When you’re selling to a new customer, can you predict their future? You probably try and you don’t even realize it. Like a palm reader, you survey their equipment and try to divine what failures are likely to occur, how much ongoing maintenance will cost, and how much money you are going to make. And, like a palm reader, I bet you are doing this with very little useful information beyond your own experience. So, how can you get scientific about this process and make accurate predictions and decisions? Data!

When a customer has a particular model of equipment that you regularly service, you should be able to tell them the average cost and frequency of repair for that make and model across your entire customer base. Imagine walking into a sales meeting armed with that information. If you are up against a low-price competitor (One Truck Chuck), who do you think customers are going to trust? The guy who tells them “Yeah buddy! I work on these all the time. No problemo. It’ll be cheap cheap cheap!” or the professional sales rep who tells them “We manage 4,065 model ANS-3214s across our entire customer base. We find about 43% in need of repair and the average repair quote is $1,136.83.”

Sounds like a lot of work to get at this data, but it’s not! Let’s walk through the steps:

Establish a list of common assets

You don’t want to perform these calculations for every make and model of equipment you service. All you care about are the most common assets under management. This is a great use case for the 80/20 rule. 20% of the make/models that you service probably account for 80% of the volume and revenue. So, let’s start with an export of all equipment data from your system. Don’t have or can’t access that data?  You can with ServiceTrade.

Next, in your favorite spreadsheet application, create a pivot table from the data that summarizes the count of each make/model pair in your database. Unfamiliar with pivot tables? Oh boy, you’re missing out. Just Google “how to pivot table” and be prepared to have your mind blown! Here’s a real example, with fake model names, from a ServiceTrade account:

Model Count in database
ANS-3214 4,065
LEN547 156
HOB2 102
CHILL-625 99


Now that you’ve determined the most common assets under management, let’s step through the quick analysis to be performed on each one.

Deficiency frequency

How often do you find deficiencies for ANS-3214 units? Well, with a quick export of deficiencies reported filtered down to this asset model, we found that 1,748 assets had deficiencies reported against them. That means that there is a 43% chance that you will find a problem with a given ANS-3214 unit.

Average repair price

Easy! Export your repair quote data, remove all quotes that include more than one asset (to avoid skewing data with repairs of different asset models), and filter down to all quotes for ANS-3214 units. In this case, the average repair quote came in at $1,136.83.

Obviously, there are caveats to this data analysis. First, the results are only as good as the data! Garbage in equals garbage out. Also, you are much more likely to find issues with equipment on your first inspection/maintenance visit. Any given asset may deteriorate differently depending on its environment and usage. Variations in a particular model may make a significant difference in the reliability of the equipment. With all that said, this type of data can make a compelling argument that you are a transparent, data-driven vendor that is more valuable than you low-price competition.

Data-driven sales enablement is just a small piece of selling the program. To learn more, check out our webinar “Don’t sell on price. Sell a premium program.”  and read our book, The Digital Wrap.

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