Predictive Analytics for Marketing

“A lot of direct marketing activity is retrospective; it’s about trying to replicate the past. With predictive analytics, you’re being proactive. It can turn the tables,” Simon Kaffel at HSBC said recently in a MarketingWeek article, touching on the excitement many marketers feel about the promise of predictive analytics.

Predictive analytics is the use of data mining, statistics, machine learning, and modeling to analyze current and historical facts to make predictions about future events. Predictive analytics is like cat nip for marketers. It holds the promise of being able to present the right message to the right people at the right time.

But we’ve all experienced marketing that gets it wrong. An ad might cue off of someone’s browsing data but miss that they already bought the product somewhere else. Marketers might have a detailed record of actions taken by potential customers, but completely miss the intent behind those actions. And so, people are inundated with marketing messages that are partly right, but largely wrong.

Marketers have more data at their disposal than ever, but we’re still in the adolescent stage of figuring out how to use it. It’s a struggle to draw the right insights from so many data sources. There’s a large gap between the promise and the reality.

As predictive analytics continue to improve, it will transform how we work. But there’s a massive learning curve. I’d love to hear your thoughts on using predictive analytics for marketing.

Here’s a cartoon I drew three years ago about the promise of Big Data.

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