We had an inquiry come through this week for mid-west hunters and although benign on the surface, this count request is a perfect example of cost vs. value. We had sourced the file through 2 compilers.

Source 1:  Fishing and shooting, 2.2 million records  — $30 per thousand records

Source 2:  Hunter lifestyle, 2.6 million records — $50 per thousand records

Source 3:  Field and Stream Subscribers, 265,000 in ‘mid-west’ — $95 per thousand records

The 2.6 million record file was about 40% more expensive but seemed to be very similar profile wise so why the extra expense?  Field and Stream subscribers were also about 300% more expensive than the 2.2 million record file and that was a more targeted type of hunter mailing list based on transactional subscription.

So the question is how to proceed.  What guidelines would we set forth in testing data?  My first thought is that we needed more lists to tests but we had enough for a first small direct mail test.  Would we test the less expensive compiled hunter mailing list for $30 per thousand with a universe of 2.2 million against the 2.6 million record mailing data file that ran $50 per thousand?  Would we work with the field and stream list later as a second test?  Tons of questions!

Here is where reality sets in – their brochure was a huge, very expensive piece, so the cost of the data being 3 verses 5 cents was truly minimal, even at 9.5 cents the Field and Stream file as also a viable candidate for the first round because it didn’t push the overall cost of the mailing up that significantly.  It got me thinking we needed more higher end data options in order to make sure the high end piece did well for conversion.

The client was ready to run with the small test even though the data was very inexpensive, the 2 cents/ 6 cents increase makes a difference if they are mailing fifty or one hundred thousand records per month.  It was also worth testing quality of response and if the more expensive data did better, it would be worth the extra expense.   Here is the cost difference at 100,000 pieces mailed.

3 cent data @ 100,000 is $3,000

5 cent data @ 100,000 is $5,000

9.5 cent data @ 100,000 is $9,500

As you can see costs do add up but testing will tell you if the costs are worth it over time.  Focusing on just the cost can leave out the biggest part, return on investment!