Metrics for Managing Your Co-op Database Circulation

For most mailers, understanding the cooperative databases is a big part of managing a catalog’s circulation, as the databases have become a larger and larger part of the prospect universe.

The first question most circulation managers have regarding a co-op database is, How many names are in its universe? Subsequent questions include, How often can they be mailed? And how have the names responded in the past? These are indeed the key questions.

Use the historical sales results and the proven list universe size to plan sales. You should plan your annual usage of the database as well as usage for specific mailings. And don’t plan just your sales. Plan all the way down to the profitability of the your prospecting.

You need to know the number of unique, profitable names being served up by each database. This ties in to your decision regarding how many databases you should belong to. The more co-ops you join, the more your buyer names will be diluted by offerings from other catalogs, so be sure the unique-name universe is large enough to warrant your participation.

How well are your database names responding over time? Are you experiencing list fatigue? Can the co-op expand your universe of profitable names with new models and new selects and by fine-tuning your proven models so that you can prospect deeper into existing models?

Use key metrics to manage your relationships with the co-op databases. If you treat the co-op databases as a business partner you’ll get better results than if you view them as providers of prospecting list on a campaign by campaign basis.

As for those key metrics:

1) Co-operative database list universe
* Co-op database prospecting list universe * Historical response rates by model * Total prospect universe for each database

2) Annual sales potential for each database
* Prospect universe * Average sales per catalog * Annual frequency * Annual sales potential (universe x sales per catalog x frequency)

3) Historical sales results
* Last year’s sales for this co-op * Last year’s sales per catalog * Last year’s circulation * Is list universe increasing or decreasing? * Are sales per catalog increasing or decreasing?

4) Profit planning
* Planned annual sales from co-op database circulation * Circulation * Sales per catalog * Catalog cost * Catalog cost as a percentage of sales * Merchandise cost as a percentage of sales * Variable cost as a percentage of sales * Available for fixed and overhead percentage or “profit” percentage * Total “profit” or contribution to fixed overhead

5) Buyer acquisition
* Annual sales from co-op prospect models * Average order * Number of new buyers planned for this year * Number of buyers last eear

6) Overlap with other databases
* Historical overlap with other databases (gross-to-net percentage of each database compared with each other individual database) * Unique names from this database

7) Response rates of co-op multibuyers and single buyers
* Historical response rate of co-op-to-co-op multibuyers * Historical response rate of co-op of singles

8) Database universe
* Database universe as percentage of total prospecting universe

9) Cost of co-op database names
* Cost of prospecting names (cost per each) * Net-name arrangements, etc. * Number of names below breakeven last year

10) How well are the database names responding?
* Percentage of total names mailed below breakeven last year * Increase in prospect universe last year * Projected increase in prospect universe this year * Projected sales per catalog this year compared to last year

11) Other co-op database modeling
* House file optimization and reactivation * Balance models * Licensed scoring * Add-a-name Jim Coogan is president of Santa Fe, NM-based Catalog Marketing Economics.

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