Catalog Analysis: The Importance of Tracking Source Codes

As catalog and Web marketers, you need to understand the role and importance of numbers in driving your business. Comprehending everything you can about your customers and prospects is essential. You must gather every bit of statistical, demographic, and psychographic data you can about them and then usethat information to better target your marketing efforts to grow the business and the bottom line. Direct marketers need data, and they need to massage and analyze that data to make certain that every prospect effort and every customer contact is better than the last.

Tracking orders via source codes (also called sales codes or key codes) is one of the most important ways to measure customer and prospect marketing efforts. In an ideal world, we would like to know where every order came from. While this may not be possible, we must be diligent in tracking source codes, because the catalog business is statistically driven, and measurement is crucial.

Most catalogers will ink-jet the customer name, customer number, and source code on the back of each catalog and on every order form. Prospect mailings carry everything except the customer number. Before ink-jetting, when cheshire or “peel-off” labels were the industry norm, the benchmark for tracking was about 85% of all orders trackable to a specific source, with 15% uncoded. With outside and inside ink-jetting, that standard is now in the 90%-95% range — or higher.

Direct mail pieces — solo, self-mailers, postcards, or even multimailers — should have similar standards. In other words, more than 90% of those orders should be trackable, thanks to the increased use of personalization.

Space ads likewise are not difficult to code by publication, issue, city, and specific offer that may vary from ad to ad. The typical trackability of space ads is in the 95% range.

The Internet is a villain when it comes to tracking orders. Push e-mail campaigns are more straightforward because the marketer is controlling to whom these promotions go. But Website traffic — specifically catalog requests and orders — is poorly tracked. The majority of marketers complain that they have no idea of exactly where their Web orders came from. While we know that catalogs drive Web orders and a growing number of consumers prefer to shop online, few catalogers require a source code to complete the order. So a growing number of Internet orders go into the uncoded category and must be matched back against the mailing list to ascertain whether the catalog actually drove the order.

A case in point

The case study in the chart to the right shows the errors and misconceptions that can occur if management does not mandate the capturing of source codes.

The example shows the results of a small catalog company for two seasons: fall and spring. The customer list, while modest in size (7,335), appears to be performing well in both catalog promotions. Each season’s buyer mailing produced nearly the same results:

  • Fall: 6.1% response, $47.53 average order value (AOV), and $2.92 sales per catalog mailed.

  • Spring: 6.1% overall response, $46.16 AOV, and $2.83 sales per catalog mailed

Compared with the marketer’s fully loaded breakeven of less than $1.25 sales per catalog, which includes all variable and fixed creative costs, this segment of the mailing was judged a success. (For more on calculating breakeven, see “Getting to Breakeven,” November issue.)

The challenge was prospecting for more customers. Two campaigns with the fall/holiday catalog and one with the spring issue were, by all standards, dismal failures. Not a single list generated $0.50 a catalog. What to do? Should the catalog give up on outside rental lists and change its marketing strategy to space or other alternative media? Can the catalog even stay in business in the long term if it can’t attract new customers via list rentals?

In digging deeper, we discovered that there indeed were other orders. Some came from the Website with no source code tracking, since the company didn’t even ask for one. And a slew of orders kept being reported each week as “no code” because the phone reps had failed to capture most of the codes. When the smoke cleared, we discovered the following:

  • Fall campaigns (two mail drops): 2,612 uncoded orders and 101 Internet orders

  • Spring campaign (one mail drop): 2,950 uncoded orders and 245 Internet orders

This totals 5,562 uncoded orders and another 346 Internet orders. We divide the total uncoded orders (5,908) by the total coded orders (1,529) to get the allocation factor, or 387.2%. Now the results of the prospecting and house file mailing take on a new dimension. Look at the last column, with uncoded and Web orders allocated back to sales per book, and note the different results:

  • The customer lists in both the fall and the spring drops reaped more than $10.00 per catalog. Recommendation: Mail the customer list more often during these seasons. Even a 50% or 75% drop in response will produce profitable customer mailings.

  • Fall prospect lists were still rather anemic in their results, but List B in the first drop produced nearly $1 per catalog and must be remailed. Similarly, Lists D and G had revenue per catalog that suggest retesting.

  • Both spring prospect lists rose above the prospect breakeven at $1.22 and $1.25 revenue per catalog when uncoded and Internet orders are allocated back. Both lists are substantial winners.

You might contend that all of the uncoded or Web orders were not a direct result of the catalog mailing, and this could be true. But most of our studies on matching uncoded and Web orders back against a mailing tape tell us that 85%-90% of those orders came from people who were mailed a catalog. This case illustrates the importance of tracking, reading, and allocating orders correctly against the original mailing plan. No short cuts here!

