This is the third in a series of articles exploring business metrics and their usefulness in the engineering software space. The last post discussed the fact that the cost for keeping a specific customer may be higher than expected and may even exceed the revenue actually obtained from the customer, effectively removing any profit from the equation. The options then are to (1) “fire” the customer, (2) take a loss on the customer, or (3) figure out how to increase revenue from the account. Of these, (3) is the most attractive — and least painful. Read on.
It always fascinates me that salespeople know exactly how much revenue they want from a particular account but how little they know about what else their client or prospect is buying. If the prospect has a budget of $X, a good sales rep has been taught to go after all of that $X. But what proportion of the total is $X? What else is the customer buying? From whom are they buying it, since they aren’t buying it from you?
Because software is often a confusing bundle of packaged goods, ratable services and training or other one-time services, let’s take a simpler product: sunscreen. In a typical month, Hula sunscreen sells 1,000,000 units to households that buy 2,000,000 units of sunscreen overall. Hula’s “share of wallet” (measured in units) is 50%. That’s very important information because of the questions it should generate in the mind of the sales manager: Why did the households buy more than one brand? Price? A feature Hula didn’t have? Retail outlets that didn’t stock Hula? And so on. What would this look like if converted to dollars?
Moving back to engineering software: Many engineering groups run MSC Nastran, ANSYS ANSYS and Altair RADIOSS solvers, or (and) ANSYS FLUENT and CD-adapco STAR-CCM+. It’s likely that each has a less than 50% share of wallet in typical accounts. Each solver has unique competencies and, for organizations where price is less of an issue, having more than one may be crucial, but each vendor needs to understand why its customers cannot rely solely on its products. It is the unique competencies or is it price, support, speed, CPU usage, parallel processing (or not) capability or something else?
Tied to “share of wallet” is repeat buying patterns. There are many names and associated concepts for this in the business literature, including heavy usage, sole usage and so on, but the point is simple: when given a choice, will your customers buy your brand again or someone else’s? In the CAE example above, sophisticated analysts often run critical models through several solvers to be sure of the conclusions; they would be considered “light” users of any one solver. An analyst using Cosmos as built into SolidWorks would be considered a “sole” user, since he likely has access only to Cosmos. The key is to classify customers according to their historical buying patterns and then to try to interpret what they mean about your business: why is your product the sole product? Are your users die-hards (good) or do they have no choice (good in the short-term but perhaps not sustainable). If it’s not the sole product, why not?
The sunscreen buyers were probably heavy users at the sunniest times of the year and non-users the rest of the year. Aside from solving its share-of-wallet problem, Hula has the opportunity to grow its business by responding to the repeat buying trends it noticed. During the warm months, are customers buying a second tube? If not, what’s wrong with the product? If they don’t buy during cold periods when most people don’t think of sunscreen, the company has options. It could run with the sun, shifting marketing and sales resources around the globe with the seasons. Or it could try to sell sunscreen in winter, marketing a lighter sunscreen for dry winter skin.
Customer buying trends are all-important, in that they measure past performance and can predict the future. Are fewer customers repurchasing than a year ago? Why? Are they repurchasing, but for a smaller amount? Are they placing larger orders the second time? (If so, why the small initial order?)
Sometimes the answers to these questions are fixable: product deficiencies, pricing, support. Sometimes they aren’t: a competitor’s offering, a layoff meaning the potential for fewer licenses. Paying close attention to customers’ needs and buying habits can only lead to product and process improvements — and that’s likely to help both the top and bottom lines.
The last few articles in this series have covered metrics that I believe are important in running a business — but, of course, the metrics are all built on data. Next time, we’ll go over how to unearth this data so that valid conclusions can be reached.
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