LON Intros ‘Catalyst’: Scoring Deals, Matching Them with Buyers
When was the last time you got a manicure special in your in-box? If you are a man, it didn’t do much for you. In fact, many deals, offers and other promotions aren’t especially relevant to their recipients. Even when they are relevant, they pop up out of context in media where recipients are unlikely to act on them.
Can promotions be filtered and personalized for interests, location and behavior? When consumers sign up for Groupon, Living Social or Amazon Deals, or loyalty based products such as Amex’s LikeLinkLove or BankAmericaDeals, they may be able to check off categories for certain interests, and more relevant deals may be aimed their way.
There isn’t always enough volume to do much with this, however, or a good way to distribute the offers. Amazon and Bank America, for instance, both have huge distribution lists. Anyone would want access to these lists.
But consumers will only see an Amazon deal when they go to Amazon, get an email from Amazon or look it up on Facebook or Twitter. BankAmericaDeals will only show its offers when people look at their online debit card statements, or via emails.
A new solution was introduced today by Local Offer Network, the company that runs Deal Radar and licenses its aggregated deals database to 150 media companies (including MasterCard, Tribune and others). LON’s new product is “Catalyst,” a personalization and scoring system.
Catalyst is able to cold start personalization via data exchanges such as eXelate and other sources, and becomes smarter over time as more descriptive and behavioral data are added to a profile. The cookies automatically capture consumer behavior and information, and then matches it against the deals and other content from Local Offer Network’s network.
CEO Dan Hess, in a briefing with BIA/Kelsey, emphasized that Catalyst is ultimately about boosting yield. “Our premise is that content should be pushed to consumers at any given time,” he says. It is also about leveling the playing field between deals, offers, circulars and other promotional data.
“Local commerce information isn’t effective if you just pump it out of a fire hydrant,” says Hess. “We’ve developed a way to select the most relevant, highest performing content for each person, wherever they happen to be. And we can do that with very light implementation requirements on the part of publishers.”