Catalog Marketing Analytics and Performance Measures
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Catalog Marketing 101 - Catalog Management, Financials & Analytics This tutorial will walk you through the process of developing and using tools for managing, tracking and analyzing catalog performance. The analysis is key to fine-tuning and improving catalog results. Breaking down a catalog's performance at the end of the promotion cycle is one of the most important exercises you should perform. It is the activity that can provide you with a framework and direction for future catalog planning. In this tutorial you will learn what the key catalog performance measures and elements of catalog financial management are and how to use them. It shows you what needs to be analyzed and what are the key catalog analysis data requirements. It's an excellent "how to guide." To download visit http://www.dwsassociates.com/marketing-tools/whte-papers-tutorials/tutorials/catalog-marketing-101/
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Catalog Marketing Analytics and Performance Measures
- 1. Catalog Marketing 101 Workshop By Dudley Stevenson, Mark Eubanks (651) 315-7588 Catalog Marketing 101 Workshop 1
- 2. Table of Contents • Part 1 – Catalog Marketing Overview & E-Commerce • • • • • • • Synergy Part 2 – Front & Back End Marketing Part 3 - Catalog Merchandising Part 4 – Catalog Creative & Design Part 5 – Catalog Copy Part 6 – Catalog Production Part 7 – Management, Financials & Analytics Part 8 – Operations & Fulfillment (651) 315-7588 Marketing Planning 101 Workshop 2
- 3. Catalog Marketing 101 Part 7 Management, Financials & Analytics (651) 315-7588 Marketing Planning 101 Workshop 3
- 4. Catalog Performance Analysis • Developing and using formal and documented tools for analyzing and tracking catalog performance data is key to fine-tuning and improving catalog results. These tools include: – – – – – – Budgeting and analysis Square inch analysis Mailing list response analysis Gross margin analysis Price point analysis Multi-product response analysis (651) 315-7588 Marketing Planning 101 Workshop 4
- 5. Catalog Post Analysis • Breaking down a catalog's performance at the end of the promotion cycle is probably one of the most important exercises a cataloger should perform. It is the “closing the loop” activity that can provide a framework and direction for future catalog editions. • We recommend marking up a catalog showing actual unit and dollar sales per item and a per-page and per-spread summary. Because catalogs work in two-page spreads, they should be judged accordingly. • Using the sales data, you should prepare a square-inch analysis by page and by spread showing how each item performed, down to the contribution level. The actual profit and loss results for the mailing should be shown, and results of each page and spread can be judged by how they performed against the original sales goal per page. (651) 315-7588 Marketing Planning 101 Workshop 5
- 6. Catalog Performance Measures • Sales demand and net per book mailed • Sales demand and net per square inch • Gross margin per book mailed • Cost per book mailed • Cost per customer acquired • Response rate • Sales per page and spread (651) 315-7588 Marketing Planning 101 Workshop 6
- 7. Elements of Catalog Financial • Gross merchandise sales • Fulfillment • Returns • Advertising • Cancellations • Contribution to • Net sales overhead and profit • List rental income • Pretax profit • Gross cost of goods • Gross margin (651) 315-7588 Marketing Planning 101 Workshop 7
- 8. Typical Tools for Analysis • • • • • • • • Sales contribution by catalog page Item and classification ranking Sales dollars Best selling units Gross profits Price point analysis Prior list usage Product space allocation and position analysis (651) 315-7588 • • • • • Catalog order lag pattern analysis Merchandise classification lag pattern analysis List performance by merchandise category Catalog design and creative critique Response analysis by list and customer segments Marketing Planning 101 Workshop 8
- 9. Catalog Analysis Data Requirements • Item id number • • • • • • • • Item description Selling price Product cost Order processing and fulfillment costs per item Returns cost (refurbishing, processing, markdowns, repayments) Advertising cost per item taking into account space occupied, the type of page (651) 315-7588 • • • • • • • Mail quantity Mail date Page number Merchandise class Space allocation Specific appeal to male, female, either Country of origin Merchandised as exclusive to the seller personalized Marketing Planning 101 Workshop 9
- 10. Types of Analysis • Item ranking • Merchandise class ranking • Price range analysis • Graph of sales by price range • Ranking by page cost categories • Mailing list source code analysis • Page and Spread analysis (651) 315-7588 Marketing Planning 101 Workshop 10
