eBureau is a predictive scoring and information service provider founded in 2004, focused on technology for very rapid model development and deployment. Using their own purpose-built modeling software, a small group of modelers developed 900 predictive models in 2009 alone. The company has been applying this capability for real-time and interactive marketing like contact centers, consumer lead generation, risk and fraud management, and display ad targeting. Not only can the models be built fast, they can be deployed in the cloud quickly for real-time scoring applications. Typical transactions take less than a second round trip. They have found that this approach works in a variety of industries like education, financial services, automotive, telecom etc. For instance, online universities are using the eBureau solution to predict which consumer leads will apply, enroll, and stay enrolled.
At its core, eBureau is focused on new customer acquisition whether helping clients understand payment risk or propensity to respond to an offer. They take historical performance data (leads, who converted, how valuable they were) and data from 50 other sources before running their predictive modeling technology. eBureau develops a predictive score (for fraud, probability to convert, payment risk, etc.) that can then be used to:
- Improve the online marketer’s cost-per-lead advertising decisions e.g. buy or no-buy decision on leads or right-pricing based on the score
- Improve contact center conversion efforts e.g. offer path management or how to route calls most effectively based on a consumer’s profile
- Improve display ad decisions e.g. find an audience that looks like your best customers and target the right creative at the right time using predictive models
eBureau has some 50 databases enabling them to cross-reference data like addresses and phones, customer purchase data like aggregated catalog sales data, demographic data, aggregated financial data like Zip+4 household wealth, and interactive data like social graphs and e-mail addresses. These 50 databases add up to a combined 300Bn records and 200TB of data covering 99% of US adult consumers. All of this is available for every modeling project – some 50,000 attributes applied to every problem. Obviously this has to be integrated in terms of identity matching and in terms of managing data granularity to ensure summary data can be used as well as individual data. This is all done in-house in a highly secure data center in St. Cloud, Minnesota just north of Minneapolis.
One of eBureau’s education clients wanted to predict which leads would result in enrolled students. Over a period of 6 months, this university purchased 537,000 consumer leads which ultimately resulted in 6,000 enrolled students, representing a 1% conversion rate. eBureau found some 120 attributes that were predictive across demographic, property, purchase history, etc. The average cost-per-enrollment was $3,100 across the whole portfolio but by focusing on the highest scoring segments they were able to reduce this to $2,300 saving them tens of millions of dollars; Classic predictive analytic segmentation.
Depending on sales cycles, it can take several months to know if a lead converts or not, but once the model is built eBureau clients get immediate feedback on the quality of a given lead. This allows eBureau clients to rapidly assess a new lead source, understand quality across a portfolio of lead sources, or simply optimize internal campaigns and creative.
Another example is a company using it to segment inbound leads from ads bought on the spot market (where they don’t control the time to run the ads). Using only the prospect’s phone number, this client used a simple green/yellow/red score to prioritize the incoming calls. When volumes are low, the red (low conversion) ones get handled but when things get busy (because lots of ads are running) only the green and yellow get handled and the green’s (top few segments) get prioritized and routed to the appropriate sales people.
Direct marketers have been doing this for years with direct mail lists. For example, credit card marketers don’t send letters to everyone, they pre-screen to find the best potential audience for the offer. Online display ads can be managed similarly, but using predictive models in real-time to understand who it is worth showing a display ad to. eBureau uses their data to build “look-alike” models for a company’s best customers – online consumers who look (statistically) like your best customers. eBureau protects privacy by placing anonymous cookies allowing advertisers to identify a high-propensity “look-alike” prospect and serve exactly the right display ad in real-time without the advertiser ever knowing who the target is or anything about them. This is a nice example of a predictive model keeping private data private while letting people use that data effectively.
eBureau has also invested in web-based reporting and analysis tools to help their clients understand the impact of models such as score trends by source over time. As with many predictive model and scoring solutions, the education of marketplace and helping new clients with organizational change implications are critical to success.
Personally I think this kind of hosted decisioning/analytics is a great way for many companies to get started with analytics, with using external data sources to enrich their own and to apply analytics in their operational systems.