Why you should use analytics to prospect


Traditional methods of sales prospecting are grossly inefficient.   Jill Konrath

You want to keep your pipeline as full as possible – and the opportunities are endless.  How many stories have you heard about companies using a brute force approach of toughing it through extensive calling lists?  Sales executives love to tout movies like Boiler Room, The Wolf of Wall Street, and Margin Call to their sales managers to motivate them to just keep powering through the calls.

But these methods rarely result in efficiency or predictability.

A client recently came to us with a problem that we’ve found applies to many companies.  The client sales team had over four times the number of prospects than their sales team could effectively manage.  A good problem to have, sure, but their sales team had no sense of direction or prioritization.  Where should they put most of their effort?  What kind of messaging would be best to reach each prospect?

Ideally, you want to know which prospects on your list are most likely to buy.  If you have seen any of our other articles, these ideal prospects would look like your Loyal customers – they align closely with your value proposition and are most likely to stay as customers.  Isn’t that what most firms really want?

LRC has effectively used Deep Analytics to profile and identify the most promising prospects for our clients.  What distinguishes these?  The most promising prospects will recognize they have a problem that’s not currently being addressed with any of their solutions (including current suppliers) and they’re actively looking for an alternative solution.   Deep Analytics builds an algorithm that puts a “promising prospect” probability on each account in your database.

The table above summarizes the findings for one program.  The first two columns take all the predicted probabilities for each prospect and breaks them into deciles – ten equally sized groups.  You can see only the top three deciles – 30% of the prospects – have meaningful probabilities.  The third column reports the results of the sales effort.  Note that more sales were achieved with higher probability account classes and that slightly over half of the sales came from the highest probability accounts.

When this was applied to the client referenced above, it reduced the prospect pool to about 15% of the original size.  Result?  By focusing their efforts on these best prospects, the team was able to generate 3 times their goal!  This changed the client’s entire LeadGen and BusDev process.

So how do you do it?

  1. Define the database of prospects and add descriptors.

Before you can begin to undergo any analytics, you need to have a database.  No database, no project!  Once the foundation is there, descriptors can be appended from outside sources/databases if needed.  This is also a good time to refine the scope.  Do all prospects need to be looked at, or are you expanding into a specific geography first?

  1. Interview a sample of prospects.

Talk to your prospects.  LRC usually recommends a blinded (un-sponsored) study with an incentive to participate to get candid responses.  In the interview, you should be seeking to understand their readiness to buy, alignment with your business model, and how they prefer to be sold to.

  1. Model: explains the fit.

Analyze the responses and score the prospects based on their readiness to buy – what we call attractiveness.  Start with a simple 0/1 code, where 1 = attractive and 0 = not attractive.  What similarities and differences are you seeing?  You might be able to segment the attractive group further by preferences, demographics, or other descriptors to really hone your approach.

  1. Use an algorithm to project onto the database.

Once the respondents are scored, you can use an algorithm to project that scoring model onto the rest of the database based on the descriptors.

We’ve helped sales teams drastically improve their outcomes using this approach.  Following these steps will cut down on calling lists and should give your sales team insight on how best to reach the prospects that are ready to buy your products or services.

If you need help with a data-driven strategy for prospecting more efficiently and predictably, contact LRC today.

Posted in Blog, Deep Analytics, Insights, LRC Blog.