Optimizing Rhenania's Direct Marketing Business Through Dynamic Multilevel Modeling (DMLM) In a...

Interfaces, � 2003 INFORMSVol. 33, No. 1, January–February 2003, pp. 50–66

0092-2102/03/3301/0050$05.001526-551X electronic ISSN

Optimizing Rhenania’s Mail-Order BusinessThrough Dynamic Multilevel Modeling

(DMLM)

Ralf Elsner • Manfred Krafft • Arnd HuchzermeierRhenania Group, 56061 Koblenz, Germany

Westfalische Wilhelms-Universitat Munster, Institut fur Marketing, Am Stadtgraben 13-15, 48143 Munster, GermanyWissenschaftliche Hochschule fur Unternehmensfuhrung (WHU), Otto-Beisheim Graduate School of Management,

Burgplatz 2, 56179 Vallendar, [email protected] • [email protected] • [email protected]

Rhenania, a German direct mail-order company, turned its catalog mailing practices aroundwithin one year and consequently moved up in market position from number 5 to number 2.A dynamic multilevel modeling (DMLM) approach uses elasticities to determine the optimalfrequency of catalog mailings, a customer-segmentation approach allows for optimization ofmailings, and a recency, frequency, monetary-value (RFM) segmentation in combination witha chi-square automatic interaction detection (CHAID) algorithm determines when customersshould receive a reactivation package—as opposed to a catalog—to optimizemailing efficiencyfurther. The DMLM approach was so effective that Rhenania acquired two competitors (onea subdivision of Springer Verlag).(Marketing: buyer behavior. Statistics: elimination.)

D irect marketing, selling goods and servicesthrough such channels as catalogs, outbound call

centers, and the Internet, is important in North Amer-ica, Europe, and Asia. For example, the advertisingbudgets for direct marketing represent more than halfof total advertising expenditures. The advertising ex-penditures for direct marketing in the US and the 15largest economies worldwide were estimated to totalaround US $380 billion in 2001. About one quarter ofthese expenditures are spent for direct mail. After theUS and Japan, Germany has the highest advertisingexpenditures for direct marketing with more than 460million catalogs mailed to end customers per year�http://www.the-dma.org�. However, the catalog-sales industry is characterized by maturing marketsand slowing growth in market volume. Over the years,the number of customers interested in purchasingproducts or services through direct-mail channels hasbecome fairly stable. Many direct-mail segments are

served by a fairly steady group of companies that tryto succeed in these mature markets with low margins.Nevertheless, selling books, CDs, videos, DVDs, and

CD ROMs through direct-marketing channels via cata-logs is a well-established business. In Germany, thisbusiness was dominated by Weltbild (with sales ofabout US $500 million), Mail Order Kaiser, and the pub-lishing company “2001.” In the mid-1990s, these threecompanies served more than 50 percent of the market.At that time, Rhenania was one of the top 10 companiesin the business, competing with such firms as Akzente,Conlibro, Frolich und Kaufmann, Humanitas, andTaubert. The German business environment can becharacterized as mature. Growth in sales can come pri-marily from gaining market share from competitors.With regard to the so-called four Ps (price, product,

place, promotion), promotion is the most effectivemarketing instrument for differentiating a company’sofferings from those of competitors. Because of legal

http://www.the-dma.org

ELSNER, KRAFFT, AND HUCHZERMEIERRhenania

InterfacesVol. 33, No. 1, January–February 2003 51

constraints, prices for new books are fixed, and pricecompetition, even for older books, is limited acrosscompanies. This price fixing (in German Buchpreisbin-dung) has been in place since 1878 and is intended toprevent large bookstore chains from dominating the

Promotion is the single mostimportant marketing instrument.

business and forcing smaller retailers out of business.Price promotions or discounting are permitted only forbooks publishers have delisted �http://publishing.about.com/library/weekly/aa080700a.htm�.Discountedbooks are usually distributed through catalog compa-nies, and price promotions therefore differentiate mail-order businesses from stationary retailers (bookstores).The products mail-order companies offer do not differsubstantially, because they all buy their assortments froma limited number of publishing companies. Naturally,the locations of mail-order companies do not play a sig-nificant role. The trend is towards outsourcing ordermanagement to international providers of logistics ser-vices that have extensive networks of warehouses andonline procedures for filling orders. Because the compet-ing companies all use similar professional subcontractorsto handle orders and deliveries efficiently, they gain nosustainable advantage in this area either.Promotion is the single most important marketing

instrument. In the mail-order business, making pro-motion effective is equivalent to optimizing mailings.Although the design, layout, and content of catalogscan also influence success, a comparison of differentcompanies’ catalogs revealed few differences. Conse-quently, companies face three major questions in de-termining their mailing strategies (Bitran andMondschein 1996, 1997):—the number of catalogs to mail over a given period

(frequency),—the customers or customer segments that should

receive mailings, and—whether mailings should be tailored to individ-

uals or to customer groups (segments).Because margins in the mail-order business are low,

sending out too many catalogs can be counterproduc-tive (Campbell et al. 2001, p. 78), while sending out too

few can lead to opportunity losses, because customerswilling to order cannot do so without first getting cat-alogs. If profitable and loyal customers do not receivemailings frequently from their mail-order company,their propensity to get interested in competitors’ offer-ings will increase.

