Cooperative databases provide a valuable source of quality names for many product offers. For most small and medium-sized catalog companies, 50% or more of prospecting circulation goes to names selected from co-operative databases. Prospect names selected from co-ops are a good value for the money. First, the names are selected according to a model, which means they are selected because they resemble your own customer database. Second, these names rent for less than most outside response lists. They are “net” of your house file, which also brings down the cost per net name (please refer to the chart “A” below). On the surface, it appears coops are about half the cost of outside lists. But, in reality, names from a coop are about one-third the cost because, once again, they are net of your house file.
| CHART “A” | |||||
|---|---|---|---|---|---|
| List |
Input
|
Cost/M
|
Est. Invoice
|
Net/Qty Mailed
|
Cost/M Mailed
|
| Coop List A |
10,000
|
$70.00
|
$700
|
8,540
|
$81.97
|
| Rental List A |
10,000
|
$150.00
|
$1,500
|
7,120
|
$210.67
|
| *Costs above are examples and do not include misc. charges | |||||
Some co-ops like I-Behavior and NextAction can base models on variables created from SKU-level data which is a real advantage that can be reflected in the revenue realized per catalog mailed.
Each co-op starts its models from a different database structure, resulting in unique variables for study. Plus each co-op creates their models using different methodologies. So, while there is a significant overlap of names available through various co-ops, the modeling techniques they use can select different individuals for mailing. Therefore one co-op may identify good prospect names that another co-op might miss. That’s the objective in working with multiple co-op companies.
In order to get the most from co-operative databases it is important that you work with them as a partner and an extension to your marketing team. Share your goals and your results with them. Let them know what you expect in terms of performance so they can help you accomplish your objectives. Yes, co-operative databases supply prospect names. However, the quality of the names obtained through a co-op can be enhanced by working more closely with them, and their resources can be applied creatively to improve mailers’ house file and prospect name performance from vertical lists. Here are a few proven strategies to consider:
- Outside List Optimization – Mailing lists contain households whose overall buying patterns are not typical of the characteristics of the list. For example, a high-end gift list may contain households that made a purchase for a special occasion, but generally makes purchases from lower-end catalogs. Optimization – the process of matching vertical list names against a co-op database and scoring them with co-op targeting tools – identifies those households that don’t otherwise fit the desired buying patterns. These less productive names can then be suppressed from the mailing and the response rate from a particular list will improve as a result. Similarly, this technique can be used to optimize the performance of marginal lists by suppressing the weakest segments—those that are performing just below acceptable response. Mailers using this technique will typically suppress 10% to 15% of a list, thus providing enough of a lift to make a marginal list profitable, or to help weaker segments achieve breakeven. It is common for the lift in response to more than justify the incremental cost of optimization.
- Suppression of Rental Singles – The most common use of list suppression is optimizing the rental singles (one-time buyers) that come out of a merge. The worst-scoring segments of the rental singles frequently perform at less than half the response rate of the average rental name. In many cases, mailers replace the suppressed names with “better” prospect names. This process allows the mailer to improve the overall performance of the outside names mailed and to fine tune the number of names on the mail tape sent to the printer.
- New-to-File Buyers – Mail new buyers even faster by selecting very recent fill additions from the co-operative database. Consider setting up a weekly or monthly program (depending on volume/quantity) to download these “hot-line” names for mailing.
- House File Modeling – You can use house file modeling for specific applications to improve your RPC, but not as a substitute for R-F-M (recency, frequency and monetary value) analysis. One effective use for house file modeling is to model all the previous buyer names you do NOT plan to mail – including older names for reactivation, plus your non-converting inquiries. Using co-op models to reactivate your house file will help identify previous customers who are most likely to purchase again. The same is true of using a model to identify which inquirers are most likely to buy. In both cases, you can increase RPC by eliminating circulation to non-performing names before mailing them. After you mail non-converting inquiries for the last time (generally 3 times), have these prospect names modeled and mail them one more time. You will be pleased with the results when you see the increase in your RPC.
