“Hidden” revenue opportunities you can find in your customer sales transactions
Imagine reaching out to a customer and recommending a new product, suggesting an upgrade to an existing system or asking if they need to restock inventory. Sound familiar? That’s because as sales professionals that’s pretty much what we do each day. Now imagine you had real data analytics behind these recommendations. Imagine your target customer has a high probability of considering your recommendation or appreciates your timely suggestion. Now imagine the reality we have all lived through at least once or twice…maybe they are already considering a recommendation from your competitor because he/she beat you to it.
Day2Leads is a business focused on helping companies improve the lifetime value of their customer base. We accomplish this by using a Predictive Analytics platform that we developed. Our engine uses advanced mathematical algorithms to find similar patterns of buying behavior among global transaction data. Using the output of this process, we develop a unique model for each company and use this model to generate leads for the “next” potential purchase of a product, service or solution. By applying filters that help focus the results based on specific and relevant criteria such as product families, transaction value, likelihood of purchase and purchase time frame, our solution becomes a recommendation to take a sales or marketing action in prospecting specific existing customers to become buyers again.
What we have found in our execution experience is that there tend to be four common categories of opportunities that result from analyzing these types of datasets:
1. Consumables. Although there are rules-based systems that “predict” a customer’s need for supplies or replacement products, an analysis that matches the patterns and behaviors of similar environments casts a larger net for an opportunity. From batteries for mobile radios to industrial centrifuge tubes, predictive analysis can not only expand your reach into your customer’s pocket, it can help to eliminate competition by anticipating needs
2. Cross-Sell. Businesses merge and expand product lines as they grow. Very often, the justification for an acquisition is the cross-selling opportunities that in theory are created through the business combination. By using analytics to assess customer behaviors in similar environments we can predict the highest probability opportunities to focus early cross-sell efforts and achieve early success.
3. Upgrades. Upgrades are a more complex problem because an opportunity is generally driven more from desire rather than need. The key to solving this problem is analyzing the behavior of similar customers and how and when they have upgraded. The difficulty is in determining what a “similar” customer is. This is where access to your global transaction data becomes essential in the analysis phase.
4. Unique Products. New or “Novel” product recommendations are probably the most complex of these categories yet offer the most revenue upside. The novel product recommendation is an entirely new product which the customer has not considered or purchased before. Providing a synergistic recommendation is similar to popular services from companies like Amazon. The key concept is that the customer is likely to buy, but not likely to have found the item or service on their own.
Day2Leads not only improves the LTV of your customer base, it also allows your direct sales teams, distribution partners, and affiliates to maximize their efficiency by focusing their energy and time on the most appropriate, highest value targets.