Predictive Analytics for Upsell and Cross-Sell
Upselling and cross-selling products and services to your existing customer base is a great way to drive cost-efficient revenue growth, increase loyalty, and grow share of wallet. No matter your company size or industry, driving these three metrics — revenue, loyalty and share of wallet — is a great way to edge out the competition and build your brand within the marketplace. Predictive analytics for upsell and cross-sell can help.
Today, with AI, you now can predict when customers are interested in buying additional services, accessories, or other add-ons. You can also predict when a customer will be more likely to purchase premium-tier products and services.
The difference between cross sell and upsell opportunities
Why are upselling and cross-selling customers so important, and what’s the difference between cross-sell and upsell?
Upselling and cross-selling to customers matters because customer loyalty is not enough to survive in today’s competitive marketplace.
- Cross-Selling: Recommending a purchase of a product that complements a customer’s existing purchase. For example, you could sell a case with a phone or add boat insurance to a homeowners policy, etc.
- Upsell: Encouraging a prospect or customer to purchase a higher-priced product.
How does predictive analytics support upsell/cross-sell opportunities?
Predictive analytics supports your upsell/cross-sell sales motions because predictive algorithms uncover complex patterns in data that humans can’t recognize.
Predictive upsell/cross-sell modeling benefits businesses of all sizes. In fact, there are a few situations where it’s especially likely to generate significant ROI. For example, if your outreach to customers is expensive — say, individual phone calls from salespeople — identifying the most likely opportunities saves significant time and resources.
Starting with your existing customer data, you can create predictive models that analyze past transactions to identify likely buyers. In addition, these models can account for seasonality, location, weather, holidays, and other variables. The models can reveal what drives purchases and when you should communicate upsell/cross-sell promotions.
Examples of Using Predictive Analytics for Upsell/Cross-Sell:
- An e-commerce brand built an upsell model in under 2 weeks that resulted in a 2x increase in conversion rates.
- A global mobile game developer identified 200% more VIP customers and converted them into high-value customers, with an average of 3X uplift of spend per user.
You can follow in these businesses’ footsteps by adopting a predictive approach to upsell/cross-sell. You’ll find it easier than ever to intrigue customers with offers. Plus, you’ll focus resources on those most likely to be interested. The customer experience is also improved with this kind of targeted outreach.
Want to start reinforcing your relationship with your customers today? Reach out now to learn more about how predictive analytics can help.