- 11. Benefits of Analysis • Performing this analysis provides the information for: – Determining items which should receive greater or smaller space allocation – Categories to be thinned or expanded – Rebalancing of offerings by price point within merchandise category – Concentration of sales during periods of seasonal strength – Evaluation of the performance of items that are repeated in successive catalogs or ads – Identification of other factors which impact sales or profit performance (651) 315-7588 Marketing Planning 101 Workshop 11
- 12. “Squinch” Analysis • • • • • • Square-inch, or “squinch” analysis — can help measure contribution or profitability of items, pages, spreads, product categories, and price points. Squinch is one of the most productive and telling merchandise analyses that catalogers must perform. It reduces all products in your catalog to a common denominator, square inches of space occupied. It equitably and precisely compares sales per product and the cost associated with the space each product occupies. It ranks your products by sales dollars per inch. The goal is to compare the sales per inch for each product both to the book average and to breakeven. Items that are above average should be allocated more space in the next catalog. Items that perform below average should get less space or be deleted entirely. (651) 315-7588 Marketing Planning 101 Workshop 12
- 13. Mechanics of “Squinch” Analysis 1. 2. 3. 4. 5. Measure how much space each product takes including both image and text. Measure all editorial and white space so you account for every inch of space on page. The editorial and white space is weighted back to each product. Enter the total into one column on a spreadsheet. Place the sales for each product into another column. Divide each product’s sales by the space it takes on the page. For a complete picture of each product, include columns for units sold, profit per item, profit per inch, item number and product description. Include an index column. In this column, divide the sales per inch of each product by the average sales per inch for the entire catalog. (651) 315-7588 Marketing Planning 101 Workshop 13
- 14. “Squinch” Reporting • The Square Inch Report, in addition to the basic ranking of products based on sales per square inch, can be resorted to: – Rank by item by profit per inch – Rank by category by sales per inch – Rank by category by profit per inch – Rank by page by sales per inch – Rank by page by profit per inch (651) 315-7588 Marketing Planning 101 Workshop 14
- 15. 1% Response Rule in Testing • The 1 Percent Response Rule: – If you mail an average-size catalog, with merchandise of average desirability, at average-value price points, to an average selection of response lists, then your average response rate (number of orders received, divided by number of catalogs mailed) will be 1 percent. • The 1% rule only applies to prospect mailings. • The 1% rule is why list rentals have a 5,000 name minimum. – if you mail 5,000 names and get a 1% response, that means you'll receive 50 orders, and statisticians have mathematically determined that 50 responses is the minimum number of responses needed to make results "statistically significant." (651) 315-7588 Marketing Planning 101 Workshop 15
- 16. Applying the 1% Response Rule • • • • If you're launching a startup, use a 1% response rate for your prospect mailings and see what happens to the rest of your projections. If you're seeking financial backing for your catalog, and if your actual prospecting rates are below 1%, decide on a response now, before you make your official presentations. If you're considering investing in a catalog, you should compare the catalog's prospecting results with the 1% rule. If you're already a successful cataloger, the 1% response rule can help improve your business in two different ways: – If your sales reports consistently show sub-1% response rates on prospecting mailings, it's time to stop and look at why. – If your prospecting response rates are well above 1 percent, take a close look at how far above 1 percent you are, and compare that with where you should be. (651) 315-7588 Marketing Planning 101 Workshop 16
- 17. The “RFM” Rule • You'll get higher response rates when you mail to customers who have (a) bought more recently, (b) bought more often and (c) spent more lifetime dollars. • Conversely, you'll get lower response rates from mailing to customers who have bought less recently, less often and spent fewer total dollars. (651) 315-7588 Marketing Planning 101 Workshop 17
- 18. How is RFM Measured? • Date of most recent contact (to measure recency). – Typically in 0 to 3, 4 to 6, 7 to 12, 13 to 17, 18 to 24 months, etc.. • Quantity of lifetime purchases (to measure frequency). – Typically single or multi buyer • Total lifetime dollar sales (to measure monetary value.) – Typically broken down into groups based on your price spread – Actually some catalogers prefer to use only total sales during the last 12 months for this measure, but they are in the minority. • Some marketers have amended this formula to include the type of merchandise purchased. (651) 315-7588 Marketing Planning 101 Workshop 18
