Predictive analytics is the closest marketers will get to having a crystal ball to peer into the minds of their potential and existing customers. It analyzes historic and current data patterns to determine whether those patterns will happen again. This data can vastly improve your marketing flows—including nurturing—and reduce your overall risk. While it can be applied in different ways across your marketing stack, predictive analytics can influence customer relationship management in ways that sometimes get overlooked.
What is Customer Relationship Management (CRM)?
50% of sales teams today use CRM data to produce relevant forecasts. It makes sense, given that 86% of buyers are willing to shell out more for great customer service. You might be able to rope in new customers with a fantastic product, but if you want to retain them, you must provide them with amazing customer experience. A big part of that is how you develop and maintain a relationship with them. That’s where CRM comes in.
The term refers to both the strategies you employ but also the software solutions you use to keep track of customers and your interactions with them. CRM platforms collect and connect data from your sales leads and customers in one place. The breadth of information you can capture and analyze includes everything from communication threads and key documentation to price quotes, purchase histories, and even follow-up and to-do tasks for each customer.
The best CRM platforms allow for easy access and organization of a whole lot of highly useful data that smart sales teams use to close sales or improve their customer service offerings. This ease-of-use makes CRM software preferable to, say, spreadsheets that require manual updates.
Where Does Predictive Analytics Come In?
As marketing and customer experience expert Jay Baer put it, “we are surrounded by data, but starved for insights.” You can collect as much data as you like, but it won’t mean much if you can’t make sense of it. For B2B marketers, predictive analytics is valuable because it’s all about finding insights into what customers may do in the future. Apart from historical data, it also seeks out patterns in repeated transactions as well as relationship cues, all three of which help your marketing and sales teams develop more personalized outreach and accelerate their sales cycle.
1. Smart Sequences
Everything your sales and marketing teams do is geared toward getting customers to perform a desired action—usually in a sequence. When you promote a whitepaper on social media to a targeted audience, for instance, you want prospects to download that asset (usually after sharing some personal information, such as name, company, position, and email address). The next step in the sequence would be for them to check out pricing models and options. The goal is to help prospects get to the point where they’re ready to make a purchasing decision.
Sequencing is all about analyzing the probability a third action is likely to occur if two specific actions occur first. This is based on the work of Russian mathematician Andrey Markov and—without having a complicated conversation about calculus (as if we could!)—is very easy to apply if you have adequate historical data. Numbers won’t lie and will give you a clear picture of the likelihood of a desired action given a sequence.
But even without a lot of historical data, you can still use the model to your advantage. You can simply consider the last successful event that predicted future behavior and assume the probability between that single event and your desired outcome is about the same. If one person followed a particular path to purchase, it’s likely others will too. It’s rudimentary, but it’s a start.
There are several ways sequencing benefits your CRM. The best example is behavior-triggered automated email campaigns—when a person’s first and/or second actions place them on a specific track of a campaign with messaging to match. As you look at the sequence of actions your prospects take throughout your campaigns, you’ll start seeing key patterns that will let you customize and personalize your messaging more effectively.
Sequencing also lends a degree of proactivity to your lead nurturing. If a pattern emerges wherein a website visitor downloads a whitepaper, checks out your pricing, then requests a demo, you can assume they’re showing intent. So, the next time someone initiates a download, you can proactively approach them and offer to show them pricing relevant to their needs and even offer a free trial. Be careful, though; there’s a fine line between anticipating a customer’s needs and giving off stalker vibes.
2. Thoughtful Cross-Sells
If you have more than one product or service—or perhaps several different pricing tiers—you’ll want to try to cross-sell (or up-sell) your customers at some point after they’ve closed a deal with you. It’s common on B2C shopping sites like Amazon, but the principles apply to B2B too. You look at both historical transactional data and demographic information to find which of your products or services have been purchased together by specific types of companies.
This works well with new customers but is even more effective with existing ones. Research shows cross-selling to current clients is 60-70% more effective than to first-time buyers. The key is focusing on customer needs. If you come off as trying to squeeze every penny out of a transaction, it will leave a bad taste in their mouths. Focus on how a cross- or up-sell has helped similar customers and can therefore help them.
The above example illustrates a reactive approach to cross-selling. But you can and should proactively apply what you learn from your predictive data analytics set. For instance, you can create bundles or upgrade offers and display these prominently on your website—with content detailing the benefits of these packages. You can also display “frequently bought together” products together at the point of sale. Hey, if it works for Amazon…
Finally, you can use this data in your marketing campaigns by targeting customers most likely to buy bundles or upgrades based on your learnings.
3. Better Inaction Reaction
Predictive analytics is effective in interpreting data based on a customer’s actions. But it’s just as useful when analyzing inaction. We’ve all experienced customers ghosting us after what seemed like a meaningful interaction. It’s one of the biggest frustrations of both marketers and salespeople and leads you to wonder what went wrong. Your instincts are to reach out and jumpstart the conversation again, right?
That’s where predictive analytics comes in. By looking back at customers who dropped off before, you can search for patterns or trends to build a fall-off model. The model assesses key behaviors and actions that are common with customers that have walked away from their cart, so to speak. Examples could be a script used by your sales team, the timing of your outreach, or how you communicate.
This is useful to your CRM efforts because you don’t want to dive right in to try to win them back—likely pushing them further away. Instead, approach winning back your customers more thoughtfully, with an understanding of what turned them off to begin with. You can then change what needs changing.
Predictive Analytics Empowers CRM
Predictive analytics is a powerful complement to your CRM. In fact, it gives your CRM greater utility and relevance. Understanding what it can do and how it can be applied gives you the power to approach your marketing and sales activities armed with the confidence of data and insight. Of course, it also helps if you work with leads that are verified and truly interested in what you have to offer to begin with. Talk to us today. We’ll be happy to help you.
