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You’ve probably heard the term ‘predictive sales’ or ‘predictive selling’. What is predictive selling, and what can it do for your business?

There’s change afoot – change that will impact the very core of how we do business.

That change is predictive selling. We’ve dipped into the topic before, talking about the basics of predictive selling and why your business needs it.  In this series, we’re going to look at predictive selling in depth. Along the way, we’ll learn how it works, what problems it can solve, and what the future will bring to predictive sales.

What is Predictive Selling?

In the past, sales were generally propelled by a combination of informed guess and gut instinct. As more data sources became available, the informed guess was replaced by purchase histories and correlations between buying patterns and customer behaviors.

Today’s businesses have access to vast libraries of consumer data. This data is being processed by advanced technologies like machine learning and artificial intelligence. Machine learning and AI go even deeper into data, delivering new insights on what to sell, who to sell it to, and how. When these insights are delivered to the sales team in an understandable, context-sensitive way, predictive selling is born.

What Can Predictive Sales Do Right Now?

Predictive selling is already helping sales reps become more efficient and effective. Think about what the average sales day looks like:

  • Planning calls and meetings
  • Chasing down leads, some of which will not produce
  • Manually analyzing CRM data and coming up with action items that may or may not work
  • Improvising talking points during client interactions
  • Searching through multiple reports, dashboards, and lists to find needed information

If you read this and thought it sounded like a lot of wasted time and frustration, you’d be correct. What is predictive sales doing right now to make this situation better?

  • Each week, a personalized sales game plan arrives in the rep’s inbox.
  • Leads are qualified and sorted into a prioritized order.
  • An analytics engine analyzes data and provides action items for each interaction.
  • Machine learning technology creates a feedback loop in the system, so recommendations are adjusted according to what works (or doesn’t work).
  • Reps prep for client interactions with specific products and items of interest.
  • All information is mobile-accessible and presented in an intuitive format.

While this undoubtedly sounds good, it also raises some questions: How does the process behind predictive selling work? What are the key facets, and how can each facet improve sales?

Predictive selling has four pillars:

  • 360-degree customer profiles
  • Comprehensive guidance
  • Intelligent analytics
  • Ease of use.

We’re going to look at each of these in detail over the next five posts. Then we’ll wrap up this series by considering the current impact of predictive selling and what the future will bring.

What’s Next?

We’ve introduced predictive selling and shown how it is already being used to improve the sales process. In our next post, we’ll examine the first of the four pillars of predictive selling: getting a complete view of the customer. Join us!

Authored by Titir Pal, VP at Absolutdata and Rajat Narang, Associate Director at Absolutdata