Three Ways Infer Increases Rep Productivity

Every company has good sales reps and not-so-good sales reps. While much about sales success relates to a rep’s personality, network, or other subjective attributes, some smart companies are starting to figure out ways they can more objectively evaluate and increase their reps’ success rates. To provide a quick illustration of how we’ve seen Infer create lift (increased win rates and conversion) for our customers, let’s look at some sample Salesforce dashboards. Below we’ll illustrate three ways predictive intelligence can help increase sales rep productivity.

1) Send them your hottest leads 

The first layer of benefit comes from knowing which leads to route to sales and which should be kept in a nurture program until they heat up. Infer uses machine learning to accurately predict which leads will convert. In this example, 97.3% of the revenue came from the top 3 tiers of hot leads Infer identified. Depending on your distribution of tiers, that means you could potentially reduce the inside sales team’s workload by 70% and still capture the majority of the revenue opportunities.

2) Don’t miss good opportunities

The second layer of benefit comes from surfacing good leads that sales might otherwise miss. For example, sales might be biased towards leads coming from contact me requests and free trials. That makes sense, because those types tend to close at a higher rate. But with predictive scoring, you can identify a good lead from any source. If you have 1,000 webinar or industry event leads, Infer can stack rank the best ones so they get proper attention. If you have a score that sales trusts, you can ensure no lead will slip through the cracks – regardless of where it came from.

3) Spot your best reps, and distribute leads accordingly 

The third layer of benefit comes from spotting your best reps. Traditionally this has been tricky because every patch of leads is a little bit different. With Predictive Intelligence, you can ensure that each rep on your team is starting with the same number of high-quality leads, and make apples-to-apples comparisons between their results.

In the example below, we’ve compared the traditional approach (left) to the new approach (right). With the traditional approach, it looks like Greg Little is your best rep. But when you account for lead score, it turns out Greg has a fat patch, meaning that he has twice as many hot leads as Michelle. If you were to take the same number of leads and distribute them evenly, based on Infer Score, it would become evident who your best rep is. Turns out Sarah Ralston is your best rep. This ability to accurately measure lead quality levels the playing field and sends a message that if you really are a top performer, the numbers will show it.

From a prospect perspective, even distribution of leads also ensures your best prospects get prompt follow up. In the scenario below, it’s going to take time for Greg to work through all his good leads. Meanwhile, Michelle has half as many good leads. She’s going to shift her focus to marginal prospects while there are still good leads in Greg’s queue that haven’t been touched. With lead distribution based on Infer Score you get to your best leads faster, which in turn increases conversion.

Those are just three of the ways that Infer helps increase rep productivity: Eliminate wasted energy, focus where you’ve got the best shot of winning, and recognize your top performers.

If you’ve got questions or other ideas of other areas where lead scores can make a difference, be sure to leave a comment below and contact us. We’d be happy to help out.

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