- 19. Creative Modifications Based on Analysis • By reviewing the marked-up catalog and squinch, the creative team — which must be part of the post-analytical review — can draw conclusions as to such design, copy, and photo issues as: – – – – – Is the proper amount of space being allocated to each item? Could the overall pagination be improved to boost sales? Are the spreads dense enough? Too dense? Has the photography helped or hurt product sales? Is the copy adequate to close the sale? Are sufficient benefits shown? – Has the eye flow on the spread helped or hindered sales? (651) 315-7588 Marketing Planning 101 Workshop 19
- 20. Sales Planning & Forecasting • Some of the other tools used for planning and forecasting catalog sales include: – Mailing list response analysis • Availability of suitable target audience quantities – House file segmentation and analysis – Customer acquisition strategy • Prospect mailings • Catalog request programs – Catalog request segment performance – Customer reactivation strategy – Price point analysis – Category/classification analysis (651) 315-7588 Marketing Planning 101 Workshop 20
Notas do Editor
- Developing and using formal and documented tools for analyzing and tracking catalog performance data is key to fine-tuning and improving catalog results. Collecting, organizing and maintaining product and sales data is important in the planning and creation of a catalog as well as in the post analysis and review phase. Measuring individual product performance is an important part of the process of improving overall catalog and multi product offer sales and profits. It will have an impact in the following areas: Product and merchandise classification selection Pricing assortment and structure of an offer Improved utilization of space Other areas affecting sales and profits You should also perform catalog or multi-product response analysis. Performing this kind of analysis provides the information for determining items that should receive greater or smaller space allocation. It will give you an indication of categories to be thinned or expanded. You will be able to rebalance offerings by price point within merchandise category and determine seasonal strengths. This evaluation will also permit you to look at the performance of items that are repeated in successive catalogs or ads as well as identify other factors which impact sales or profit performance.
- The elements of a catalog financial statement include gross merchandise sales These represent gross demand by customers for your products, or the sum total of all customer orders — filled or not. You must track demand for goods even though you will always have some returns and cancellations. For gifts/hard goods companies, gross demand is typically about 13% higher than net sales, or 113%. returns This represents products being returned for credit or a refund, which must be deducted from gross sales. (Merchandise exchanges are not a reduction of gross sales, but they are included with fulfillment costs.) Returns occur throughout the promotional cycle. For the gifts/hard goods model, returns typically account for 5%-10% of all orders. Note the dramatic differences of returns by type of company, especially apparel, in which return rates of 25% are not uncommon. cancellations Another reduction from gross sales, cancellations come in two types: credit or check related, and product availability or inventory related. If a customer gives you a bad check or his credit-card number is wrong, maxxed out, or fraudulent, you can't complete the sale. Merchandise-related cancellations may occur if product is not available at the end of the selling cycle. In the gifts/hard goods example, cancellations of 2%-3% are normal. net sales This is what is left from gross demand after deducting returns and cancellations — or simply put, it's the dollars you take to the bank. Final net sales cannot be truly read until all demand is complete and returns are in, usually about six months after the start of a campaign. In all cases net sales are the starting point from which all other costs are figured. In each of the financial models, net sales is 100%. gross cost of goods This number represents the total cost of product, including freight to your warehouse (freight-in), plus any expenses incurred for markdowns and remaining merchandise at the end of a season. You should also include certain credits or incomes here, such as co-operative allowances given by vendors for promoting their products and discounts for early payment of accounts. Cost of goods varies dramatically by type of company. In