More on Lists: How to Read a Datacard Like a Pro
What’s the best way to glean the information you need in order to make a decision on whether you should test or not test a particular list? This week, I’d like to lay that out for you.
For those of you new to renting lists, or if you’ve never seen a datacard, you can view thousands of datacards by visiting www.nextmark.com. Nextmark is a tool that industry professionals use to find and recommend lists to mailers. According to Nextmark, there are 60,000 lists currently on the market. Most of the people using this tool are lists brokers — and for the average person, the site can be overwhelming. But if you are a hardcore do-it-yourself-er, you can certainly use this online tool to define your own mail campaigns.
Caveat: While DIY is great for industry vets, if you are new to renting lists, you are MUCH better off using qualified list brokers. They know through direct knowledge which lists work or don’t work, who’s mailing what, etc.
So when you go to Nextmark.com, do a search for a catalog list. For this illustration, search for the list named “Athleta” and let’s look at what we can learn about their list. Forget about whether this list is good for your offering and just concentrate on the numbers side of the datacard.
Measure a Suspect Company’s Worth
Under the heading “segments,” you’ll see their total universe of names, plus breakdowns for buyers by recency of purchase. There’s a lot you can learn just from the segments box on any datacard. For instance, you can get a down and dirty measurement of the company’s size and sales volume by multiplying the last 12-month buyers by the $185 average order (located on the left side of the datacard as “spending”). This is just a rough measurement, but it will give you an idea of the size of the company you suspect may be a fit for your next mailing.
Is Your Suspect List in a Prospecting Mode?
Another thing you can learn from the segments section of the datacard is whether the company is actively prospecting and growing. Compare the last 3-month buyers to the last 6-month buyers. If the more recent segment is higher, then it should be actively prospecting in the last few months. Also take a look to the right of the “segments” header. You’ll see that it states counts through 1/18/07, so you know the list is relatively up to date. Some lists update monthly, some quarterly. Updating means the list owner sends the list manager updated names, adding in the most recent purchasers since the previous update.
When you’re planning a campaign you want to use the most recent buyers. If a list hasn’t updated in a while, you may not want to mail it or do more research on the list. If you can’t find an update schedule on the datacard (looks like Nextmark doesn’t include this for some reason), find out when the next update is by contacting the list manager (or have your list broker do it). If an update is coming up soon and you have some time in your schedule before you order the names, I’d suggest waiting for the file to update.
Seasonality, Prospecting and List Segment Size
When comparing buyer segments to determine activity as described above, you’ll also need to take into consideration the seasonality (best selling season) of the company whose list you’re considering. Does the company have a more seasonal business? If you think so, then it would be natural to see big jumps in activity at certain times of the year (as reflected in list size for specific segments).
If you look at the Athleta datacard, updated in January, the last three months would take in the holiday (and post-holiday sale) season. To me, given the fact that in the six months prior to the January update, 70 percent (57K last three months, 81K last six months) of their orders came in from November 2006. My guesstimate would be there was some heavy prospecting going on in Q4 of 2006; in essence, fishing when the fish are biting.
FYI, Athleta was a random selection I chose, although I did rent and mail its list in 2002 with one company I worked for. I have no actual knowledge of its business. But based on my interpretation of Athleta’s datacard, I made some assumptions just like you can.
I invite your comments, additions, criticisms, challenges etc. Post below!
More on reading datacards next week.