The Rhenania CompanyRhenania was founded in 1946 and is a medium-sized(with revenues between US $10 and 50 million) mail-order company, selling books, CDs, and related prod-ucts through catalogs. In the mid-1990s, the companymailed up to 20 catalogs per year to a large number ofcustomers and prospects. Rhenania’s customer data isstored in a house list, a proprietary customer databasecontaining the addresses of customers who have or-dered in the past. It augments this list of customerswith addresses that it either rents from commercial ad-dress brokers (rental lists) or gains directly throughcampaigns in print or similar media. In the mid-1990s,Rhenania’s database contained about 600,000 names,and every year it mailed about 2,400,000 catalogs andother materials. In 1996, the then-CEO of Rhenaniafound that the company was in trouble: it had a sus-tained trajectory of declining sales, a declining marketshare, and declining profits. He hired Ralf Elsner asthe new marketing director. The company downturnhad occurred even though Rhenania had followed thestandard marketing approach to managing customercontacts and choosing the best customers for eachmailing. In other words, it sent clients catalogs only ifexpected revenue was higher than the cost of the mer-chandise, the order fulfillment, and the mailing itself.Rhenania had increased the productivity of its sepa-rate, single mailings, but its customer base was shrink-ing (Figure 1). Elsner’s task was to increase Rhenania’scustomer base and to increase the firm’s profitability.Elsner, with a background in economics and consid-

erable experience in modeling and quantitative meth-ods, recognized that mail-order companies’ traditional,worldwide practice of optimizing the productivity ofindividual mailings is actually suboptimal even in themedium term, since it leads to shrinking bases of activecustomers (customers who ordered in the last 12

http://publishing


Interfaces52 Vol. 33, No. 1, January–February 2003

Rhenania’s Active Customers

January 1994–April 1998

Figure 1: Optimizing single mailing campaigns is the accepted practicein the mail-order industry, that is, firms send catalogs to individuals forwhom they expect marginal sales to exceed marginal costs. Conse-quently, they increase the effectiveness of the mailing campaigns (asmeasured by the profitability of the mailing per customer). However, totalannual sales, total annual profit, and the number of active customersusually decline over time.

months) and lower profitability over time. It can beproven, using straightforward mathematical modeling,that it pays in the long term to mail to clients whowould be considered unprofitable from a traditional,short-term standpoint (Elsner 2002). Depending on theresources available, even customers who have not or-dered for a while or have placed only small orders cancontribute to a mail-order company’s bottom line.Elsner proposed a nontraditional, analytical approachthat uses a rolling-horizon model to optimize catalogmailings. His generic, basic model proved that short-term losses with low-valued customers can be morethan compensated for in the long run because of econ-omies of scale per mailing and per customer, and be-cause low-valued customers can become active custom-ers in the near future. However, this approach was socontrary to common knowledge and conventionalthinking in the mail-order business that Elsner did notget the then-CEO’s approval to move forward. Rhen-ania’s downturn continued. It replaced the then-CEO in

April 1998 with Frederik Palm. Palm was very openminded and recognized the potential value of Elsner’snew basic model. He agreed to give it a try.

Best Practices in the Global Mail-Order BusinessUntil 1998, Rhenania sent 20 catalogs a year to its activecustomers following the traditional industry approachof optimizing the performance of individual mailings.Until 1997, Rhenania did not even evaluate whether 20catalogs a year was the optimal frequency—it just fol-lowed traditional practice in the industry. Its activecustomer base shrank continuously. Elsner and Palmrecognized this shrinking as a consequence of a sub-optimal mailing frequency and a suboptimal choice ofcustomers or customer segments to receive the mail-ings. Rhenania’s active base of profitable customers de-creased, as their addresses changed or their creditwor-thiness deteriorated or as they switched to competitorsor died. In following the traditional mailing strategy,Rhenania optimized single mailing campaigns, that is,it sent mailings only to people for whom it expectedmarginal sales to exceed marginal costs. Although thismethod increases profitability per mailing per cus-tomer, it also leads to contacting a shrinking numberof customers. Given its resources in 1997, Rhenaniacould handle contacting as many as 400,000 customers.However, in applying the traditional method, it con-tacted only 200,000. Over time, Rhenania’s volume andmarket share decreased dramatically. In 1995, 1996,and 1997, the market volume grew annually by aboutfive percent, while Rhenania lost around 10 percent insales volume per year. In early 1998, Rhenania’s prob-lem was to identify the optimal frequency and timingof mailings to which customer segments. With FrederikPalm as CEO, Elsner got the opportunity to apply hisnew dynamic mailing approach.

Thinking Outside the Box—Dynamic Multilevel Modeling(DMLM) at RhenaniaThe dynamic multilevel modeling (DMLM) approachis a multiperiod optimization model of catalog mail-ings that allows for dynamic promotion or demotion



Level 1

Level 2

Level 3

Direct Marketing Test Series

Regression and Elasticity Analysis

Profit Maximizing Number of Mailing

Campaigns

Cluster Analysis for Customer Segmentation

Forecasting of Customer Behavior

and Paths of Migration

Profit Maximizing Number of Mailing

Addresses

Dynamic Data Mining

CHAID Analysis

Profit Maximizing Customer Segmentation

Approach Method Decision

Level 1

Level 2

Level 3

Direct marketing test series

Regression and elasticity analysis

Profit maximizing number of mailing

campaigns

Cluster analysis for customer segmentation

Forecasting customer behavior

and paths of migration

Profit maximizing number of mailing

addresses

Dynamic data mining

CHAID analysis

Profit maximizing customer segmentation

Approach Method Decision

Figure 2: Rhenania has applied the dynamic multilevel modeling (DMLM) approach since May 1998. Using arolling horizon, Rhenania considers a period of one year to optimize the number of mailings per year (Level 1),to determine the customer segments and number of customers who receive catalogs (Level 2), and to identifythe economic value of small customer segments, especially of low-value customers who should still be includedin mailings (Level 3). It updates parameters for optimization frequently, and every catalog Rhenania sends outis dictated by the model.

