How to Implement High-Converting Cross-Sell and Upsell Strategies with Predictive Analytics

This article is part of our Predictive Frameworks series, which explores the most effective use cases for predictive analytics — such as predicting upsell and cross-sell offers.

Have limitless funds for sales and marketing? Nah, us either. That’s why you’re reading a blog post instead of watching our custom-produced feature film, starring famous action heroes using predictive analytics to save the world — with a dash of romance, of course.

We all want to use sales and marketing resources as efficiently as possible. And we all know that once you’ve acquired a customer, keeping them and building on their existing business is far easier and cheaper than acquiring new customers. In fact, strategies that optimize your cross-sell and upsell programs will bring immediate bang for your buck, focusing time and money where they pay off.

Fortunately, today’s predictive analytics methods mathematically analyze your existing customers’ data, including transactions, demographics, and user behavior, identifying who will be most likely to take you up on a cross-sell or upsell offer. 

Predicting success: more conversions, more revenue, and better processes

Predictive analytics finds patterns in massive amounts of customer data that are too complex for humans to recognize. To be sure, it’s a far more dynamic approach than BI-based methods, like static business rules or manual segmentation. Those methods can’t adapt on the fly to rapidly evolving conditions. (Want to learn more about the basics of predictive analytics? Get up to speed quickly with our guide to the essentials.)

As the Harvard Business Review recently explored, using integrated customer data with advanced analytics and automation is key to successful personalization and streamlining of omnichannel sales and marketing at scale. Moreover, companies that implemented personalization efforts, like customized cross-sell offers, have seen revenue growth of 6 to 10%. Additionally, they’ve benefited from increases in net incremental revenue due to those personalization initiatives from 40 to 100%. 

You’ll achieve more conversions, generate more revenue in the same amount of time, and enhance your selling or marketing processes. And with Pecan, you won’t even need data scientists to achieve these goals.

Predictive modeling boosts the breadth and depth of customers’ purchases

First, let’s quickly review what cross-sell and upsell strategies are. Then, we’ll dive into how predictive analytics enhances them.

Cross-sell strategies try to identify which items or services existing customers might like to add to their current purchases. For example, an insurance company might like to determine which auto policyholders would be most inclined to add homeowner’s insurance to their coverage. 

In contrast, upsell strategies encourage customers to elevate their selections to the next level, such as a higher level of service or an upgraded version of a product. For instance, a pet supplies subscription service could see which customers would be most likely to upgrade from a basic subscription to one that included additional grooming items and snacks.

Predictive cross-sell/upsell modeling benefits all businesses, with extra payoff for some

In general, there are a variety of ways business teams might use cross-sell and upsell predictions. But regardless of who ultimately acts on the information, you’ll achieve more with available resources.

There are a few situations where investing in this kind of predictive modeling can generate really significant ROI:

  • If your outbound sales method is fairly expensive. If you invest substantially in each outreach to customers, you want to allocate resources to the most likely prospects. For example, outbound call centers need to assign staff time to the most promising customers. (Here’s an example of how this worked for one Pecan customer.)
  • If your customers are easily put off by unwanted outreach. Indeed, reaching out to customers who don’t want to be targeted could damage your relationship with them. Instead, focus on customers who will welcome your attention.
  • If you have many customers and not enough sales resources. Using predictive modeling to prioritize customers can direct limited sales attention fruitfully.
  • If every deal means a lot. When your product is relatively expensive and gaining a fairly small number of new purchases can be meaningful, the ROI from predictive analytics is fast and rewarding.

Sound appealing? Let’s look at what you’d need to try this out.

Put your data to work today to predict cross-sell/upsell success 

In truth, your existing CRM data is a fantastic starting point for cross-sell/upsell prediction models. Transaction data showing past sales is key to building these kinds of models, plus demographic data on customers. Additionally, product master data that includes pricing and promotional information is important. 

For the purpose of making these predictive models perform even better, supplemental internal and external data can also be integrated. For example, information on special dates, important locations, holidays, weather, events and more can enrich your existing data. (And even if you don’t think your data is ready … it probably is.) In truth, the beauty of predictive modeling is that it can ingest, combine, and find relationships within more data than traditional analytics approaches. 

With your data in hand, cross-sell/upsell predictive models score each customer to show how likely they are to respond positively to your offer. Typically, those scores are fed back into your CRM, marketing automation software, or call center software. You can then prioritize your outreach through whichever channels matter most for your business.

An example of integrating your data and Pecan’s predictions into your business workflows; many other combinations of platforms and tools are possible
How to act on upsell and cross-sell predictions

You might be wondering, though, what exactly you should offer these prioritized customers. It’s possible to combine cross-sell and upsell modeling with a model that recommends the “next best offer” for each customer — not only suggesting who is more likely to buy, but also what might excite them the most.

And finally, it’s often even possible to predict when and through which channel a customer might most effectively be reached. In fact, we’ll talk more about these exciting opportunities in future blog posts, so stay tuned (or reach out to us if you’d like more info now).

These predictions can transform your entire approach to sales and marketing. Instead of making informed guesses based on overly broad customer segments and historical data, you can look into each customer’s future, positioning yourself perfectly to fulfill their desires. 

 

Up next: success stories and a glimpse behind the scenes

Now that you know how predictive modeling can refine your cross-sell/upsell strategies, be sure to take a look at our next blog post, where we’ll share some success stories and some great ways these strategies are used in various industries. In addition, we’ll take a peek inside how these models actually work. (Don’t worry – we’ll skip the equations and Greek symbols.) 

Who needs celebrities? With cross-sell and upsell modeling playing a key role in your business, you’ll be well on your way to becoming a predictive-analytics action hero yourself.

If you’re looking for the right predictive analytics solution for your team, maybe you’ve found it! Pecan automates the predictive analytics process, without needing data scientists. If you’d like to assess your predictive readiness, request a use-case consultation. We’ll help you find the best way to get future-ready.

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