Why Recommendation Engines Are About To Get Much Better
Expanding data sources, including social media sources, are making recommendation engines much more powerful.
Recommendation engines aren't only making a difference for consumer-facing shopping sites. Three-year-old SalesPrism from Lattice Engines offers recommendations to B2B salespeople.
"By combining public and company data, we mine thousands of different attributes that are predictive of buying behavior," company director of product marketing Rob Bois told InformationWeek in a phone call.
The Lattice tool, which can be integrated into sales-enablement platforms such as Saleforce.com, has more than 75 customers to date, including Dell. The tool helps a salesperson by recommending prospects on their likelihood of buying, and also suggests which product to promote and talking points.
The application pulls information from a variety of sources, such as Lattice's own Data Cloud service, credit bureaus and a company's own sales and customer databases.
Machine learning then helps find relationships across all the data that a salesperson might miss. Similarly, the analysis has revealed that some sacred cows of sales and marketing aren't very predictive when it comes to determining a propensity to buy.
"B2B marketers think [a prospect's] title is very important ... but it turns out to have very little correlation to likelihood to buy," he said. Better markers for buying proclivity are items that indicate growth, such as new locations, stock movement, press attention and even an increase in the number of job listings on social media sites, Bois said. In the third quarter, Lattice will release a new version of its tool that will expand the information from social media feeds, which will be analyzed over time and combined with other data sets.
But Bois doesn't believe ever-increasing volumes of data are the most important change when it comes to refining recommendation models.
Rather, "democratizing the ability to build predictive models," or letting business users experiment with models and look at their predictive output, will be the game-changer, he said. "That's where I think we're going to see the explosion in predictive model use," he said.
Nevertheless, one barrier to deploying an effective recommendation engine is a lack of data, according to Think Big Analytics' Mallinger.
"Recommendation engines require extensive data on user behavior from a large number of users to generate reliable results," he said. "In a common-but-losing scenario, many of our clients attempt to start data collection only after they decide to build a recommendation engine. This approach typically limits early successes and requires more iterations."
Mallinger said successful companies collect data early and leverage the value of historical records.
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