A Sales SLA (Service Level Agreement) ensures that all of the leads marketing generates are followed up on by sales in a timely fashion. Most companies will create a tiered SLA based upon lead type — i.e. Contact Me Requests and Free Trials are top priority, while Webinar attendees and eBook downloaders may only warrant a single phone call.
The challenge is that not every Free Trial sign-up warrants 3 calls. There are people who try your product who simply aren’t a fit to buy. And on the flip side, there are certain Webinar and eBook leads that you’d be foolish to give up on after one call. The trick is identifying the diamond in the rough. Without using some form of data science to single out good leads and flag bad ones, you’re bound to have inefficiency. Wasted energy on one side, missed opportunities on the other.
Infer’s predictive scoring technique uses many signals that wouldn’t otherwise be available to the sales rep. Things like relevant job postings, accurate employee count, patent filings, social presence, website traffic, spam indicators, and even the technology vendors they use. This lets you align your Sales SLAs with a true likelihood of conversion vs. gut instinct.
Here’s an example of what an SLA based upon Infer Scores might look like:
So how much better is it to use predictive scores vs. lead type as the basis for a Sales SLA? With the traditional approach, a company might spend 30% of its sales energy on a set of leads that account for just 2% of its pipeline:
The opposite could also hold true — you might be missing out on great opportunities that just happened to come in via a “less promising” lead type. By leveraging predictive scoring for your Sales SLAs, you can identify good leads that aren’t getting enough touches. Maybe that’s because reps have more leads than they can work, or because they’re plucking off low hanging fruit.
Once you can accurately predict your winners, you’ll be able to invest your sales teams’ time where there’s the greatest opportunity. With SLAs based upon predictive scores, you may decide to send everything that scores a “70” or below directly to nurture, because those leads represent such a small pipeline opportunity.
Infer provides a threshold explorer tool, which stack ranks all your leads based upon their Infer Score. As you drag a slider to determine the cut-off point for sales-ready leads, it displays metrics associated with the selected set of leads, so you can see how much more likely you are to win, what percentage of pipeline this group of leads represents, etc. You may find that anything Infer scores at say “80” or above (regardless of lead type), should get at least 5 touches before it’s flipped to archive.
And its worth noting that marketing should be aligned to a similar framework. For example, you could have different email journeys, different offers, and more high-touch programs for leads with the highest predictive scores. Gone are the days of one-size fits all marketing or mis-aligned sales and marketing resources.
Infer is working with some of the fastest growing companies on the planet to re-imagine how they operate with scenarios like these. If you’re interested in learning more about supporting data-driven decision making, contact us to join our next networking event where you can hear from other companies that have put these ideas into practice.