Julia Cook
Julia Cook 19 December 2017

Use Machine Learning to Widen the Funnel: Here's How

Understanding your buyer is essential to closing the deal.  And with recent advances in information technology, our understanding of prospects goes deeper than language cues and handshakes. 49% of marketers were using predictive analytics to power their efforts at the beginning of 2017. As lead generation shifts its focus to "long-tail" prospects, can your marketing campaign compete?

B2B marketing used to be shrouded in mystery, reliant on buzzword-heavy strategies and antiquated measures of success.  But according to Forbes, about half of marketers in the space have anticipated the needs of their consumer using success rates from the past.  It’s now possible to build data-based profiles of companies big and small, see their place in the buying cycle and target them accordingly.  In our own database over 1,500 marketing and advertising businesses who implemented a Big Data solution in the past year.

The good news is, tools for Database Management, CRM, and Marketing Automation make it easy to track your prospects from contact to close.  But all this information leaves a lot of room for human error.  That’s where data science comes in.  A data profile can call out traits of interest, like pain points and re-orgs.  The more you know about a company’s problems, the easier it is to position your service as the solution.  Closing (or losing) a deal will only make your system better at interpreting these signals.

The more target profiles you input, the easier it is to predict which businesses will buy in the future. Marketers using this strategy are almost three times more likely to experience rapid growth and shorter sales cycles.  But the real game-changer is the accuracy Machine Learning can offer.  Ronald Van Loon has written extensively on this phenomenon.

The system can make data-driven choices, given a volume of information humans can’t begin to comprehend.  And it can do that in seconds.

Machine Learning has been pivotal, for example, in the world of display advertising. With with Google and Facebook leading the way, consumers are growing accustomed to relevant, contextualized content.  What we see adapts with our buying habits, so consumers instantly find products that would have taken weeks to uncover and assess.  While it's a bit more difficult with B2B (we typically want surefire interest and points of contact) companies in our space are starting to grasp that by identifying key prospects from the get-go, Machine Learning has the potential to cut the sales cycle in half.

For Lead Generation, marketers have previously relied on a segmentation process to split the audience into slices—typically based on the prospect’s size, industry, and location.  They expected more than 95% of the leads generated from the campaign would not convert into a new customer.  That’s a wildly inefficient way to spend your time.

Increasingly, we're turning to Account Based Marketing, building relationships instead of spray-and-pray contact lists.  Even this tactic requires a sizable time commitment and reliable data.  Unlike a sales team, Machine Learning isn't governed by a daily schedule or 'gut feelings.'  It's also cheaper than continuous follow-up.  But perhaps the most compelling argument is that it asks very little of your prospect.  A Machine Learning solution could analyze billions of data points to create deep profiles on organizations, generating thousands of attributes each, versus the handful used in the traditional segmentation process.  The platform could review historical data within your own business and learn the ideal buyer persona.  This persona is your new customer.

Quality assurance comes from the accuracy of your own records, but it also depends which partner you choose to supplant your data and build your engine.  Check out Engagio's Account Based Everything map for some quality recommendations.  A good model improves as it operates, but good data is essential from the get-go.  Once you’ve painted a picture of your prospect, matching it against millions of businesses creates a list of targets likely to convert. 

The result is higher quality leads, better conversion rates, and more revenue per customer.

It’s essential to include a predictive spend in your 2018 marketing budget.  If you're a stranger to Python, consider one of the businesses above or a contract data scientist (you'll find more than a few on Quora.)  Thanks to advances in data science, B2B marketing will become highly targeted, deeply personalized, and time-sensitive in the next five years—in fact, it’s already begun.  Responsive email, web, and content campaigns aren’t just necessary—they’re urgent.  Luckily each audience profile can change with its segment’s behavior, creating conversion paths that improve as they make successful predictions.  The only question is how quickly you can implement these models—or fade into the noise.

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