What is the difference between Infer and traditional lead scoring?
November 12, 2013
How does Infer work?
With nothing more than an email address, Infer can go out and grab thousands of signals about the individual and the organization they work for. Things like relevant job postings, employee count, patent filings, social presence, website traffic, and even the technology vendors they use. That lead is then pushed into the model.
The model uses machine learning to understand what a good customer looks like. To train it we use historical data from your sales and marketing applications. Things like converted leads, wins and losses, account profiles, and purchase history. All that goes into the model.
How does traditional marketing automation work?
With traditional lead scoring, like those found in Marketing Automation Systems you manually define point values. Obviously this breaks down when you’re talking about hundreds or thousands of attributes. With the Infer approach, nothing is done manually. We use the most advanced predictive intelligence and machine learning algorithms available. Therefore, you can have confidence your lead scoring is statistically proven to be accurate.
Why are Marketing Automation Systems critical to a successful lead scoring strategy?
First and foremost, Marketing Automations Systems do a terrific job of aggregating activity data. You need a system in place to track the clicks and form fills across all your various channels. Marketing Automation Systems also do a terrific job of mapping the customer journey. Once an Infer score is synced to your MA system, you can filter leads before they go to sales, target nurture activities, and assess campaign effectiveness. So as you can see Marketing Automation and Lead Scoring from Infer are entirely complementary. In fact, many of Infer’s customers report that Infer scoring is a key capability for getting the most out of their marketing automation investment.
*This post appears under my by-line but really it was a collaborative effort with Karl Rumelhart. We affectionately call him “the professor” because he knows so much about predictive scoring. It is the first post in a series so stay tuned for a post under Karl’s name later this week.
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