Lights, Camera, Campaign!

Jul 01, 2005 9:30 PM  By

Developing a seasonal campaign — a multichannel, multiple-impression integrated marketing effort designed to run across an entire season — is akin to making a movie in which every scene is crafted carefully to tell an unfolding story. Each scene — or in the case of a seasonal campaign, each individual customer contact — is coordinated with the previous one and plays a role in telling the entire story over time.

In an article in the May 1 issue, “Calculating incremental response,” we discussed creating a “longitudinal analytical framework” to support the continuous creation of knowledge. This framework can be defined as a robust repository of complete customer history — that is, a best-practices marketing database — and the implementation and subsequent analysis of continuous waves of seasonal test treatments to clarify the complex interactions of channels and promotions.

It is these “continuous waves of seasonal test treatments” that are so critical to seasonal campaign optimization. This progressive approach is far more effective than the traditional one-off promotions that are conceived and executed in a vacuum. This is particularly true in complex environments, such as when direct channels interact with brick-and-mortar retail, or in business-to-business marketing, especially those situations that include a field sales force.

But just as crafting a compelling movie requires more than pointing the camera at the actors and yelling, “Action!” creating an effective seasonal campaign or test requires a fair amount of preproduction work.

SETTING THE SCENE

Because of the myriad channels and contacts involved, it is impossible to produce the optimal seasonal campaign on the first attempt. You can, however, evolve toward optimization by starting with a “best guess” option and then testing your way to improvement.

To help you determine the specifics of your seasonal test treatments, you should first answer the following five questions:

  1. Which messages should extend with consistency across the season, and which need to unfold as the season progresses?

    For example, “appropriate gifts for the holidays” can act as a seasonwide theme, while “rapid shipment for last-minute shoppers” can be overlaid at the appropriate time.

  2. What should be the level of marketing intensity for each customer segment?

    The objective is to match marketing intensity with each segment’s potential. A primary method is to adjust the number of catalogs, including remails. Additional ways to affect marketing intensity include supplemental contacts such as e-mails, including “priming” efforts in advance of and reminders just after catalog drops. Sometimes outbound calls are appropriate, especially in b-to-b environments; other means include increased catalog page counts and the judicious use of more expensive postage; efforts by the field sales force; and in-store tie-ins such as postcards announcing specials only for best customers.

    Promotional contacts with customers aren’t the only way to alter marketing intensity. Offering best customers special toll-free phone numbers or expedited shipping, and implementing loyalty programs are among other tactics that can heat up the intensity.

  3. What is the timing of the various types of promotions?

    For example, should the second catalog drop be closer to the first or to the third? Should e-mails be sent in advance of the catalogs to announce their impending arrival, or should they be positioned as a trailing reinforcement?

  4. What audiences should be selected for each promotion?

    How deep into the file should we go for each of the catalogs, for instance? Which poor-performing customer segments should receive only e-mails?

    Before you can select the optimal audiences, you need to conduct a promotional cost/benefit analysis that incorporates the estimated baseline order activity. This is the revenue that would have occurred in the absence of any promotional activity; it generally differs dramatically by customer segment. You can establish these estimates through ongoing testing and analysis. Failure to include this baseline order activity generally results in the overpromotion of certain customer segments and the misallocation of marketing dollars.

  5. Should the messages be personalized? If so, in what way?

    For example, should the catalogs be mass-customized with the selective binding and digital messaging capabilities offered by today’s sophisticated printers? If that’s the case, how should you define the business rules that drive such efforts? Likewise, should the e-mails be tailored to individual customers?

MAPPING THE PLOT

By its very nature, seasonal campaign optimization will vary from season to season, let alone from business to business. But here’s a basic four-step approach to seasonal campaign optimization that you can adapt to the specifics of your business:

STEP 1

Start with your existing “control” promotional strategy for each of your customer segments, including the sequencing. Generally this will be the approach you used during the same season last year.

For the sake of this exercise, let’s assume that you are a hybrid merchant — one that sells to consumers as well as to businesses — with three channels: a catalog, a Website, and a national retail presence. Your existing (control) strategy for your peak holiday season is to mail between one and three catalogs to each of your active customers based on the prediction of upcoming purchase activity. Only the best customers receive all three catalogs.

The catalogs are supplemented by two e-mails, just prior to the first and the third catalog mailings. Before the holiday season, your telemarketing team calls your b-to-b customers to update contact information and ship-to addresses. Most of your b-to-b orders are, in fact, ship-to gifts.

The baseline order activity is modest at the beginning of the season and builds steadily as Dec. 25 approaches. Order activity drops dramatically after Christmas.

STEP 2

Create multiple seasonal test treatments with different marketing intensities than that of the control strategy.

Let’s assume that, for reasons of operational complexity and expense, your company is willing to create no more than two test treatments a season. One test will add a third e-mail to the control strategy as well as a postcard that advertises a retail tie-in. The other test will add a fourth catalog mailing to the very best customers, plus a third and fourth e-mail and a postcard that advertises a retail tie-in. For the sake of simplicity, we will not consider strategies beyond promotional touches such as dedicated 800-numbers for best customers.

The chart above illustrates test #2, which has a greater marketing intensity than both the control and test #1.

Note that, within the limitation of two seasonal tests, many of the possible permutations are not being considered, such as the addition of a third e-mail only; of a retail tie-in only; of a fourth catalog remail; of a fourth catalog remail with only three e-mails; and so on. Therefore it is important to use judgment when determining which treatment permutations are most likely to be successful.

STEP 3

Measure the effectiveness of each seasonal test as well as of the control strategy:

  • Calculate total revenue across the season.
  • Subtract the operational costs of sourcing, handling, and shipping the merchandise associated with the revenue.
  • Subtract total promotional costs. The result is total contribution
  • Normalize total contribution as necessary, such as per starting customer.

The strategy with the most favorable normalized contribution is the winner and will act as the control going forward.

STEP 4

Focus in future seasons on fine-tuning the control by methodically altering the inputs that define the seasonal contact strategy: message, promotion, timing, audience, and personalized content. It is here that you can examine previously untested permutations. In this way, you can make constant progress toward seasonal campaign optimization.


Jim Wheaton is cofounder/principal at Wheaton Group, a data-mining and decision-sciences practice in Chicago. Omer Artun, Ph.D., is a senior director at Best Buy Co., responsible for business-to-business marketing for the recently created Best Buy for Business division.