the case of consumer electronics, cost of goods might be in the 60% range. For gifts/hard goods catalogs, cost of goods in the low- to mid-40% range is typical. gross margin The cost of goods subtracted from net sales is gross margin. Improve the margin by 1% and that gain drops directly through to contribution to overhead and profit. Lose a single point in margin and it directly hurts the bottom line. fulfillment The total cost of receiving customers' orders, inquiries, and catalog requests, and shipping products to them. Fulfillment also includes the cost of order entry; computer hardware and software; warehousing; product picking, packing, and shipment; and returns processing, as well as fees for credit-card authorization. You should include shipping and handling revenue from customers here as a credit to fulfillment, rather than as gross sales. Most mature marketers have net cost of fulfillment in the 10%-15% range. advertising The leverage of advertising is three-fold. For one, fixed costs of creating the catalog or the Website and developing color separations are enormous for marketers mailing only a few thousand books. For example, if your fixed cost is $50,000 and you're mailing only 200,000 test catalogs, fixed costs represent $250/M, or $0.25 per book mailed. If you mail the same catalog with the same $50,000 fixed cost to 1 million names, fixed costs drop to $50/M, or only $0.5 per book. Second, the higher the response, the lower the advertising cost as a percent of sales. Let's say a cataloger in prospecting mode mails 90% of its books to rental names, spending in variable printing, mailing, and postage $600/M ($0.60 per piece) to mail 500,000 catalogs, or $300,000. It pulls 1% with a $100 average order, or 5,000 orders and $500,000 in revenue. Advertising as a percent of sales = 60%. Another cataloger mails mostly to customers, sending 60% of its books to buyers. It has the same variable printing, mailing, and postage cost of $600/M or $0.60 per piece to mail 500,000 catalogs or $300,000. It pulls 3% with a $100 average order, or 15,000 orders and $1.5 million in revenue. Advertising as a percent of sales = 20%. And finally, the more catalogs mailed, the lower the unit costs. Catalogs with smaller quantities will always fight a higher per-piece cost in the mail, while more-mature marketers have better bargaining power and more leverage to reduce the cost in the mail because of their higher volumes. contribution to overhead and profit This is simply what's left from gross margin after subtracting fulfillment and advertising costs. Since most direct marketers like to keep fixed costs to 6%-8%, this line in the financial model is an important control point. To realize an overall goal of a 10% pretax profit, you must achieve this contribution line in the model. list rental income Rental of buyer names is generally, but by no means always, limited to 12-month buyers, and it is not uncommon for a strong, in-demand list to turn its 12-month-buyer list rentals 20 or 30 times a year. List rental income is normally reported at the bottom of the financial model after contribution to overhead and profit. pretax profit The starting point as a goal and the ending point after all expenses are deducted from revenue, this line is sometimes referred to as EBIT. Every company's tax situation is different, so this is typically the line for which a catalog's general management is held accountable. The bottom-line goal of every mature consumer cataloger should be at least 10% pretax profit or 10% EBIT (earnings before interest and taxes). New ventures should strive to meet this level by the fifth year, although some catalogs take 10 years and others just three years. If your projections don't come close to a 10% pretax profit by the fifth year, you need to seriously question the venture's feasibility.
- Need for Formal tools - Developing and using formal and documented tools for analyzing and tracking catalog performance data is key to fine-tuning and improving catalog results. Collecting, organizing and maintaining product and sales data is important in the planning and creation of a catalog as well as in the post analysis and review phase Above are examples of tools for analysis
- Data required for performance measures
- Square inch (squinch) analysis can be used to determine the relative strength of your customers’ demand for each and every product. This invaluable information then is used to make decisions about the catalog, such as featuring high-demand products and eliminating those with little or no demand. More importantly, however, squinch analysis provides a guide for correcting marginal items and shows you how to make them winners. The result is often an increase in total sales per catalog –- not just products featured. By featuring the products your customers are most interested in, you make the entire catalog more attractive.
