Why the Future of Sales and Revenue Enablement Hinges on Predictive Analytics

Taking a data-driven approach to sales and revenue enablement is not a new idea. For years, companies have leveraged data to understand what their customers are doing, customize sales pitches and help optimize revenue.

But traditionally, vendors used data mostly in a backwards-looking fashion. They ran analytics on historical data to see what happened. They might have tried to extrapolate from historical data to predict what was coming, but they did so largely in an ad hoc, unscientific way.

Thanks to the emergence of a new breed of analytics and machine learning technologies, a better world has become possible. No longer do businesses need to rely on guesses to take a proactive approach to sales and revenue enablement. They can instead leverage predictive analytics to identify challenges and opportunities in real time.

How Predictive Analytics Supercharges Business Success

To articulate fully what that means, allow me to dive into the role that predictive analytics plays in sales and revenue enablement, explain how technological shifts have opened up key new opportunities in this space and discuss how businesses can take advantage of them.

Predictive analytics, meaning the use of data to forecast upcoming trends or events, is valuable in any industry. But it’s especially helpful for companies whose revenue is tied to subscriptions. To optimize revenue, these vendors need to optimize their ability to renew customer subscriptions. They also need to identify and act on opportunities to upsell customers, and to identify new prospects.

Critical Tool

As an example of how predictive analytics benefit companies, imagine a scenario where a major client decides not to renew a subscription because they don’t think they’re getting as much value as they hoped for from your product. If you don’t discover that issue until renewal time, your ability to react effectively is limited. In a best case, you’ll likely be forced to make concessions (like pricing discounts) to convince the customer to renew. And in many instances, they won’t renew at all because they’ve already made the decision before you found out about it.

But if you get early warnings of customer churn risk, in the form of signals like a decrease in how often the customer uses your product or services, or a declining net promoter score (NPS), you can get ahead of the issue and take steps to mitigate it. For example, you could educate the customer to increase the value that the product brings them, or make new support channels available to resolve challenges the customer is facing in using the product.

In short, predictive analytics aren’t just a nice-to-have capability for a business. They’re critical for stabilizing and growing revenue.

Easing Access to Predictive Analytics

Recognizing the value of predictive analytics is easy enough. The real challenge lies in actually taking advantage of it.

Historically, that was tough to do. Although predictive analytics as a field has existed for decades, building predictive analytics engines has traditionally required organizations to invest in data science teams, a resource that was out of reach for many companies. And even for businesses that managed to implement their own predictive analytics solutions, the process often took years.

That’s no longer the case. Today, businesses can take advantage of ready-made predictive analytics tools. By collecting and analyzing data from various systems that the typical company already deploys (like CRM platforms, revenue optimization tools and customer support systems), these solutions can predict customer behavior and identify risks and opportunities.

This means that predictive analytics is, in a sense, becoming democratized. It’s no longer the exclusive domain of extremely large, well-resourced enterprises that can afford to build custom solutions. Today, businesses of all sizes can take full advantage of predictive insights.

Reacting to Sales and Revenue Opportunities With AI

And that’s just part of how the technology landscape surrounding predictive analytics has shifted. The other key part of the story is the emergence of artificial intelligence (AI) tools that help businesses react effectively to the insights they generate using predictive analytics.

This is critical because reacting to challenges and opportunities has traditionally been more of an art than a science. You might realize that a customer was considering ending a contract, but figuring out what to do about it required manual, ad hoc assessment of what it would take to secure a renewal.

Using AI, however, it has become feasible to automate much of this process. AI tools can generate playbooks tailored to different customers and scenarios, based on data that shows what works under varying conditions. The result is instant, data-driven guidance that helps sales and customer relationship teams determine exactly how to acquire, retain and upsell customers.

Predictive Analytics and the Future of Revenue Optimization

Admittedly, the integration of predictive analytics into all stages of companies’ sales and revenue operations remains limited at present.

But as the access barriers to the requisite technologies become much lower, expect the floodgates to open. The ability to predict customer behavior and, just as important, react to it in a consistent, data-driven manner will become table stakes for sellers.

The companies that thrive will be the ones that succeed in transforming customer forecasting from an art into a science.

As the CEO of Mediafly, Bill Walsh leads a dynamic and innovative company that provides sales enablement and content management solutions to over 100,000 sellers and marketers worldwide. Walsh has over 30 years of executive leadership experience in the software and analytics industry, with a focus on building successful and diverse teams that deliver exceptional results.