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

Catalog Case Study — Fall/Holiday and Spring Drops
Name Mail Date In Home Quantity
Order Value
Sales per
Allocation Factor
BUYERS TOTAL Sept. 22 Oct. 6 7,335 451 6.1% $47.53 $21,436 $2.92 $11.31
Prospects Drop 1
AL010A List A Sept. 22 Oct. 6 4,976 15 0.3% $21.33 $320 $0.06 $0.25
BB010A List B Sept. 22 Oct. 6 5,795 50 0.9% $29.62 $1,481 $0.26 $0.99
BS010A List C Sept. 22 Oct. 6 5,653 28 0.5% $28.11 $787 $0.14 $0.54
HL010A List D Sept. 22 Oct. 6 5,738 25 0.4% $40.84 $1,021 $0.18 $0.69
JM010A List E Sept. 22 Oct. 6 4,772 20 0.4% $31.00 $620 $0.13 $0.50
OH010A List F Sept. 22 Oct. 6 4,724 23 0.5% $23.13 $532 $0.11 $0.44
PA010A List G Sept. 22 Oct. 6 4,891 14 0.3% $66.21 $927 $0.19 $0.73
RB010A List H Sept. 22 Oct. 6 5,759 17 0.3% $30.47 $518 $0.09 $0.35
TG010A List I Sept. 22 Oct. 6 4,886 11 0.2% $48.27 $531 $0.11 $0.42
PROSPECTS DROP 1 TOTAL 47,194 203 0.4% $33.19 $6,737 $0.14 $0.55
Prospects Drop 2
AP010B List J Oct. 6 Oct. 20 4,846 63 1.3% $31.75 $2,000 $0.41 $1.60
BC010B List K Oct. 6 Oct. 20 4,973 4 0.1% $33.50 $134 $0.03 $0.10
BI010B List L Oct. 6 Oct. 20 4,796 5 0.1% $38.60 $193 $0.04 $0.16
BL010B List M Oct. 6 Oct. 20 4,441 21 0.5% $36.57 $768 $0.17 $0.67
GC010C List N Oct. 6 Oct. 20 5,003 4 0.1% $68.25 $273 $0.05 $0.21
HB010B List O 0Oct. 6 Oct. 20 4,962 1 0.0% $41.00 $41 $0.01 $0.03
JM010B List P Oct. 6 Oct. 20 4,771 10 0.2% $22.70 $227 $0.05 $0.18
OH010B List Q Oct. 6 Oct. 20 4,724 6 0.1% $38.17 $229 $0.05 $0.19
HC010B List R Oct. 6 Oct. 20 4,969 5 0.1% $50.60 $253 $0.05 $0.20
LE010B List S Oct. 6 Oct. 20 4,982 6 0.1% $30.17 $181 $0.04 $0.14
PROSPECTS DROP 2 TOTAL 48,467 125 0.3% $34.39 $4,299 $0.09 $0.34
TOTALS – DROPS 1 AND 2 102,996 779 0.8% $41.68 32,472 $0.32 $1.22
Uncoded Orders – October through December 2,612 $35.85 $93,634
Website Orders – October through December 101 $33.23 $3,356
Buyers Drop 3
BU021A Last 12-month buyers Jan. 25 Feb. 1-4 3,663 326 8.9% $47.56 $15,503 $4.23 $16.39
BU031A Last 13-24-month buyers Jan. 25 Feb. 1-4 2,337 103 4.4% $43.37 $4,467 $1.91 $7.40
BU041A Last 25-35-month buyers Jan. 25 Feb. 1-4 1,335 21 1.6% $42.43 $891 $0.67 $2.58
BU011A New to file buyers Jan. 25 Feb. 1-4 360 22 6.1% $42.09 $926 $2.57 $9.96
BUYERS DROP 3 TOTAL 7,695 472 6.1% $46.16 21,787 $2.83 $10.96
Prospects Drop 3
AP010A List T Jan. 25 Feb. 1-4 19,869 203 1.0% $30.82 $6,256 $0.31 $1.22
ABO11A List U Jan. 25 Feb. 1-4 4,983 72 1.4% $25.03 $1,802 $0.36 $1.40
PROSPECTS DROP 3 TOTAL 24,852 275 1.1% $29.30 8,058 $0.32 $1.26
TOTALS BUYERS AND PROSPECTS 32,547 747 2.3% $39.95 29,845 $0.92 $3.55
Uncoded Orders – Jan. 1 to date 2,950 $35.91 $105,920
Website Orders – Jan. 1 to date 245 $33.78 $8,277
Uncoded Orders – October to April 5,562
Website Orders – October to April 346
Uncoded – % of Coded Orders=387.2%

Why You Must Track Source Codes

  1. To judge prospect or outside list testing. Bringing in new customers is the lifeblood of a successful catalog business. Profitable mailers measure what it costs to get a customer and they are precise in new list testing and continuation mailings. Inaccuracy or lack of information here can ruin any consistent customer acquisition effort.

  2. To judge customer list communication efforts. Lifetime value (LTV) has become the standard by which most catalogers measure their house file. With a knowledge of LTV, you can change or adjust your prospecting efforts to focus on those sources that are producing the greatest name value. Inaccuracy here will hurt your ability to maximize the growth of the list and the ability to target differentiated offers to your customers.

  3. To judge the average order value by method of ordering. We know that historically phone orders have a higher average order value (AOV) than mail orders — as much as 15%-25% higher. But what about orders from the Internet? Are they equal to or less than phone and mail orders in terms of AOV? Should you offer an incentive to people ordering on the Web, or are you just giving away part of your margin? Accurately tracking order method becomes an important factor in improving your bottom line.

  4. To judge the true value of your online and e-mail efforts. Is LTV from Internet customers as good as that of customers from rental lists? Or space ads? Do the customers who respond on the Internet or to opt-in e-mail promotions behave differently from traditional catalog customers? If we agree that the Web will be a significant part of future catalog marketing, we need to get better at tracking source codes and measuring promotional results from that medium.

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