- 1. Measure how much space each product takes up on each page including both image and text. 2. Measure all editorial and white space so you account for every inch of space on page. The editorial and white space is weighted back to each product and the total is entered into one column on a spreadsheet. 3. Place the sales for each product into another column. 4. To find sales per square inch, divide each product’s sales by the space it takes on the page. For a complete picture of each product, include columns for units sold, profit per item, profit per inch, item number and product description. 5. Finally, include an index column. In this column, divide the sales per inch of each product by the average sales per inch for the entire catalog. An index of 1.00 indicates the item is performing equal to the average; whereas, results greater than or less than 1.00 indicate the item is performing better than or less than average, respectively. This provides an instant gauge of the relative strength of each product. The goal is to compare the sales per inch for each product both to the book average and to breakeven. Items that are above average should be allocated more space in the next catalog. Items that perform below average should get less space or be deleted entirely. A good rule of thumb is to expand the space and improve the presentation of the top one third of the products; eliminate the bottom third; and individually examine each of the products in the middle third – some will get more space and others less.
- Where did the 1 percent response rule come from? To the best of my knowledge, there is no theoretical foundation for the 1 percent response rule—no psychological, sociological, economic or business reasons for it. The rule exists simply because catalogers see it happen over and over and over again. Actually, it's surprising that such a widely recognized rule should have no theoretical justification. But it doesn't. It just is. How can you apply the rule to your catalog? Before you can use the 1 percent response rule in your catalog business, you will need to modify it wherever its assumptions deviate from the realities of your business. To see how that's done, we'll examine each of the assumptions on which the 1 percent response rule is based: "Average-size catalog." This means generally between 24 and 32 pages. If your page count is much less (such as the 12- or 16-page catalogs that startups sometimes launch), your response rate from response lists will be lower—not vastly lower, but lower. And conversely, if your page count is higher, your response rates will be somewhat higher—not vastly higher, but somewhat. "Merchandise of average desirability." If you're selling merchandise that is very hot at the moment (like Ty Beanie Babies), your response rates will be higher—as long as the unusual popularity lasts. And conversely, it's quite easy (especially for start-ups) to misjudge the desirability of their merchandise and to launch a catalog filled with less desirable merchandise. In that case, your response rates can be much less than 1 percent ... all the way down to near zero. Generally though, most catalogs (at least after the first year or two) are stocked with "merchandise of average desirability." "Average value price points." Nothing can depress your response faster than overpricing your merchandise, particularly if you overprice significantly. Yet many catalogers resist, preferring to believe that attractive photos, compelling design and persuasive copy will overcome high price points. But I rarely find that to be so. The key factor, of course, is "value," not absolute price point. For example, a price point of $100 might represent a tremendous value (a women's silk suit), or an extremely poor value (a box of cookies). To earn a 1 percent response, your price points must represent average good value for your merchandise. Otherwise, you'll need to adjust the rule upwards (higher response rates for extremely good value pricing) or downwards (lower response rates for unusually poor value pricing). "Average selection of response lists." All bets are off if you don't do a reasonable job of list selection. You can easily get a 0 percent response if you select lists unwisely. Additionally, the 1 percent rule is based on response lists, meaning the buyer files of other catalogers. If you're mailing compiled lists (names collected from surveys, census data, warranty cards, etc.), then your response rates in general will be lower (up to 50 percent lower). If you're mailing publication subscriber lists, your response rates may be even lower still.
