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

Inside sales teams are at it every day, making thousands of calls to prospects, seeking an appointment or a sale. They are given scrubbed lists with contact names, job title, 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 be aware. But there are easy fixes. Here we explore three cases where potentially good leads are thrown out, and 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. But don’t overlook the potential opportunity if analytics is sending signals.

As an example, say you sell technology products, and religious institutions are not known as leading technology buyers. But recently 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 so they lead with iPads and related iPad cross-sell products instead of laptops, printers or software.
  • Show reps the sales potential of these calls when they may understandably question this. 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 valued by sales reps for their calls may be missing.

In one case, sales reps for a technology provider perceived the presence of a web site as a surrogate for computer purchases. In a B2B database of 14 million businesses, this field was available for only 22-25% of sales prospect records. In our scored file this info was available for 35% of records – certainly an improvement, but reps were concerned about the two-thirds of leads without this info. The scoring rated these leads as strong prospects but reps were uncertain about calling them. What do you do?

  • Have them do a quick Google search on company name and state. The company may have reviews, social media presence, etc. that would otherwise confirm they’re “tech-savvy,” and if there’s a website it will likely turn up.
  • Show cases where leads without this information have progressed through the pipeline and converted successfully.
  • Explain that the process of data collection, while well-defined, is not always perfect. Set up specific steps they can follow to cross-corroborate other fields and deduce the missing information.

3. Give Counter-Intuitive Recommendations a Chance

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

One of our projects produced recommendations where the prospect company size varied from 100 employees to as low as 8. Most records were towards the lower end, which reflects the business universe. Most businesses are small. But in this case, perhaps employee size was not an important predictor of sales.

Indeed, 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 are not completely trusting the sales potential:

  • Provide them with evidence that shows why these prospects scored high. Share the desirable attributes of these prospects.
  • As one option, 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.”

Build Trust

A benefit of predictive analytics is the ability to score based on a large number of attributes. For our models, 500+ variables is not uncommon. This reaches beyond the boundaries of human intuition, which no doubt can bring profitable insight. We don’t discount that intuition. Rather, predictive analytics should lead you to new sales from new markets where your intuition based on past experience may not lead you. The flip side is that these new markets and new customers may not intuitively look like great sales opportunities at first glance, precisely because they’re new and different.

Acting on model recommendations can bring faster growth and higher sales. This is where sales managers can mentor reps to trust and to see the opportunities ahead. Help sales reps trust the idea of calling upon these new opportunities, and the potential conversion rates and quota attainment, even when reps may have previously rejected some of these opportunities. To get the true 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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