Inbound marketing agency SmartBug Media does a great job of educating its clients on new ways to increase marketing ROI and grow their business. Recently, the company’s director of marketing, Dolly Howard, sat down with our own Sean Zinsmeister to talk about the value HubSpot customers can enjoy by adding predictive scoring to their technology stack
Here’s a an excerpt from their Q&A:
How is predictive lead scoring different than custom lead scoring in HubSpot? There are two big differences between predictive lead scoring and the kind of custom lead scoring in marketing automation platforms (MAPs). The first is that predictive uses both internal data from your CRM and MA systems plus thousands of external signals from a variety of data sources outside your company. The second major distinction is that predictive scoring solutions use machine learning to look at all kinds of combinations in the data that humans could never grok on their own. Whereas MAPs require you to manually come up with points-based calculations formed through your gut instincts, predictive solutions take the guesswork out of the equation and do all that work for you in order to better predict higher converting leads.
Would it ever make sense to use them both? Depending on your use case, it often makes sense to leverage multiple different flavors of lead scoring. Fit scoring, which looks at a lead’s demographics and a company’s firmographics to find prospects who look like past won opportunities, can give you different insight than behavioral scoring, which looks at a prospect’s intent signals from activity on your web site. Across Infer’s customers, we’ve found that a helpful best practice when first engaging with predictive is to focus on building a really solid fit score that demonstrates success to the business. Once you’ve seen a lift in your average order value and conversion rate, at that point it’s a good idea to consider a two-filter system – which first uses a predictive model to find your best-fit leads, and then uses custom behavioral scoring inside your MAP to pinpoint which of those leads are most likely to buy soon. And after that’s working well, many companies take it a step further to make the second filter less manual by replacing it with a predictive behavioral model.
Check out the full blog post here to read what Sean had to say on a variety of other topics, including:
- How does predictive lead scoring help marketers on a day to day basis?
- For a company to benefit from predictive lead scoring, who should be involved in implementation and rollout of this integration?
- What specific data from Infer goes into scoring leads?
- How would companies use Infer to improve/modify their SLA?