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.