of customers across a large number of customer seg-ments. In DMLM, the customers within segments arecharacterized by similar responsiveness to mailingsand homogeneous value. The DMLM approach isbased on the idea that low-value or low-frequency cus-tomers that yield short-term losses are profitable in thelong term because a fraction of these low-valued cus-tomers can be expected to become good customers inthe near future and because the company can obtaineconomies of scale per mailing. The economies of scalestem from lower costs per unit for printing, handling,and shipping larger numbers of catalogs.The model operates at three levels (Figure 2). First,

in a test phase, we vary the frequency of the mailingsto estimate the elasticities for order size, purchase fre-quency, and customer response (when receiving themailing on a given day of the week). Any firm couldadopt this Level 1 analysis of the direct-marketing ap-proach. Second, we segment the pool of customers fur-ther, for example, based on customers’ actual purchas-ing behavior. The company must decide whether to

include or to exclude particular customer segmentsfrom the catalog mailings (Level 2 analysis). We seg-ment customers based on the recency of their last pur-chase only when we observe a decline in marginalsales. What is unique about our approach is that wederive a threshold level u* (a minimum required saleslevel) that we can use to evaluate the attractiveness ofindividual customers (in the Level 3 analysis). Third,we can fine-tune the firm’s mailing strategy further byanalyzing whether customers who belong to a partic-ular customer segment should be demoted or treatedin a special way. We can base this decision either onthe RFM criteria (recency, frequency, monetary value)alone or on the CHAID-analysis (chi-square automaticinteraction detection).For example, a customer who places an order moves

up immediately into the highest customer segment.However, the customer may not order again for awhile, and thus, at the end of a prespecified period, thefirm sends a reactivation package to stimulate addi-tional purchases. It (temporarily) excludes the cus-



tomer from the catalog mailing list. It can apply thesame type of review and reactivation procedure toother segments as well (Level 3 analysis). While thefirm does not have to alter the overall number andchoice of customer segments, it does change the sizeof each segment in each period to curtail mailing toone-time buyers or to unprofitable accounts. The firmbases its classification of customers as active and in-active and its decisions to send reactivation packagesinstead of catalogs on demographic data combinedwith customer-specific RFM values. We analyze Rhen-ania’s customer base periodically and label individualcustomers that do not contribute to profitable sales asinactive. To remain on the catalog mailing list, custom-ers must exceed the critical threshold level of sales u*.Consequently, the termination rate of customer rela-tionships based on the Level 3 analysis can be explicitlyaccounted for in a slightly altered model formulationfor the Level 2 mailing optimization (Appendix).The major challenge we faced in developing this

direct-marketing approach was coordinating the ex-perimental testing and analysis with the data-estimation procedures. For example, it took almost two

In 1996, the CEO found the companywas in trouble.

years to estimate the parameters for Rhenania’s cus-tomer base. Our model is in the new tradition of eval-uating customers over their lifetimes rather than eval-uating individual mailings. Overall, the basic DMLMmodel is only moderately sophisticated technically, al-though the approach is nontraditional and pioneering.

DMLM Level 1 AnalysisIn the DMLM Level 1 analysis of the firm’s customerdatabase, we decide how many mailings to send dur-ing the following 12 months (or during any given pe-riod of time) and on what day of the week to mail thecatalog to customers. (In Germany, the postal serviceis very reliable, and thus delivery lead times arepredictable.)Regression analysis is the major method we used to

determine the elasticity of how mailings affect themonetary value of customer orders, the purchase fre-

quency, and the response rate across all segments. Wedid two years of experimental testing to develop stableelasticity coefficients. Sometimes Rhenania learnedpainful lessons from errors made by the logistics ser-vice providers. For example, mailing identical catalogsto the same customers over short intervals does notincrease revenue. To the contrary, it has a negative ef-fect on customer satisfaction.Given a customer base of 1.1 million addresses, 30�

employees primarily in sales and marketing, and thelimited financial resources of a medium-sized com-pany, Rhenania saw 25 mailings as the volume that itcould handle efficiently to obtain substantial gains inprofit. Today, it sends out a total of 75 catalog mailingsa year across all of its three subdivisions (consisting ofRhenania and the two acquired companies).

DMLM Level 2 AnalysisBased on the optimal number of mailings per year asdetermined in the first level of the DMLM model, inLevel 2, we determine how many customer segmentsRhenania should contact in each mailing to optimizeexpected profits over the entire planning period.In Rhenania’s case, we divide the house list into

three segments of customers based on the time sincetheir last purchase; that is, we focus on recency only(not the frequency or monetary value of orders). Re-cency is a reasonably good predictor for the customer-response rate. Customers in the first segment pur-chased within the past 12 months. Customers in thesecond segment purchased within the past 24 months,and customers in the third segment purchased evenless recently.We suppose a Markov-like process whereby, given

the stimulus of a catalog, customers will migrate fromall lower segments to segment number 1 when theymake a purchase (Biggs et al. 1991; Bitran andMondschein 1996, 1997; Pfeifer and Carraway 2000).Over time, customers who do not purchase for thespecified period of time will migrate from segmentnumber 1 downwards. In addition, the model takesinto account the possibility that names will disappearfor a variety of reasons (death being one). Further-more, the firm periodically replenishes the customerdatabase through list rentals and ads (Figure 3).The data analysis revealed that the rate of promo-



