns. It since has been extended to handle Web sites and e-mail, and the vendor is working on a version for call centers. The product was launched in late 2000 and has three major clients plus some pilot projects.
Prices are based on volume and application and generally run $250,000 to $500,000 a year.