What to do when your sales reps think good leads are bad | ProsperFleet by Valgen

What to do when your sales reps think good leads are bad

Inside sales teams are at it every day, calling thousands of prospects, seeking appointments and sales. They are given scrubbed lists with contact names, job titles, phone numbers, and a good luck pat. On the back end, managers track calls, appointments, and sales. The cycle continues when reps deplete their assigned leads and it starts over again.

There are times when this well-oiled prospecting machine can under-deliver – and you may not know it. Here we explore three cases where potentially good leads are thrown out. We share what to do instead so you don’t lose the opportunities.

1. Test New Markets

When looking at sales data, you may find strong traction among companies that don’t fit the best customer profile – or at least what the rep thinks is the best profile.

This finding is usually uncovered by in-depth profiling, micro-segmentation, or modeling analysis. These customers may not be among the largest customers. Don’t overlook the potential opportunity if analytics is sending signals.

For example, say you sell technology products. And religious institutions are not known as leading technology buyers. But we came across a church buying hundreds of iPads for one of its programs. This is opportunistic entry into a market if other religious institutions have similar programs. Here’s what you can do:

  • To aid sales calls, share historical product purchase data with reps. They can lead conversations with iPads and related iPad cross-sell products instead of laptops, printers or software.
  • Reps may understandably question the sales potential of these calls. So show them the data. Pull examples to show similar sales to this market and the value of the deals.
  • To help target this new vertical, it is important to match the highest product propensity to the leads in this vertical.

2. Find Missing Information

Even when providing a scored list through predictive modeling, some information that sales reps value for their calls may be missing. Don’t throw out the lead.

In one case, sales reps for a technology provider perceived the presence of a web site as a surrogate for computer purchases. But in their B2B database, this field was available for only about 25% of prospect records. Then, another source provided this info for 45% of records. Certainly an improvement. But reps were concerned about the leads “without web sites.” (Many companies had web sites; the data source just didn’t provide this info.) The scoring rated these leads as strong prospects but reps were uncertain about calling them. What do you do?

  • Have reps do quick Google or Facebook searches on company name, city and/or state. The company may have reviews, social media presence, etc. that would otherwise show they’re “tech-savvy.” And if there’s a website it will likely show up.
  • Show cases where leads without this information have progressed through the pipeline and converted successfully.
  • Explain that the process of data collection is not always perfect. Set up steps that reps can do to cross-corroborate other fields and deduce the missing information. For example, if there’s an email address, the website might be the domain in the email address.

3. Give Counter-Intuitive Recommendations a Chance

Recommendations from predictive modeling may favor market segments that reps do not associate with large sales. But yet, the model shows there are easy smaller sales opportunities.

One of our projects produced recommendations where the prospect company size varied from 8 to 100 employees. Most records were towards the lower end, which reflects the business universe. Most businesses are small.

These leads were produced by the scoring algorithm, and further fine-tuned as the leads most likely to respond. However, reps were suspicious of the potential of prospects with fewer employees. When reps don’t trust the sales potential:

  • Give them evidence that shows why these prospects scored high. Share the desirable attributes of these prospects.
  • Using Industry (SIC) distribution, create “blocks” of leads that contain a mix of company sizes and provide guidance that x% are expected to convert for each of these “blocks.”

A Matter of TrustBuild Trust

A benefit of predictive analytics is the ability to score based on many attributes. For our models, 500+ variables is not uncommon. This reaches beyond the boundaries of human intuition, which no doubt can bring profitable insight.

Predictive analytics can lead you to new sales from new markets where your intuition based on past experience may not lead you. These new markets and new customers may not intuitively look like great sales opportunities at first glance, precisely because they’re new and different. So it’s understandable that it’s not easy to trust.

But acting on model recommendations can bring faster growth and higher sales.

Sales managers can mentor reps to see the opportunities ahead. Help sales reps to test the idea of calling on these new opportunities. Lead them to see the potential conversion rates and quota attainment, even when reps may have previously rejected some of these opportunities. Once they see results, they will begin to trust.

To get the gains possible with predictive analytics, you need to build the trust and confidence to push beyond a business-as-usual routine.


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