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As top distributors become more proficient with data analysis, others are falling behind, creating a critical gap in business intelligence. Four of the speakers at MDM’s upcoming Analytics Summit share their recommendations here for optimizing data analytics across a range of business operations.
Understanding the power and potential of proper data analytics is becoming increasingly important for distributors looking to get a leg up on the competition or simply keep up with those who are already putting analytics to their advantage. But there’s a competitive gap forming between companies building analytics capability and those not addressing this aspect of operational improvement.
While smaller shops may be able to get away with less sophisticated analytics programs, anyone in the $20 million and greater revenue range has “got to be on top of the game with regard to analytics,” says Al Bates, principal at Distribution Performance Project and formerly of the leading financial performance benchmark service for more than 40 distribution associations across three decades.
“I don’t want to predict their demise (for those not invested in analytics), but I think they will begin to slowly be marginalized. Less sales growth. Less gross margin. More expenses than their competition,” Bates says.
The quality of analytics directly affects two components of a company’s operations: profitability and growth, says Charley Hale, chairman of Liquid Technologies and former CFO and CEO of FCX Performance, a company he grew over 18 years from $30 million to more than $300 million with a focus on analytics.
“Clearly, better analytics and understanding pricing is critical,” he adds. “I’m still amazed when I look at a lot of distribution businesses that don’t have any kind of strategic pricing approach or pricing analytics.”
The industry has evolved from basic, transactional data analysis such as understanding what a customer is buying and what the margin on the product is, to applying strategy to that data to make pricing more effective, Hale says. It’s critical for distributors to have the capability to understand what customers are buying across a range of product offerings, know where the “holes” are and how to attack them, he adds.
For example, for a lot of distributors, business is concentrated in a sales territory where the focus is on the needs of the top 10 customers. Sales people are “not doing much work beneath that,” Hale says. Using data to understand where potential or neglected customers are and how to access them is a helpful initial step, says Hale, who will be presenting “A growth-by-analytics success story” at the Analytics Summit.
The Will to Do It
While some companies will do well in spite of themselves, simply by being in the right market at the right time in the right place, “for most companies, if they aren’t keeping on top of their inventory analytics, then they are going to fall behind for sure,” adds Mike Brockway, consulting director at Dimensional Insight.
This means tracking inventory to know what’s selling and what’s not selling, and how the product mix is lining up with customer buying patterns. Too many companies aren’t carefully tracking inventory overhead and how much their warehousing costs are affecting profitability. “If you’re not keeping your eye on it, it can quickly snowball and you’re going to get eaten up by the competition because you’re not in control of your costs,” says Brockway, who is presenting the session “Inventory Management Analytics: Maximize Productivity” at the Analytics Summit.
Distributors already tracking data “get a lot of power” from working with large data sets and historical data, adds Bob Sherlock, CMO of Chief Outsiders, a marketing consulting firm. But companies often miss other ways to segment their business by taking a look at smaller data pools, he says. One example is looking at differences between transaction types – large contract jobs versus small one-offs. “Where they may not have masses of data, is there still enough data to do analysis with, even if it might not be analytics in the sense of massaging data sets looking for patterns?” he says.
One example from Sherlock, who is presenting the session “Hidden Value: Pricing & Margin Improvement Levers” at the Analytics Summit, is the correlation between buying behavior and price sensitivity; these can be very different between a large project and a small, routine one, such as rewiring a strip mall store for a new tenant. “I don’t know how many distributors are tracking their transactions to analyze the buying situation and so on,” he says. “So that might be another type of gap in analytics; they’re just not tracking that type of data.”