Having a complete 360 degree view of each customer is imperative for predictive sales success. Where does the data for this comprehensive customer profile come from? And when should you start creating your profiles?

Last week, we looked at what AI-based predictive selling, also known as predictive sales, is doing right now. It’s making sales teams more effective and productive by eliminating most of the guesswork. And it’s providing a glimpse into a future that is much more data driven.

But what makes predictive selling work? The concept rests on four key areas. We call them the four pillars of predictive sales.

The Four Pillars of Predictive Sales

Let’s briefly enumerate the four pillars on which predictive selling rests. We’ll delve into each of these over the course of this series:

  • 360 Degree Learning
  • Comprehensive Guidance
  • Intelligent Analytics
  • Ease of Use

Today, we’ll focus on the first aspect: 360 degree learning, or compiling a complete data-based view of a customer.

What Is 360 Degree Learning?

A complete view of the customer ‘ also known as 360 degree learning ‘ is about looking at your customer from every possible angle. And to do that, you need data from multiple sources.

Suppose your customer view was restricted to a single source of data, like their purchase history. That would give you a good representation of their past. But what if you combined this one angle with information about your customer’s social media activity and with geographic and demographic data? Wouldn’t you have a much better idea of how to pitch to them? You would. And the more information you could compile, the better your pitch would go.

In short, a 360 degree view of your customer is about using all the available dimensions of data ‘ pretty much everything you have in your systems and can gain from the usual third-party sources.

So where exactly does all this data come from?

Five Data Sources That Power Predictive Selling

There are no less than five places where this data can be found:

  1. Core Data from Your Internal CRM: Customer/prospect details, purchase history, historical and current opportunity details, and contact info.
  2. Other Internal Data: Usage data, customer feedback, marketing response data, customer interactions with digital assets, abandoned carts, tracking cookies.
  3. External B2B Data: Firmographics, business financial information, additional contact details, industry-specific information.
  4. Social Media: Activity on social media sites like Facebook, LinkedIn, and Twitter; articles your client or contact has read; posts they’ve liked; news feeds they subscribe to, etc.

But there is another very crucial data source your own sales teams, the most effective people, activities, and processes within your own sales department. This becomes part of the self-learning process that makes AI-based decisions smarter and faster.

That’s quite a lot of information. Naturally, it has to be integrated to create a complete picture of your client.

This raises another question: Why are different forms of data used to create a complete customer view? Well, consider what we learn from each type. Internal data shows customer behaviors and preferences. It also shows which sales techniques are working and can be implemented across the team. External data can be used to draw correlations between various factors and consumer behaviors. External sources are also a good way to round out your internal data, which tends to be specific to your company’s offerings.

When and How to Use Your Data

The more data you can add to your profile, the richer and more complete it will be. But there’s no reason to wait until you can gather input from every possible source. Rather, start with what you already have in your CRM system. You can always add more info later.

What’s Next?

Next week, we’ll begin on the comprehensive guidance aspect of predictive selling. We’ll zero in on finding answers to five important sales-related questions. Be sure to read this post!

Feel like you’re missing something Click here to read last week’s post on predictive selling.

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


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