Using Predictive Sales Analytics to Retain B2B Customers

While understanding the reasons why customers leave is essential, spotting the signs of that departure early on can make all the difference.

Very often, before leaving, customers will change their behavior: the key is to spot these changes at that stage, so as to try to retain them.

"We haven't seen Mrs. Wilkinson today…"

"What is it going to be for you today Mr. Morris, same as usual?”

These daily phrases, straight out of an old-fashioned mom-and-pop store, seem deliciously quaint to city folks and e-commerce enthusiasts. Yet, they are the perfect illustration of the deep knowledge of the individual habits of each customer: day and frequency of purchases, products preferences, etc.

This customer habits knowledge is precisely the benchmark against which one should spot the warning signs of a potential departure. But how can you do this at scale?

There are many sources of information to know the behavior of a given customer:

• In B2B and B2B2C, the most accurate, up-to-date and complete source is of course the sales representative’s own knowledge, based on one-to-one interactions with customers. You will typically find this in your CRM.
• When available, tracking the navigation data of customers on your website or application is a treasure trove as well: frequency of visits, traffic sources, orders data, shopping cart abandonments… There is a lot to analyze there.
• Last, you should turn to your Customer Support tools to check on issues most often reported by your customers.

The number one source of information about your customers is data you already have

The data recorded in your ERP software – specifically, orders data - is the #1 source of customer knowledge.

Just think about it: look at this at the individual customer level and it will tell you each customer’s behavior to date! You can find out when they order, what they order, and how much they spend - the single most valuable information!

Even better: once you know what their ordering habits are, you can very quickly identify when they deviate from them, by applying predictive statistics.

You now know if a customer is active, if they’re at risk of leaving, or if they’re gone. How about that for an info?

You will then be able to sort customers by contact priority order, making sure your sales reps spend their time in the most efficient manner. And reach out to the customers that matter most.

The reality is your sales reps probably should not be reaching out to all their customers, even if these customers are at risk of leaving: while some are valuable, others not so much, making the ROI your sales rep’s actions questionable.

Should such lower-value customers be left to their own then? Absolutely not! Otherwise you’ll end up losing revenue.

What you need to do then is to handle such tier-2 and tier-3 customers with automated tools that will send them messages (think emails or text messages, for instance) when they reach specific criteria. This way, re-engaged customers reach out to your sales reps, instead of sales reps reaching out to fading-out customers. Sales reps can then focus on 1) VIP customers 2) smaller customers that want to start ordering again. Less customer-chasing time, more revenue: they will thank you for this!

According to the Gartner Institute, by 2025 60% of B2B companies will have moved from an experience and intuition-based sales system to a data-driven system by merging sales, applications, data and analytics into a single operational tool.

Will you be an early adopter? The data exists and is at your fingertips: it's up to you to act upon it!