Kefta's dynamic targeting tailors visitor's site
The components needed to tailor treatments to individual customers have been understood for years. Touchpoint systems send interaction data to a central customer profile and rules engine. These select the appropriate treatments and send them back to the touchpoint for execution.
But drawing the picture is one thing and making it happen is something else. Many software products have enabled such targeting or provided pieces of a solution. They differ in technical approaches, channels served, selection methods, degree of automation and user skills needed. Selection methods for Web site personalization choices have included rules engines, collaborative filtering, behavioral targeting, event detection and multivariate testing.
Kefta Dynamic Targeting (Kefta Inc., www.kefta.com) fits somewhere within this universe. Kefta itself says it competes primarily against behavioral targeting systems such as Touch Clarity (recently purchased by Omniture), Certona or [x+1] (formerly Poindexter Systems). Like those systems, it inserts code snippets into Web pages that gather visitor information, sends this to a server with visitor profiles and selection rules, and receives the content to display.
But behavioral targeting systems automatically create segments based on which visitors select which content. Kefta users define the segments in advance and specify which content is presented to each group. Multivariate testing systems including Optimost, Offermatica, Memetrics and SiteSpect use a similar approach.
On the other hand, Kefta and the behavioral targeting systems can automatically direct larger portions of visitor traffic to the best-performing content. Most multivariate testing systems keep the content mix steady until users intervene. Offermatica is an exception.
Kefta's primary offering is a full-service system that extends beyond ads within Web pages to support follow-up e-mails, page layers, exit pop-ups and off-site banner ads. A self-service system, introduced late last year, is limited to Web pages and lacks many advanced features.
Both products use the same underlying engine and both are organized around campaigns. To set up a Web campaign, users specify the pages, placeholders within each page and content elements that can populate the placeholders. Each placeholder is defined by a "Kefta probe" which contains HTML that calls the Kefta server when the page is viewed. The server will refer to the campaign rules to determine which contents the particular site visitor should receive.
Placeholder probes also store a default content definition, which ensures that visitors see something relevant even if the connection to the Kefta server is lost. Other probes can track "actions" which are accumulated for reports and can be used as the object of a test campaign. Actions can be defined as the number of times a given probe has executed or as the sum of a value, such as order amount, which is gathered when the probe is fired.
The full-service system allows any number or type of actions per campaign, while self-service is limited to 10. Kefta staff creates probes for full-service users, while self-service users can produce their own placeholder probes and one action probe. Kefta builds additional action probes for them.
To conduct a test, self-service users attach multiple content items to one placeholder, specify the number of splits for the placeholder and then select the content items to attach to each split. Users can assign control content for each placeholder and specify at the campaign level what percentage of visitors will be in the control group.
Kefta offers several ways to allocate test contents among visitors. Users can manually assign the percentage of visitors who will receive each item. They can specify percentages of visitors to receive the best- and worst-performing combination and let the system implement this based on actual results. Or the system can execute a "full factorial" test plan, meaning it tries all possible combinations of contents across all placeholders.
Although Kefta can also test a subset of combinations and estimate results for the remainder (the "Taguchi" method), it has found the full factorial approach to be significantly more reliable.
The simplest tests rotate the same contents among all visitors. However, Kefta argues strongly that finding the best contents for an "average" visitor is less effective than finding the best contents for different segments. Users can define visitor segments and assign test contents separately for each segment.
In the self-service system, users must choose one of several segmentation factors: search engine key words; referring site URL; tracking codes or values within the referring URL; geographic location (usually state); or connection speed.
The full-service system can also apply statistical scoring systems to identify the best contents to offer individual customers, drawing on their segment, life stage, previous contents viewed and available content. Business rules can further control the contents selected. Optimization uses logistic regression to automatically read test results and deploy the best-performing
Full-service also creates third-party cookies for visitors to external Web sites, allowing it to coordinate messages outside of the client's own site when such cookies are not blocked.
System reports show click-through rates, actions and lift versus control, with trends by days and details by segment. Other reports show exposures by placeholder combinations and detailed results per placeholder. The optimization system can estimate the incremental impact of individual content items on final results, even across multiple site visits.
Both versions of the Kefta solution are hosted. Reports and the self-service interface run in a Web browser. Kefta was founded in 2000 and has more than 30 users on its full-service system. Pricing is based on volume and services provided. It starts at $10,000 per month for the self-service system .