- Does the 1 percent rule affect more than just response rates? Actually, the 1 percent response rule is why list rentals have a 5,000-name minimum. This large minimum often frustrates start-up catalogers, who always ask, "Why must I rent so many names, when I could tell if a list is good or bad by mailing just 1,000 or so?" And the answer is, if you mail 5,000 names and get a 1 percent response, that means you'll receive 50 orders, and statisticians have mathematically determined that 50 responses is the minimum number of responses needed to make results "statistically significant." How can I use the 1 percent response rule to improve my catalog's results? This is the heart of the matter—how to use the 1 percent response rule to improve your cataloging results. The techniques vary depending on how you're involved in cataloging. If you're launching a startup, plug in a 1 percent response rate for your prospect mailings and see what happens to the rest of your projections. Does the resulting bottom line look too awful? Then you need to rethink other parts of your program. Are your projected costs too high? Is your cost of goods excessively high? What changes in pricing, merchandising and catalog presentation can you make that will make your catalog more-than-typically appealing to prospects? This is the hard work of cataloging, and the 1 percent response rule is a good tool for forcing start-up catalogers to face the stern realities of catalog marketing. If you're seeking financial backing for your catalog, and if your actual prospecting response rates are below 1 percent, decide on a response now, before you make your official presentations. If you can't do anything about your low response rate, at least realize that you'll need to emphasize other, more positive numbers in your business plan. And conversely, if you're beating 1 percent in your prospecting results, make sure your banker or venture capitalist knows all about it. If you're a venture capitalist or a loan officer considering investing in a catalog, you should compare the catalog's prospecting results with the 1 percent rule. If they're beating it, try to understand why—is it a transient effect, or something you can count on? And if results are less than 1 percent, that's a red flag to investigate. There are many good reasons why prospecting results may legitimately be under 1 percent (for example, they're mailing a very small catalog)—just be sure you understand why you're seeing response under 1 percent before you invest money in this catalog. If you're already a successful cataloger, the 1 percent response rule can help improve your business in two different ways: A) If your sales reports consistently show sub-1 percent response rates on prospecting mailings, it's time to sit back and take a long hard look at why. As an experienced cataloger, you're aware that few catalogers can break even on the first sale to a rented name—costs are too high and response rates are too low. But an average response under 1 percent on prospect mailings is too low, and deserves closer examination. For example, a frequent cause of prospecting response rates under 1 percent is lack of clarity in catalog design, copy and merchandise. As catalog glut becomes more and more widespread, prospects are spending less and less time with each catalog they receive. If you're sending your prospects the same catalog that you're sending to your house list, chances are that your catalog isn't clear enough for new prospects. Long-time catalogers often fall into the trap of targeting prior buyers, who already understand the fundamentals of your offer and forgetting that prospects may be confused about who you even are or why they should want your products. The solution may be a special prospecting catalog, with special aids for prospects, or it may be better clarity in your main catalog (which will boost response rates from you house file too). B) If you're a successful cataloger and your prospecting response rates are well above 1 percent, take a close look at how far above 1 percent you are, and compare that with where you should be. Are you mailing a 96-page catalog filled with high-value, highly desirable merchandise to extremely well-targeted lists? Then you should be doing a lot better than 1 percent—make sure that you are. And that concludes our examination of rule of thumb #1: the 1 percent response rate. Next time, we'll examine a different cataloging rule of thumb. Until then, best of luck with your cataloging success.
- Is RFM obsolete? If you're thinking that RFM is too old-fashioned for our post-modern world of neural nets, synergy models and so on, think again. Buzzwords like CHAID, regression, data mining and the like all refer to analytical techniques, but to function, these analytical techniques must be applied to real-world data. And that's what RFM is—three vital chunks of real-world data that we maintain for each customer, and that we use to predict how they'll respond to our offers. In fact, RFM is a central part of the power of most current statistical tools used by catalogers. Take RFM data away, and the power of modern statistics for catalogers would be drastically reduced.