CustomerSegment 1

(R < 12 months )

CustomerSegment 2

(12 months ≤ R < 24 months )

CustomerSegment 3

(24 months ≤ R)

Moveup tocustomer segment 1

No purchasein period t

Purchasein period t

New customers

Closedaccounts

Figure 3: Customers are segmented using the recency (R criteria) of the last purchase. The rate of promotionsand demotions for the chosen customer segments is almost constant throughout the year. We can make thesame assumption for new customer accounts and for closing accounts. Consequently, a fixed number of custom-ers per mailing either buy and thus move up into customer segment number 1 or do not buy and thus eitherremain in their current segment or drop down into a lower customer segment.

tions and demotions for the chosen customer segmentsis almost constant throughout the year. Consequently,we can assume that a fixed number of customers permailing either buy and thus move up into customersegment number 1 or do not buy and thus either re-main in their current segment or drop into a lowercustomer segment. We can make the same assumptionfor new customer accounts and for closing accounts.Thus, to simplify, we have assumed that a constantnumber of customers are being added for eachmailingand a constant number are being dropped per mailing.The optimization approach works as follows. First,

for the chosen planning horizon, we determine the to-tal number of addresses in each segment. Second, wedetermine the expected profit assuming that mailingsgo either to the top one, the top two, or all three cus-tomer segments. The model accounts for scale econo-

mies in mailing. Third, we determine the profit-maximizing number of customer segments to includein the mailings.In the application case study, we concluded that

mailing to all three customer segments of Rhenania isoptimal. Moreover, we can analytically derive thethreshold level for the minimum required sales percustomer per segment per mailing u*. This thresholdvalue u* depends on the overall number of mailings,the rate of customers becoming active, and the mar-ginal costs of mailing for the lowest customer segment.It captures the trade-off of costs for continued catalogmailings and the expected long-run profitability fromrepeat buying. Moreover, we show that, in the opti-mum solution, the marginal revenue of the lowest seg-ment to be included in the next mailing is less than themarginal cost of the catalogs (Elsner 2002).



In an early feasibility study, Rhenania used 1997data to verify that adoption of the DMLM approachwould in fact improve profitability. The impact onprofit of mailing to customer segments 1 and 2 resultedin an increase of two percent, the impact on profit ofmailing to all three customer segments was estimatedas close to six percent.

DMLM Level 3 AnalysisSo far, we have discussed classifying customers basedonly on the recency of their purchase. Another methodtypically used in segmenting customers is RFM, whichfocuses on three factors: recency, frequency, and mon-etary value of orders (Hughes 1994, p. 87). In addition,the CHAID algorithm can be applied to partition thethree customer segments into smaller segments (Kass1980). Besides the standard R, F, and M factors, theCHAID algorithm uses demographic data, such as age,sex, academic degree earned, profession, and mailingdestination. Thereby, in each segment, one can sepa-rate from the active customers the inactive customerswho should no longer get the regular mailing. The de-cision is based solely on the predetermined thresholdlevel of sales u*. Rhenania sends the customers iden-tified as inactive reactivation packages in an attemptto motivate renewed activity. This reactivation pack-age is more costly than a standard mailing and con-tains special offers, such as price promotions or freegifts. The process of reactivating a customer at Rhen-ania is personalized (for example, each customer ispersonally addressed by the CEO of Rhenania or thehead of marketing at the various phases of the reacti-vation process) but highly standardized.Overall, Rhenania’s scheme for classifying custom-

ers into segments remained the same as in Level 2, thatis, it grouped customers by recency of last purchase.However, we introduced a new distinction. For ex-ample, in customer segment 1, we reviewed all cus-tomers who made their last purchases more than ninemonths ago and separated them into two categoriesbased on past purchase frequency F, that is, customerswho had bought once (F� 1) or several times (F � 1).In the first case, the monetary value of their orders hadto exceed M1, for example US $75, for them to remainin customer segment number 1 and continue to receive

the standard package (up to 18 months); otherwise weview them as inactive customers and immediatelysend them the reactivation package (Elsner 2002). Sim-ilarly, customers in segment number 2 and segmentnumber 3 are being reviewed as well, however, withdifferent threshold levels for the monetary value oftheir orders M2, M3 respectively.

This analytic method does not use scoring systemsor divide up the house list arbitrarily into equal partswith regard to the customer’s last purchase, number ofpurchases, or amount of purchases (which is commonpractice in the mail-order industry today).The CHAID analysis produces a strict segmentation

of the firm’s available customers so that it can maxi-mize profits. This third-generation RFM analysis has

We had very little time to conductexperiments.

none of the evident disadvantages of its predecessors.It does not show address segments that are probablybetter than others, but it identifies the profit-maximizingsegmentation. Moreover, it can be combined with vari-ables other than recency, frequency, or monetaryvalue.

