Catalog Analysis: Following the Response Curve

Nov 01, 2003 10:30 PM  By

This month we look at another analytical technique or process that direct sellers need to know, understand, and apply to their daily operations: tracking response curves. Successful and profitable catalogers will likely recognize the applications we are about to discuss; newer and smaller catalogers and Internet marketers may find that the information can provide them with new ways to help improve their bottom line.

A response curve is the historical calculation of orders — and percentage of orders — received weekly from a direct response campaign. Years ago, when catalogers had to maintain order information by hand, it was difficult to manually reconstruct response curve order information at the end of each campaign. With today’s computer tracking, however, it should be automatic and quite simple to build the kind of weekly response curves shown below.

Here are several suggested steps in getting started and applying the knowledge of response curves:

Step 1

The first step is understanding monthly seasonal mailing variations. Successful catalogs rank months of the year on a basis of 100%: The top response month is 100%, and other months are percentages of the top month. For example, if the historical monthly response winner is November, it is ranked at 100%. October may not be quite as good but still strong — 95% of November, perhaps. December might be further down in typical response and ranked at 85%. The “Best/Worst Months for Mailing” chart below represents an average seasonality of all direct marketing efforts compiled a number of years ago by a list broker.

Step 2

Construct, from the past two years, a weekly response pattern of each promotional campaign. This is the percentage of orders received by week for the drop. If you have four mailings a year, you need to develop four weekly response curves, one for each season. If you have remailings of each major campaign, those need to be tracked and a separate response pattern built for each drop. Ideally a cataloger will have two or three years of history to compare for each seasonal drop.

From this information you can identify the “half life” of each catalog — the point when 50% of the orders are in. The control buying team will use this “doubling point” when placing merchandise reorders.

Step 3

For your next promotional campaign construct a simple Excel spread sheet similar to the “Catalog/ Internet Response Curve” chart shown below, applying past percentages to the mailing. You will have to estimate the overall mailing response, again based on past performance. The worksheet will automatically calculate how many orders you should receive each week following a mailing, based on historical response patterns. This step allows you to track results on a week-to-week basis and on a cumulative basis.

Applications and uses of response curve data

Quite a few people in your company will want to access and use the actual response information compared with the campaign’s plan:

  • Senior management will want to know how a specific promotional campaign (catalog drop, push e-mail effort, or direct mail promotion) is doing. Is the promotion on plan, ahead of plan, or behind plan?

  • The marketing team must get order information from operations and view actual results against plan. If a promotion is behind plan, the team may want to consider remedial or short-term marketing efforts to drive additional sales. In addition, the circulation team will use the monthly seasonality history in forecasting the differences in response and average order value.

  • The merchandisers, particularly those responsible for rebuying, generally start reviewing response data the first week that order activity begins. Three types of product situations will quickly become apparent:

  1. Some products will be on forecast, or expected to generate as many orders as were originally forecast in the buy plan. Placing the second or third order for the product can take place in week three or four. No unusual problems are anticipated.

  2. Some products will be ahead of forecast — these are known as high-flyers. Merchandisers will need to alert the vendors of the demand and activate reorders or hold-for-confirmation orders. To minimize back-orders, taking action as soon as possible is critical. A cataloger cannot manage inventory from a back-order report. The crucial timing will have been missed in getting orders moving from the vendor.

  3. Some products will be behind forecast. As soon as under-forecast response is quantitatively confirmed, rebuyers should cancel hold-for-confirmation orders and start planning how to dispose of overstocks.

Most catalog operating systems have some inventory forecasting subsystems and reports that can help the rebuyer project response based on one or two weeks’ results. This type of report is tied into a stock status report and the weekly response curve. Marrying the two reports gives the rebuyer a quantitative tool to project total demand as well as sales of any individual item in the catalog. Once rebuyers reach the doubling point, they can project quite accurately to final demand.

Understanding and using historical seasonality and response curve information gives catalogers a dramatic advantage over retailers, who must rely heavily on intuition and instinct in managing their businesses.


Jack Schmid is president of J. Schmid & Associates, a Shawnee Mission, KS-based catalog consultancy.

Inventory Management: A Key to Catalog Profitability

Managing inventory is truly a core competency of profitable catalogs. Every catalog business has its unique inventory challenges. Apparel mailers, for instance, can expect high returns and cancellations — often ranging from 25% to 30% of sales — and they typically have very few items repeated from season to season. Business catalogs, conversely, often have a high number of repeated items from catalog to catalog, typically introducing only 20%-25% new products. And many high-margin businesses have a greater percentage of direct imports and the increased inventory risk associated with importing. Every company will be different, but since inventory is such a major asset of a catalog, it needs careful attention and management.
JS

Catalog/Internet Response Curve

Fall 2003 Catalog 3.65% response for drop 1; 2.15% response for drop 2

MAIL DROP QUANTITY ORDERS 9/6 9/12 9/19 9/26 10/3 10/10 10/17 10/24 10/31 11/7 11/14 11/21 11/28 12/5 12/12 12/19 12/26 1/4 1/11 Total
8/25 100,000 3,650 120 511 555 456 303 270 204 175 172 146 139 128 117 106 91 91 66 0 0 3,650
9/28 100,000 2,150 71 301 327 269 178 159 120 103 101 86 82 75 69 62 146 2,150
Total 200,000 5,800 120 511 555 456 374 571 531 444 350 305 259 231 218 192 173 167 135 62 146 5,800

RESPONSE PATTERN

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Balance
Weekly response 3.3% 14.0% 15.2% 12.5% 8.3% 7.4% 5.6% 4.8% 4.7% 4.0% 3.8% 3.5% 3.2% 2.9% 2.5% 2.5% 1.8% 100%
Cum. response 3.3% 17.3% 32.5% 45.0% 53.3% 60.7% 66.3% 71.1% 75.8% 79.8% 83.6% 87.1% 90.3% 93.2% 95.7% 98.2% 100.0%