- How is RFM measured and stored? The RFM rule is relative—that is, it refers to "higher" response rates, "more recent" buyers, "higher" lifetime sales and so on. But computer databases can store only specific, absolute numbers, not abstractions like "higher" or "more." So to save RFM data about our customers, most modern mail-order software packages store three specific data variables for RFM in each customer's master record: 1. Date of most recent contact (to measure recency). 2. Quantity of lifetime purchases (to measure frequency). 3. Total lifetime dollar sales (to measure monetary value.) Actually some catalogers prefer to use only total sales during the last 12 months for this measure, but they are in the minority. For example, if we looked up John Doe in our master customer file, we might find (among other data) these three pieces of data (separated here by commas and enclosed by double quotes): The first is recency—John last bought from us on June 6, 1999. The second is frequency—John has bought from us five times. The third is monetary value—John has spent $230.35 with us since he first became a customer. How can I use RFM to increase my catalog response rates and sales? In a nutshell, you will boost your overall response rate and total sales if you mail more often to customers with "higher" RFM values, and less often to customers with "lower" RFM values. How much more, specifically, can you gain from this strategy? Absolute numbers will vary from cataloger to cataloger—but in general, the older your house file is, and the less rationally you've been mailing it—for example, if your list is 4 years old and you're still mailing equally to every name on it—the bigger the benefits that RFM-based mailing will bring. Every cataloger I've ever seen who switched to RFM-based mailing has concluded that the improvements were "very significant." Why is RFM such a good predictor of future customer buying behaviors? This is an interesting question—and the closer you look, the more interesting it becomes. Intuitively, it's fairly easy to rationalize why M (monetary value) is a good predictor of a customer's future buying behavior: If Mary Smith has a high lifetime value, chances are she's happy with what you offer, has a continuing need or desire for it and will probably buy again. Of course we can't know that for sure—Jane's last big purchase may have finally satisfied all her needs, and you may never hear from her again. But big spenders like Jane tend to spend again—hence the power of M for predicting future sales. And also intuitively, it's fairly easy to rationalize why F (frequency) is a good predictor of a customer's future buying behavior. If Mary Smith has ordered from you several times, chances are she is getting what she wants and will probably buy again. Of course, once again we can't know that for sure—Mary's need may have terminated with her last purchase, and she may never buy again. But generally, repeat buyers keep repeating. Which brings us to recency—and intuitively, this is the real puzzler. The rule of RFM says that if Mary Smith has bought recently, she's highly likely to buy again, right away. And for me, that's not intuitively obvious at all. If Mary buys a new outfit, why should that make her likely to buy another outfit right away? Won't she be feeling a bit poor from her recent purchase, won't she perhaps wait to "save up" for the next purchase? And even if Mary isn't feeling poor, won't she be diverted from buying again for a while as she enjoys her recent purchase? My intuition answers "yes" to both questions. But clearly, my intuition is wrong—because in fact, recency isn't just a good predictor of a high likelihood to re-buy, many catalogers consider it to be the strongest predictor of the three RFM variables. Why? Perhaps buying something puts people into a "buying mood," so they feel like buying again and again. Or maybe a first purchase creates a need for additional, supporting purchases—a new sweater to go with the new skirt, then new shoes to finish the outfit, then a new necklace to match. Whatever the reason (and no one really knows for certain why recency is such a powerful predictor of future sales), recency is a very strong predictor of immediate re-buying. Which is why when you rent lists, you will almost always do a recency select (12-month buyers, six-month buyers, hotline buyers), but you will only sometimes do a dollar value select, and least of all will you do a frequency select. What practical issues must I deal with to use RFM data to boost response? The rule of RFM says that customers with "stronger" RFM values will produce "higher" response rates and sales. Which is intriguing, as far as it goes. But to create real-world mailing plans, we usually need to know much more—specifically, we need to know exactly how to rank our customers, and exactly how much stronger our results will be from our higher RFM-value customers. And the only way to get that kind of information is to start coding your mailings. Specifically, you need to devise a mailing code system that encodes each customer's individual RFM values into their mailing code (see Figure 1 on p. 62). That mailing code should be applied to each catalog you mail, often in a yellow or pink box (which is where we as catalogers have trained our customers to look for it.) Then, as orders arrive, your operators should ask each customer for this code and enter it into each order, along with the customer's name and address. Then, at season's end, you should produce a sales report with a separate row for each code, so you can see individual response rates and sales for each mailing code (see Figure 2 on p. 62). This is easier than it sounds—you can do the whole thing on a simple spreadsheet, and when it's done, you'll be able to see exactly what combinations of RFM are strongest for your business and your customers, you'll know exactly how much stronger some segments are in real dollar terms and you'll be able to figure out who to mail more to, and who to mail less to. What's a good coding scheme for including RFM data in mailing codes? Your catalog software is already saving and maintaining RFM values in absolute terms for each of your customers—that is, it knows the exact day that each customer last bought, the exact quantity of lifetime orders and the exact