Calibrating the Model: Mail Tests atRhenaniaSince 1997, Rhenania has constantly tested new mail-ing approaches. In that we developed the DMLM ap-proach for application to all mailings, Rhenania defi-nitely needed to test the model before the first full runin the summer of 1998. We ran multiple tests to deter-mine the optimal frequency of mailings per year. Forthe mail test, we chose a minimum of 10,000 customersand randomly assigned them to the test group and thecontrol group. Rhenania sent the control group mail-ings according to its standard mailing procedure. Itsent those in the test group catalogs at a lower orhigher frequency, ranging from one mailing a year upto weekly mailings. Prior to 1997, Rhenania usuallymailed up to 18 catalogs per year. On the one hand,we checked whether this number was far too high bymailing eight, six, four, two, and one catalog to the test



n eA er

1 � n � 3 13.4% 17.7%4 � n � 12 �11.0% �7.2%

13 � n � 19 �9.4% �17.6%20 � n � 26 �8.9% �24.8%27 � n � 48 �31.0% �58.0%Range of R 2 83% � 95% 74% � 95%

Table 1: Each entry in this table represents the elasticity of the averageorder size (eA) and response rate (er) if Rhenania mails one additionalcatalog. For example, if it mails two catalogs instead of one (�100 per-cent), the average order size per catalog increases by 13.4 percent. Wecomputed the elasticities based on OLS regressions with average ordersize and response rate as dependent variables and frequency of mailings(n) as the only predictor. The high R 2 values indicate that the standarderrors of the regression coefficients are extremely low.

groups. On the other hand, we checked the outcomeof frequencies similar to those Rhenania traditionallyused (20, 15, and 10 mailings) and of more frequentmailings, with a maximum of weekly mailings(actually 48 mailings a year because mailings at Easter,Christmas, and similar holidays do not generate sub-stantial revenue and have to be considered blackoutdates).In the spring of 1998, we obtained strong evidence

that 20 to 25 mailings was the optimum range of fre-quencies of mailings for the most attractive customers.We ran a series of large-scale tests to compare the per-formance of these frequencies. We identified the opti-mum frequency by comparing the elasticities of addi-tional mailings with regard to average order size (eA)and response rate (er). As long as these elasticities arelarger than 0, additional mailings generate above-average changes of order sizes and response rates. Ifthe elasticities are smaller than –1, additional mailingscannibalize response rates and order sizes. Elasticitiesbetween 0 and –1 are equivalent to below-averagechanges in order sizes and response rates, meaningthat additional mailings still produce higher total or-der sizes and responses, while the mean order size andresponse rate decreases. However, such mailingswould still lead to higher earnings. The tests clearlyshow that higher frequencies of mailings still generateadditional revenue (Table 1).We conducted different series of mail tests to check

which day mailings were delivered to customers andwhich minimum interval between mailings to individ-ual customers generates the best results (Figure 4). Wetested days of delivery to customers (Monday throughSaturday) and time interval between mailings (180, 90,45, 30, 14, seven, or one day(s)) in depth against eachother. Taking into consideration the result that bi-monthly mailings of catalogs generate the highestprofit gains, we concluded that 26 mailings a year isthe optimum frequency for Rhenania’s clients. How-ever, because of the holiday blackout dates it wouldnot actually send out mailings every second week.With these blackout dates dropped, a realistic fre-quency of 25 mailings to the most attractive customersegment is optimal. Given the size of Rhenania’s cus-tomer base, its staff and financial resources, 25 mail-

ings is also a frequency it can handle while still increas-ing profits.We analyzed subsamples of the test groups in a simi-

lar way. All in all, we identified 28 different subseg-ments based on 24 groups of active customers(grouped by recency of order, that is, date of last order,ranging from one month ago to 24 months ago), andfour groups of inactive customers whose most recentorder was more than 24 months ago (basing our fourcategories on average order size while the customerswere still active, covering order sizes of less than US$50, US $50–250, US $250–500, andmore thanUS $500).To assign the members of the test groups to these clus-ters, we ran regressions on the responsiveness of eachsubsegment to different frequencies of mailings. Weused this information to identify the economic valueof smaller segments.

Implementing DMLM at RhenaniaSince Rhenania’s economic situation in early 1997 wasserious, we had very little time to conduct experi-ments. Althoughwe gained some insight from themailtests in 1997 about the optimal range of mailing fre-quencies, we never tested Level 2 of the DMLM ap-proach prior to running our first test series in May1998. After verifying the first positive results, we begana full run of the basic model in August 1998. However,at the same time, a senior member of Rhenania’s hold-ing organization who was responsible for supervising



Figure 4: A series of mail tests provided clear evidence that 26 bimonthly mailings delivered to customers onSaturdays is the optimal mailing strategy.

the company intervened because mailing costs wereincreasing and revenues were more or less stable. Healso objected to investing in midterm customer rela-tionships because doing so was contrary to traditionalindustry wisdom and risky. Furthermore, the newmailing strategy based on DMLM’s recommendationsalso led to short-term deteriorations of accountingmet-rics, such as the measure of the productivity of mailingbudgets (the ratio of total order volume divided by thecost of mailings). Frederik Palm and Ralf Elsner ex-plained why they expected (even intended) accountingmetrics to deteriorate. They convinced this seniorman-ager to permit a few more mailings, persuading himthat he would see that the new approach would pro-duce higher earnings and lower costs per unit of mail-ings. After two more months, Rhenania’s holding or-ganization was more than happy with the newapproach and never again complained about short-term deteriorations in accounting results. To the con-trary, when we applied DMLM to Akzente, the com-pany Rhenania acquired in late 2000, the holding

organization never intervened as the short-term met-rics initially deteriorated. In other words, we overcamethis major resistance for good in late summer 1998.Since then, every catalog Rhenania has sent out has