- Add a Mailing – If you can squeeze-in one additional mailing to your house file and to the “best” performing names from a co-operative database during your busy season, do it. Let’s assume you mail to your house file 12 times a year … every month. Next, assume Holiday is your best season. During the months of September, October and November you might want to consider mailing 3 weeks apart vs. 4 weeks so you can mail one additional time to your house file. This additional drop should perform at 70.0% of a regular house file mailing to like R-F-M segments of your customer list. Some companies mail to the best segments of their house file in early December as a “Last-Minute-Ideas” mailing.
- Leveraging SKU-Level Data for Modeling – SKU-level data not only offers greater precision in categorizing buyers, it also can be used to build custom models designed to clone item/category/or season-specific buyers in a way that marries the analytic tools to mailers’ merchandising strategies. It is sometimes more difficult for a catalog to provide SKU level data, but the superior results will almost certainly justify the extra cost and effort.
- Enhanced Data Overlays – External data can enhance results with co-op databases. For example, I-Behavior is the only co-op offering attitudinal segmentation through their partnership with Yankelovich and its MindBase ™ segmentation system. This unique partnership allows I-Behavior members to link behavioral targeting with the underlying attitudes and motivations driving purchase behavior. Results to date show this extra dimension offers significant potential in opening up new co-op universes for catalogers as shown in these results below:
| CHART “B” | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Holiday 1 Catalog mailed 09/29/03 | YANKELOVICH MODEL I-Behavior TEST | ||||||||
| Results as of: 01/16/04 | |||||||||
| MAIL DATE |
KEY CODE |
DESCRIPTION | SELECTION | NET MAILED |
DEMAND AT 100% |
ORDERS AT 100% |
AVG. SALE |
% RESP. | RPC AT 100% |
| 09/29/03 | H1350 | I-Behavior | Yankelovich Model – Segment 1 | 12,750 | $20,800 | 295 | $70.51 | 2.31% | $1.63 |
| 09/29/03 | H1352 | I-Behavior | Control | 12,750 | $11,607 | 165 | $70.35 | 1.29% | $0.91 |
| Variance | 0 | $9,192 | 130 | $0.16 | 1.02% | $0.72 | |||
| Percent Variance | 0.00% | 79.19% | 78.79% | 0.23% | 78.79% | 79.19% | |||
In our test, the use of the Yankelovich Model for this food mailer increased the response rate and revenue per catalog significantly. This enhanced data overlap model has proven to be more effective with niche offers. (Note: Results will vary which is why testing is critical to knowing if this model is right for your offer.)
- Keep Data Up-to-Date– Most important, update the co-operative database monthly or even more often if possible. Keep in mind that all models are only as good as the data you provide. Be sure to update the co-op often and with all of the requested data. The “freshness” of your data is critical to the results achieved.
Some catalogers have a problem joining “other” coops (or more than one coop) because they are protective of their names. Since coops do not release any names that are unique to your company, this should not be a barrier for joining. Your names are being mailed to other offers regardless of your participation in coops. So, you may as well take advantage of coops as another source for names. Another barrier mailers have is the question of whether or not they need another coop in their mail plan? If you look at the coops similar to mailing an outside test list, then of course it’s worth a test. If the coop model does well, stay in the coop. If it doesn’t, don’t stay in the coop. It doesn’t have to be a complicated decision but rather a choice that is made on actual results.
Another point to consider is the fact that some of the coops supply customer profiles and/or demographic information on your file which can be extremely helpful. It’s worth finding out what added value information they are willing to share with you. They can be a resource beyond a source for names that is more valuable than an expensive research study you would have to conduct in order to obtain the same information.
Work with your marketing representative. Share results with them. Let them know what is working and not working so improvements can be made. Co-operative databases can be a huge asset to catalogers in their search for qualified prospect names.