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Published on 2018-05-16

Don’t Go Postal! A case of polyamorous zip codes causing headaches for territory management.

By Denny Dow, Product Manager at Black Ink Technologies

Everyone’s seen a matryoshka (MAH-tree-YOSH-ka) doll – Russian nesting dolls where several smaller, identical versions of itself reside in a larger figure. I would imagine most people have the same level of functional understanding of American districting – States, Counties, Parishes, Zip codes, all stacking up nicely inside one another. You may even have an understanding of the responsibilities of each level – State Troopers for the State, Sheriffs for the County, Local police for the parishes and cities. But the reality isn’t so clean-cut.

Imagine you have a little doll with a ‘County’ label on it, and you open it up to find that the next smallest doll called ‘zip’ exists partially there, partially in another doll, or perhaps in several. Imagine that it might be true for the next largest doll called ‘State’ as well, that you find a half or a third of the smaller doll in yours, and the rest in others. Sure, if you put the thirds of ‘zip’ together they make a whole ‘zip,’ but they don’t fit neatly into just one larger ‘county’ or ‘state’ doll – they belong to three. At this point, you might think there’s a manufacturing defect or some quantum superposition at work. But, if you’re talking about actual zip codes instead of dolls, you’re talking about reality.

Not counting P.O. Boxes or special postal zones that exist as a point or a city block, there are around 42,000 zip codes in the United States. 9,500 of them are in more than one county. In fact, more than 20 zip codes are split across FIVE counties, and one is split across six. Commit a crime there and you may have a municipal cop and six sheriff’s departments to deal with. Police actions and questionable political districting aside, these zip codes spreading their love can also have some negative implications when you’re trying to understand the landscape of your sales territory.

Let’s say you cover Oregon. Specifically, you cover South-East Oregon, including Malheur County, around US Highway 95. As you are visiting dealers and prospecting the area, you travel South down US-95 and, with the Nevada border looming, you pull into a parking lot and turn around. You’re in a town called McDermitt, zip code 89421, that’s for sure. But whether you are in Oregon or Nevada depends upon how far into that parking lot you drove, because McDermitt, 89421, covers both states. It’s one of about 100 zip codes in the United States that doesn’t recognize state sovereignty. The nerve.

You may think nothing of this – “not everything’s perfect, not everything needs to fit” – “my territory stops when I see a ‘welcome to Nevada’ sign, so what’s the big deal?” I applaud your easy-going mentality if so, but the data you use to inform and define your territory has a much colder reaction. Data is empirical, and if your data is based on zip codes (for instance, customer addresses or product registrations), then data doesn’t care that your jurisdiction stops in that Oregon parking lot or where the sign is. It will be attributed to Oregon and perhaps a counterpart in Nevada. Why? Because data shows what data knows. And that Zip objectively exists in two states.

In less extreme but more common examples, your territory ends at county lines and another sales person has the next county. When you look at a map of your and his territory, you see a nice clean delineation. But all of your data is based upon zip codes, so who owns the customers? Who owns the prospect dealers? If you open that matryoshka doll and look at the zip codes, should you see the whole thing or just the part that’s in your county? What should the other sales person see? If you see just a bit, you aren’t seeing the whole picture, and if you’re looking at market penetration or market size, you likely aren’t getting the whole story either. But if you see the whole thing, then your nice clean county line becomes a muddled mixture of your territory and his? The data shows what the data knows, and if you base your territories on counties – but your data is based on zips (who lists their county in their address anyways) you are going to have to employ that easy-going mentality when it comes to soft borders.

To have a more precise approach for territory management planning and redistricting, it is best you collect, aggregate, process and have an attribution model that mixes the best of both worlds – You want to see how many buyers of your products live in a certain area, no matter if they bought from a dealer you own or represent, or a dealer one of your comrades-in-arms sells to. You both should see the WHOLE Zip code, so that you know just how saturated it is, and see all customers who live there and buy anywhere. That’s objective market penetration, and that’s a good place to start. At the same time, why not calculate how many customers your dealer sells to in that zip code (regardless of what counties it spans), and divide that into the total buyer count? That’s dealer-customer penetration. With the two of them together, you’ll not only see the total functional buying range and performance of your dealer, but you’ll also see the total objective buyers and penetration of the area itself, no matter where they bought.

Is it neat? No. If you’re someone that needs to see a territory map look clean and sharp, you will probably be disappointed. But if you’re someone that needs to see the objective truth of an area and customer base you are trying to grow, then you see things our way. It beats only having half a matryoshka doll.

About the author:
Denny Dow is a Product Manager at Black Ink Technologies, which helps the premier manufacturing industry sell more, faster and smarter. The SaaS platform provides more visibility across the entire supply chain—from a manufacturing plant, to distributor, to territory managers, to dealers, to the local marketplace. Black Ink combined the best of CRM, business intelligence, geo-mapping, data management, industry-specific data, and pre-built library of statistical models in one easy to use, and affordable platform. This helps accelerate customer acquisition and customer relationship management—and that helps the OEM, their distributors and the dealer grow. Connect with Black Ink @BlackInk_Tech on Twitter, Facebook, and LinkedIn. For more information, please visit http://blackinktech.com/.



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