been dictated by themodel. Rhenania relies completelyon the model in making all of its mailing decisions,which are at the heart of the mail-order business. Weupdate DMLM continuously; however, the parametershave shown little variation so far. The modeling sys-tem runs on a COMPAQ/SIEMENS client-server sys-tem, operates on a database system called PROGRESS,and uses 4GL (a fourth generation language similar toSQL). For the multivariate analyses, we use SPSS andSAS. The cost of DMLM is less than two percent ofRhenania’s total IT budget, with out-of-pocket expen-ditures of about US $7,000 for additional software. Af-ter acquiring Akzente in December 2000 and Mail Or-der Kaiser in June 2001, Rhenania has based its mailingdecisions for both companies on DMLM, keeping ev-erything else (management systems, employees, prod-uct offering, and so forth) constant. The results of



Figure 5: While the overall market volume grew by an average of aboutfive percent between 1995 and 2001, Rhenania was losing market sharebefore it introduced DMLM in 1998. In every year since then, Rhenaniahas outperformed the market by a considerable margin.

applying this nontraditional mathematical model havebeen startling.

DMLM’s Impact on CompanyPerformanceRhenania was in serious economic trouble in late 1997.Customers, sales, and profits were spiraling down-ward. When Frederik Palm was appointed CEO, histrust in Elsner’s nontraditional approach helped Rhen-ania to turn around, and the DMLM model literallysaved the company. Since we implemented the model,the numbers have proved that the company’s businesshas improved across the board and that the model weapplied was the turning point. The company has gonefrom fifth in the industry (closer to US $10 million) tosecond (closer to US $50 million) since the first modelin the summer of 1998. Of course, its growth most re-cently came from its acquisition of Akzente and MailOrder Kaiser, two of Rhenania’s direct competitors.However, even considering only Rhenania, we canclearly see that before applying DMLM, Rhenania un-derperformed the market, and after applying themodel in 1998, it has overperformed the market (Fig-ure 5).Rhenania’s profitability has also increased. While

Rhenania showedprofit losses in the late 1990s, its prof-itability quadrupled in 1999 and grew even further in2000 and 2001. The project paid for itself within weeks.Another important success metric in the catalog-salesbusiness is the number of active customers. Rhenania’scustomer base was decreasing until it introducedDMLM, and then it grew by more than 55 percent be-tween April 1998 and the end of 2000 (Table 2). WhileRhenania had lost 11.5 percent of its customer basebetween January 1997 and April 1998, it took only fourmonths of following DMLM’s recommendations to re-gain these customers (Figure 6). Interestingly, the newmathematical approach to optimizing the mailing fre-quency and the choice of customers to receivemailingsseems to become less effective over time. Since DMLMhelps the firm to take full advantage of the potentialof its current customers, how much it can gain by ap-plying DMLM has an upper limit. We modifiedDMLM in 1999 and further extended it in 2000 whenManfred Krafft and Arnd Huchzermeier became an in-

tegral part of the DMLM team. The extension ofDMLM’s third level helped to increase the customerbase in 2000. Currently, the team is working on twoadditional modifications of DMLM.The increase in profitability in 1997 resulted from

Rhenania’s decision to reduce mailings to a minimumand send out catalogs only to its most profitable cus-tomers. In followingDMLM recommendations in 1998,the firm sent catalogs to many “unprofitable” custom-ers. In that year, the profitability was reduced becauseRhenania also changed its location and had to moveits stock.Applying DMLM helped Rhenania to gain economic

strength and a substantial competitive advantage. Itssuperiority also led Akzente, one of its competitors, toseek acquisition by Rhenania in 2000. Rhenania ac-quired Akzente in December 2000 along with its cus-tomer database, which contained individual clients’purchase histories (timing, order size, returns, somedemographic data, and so forth). Rhenania immedi-ately used this data in the DMLM model. For this ac-quisition, Rhenania changed only the mailing proce-dure. It wanted to maintain the appearance of the oldcompany, retaining the same merchandise, catalog,and name. It even kept the same people in importantmanagement positions (for example, procurement).Rhenania first applied the DMLM model to Akzente’scustomer list in February 2001. A startling and imme-diate turnaround is evident in the numbers. While Ak-zente had lost close to one third of its active customersduring the previous two years, Rhenania stopped this



YearAnnual Rate ofMarket Growth

Rhenania’s Rate ofGrowth in Sales

Rhenania’s Rate ofGrowth in Profit

Rhenania’s Rate ofGrowth in Active Customers

1995 �2.8% �7.0% �29.2% �3.7%1996 �9.8% �13.2% �47.9% �15.9%1997 �4.7% �6.0% �51.2% �9.4%1998 �2.6% �7.7% �69.9% �24.0%1999 �6.0% �13.2% �417.7% �3.7%2000 �5.0% �11.7% �10.4% �14.4%2001 �1.0% �5.0% �6.0% � 0.0%

Table 2: Rhenania’s performance lagged that of the market until 1997. Since introducing DMLM’s basic modelin 1998, the company has shown dramatic increases in growth, profitability, and the number of active customers.

