Enough Intuition: Base Your Channel Strategy on Analysis

It sounded so great. Catalogs would disappear as we headed for a paperless utopian society.

Well, it hasn’t quite happened despite ever-increasing postal costs. But many mailers wish it would.

For example, several clients have asked: “Can we reduce our catalog mailings and rely on e-mails to keep in touch with our customers?”

Good question. But before making such risky changes to your contact strategy, there are additional questions that come to mind:

*How do e-mails alter the behavior of our customers?

*Are we training customers by sending out promotional offers on a weekly basis to wait for e-mail promotions before placing their orders?

*Does the e-mail remind shoppers that they intended to place an order and drive them back into the catalog, or does the e-mail alone spark the desire to place the order?

*How do you treat e-mail transactions in a matchback?

There are many opinions out there, and most of these questions will not be easily answered. But let’s give it a try.

I receive five or six e-mails a week from some catalog companies, but there are pure play dot-coms that send out only a few targeted e-mails a month.

Let’s say that cataloger X has decreased the number of catalogs sent to buyers during the spring season from six down to five, hoping that e-mail contacts will continue to drive sales at the same or higher level. This strategy would obviously save a great deal in expenses, but how can we measure the impact on sales?

In addition to the standard segmentation, you can add an e-mail address flag to the records in the merge for a season and code them differently. For example, if you have 50,000 0-24 Month buyers on file, and opt-in e-mail addresses for 30,000 of them, you can split the total universe into three groups and watch their behavior both at a promotional level and at a customer level for a season–or longer.

In this scenario, we have the group of 20,000 customers without an e-mail address (group A), and two groups divided randomly of 15,000 each (Groups b and c) of customers with valid e-mail addresses who have opted-in to receive e-mail from us.

Groups A and B receive the standard catalog schedule of six drops throughout the season, while only group B concurrently receives the standard e-mail treatment. Group C receives only five catalog drops throughout the season along with the standard e-mail treatment.

At the end of the season, results can be gathered and collated at the customer level and at the campaign level to determine what the impact of removing a catalog contact was to bottom line.

When gathering results at the promotion level, business rules must allow e-mail orders to be matched back to mailed promotions, or e-mail results must be included in the totals. Promotional results can be compared to total results, at a customer level, throughout the season in order to determine the overall impact of the shift in contact strategy.

There are several difficulties to developing a perfect test strategy, as it is nearly impossible to isolate only one variable for testing. But performing analysis both at the promotional and customer levels over a longer time period can boost your confidence in making shifts in your contact strategy, as opposed to your acting merely on intuition alone.

Tom Blake is a marketing consultant with San Rafael, CA-based catalog consultancy Lenser.

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