dollar value of lifetime sales. But we can't use those exact numbers directly for coding, because we'd need literally thousands of different codes to distinguish between customers whose lifetime value was $200, $201, $202, etc. To cut the number of mailing codes to manageable size, we need to create RFM GROUPS. Figure 1 on page 62 shows a simple mailing code system that divides customers into a reasonable number of RFM groups. For RECENCY, rather than coding by each specific date of most recent contact, this system codes by YEAR Why not by half year or by quarter? Many catalogers use finer recency gradations, but if you're just getting started, coding by year will give you a useful number of recency groups without overwhelming you with codes. After you gain experience with how your customers are responding, you can try going to finer recency groups. For FREQUENCY, rather than coding by individual lifetime order count (which again would require too many codes), we usually create just two groups: one-time buyers and multi-buyers. Why not assign different codes to three-time buyers, four-time buyers, five-time buyers and so on? Larger catalogers do this, but the major difference in response comes between one-time buyers (who may only be testing you out, and may yet be disappointed by what they receive), and multi-buyers (who have come back at least once, proving that they really are satisfied.) So smaller catalogers often opt for the greater simplicity of just two frequency groups. For MONETARY VALUE, rather than coding by individual lifetime dollar sales (which again would require too many different codes), we create instead a small number of lifetime dollar sales groups, (e.g., $0-$50 buyers, $51-$100 buyers, etc.) What specific dollar thresholds should you use? If you've been in cataloging for a while, you probably have a good sense of where the breaks should be. For example, if you're a gift cataloger with an average catalog price point of $30, you might try segments like this: $0-$30, $31-100, $100+. The goal is to select thresholds that combine similar buyers into one group, and divide dissimilar buyers into different groups. So, by bringing all the above together, we have about five recency groups (current year, one year ago, two years ago, three years ago, four or more years ago), two frequency groups (single-buyer, multi-buyer), and three monetary groups ($0-$30, $31-$100, $101 and up), which means we will need just 5x2x3=30 different mailing codes, which means you'll need just 30 rows in your end-of-year "Sales by Mailing Code" report. What does a sales report for RFM analysis look like? Figure 2 below shows part of a typical sales report that breaks out sales and response rates by mailing code. By studying the columns for response rate, sales per catalog, and profit per catalog, you can see that this cataloger is leaving money on the table by under mailing his top performing RFM groups, and by over mailing his worst-performing RFM groups. This cataloger could significantly boost his results, even without printing any more catalogs, just by mailing less to the RFM groups identified in this report as weakest, and using the freed-up catalogs to mail more often to the RFM groups that this report shows to be more responsive. What trouble spots should I avoid when using RFM to create a mailing strategy? There's really only one: You must take special care when purging duplicate names from your house file. The reason is that modern cataloging software saves variables for RFM directly in the customer master file, along with each customer's name and address. So if John Smith appears twice in your file both as John Smith and as J. Smith, you can't just erase the record for J. Smith, because that would also erase whatever RFM data has been stored in J. Smith's record. Before removing any dupe, you must first intelligently transfer the dupe's RFM data into the surviving non-dupe record. So for example, if John Smith's date of last contact was 6/9/1999, frequency 5, monetary value $200, and J. Smith's date of last contact was 8/9/1999, frequency 2, monetary value $50, the new combined John Smith's record should be adjusted as follows: date of last contact should become 8/9/1999 (the most recent date in the two records), frequency should become 7 (the sum of the frequencies in the two records), and monetary value should become $250 (again, the sum of monetary values in the two records.) If you aren't transferring data in this way before purging duplicate names, you're making your better customers look worse (by throwing away part of their RFM data). This will cause you to mail your best customers less than you should, reducing your sales and raising your costs. When can RFM can be ignored? If you're just getting started in cataloging, you can't use RFM to guide your mailings for a while, because you won't have any RFM data to guide you (all your customers will be current-year single buyers with relatively low lifetime sales). But that doesn't mean you should ignore coding. If you start coding your mailings immediately, even when your customer file is small and new, you'll be all ready to take advantage of RFM as soon as your house file becomes large and old enough to benefit from it, which should be two to three years after you start. Today's complex statistical techniques can produce remarkable improvements in response rates and sales—at a price that is almost always rather high. But RFM is almost always effective and usually quite inexpensive too, since RFM analysis uses only data that your cataloging software is probably already saving for you, and you don't need to be a statistical guru to make it work. Cheap, easy to understand, reliable, foolproof—that's the magic of the RFM rule of thumb.