Figure 6: After losing customers steadily between January 1997 and April 1998, Rhenania increased its activecustomer base substantially by using the mathematical model DMLM.

trend immediately and reversed the downturn withinless than a year (Figure 7). The results also show thata substantial number of new active clients came fromthe list of customers who had not ordered during theprevious year. In other words, contacting customersthat traditional mailing strategies would have ignoredclearly pays off. Following DMLM’s recommendations

to contact customers from low-value segmentswas keyto saving and revitalizing Akzente. In acquiring Ak-zente, Rhenania moved from fifth to third in the Ger-man mail-order market for books, CDs, and relatedproducts.Rhenania obtained similar results forAkzente’sprof-

itability. Before acquiring the company, Rhenania



Figure 7: After the acquisition of Akzente, one of Rhenania’s major competitors, the application of DMLMhelped to turn around Akzente’s loss of customers, leading to a recovery within less than a year. (Numbersmarked with * are estimates.)


Akzente’s Rate ofGrowth in Sales Akzente’s Annual Profit

Akzente’s Rate ofGrowth in Active Customers

1998 �2.6% �11.7% �301,000 not available1999 �6.0% �2.8% �147,000 �2.4%2000 �5.0% �23.2% �640,000 �24.2%2001 �1.0% �12.5% �130,000 �29.1%

Table 3: Rhenania’s direct competitor Akzente lost money three years in a row. After Rhenania acquired Akzentein December 2000 and applied DMLM in February 2001, it won back Akzente’s lost customers and returned thecompany to profitable growth.

checked its performance. Akzente earned no moneyin 1998, 1999, and 2000, even though its revenueincreased substantially in 1998 and 1999. It had triedto gain market share by increasing unprofitable sales,which did not pay off in the long run. After Rhenaniaacquired Akzente and applied DMLM early in 2001, itreversed its loss of market share and made the com-pany profitable for the first time in three years (Table3). According to Frederik Palm, this development is

clearly a consequence of applying DMLM to Akzente,which had previously followed conventional mail-orderstrategies. Moreover, the changes in Akzente’s customerbase were nearly identical to the changes in Rhenania’safter following DMLM’s recommendations.In June 2001, Rhenania acquired Mail Order Kaiser.

Although this acquisition contributed to taking Rhen-ania from third to second in the market, turning MailOrder Kaiser around is taking much longer than




Mail Order Kaiser’s Rateof Growth in Sales

Mail Order Kaiser’sAnnual Profit

Mail Order Kaiser’s Rate ofGrowth in Active Customers

1999 �6.0% �8.6% �1,314,000 not available2000 �5.0% �8.6% �2,148,000 not available2001 �1.0% �25.1% �160,000 �21.4%

Table 4: The acquisition of Mail Order Kaiser’s name and stock helped Rhenania to become second in themarket. However, it did not acquire the firm’s customer data, which prevented full application of DMLM. Datafrom current mail tests and improved mailings will help Rhenania to fully utilize DMLM in 2002.

turning Rhenania and Akzente around. In this acqui-sition, Rhenania acquired only Mail Order Kaiser’sname, stock, and customer names and addresses.However, customer purchase histories were not avail-able. While Rhenania reversed Mail Order Kaiser’ssubstantial losses within half a year and made a smallprofit, the active customer base and the overall salesvolume are still decreasing (Table 4). As it obtainsmore information about customer behavior throughmail tests and purchasing records, Rhenania expects tofully utilize DMLM in 2002.DMLM helped Rhenania to turn around in ex-

tremely difficult times, to recover from its poor eco-nomic performance in the mid-1990s, and to gain acompetitive advantage. It went from fifth in themarketin late 1997 to second in 2001, partly through its ac-quisition of larger competitors (Figure 8). Industry ex-perts consider its record of achievement over the lastfour years astonishing (Borsenblatt 2001; �www.buchmarkt.de�).On the one hand, Rhenania’s success can be attrib-

uted to the courage and leadership of Rhenania’s CEO,Frederik Palm, and his willingness to try innovativeand perhaps risky nontraditional approaches. On theother hand, the evidence shows Rhenania succeededbecause of the strength of a superior mathematicalmodel. Its competitors had similar merchandise andservices. What differentiated Rhenania in a maturemarket was the DMLM model.

Extensions and ModificationsThe DMLM approach is a modular concept that can bemodified or extended. The application of DMLM’sLevel 1 and 2 analysis led to an increase in Rhenania’s

active customer base. However, the effects of the op-timization of mailing frequencies and the rough seg-mentation of Rhenania’s customer base diminishedover time. We extended DMLM to Level 3, followingManfred Krafft’s suggestion to fine-tune the segmen-tation of the customer base. This disaggregated seg-mentation greatly increased the number of active cli-ents, but its effect also leveled out after some time.Huchzermeier and Krafft proposed two major modi-fications and extensions of DMLM intended to lever-age the current customer base.Krafft suggested that Rhenania should further ex-

ploit the customer base by managing customer recov-ery more systematically. Based on his recent research,he argued that inactive or lost customers are an attrac-tive target group for direct-marketing activities. How-ever, systematic procedures for recovering customerrelationships are the exception rather than the rule inthe mail-order business. Krafft initiated a researchproject to determine the expected value of formalrecovery-management activities. Uwe Rutsatz, a full-time doctoral student at WHU’s marketing depart-ment, was responsible for this project. Using the neg-ative binomial distribution (NBD)/Pareto model, heran extensive analyses of Rhenania’s inactive custom-ers to determine individuals’ activity levels based onthe timing and frequency of their orders. This led toan even more differentiated segmentation of inactivecustomers. In a series of tests of different mailing andregaining strategies, Rutsatz identified a highly re-sponsive and profitable group among the inactive cus-tomers. While Rhenania’s and the mail-order indus-try’s traditional response rates for recovery activitiesis about 1.5 percent, Rutsatz could increase the re-sponse rate to more than four percent (Rutsatz 2002).



