With all the scaremongering going on these days about how much advertisers and websites know about us, you'd think certain online sites would be able to do a much better job in matching their ads to our interests.
And what goes for ads goes for content. When you go to the website of retailer where you frequently shop, or to your bank's website, for example, why do you still have to wade through a bunch of generic content about stuff you have no interest in? Once you've logged in, shouldn't they know enough about you to serve up more of the things you'd be psyched to see and less of the things you wouldn't?
Thanks to a San Mateo-based startup called Causata, more and more companies probably will.
The company leverages the power of big data, machine learning, and real-time intelligence to allow retail companies and financial services to customize their interactions around a customer's specific interests. In theory, that should mean that, when you go to one of these companies' websites, they should be able to serve up offers and deals for things that you'd be interested in, or at least the kinds of clothes they predict you like, based on past purchases.
Similarly, when you walk in their storefront, their clerks should know more about you and steer you toward blouses you like and away from the pants that (they now know) you ordered online but ended up returning last week.
In other words, less one-size-fits-all treament and more concierge-like service. "It will be able to use your time more efficiently and make sure you're a happier customer," Bruce Golden, of Accel, which has invested in Causata, tells Fast Company.
Causata was founded in 2008 by Paul Phillips, a serial entrepreneur who previously founded a content targeting platform that was sold to Ominiture. The company has a little over $10 million in investment, including a Series A led by Accel.
Until now, information about customer interactions has tended to be siloed with very little crossover between what happens in the store, in call centers, and online. And even when there's some visibility from one sector to another, it tends to be about what's happened in the past. It's not necessarily updated in real-time. And it's certainly not run through algorithms to make predictions about what might be the best way to serve you going forward—and potentially nab another sale.
Thanks to advances in technology—faster processors, better storage, and the ability to crunch large amounts of data on the fly—combined with the vast amounts of data that companies now collect about their customers, these kinds of systems are now possible.
"Now you can quickly assemble the data to say, 'This is what we know about this person,'" Paul Wahl, the former COO of Siebel Systems and Causata's new CEO, tells Fast Company. "And 'These are the best guesses we can make about their interests.'"
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