4 Easy Tactics for Infusing AI and Predictive Analytics Into Sales Processes
February 7, 2017
This article was originally published on the Salesforce Blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.
Unless you were hiding under a rock this year, you probably heard a thing or two about the rise of artificial intelligence (AI) for sales. As machine learning and predictive analytics technologies have rapidly matured, a whole community of forward-looking sales and marketing leaders are emerging as predictive innovators. Rather than relying on human intuition to inform their processes, these early adopters are leading the arms race for data by reinventing how their businesses operate based on intelligence that’s generated by AI and other related data science techniques.
In this environment, I’ve noticed four easy ways that smart sales leaders are hacking their team workflows to insert valuable data signals and key insights into day-to-day tasks—saving vast amounts of time and making sure all of their rep’s hard work is tightly aligned with the impact it delivers.
1. Use analytics to inform sales follow-up
There’s no doubt that confident and focused reps bring more opportunities into the pipeline. But it’s hard for them to feel confident when they’re given sparse lead records with little or no information about key buying signals – like a prospect’s fit for your product, or their likelihood to make a purchase soon based on marketing engagement. In order to avoid wasting hours every week researching leads, many teams are leveraging the latest predictive scoring and profiling technologies to create a habit of fast and efficient follow-up. When it’s easy for reps to prioritize the right prospects and plan their outreach, they follow-up more consistently, and as a result are more likely to hit their numbers each month.
For example, Shoretel is a company with a huge influx of leads, which market development reps individually call in order to qualify opportunity-ready MQLs. After adopting predictive analytics, the team started prioritizing their best-fit leads to qualify first, and MDRs went from having to call 100 leads to find 1 MQL, to just 12 calls per MQL – a huge productivity improvement.
With detailed information about each prospect, sales reps can also personalize every conversation for better engagement. By using advanced profiling techniques to create highly-segmented lists of prospects based on specific attributes and data signals (such as “VPs of Sales, in California, who use Salesforce, and have interacted with one of our marketing campaigns in the past 6 months”), reps can quickly sort out the best way to approach each group. For instance, that might send a particular piece of content or invite the prospects to a local meetup. Some tools even let you set up alerts for important events, auto-assign tasks to reps in Salesforce, and get recommendations powered by machine learning on which segments to invest more time into.
2. Assign territories based on predictive account scores
When your business relies on outbound prospecting to named accounts, it’s especially crucial to go after the best accounts first. But if reps’ assigned territories don’t contain an even distribution of top-scoring accounts, some members of your team will have more promising prospects than others. Predictive analytics lets companies like Xactly, New Relic and Okta make data-driven territory assignment decisions based not just on arbitrary geographical borders, but on where their best accounts are concentrated, giving each rep an equal shot at closing new business. This both improves the structure of their teams, and eliminates pressure on reps in low-concentration regions to manually search for new accounts that might not be a fit at all.
3. Tap into AI for expansion into new markets
In addition, this same insight can help teams identify good opportunities for expansion. Predictive account modeling can be a great help as you build your hiring roadmap, because it lets you hone in on industries or regions where it’s easy to justify an investment based on clear revenue potential. You can also use these data points to validate marketing hunches as you test-and-invest in new markets. For example, theSMB customer loyalty company, Belly, was looking for additional brick and mortar verticals to attack, and used predictive analytics to score all of the new lists they sourced. This helped the company find new pockets of good-fit buyers before ramping up their sales and marketing investment.
4. Raise awareness with target accounts via predictive ABM
Lastly, by partnering with your marketing team to combine ad retargeting with best-fit account predictions, you can ensure your brand is staying in front of the accounts your reps are going after. AdRoll’s marketing team uses predictive scores to filter top accounts that are in talks with their sales reps, and then display re-targeting ads to key contacts at those accounts. This kind of air cover is the perfect example of true sales and marketing alignment, showing sales reps exactly how the marketing team is supporting them and making an impact on conversions.
Each of these four approaches can make a massive impact on sales productivity and revenue contribution with relatively little effort. There’s really no reason why you shouldn’t at least try out some AI-powered sales hacks to get a leg up on your competition. And the reality is that before 2017 is over, you won’t have much choice – most of the other players in your industry will be using predictive analytics and machine learning to fuel smarter sales strategies and tactics.
Transform Your Pipeline Today
See Firsthand How Infer Uses Your Own Data To Create Custom Scoring Models