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Figure 8: Rhenania (grey) was fifth in the market in the late 1990s. With its application of DMLM, its acquisitionof Akzente (black) in January 2001, and its acquisition of Mail Order Kaiser (white) in June 2001, the Rhenaniagroup became second in its market.

He found that reactivation efforts after a fairly longperiod of inactivity are more promising and effectivethan mailings sent immediately after a short period ofinactivity. Furthermore, personalized reactivationpackages for test groups led to response rates threetimes higher than those effects for nonindividualizedmailings to the control group. The integration of suchtiming effects of recovery activities in DMLM will befully implemented in 2002. The analytical part of thisproject was completed in March 2002. Uwe Rutsatzjoined Rhenania in the spring of 2002, and his majortask will be to apply this modification to Rhenania, toAkzente, and in 2003, to Mail Order Kaiser’s list ofcustomers.Huchzermeier is working on different extensions of

the model. First, Rhenania has not coordinated its or-dering decisions across the three companies’ catalogs,because each division has its own purchasing depart-ment. The main argument for retaining separate opera-tions is that each mailing company has its ownmailing

list and thus targets nonoverlapping customer seg-ments. However, there are pooling effects in procure-ment. Second, the three companies plan their catalogsindependently and launch them simultaneously, withno category optimization. The companies do not fullyutilize sales information, for example, by testing mar-ket response to a focused offering in one catalog first.Because most customers respond within two or threeworking days of receiving a mailing, the companycould use information about their responses almostimmediately in designing the other catalogs and in(pooled) restocking decisions. Third, we could furtherrefine the model setup, for example, by using a mixed-integer programming formulation with piecewise-linear cost functions and inventory-balancing equa-tions for the customer segments. This would cutfurther the number of mailings sent to customers whopurchase the least and make up the largest group.Rhenania could thus reduce its overall mailing costsand obtain maximum customer purchases. Fourth, for



each of the low-level customer segments, an optimalmailing frequency exists that may be lower than theoptimal mailing frequency for the highest customersegment. We could find an optimal mailing frequencyfor all customer segments that reduces redundantmailings by using the elasticities for each segmentinstead of those for the entire customer pool. Fi-nally, we could easily fine-tune the model by ex-plicitly incorporating the purchase histories of allcustomers and customer segments. Small seasonalvariations in the likelihood of purchases exist, how-ever, and the assumption that the demand rate isuniform could be challenged. However, Rhenaniatargets customers who buy books at a fairly stablerate mainly for their own personal consumption.Thus, we do not see the lumpy ordering behavioraround particular times of the year that we see insome mail-order businesses.

ConclusionAlthough Rhenania followed the industry’s “best prac-tice” to optimize single mailings, its customer base, to-tal sales volume, and profitability decreased substan-tially in the mid-1990s. Ralf Elsner, as a new hire,identified the economic causes of this downturn and

Business has improved across theboard, and the model we applied wasthe turning point.

recommended a nontraditional approach to reverse thisdevelopment. However, he did not get the then-CEO’sconsent and had to wait until Frederik Palm was hiredas Rhenania’s new CEO. A basic version of DMLM wastested in a series of mail experiments in the spring andsummer of 1998. After overcoming some resistance in-side the company and from the holding organization,this approach was fully implemented in summer 1998and has been used since then to optimize Rhenania’smailing strategy. A substantial extension of DMLMwas

implemented in early 2000 when Manfred Krafft joinedthe project team and suggested a fine-tuned segmenta-tion as an additional component of DMLM. All in all, thecombination of three optimization principles contributedto the successful turnaround of the Rhenania group: (1)a long-term planning horizon that allows for the exploi-tation of customer buying behavior, (2) an optimizationapproach ofmailings across customer segments to deriveanalytically a minimum required threshold level of salesas the basis for making decisions on whether to continuecustomer relationships or not, and (3) a further segmen-tation of the customer base to differentiate inactive ac-counts from potentially active accounts, that is, to sepa-rate one-time buyers from repeat buyers.Further modifications and extensions of DMLM are

currently being developed or have already been tested.The dynamic model not only turned around Rhen-ania’s serious economic situation, but helped it to gaina competitive advantage and to prosper in a difficultenvironment. The company was even able to acquiretwo of its major competitors and to convert the lossesat Akzente into substantial profits, changing nothingother than implementing DMLM. Our current projectsare to collect further customer-response data and torunmail tests forMail Order Kaiser’s customers so thatwe can apply DMLM soon to this recent addition tothe Rhenania group. Although our analytical approachhas proven very effective, its effects are diminishingafter two years of application. The DMLM team con-tinues to work on integrating promising extensionsand modifications to further increase this model’s highperformance.

AcknowledgmentsWe are grateful to Leon Schwartz and Peter Norden, who coachedthe team, for their comments and suggestions. We also acknowledgeSonke Albers, and Surya Mantrala as well as Murali K. Mantrala fortheir valuable comments on preliminary versions of this manuscript.

Executive summaries of Edelman award papers are presented here. The complete article was published in the INFORMS journal Interfaces [2003, 33:1, 50-66]. Full text is available by subscription at http://www.extenza-eps.com/extenza/contentviewing/viewJournal.do?journalId=5

http://www.extenza-eps.com/extenza/contentviewing/viewJournal.do?journalId=5

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