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Why Predictive Sales Analytics Is So Frustrating

Over the last couple of years, I’ve grown increasingly frustrated with predictive sales analytics. Not the concept or the solutions, mind you, but the phrase itself.

You see, it’s my job to highlight the things that leading sales operations are doing to enhance their performance and gain competitive advantage. And leveraging predictive sales analytics happens to be one of the biggies.

But it seems that whenever I utter the phrase “predictive sales analytics”, what too many people are hearing is more akin to “quantum particle acceleration.”

For some reason, “predictive sales analytics” seems to evoke thoughts of problems so unique and esoteric that they require exotic solutions. And as a result, people are very quick to dismiss the whole idea of predictive analytics as being irrelevant, given the rather “ordinary” problems their sales operation is working to address.

While predictive analytics solutions can indeed be designed and/or configured to address some pretty unique problems, take a look at the five most common applications we hear about through our research:

  1. Identifying Better Prospects in the Market. By combining internal performance data and external prospect data, predictive analytics is helping sales and marketing teams zero-in on only those prospects that represent the best combinations of winnability and profitability.
  2. Identifying “Whitespace” In Existing Accounts. These types of predictive analytics solutions are helping sales operations grow wallet-share with their existing customers by finding and surfacing untapped opportunities to sell greater volumes and/or additional product lines.
  3. Identifying Customers At-Risk for Defection. Many sales operations are using predictive analytics to curb revenue attrition and customer defection by zeroing-in on small, but extremely telling, changes in buying patterns and alerting salespeople in plenty of time to turn things around.
  4. Developing More Accurate Sales Forecasts. These types of solutions are being used to reduce forecasting guesswork (and misses) by taking dozens, or even hundreds, of internal/external factors into consideration when projecting the most-likely revenue and profitability outcomes for a given period.
  5. Determining the Optimal Prices for Every Deal. By automatically analyzing a variety of deal attributes—things about the customer, product, and order—these types of predictive analytics solutions (also known as price optimization solutions) are delivering salespeople deal-level pricing recommendations that maximize revenues and margins at the same time.

As you can see, these problems aren’t very exotic at all. Nor are they in any way unique. In fact, these issues are so pervasive that you’d be hard-pressed to find a single B2B sales operation that isn’t grappling with a few of them on a daily basis.

So don’t let the terminology put you off. When you assume that predictive sales analytics is just an esoteric solution to some “edge case” problems only experienced by a select few, you’re not only missing out on the tremendous performance upside, you’re putting your entire sales operation at risk of falling behind.

    As always, your answers are confidential. Stay tuned for the overall results.

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