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Strategic Advisory
Revenue Acceleration
Data gives us clues, often big ones, we can use to refine targeted selling behavior. What is it about a prospect that makes them more likely than another to buy from you? Perhaps your organization experiences inconsistent sales performance across regions, divisions, and product lines. And you may enjoy high performing salespeople disguised as “stars” or uniquely gifted when compared to low performers.
In truth, analyzing and acting on your data’s signs and signals makes every salesperson better, while smoothing out the bumps between trendy products, outlier regions, and the rest of your business.
Some call it land-and-expand. Others call it upselling. The idea is simple. Over time, grow revenue from existing customers by offering more products, enhanced services, and greater value. Clean Data utilizes analytic methods that deduce when an installed based customer is ready for an add-on sale, and what add-on they’re ready for.
Nobody likes to lose customers. How can we use data to stave off attrition? We can’t read customers’ minds. But customers offer behavioral clues and cues, often patterned, that can predict when quitting is under consideration. Imagine getting the chance to talk to someone, adjust a price, or fix a quality problem before it’s too late.
Pricing is the last piece of the revenue-acceleration puzzle. Most organizations stress “sell more” to grow their revenue. But optimizing price, continuously, can deliver extraordinary financial gains. Clean Data evaluates customer buying preferences, brand loyalty patterns, and sensitivity to price fluctuations. The data can help us make decisions about price increases, price decreases, and bundling.
Profitability Enhancement
Most companies do a stellar job of keeping score. Whether public and regulated, or private and with the eye on an exit, firms keep their financial accounting on the straight and narrow. But, when it comes to cost accounting, it can be another matter. Does your exec team understand profitability by region? By product? By salesperson? By customer? The answers, as always, are in the data.
