A few days ago, I received a nice, well designed sales/marketing piece in the mail yesterday. In it, a local window company warned me of the dangers of old windows and the costs associated with them (higher energy costs, etc etc). Note: This was the third such piece I’ve received from this company in about 3 months.
It was a well thought out piece of sales/marketing material. If I had been thinking about new windows, I most likely would be given them a call.
However…my house is less than a year old. So is every other house in the neighborhood of about a thousand homes that are all less than 5 years old. Talking to my neighbors, everyone got a similar sales pitch. I’m not a window salesperson, but I wouldn’t think we are the target market for these types of pitches.
That said, the neighborhood directly beside us is a 20+ year old neighborhood that would be ideal for the pitch. I hope this window company pitched them as well as they pitched me (and I’m assuming they did).
What I suspect happened is this window company bought a ‘targeted’ list from a list broker that promised ‘accurate and up to date’ listings of homeowners in a zip code. Sure, the list is accurate (I am a homeowner) but its not really targeted correctly.
The cost of bad data
I won’t get into the joys of buying lists like this because we all know some mistakes are made. There will always be bad data regardless of what your data management practices are but a good data governance/management process will help eliminate as much bad data as possible.
Of course, in this example we’re talking about a small business. What do they know about data management? Probably nothing…and most likely they don’t need to know too much but they do need to understand how much bad data is costing them.
Let’s look at the costs for this window company.
I went out to one of those list websites and built a demographic to buy lists of homeowners in my zip code. The price was about $3,000 for about 18K addresses. Next, I found a direct mailing cost estimator website that helped me estimate the cost to mail out the material that I had received from the window company. The mailing cost was about $10,000 (which seems high to me…but what do I know about mailings?). This sounds about right considering it would cost about $8500 to send out 18,000 letters with standard postage.
I’m going to assume this company got a deal for their mailings and paid $20,000 for the 3 mailing campaigns that I received a letter. With the price of the list, that brings us to $23K total cost, or about $1.28 per letter sent. That doesn’t seem like a lot of money to spend on sales/marketing until you realize how much of that money was wasted on homes that don’t need the service.
We have roughly 1,000 homes in our neighborhood. A random sampling of the homeowners tells me 90% of them received more than one mailing from this window company. This gives us 900 homes. I’ll assume each home only received 2 mailings, which brings a cost to this window company of about $2,300 ($1.28 x 900 x 2).
That’s $2,300 spent trying to sell windows to homes that don’t need it. That’s 10% of the budget.
So…for this small company trying to sell windows, 10% of their budget was wasted on marketing their services to homes that didn’t need their services. That’s a big number, even for a small company.
Of course, some of you may argue that these costs aren’t all wasted because some of the marketing material might have made it into the hands of friends / family or a homeowner may remember this window company in future years – and you are probably right. But…is the possibility of maybe potentially getting work in the future worth spending 10% of your marketing budget?
To me it is, especially since that 10% could have been redirected to a higher potential marketing opportunity.
The cost of bad data is high regardless of what the number actually shows. If you spend $1 because bad data ‘tricked’ you into doing so, that cost is wasted.
The real question is – what are you doing to understand how good or bad your data is?
from Eric D. Brown http://ericbrown.com/cost-of-bad-data.htm
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