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Searching for Reps the Amazon.com Way By Charles M. Cohon Jan 1, 2011 12:00 PM Data mining can help manufacturers find the right reps. Savvy electrical sales managers already know the five most effective ways to build a list of reps to interview for an open sales territory, but now there is a sixth way to search for potential reps based on lessons learned from Amazon.com. Five traditional techniques to search for potential reps include:
The sixth tool that sales managers can employ is data mining, with inspiration supplied by Amazon.com. To see how data mining works, search Amazon.com for Robert Calvin's Sales Management Demystified. Amazon finds the book you asked for and also displays on the same page a note to tell you that customers who bought Sales Management Demystified also bought ProActive Sales Management: How to Lead by William “Skip” Miller. From its own sales data, Amazon knows that Calvin's book and Miller's book often go together. And with data readily available on the web, you can use the same kind of “these things go together” calculations to find great rep companies to add to your list of candidates next time you are interviewing for reps. Let's look at a real world example of how this system works. I started with the line cards of 23 manufacturers' rep companies that have a large principal in common. For the purpose of this example, we will call this large common principal “Smith Manufacturing.” Imagine you are the sales manager of Smith Manufacturing and you have a vacant territory to fill. Start by capturing the line cards of all of your current reps from their web sites. Now let's look at two of those line cards side-by-side in Table One “Comparing Company Alpha and Company Beta.” Rep Company Alpha represents nine principals, Smith and eight others. Rep Company Beta represents 14 companies, Smith and 13 others. In addition to having Smith in common on their line cards, these two Smith reps also have EGS SolaHD and Federal Signal Industrial Systems in common. If we studied the data from just these two line cards the way Amazon.com studies its data, we might draw the conclusion that “lines that go together” with Smith would include EGS SolaHD and Federal Signal Industrial Systems. But the data from just two line cards might be a coincidence, so we need to add more raw data before we start data mining. With the line cards of 23 Smith reps in hand, we have enough raw data available to make a more compelling correlation between Smith and other brands on those line cards. And the technique illustrated in Table Two, “The Three-Step Process,” is extremely simple. Step one. List all the lines from all the line cards in one column of an Excel spreadsheet. Step two. Sort it alphabetically. Step three. Count how many times each principal appears. In Table 2 on page 35, for example, we see that 3M appears twice, and Acme Electric appears three times. The format of this magazine does not allow us to show all 324 rows, but scrolling through the entire spreadsheet reveals that Federal Signal Industrial Systems appears 10 times, Mersen USA (formerly known as Ferraz Shawmut) appears nine times and EGS SolaHD appears eight times. So if you were Smith's sales manager and you had a vacant territory, you would want to be sure to include that territory's rep for Federal Signal Industrial Systems, Mersen USA and EGS SolaHD in your review of potential reps. Does this kind of review replace one of the traditional five ways sales managers identify potential reps in a sales territory? Absolutely not. After all, there is no way to know from the data available if correlation between Smith and Merson grew organically because the fit is excellent or if the only reason so many reps have both Smith and Merson on their line cards is because a Ferraz Shawmut sales manager started his or her rep search from a Smith rep list decades ago and only eight of the reps hired at that time somehow held onto both lines. Data mining is simply the sixth tool sales manager can use to assemble a list of candidates to interview. This system works just as well for sales managers who don't have an existing rep network from which to mine this data. Just mine the data available for the company with which you want to compete. For example, suppose you are a company that wants to compete with Smith but are just starting with reps. Smith's list of reps and their line cards are easily located on the internet — complete the same exercise that you would have completed if you were Smith's sales manager and you too will conclude that your list of potential candidates also should include reps for Federal Signal Industrial Systems, Mersen USA and EGS SolaHD. Data mining is a cheap, simple sixth method to add to the traditional five ways sales managers identify potential reps. And when the difference between a good rep and a great rep could mean a seven figure difference in your company's sales, shouldn't you be following the lead of Amazon.com and making data mining a key element of your business practices? Charles Cohon is president and founder of NEMRA member electrical manufacturers' representative firm Prime Devices Corp. and is active in rep associations. He earned an MBA from the University of Chicago Graduate School of Business in 2005 and now serves on the admissions committee of that school. You can reach him at ccohon@primedevices.com.
sales staff Market Leader DAVID MILLER National Sales ManagerSoutheast/Southwest/Interim Midwest U.S. & Eastern Canada VINCENT SAPUTO Western U.S. & Western Canada JAMES CARAHALIOS Northeast DAVID SEVIN Europe JULIAN MADDOCKS-BORN Classified Sales Representative DIANE MASON Acceptable Use Policy blog comments powered by Disqus |
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