Presentation Transcript

1. Markdownmanagement

   Lecture 5
2. „Waiting for the sale“ is a time-honored strategy for savvy shoppers The fashion-conscious may splurge on spring fashions shortly after they arrive But budget-minded know they can save by waiting Many items will be marked down by 70% or more by the end of the season This strategy, however, is not without risk – as the item might be sold out eventually Nonetheless, an increasing number of people are willing to take the risk in order to realize the savings In many categories retail list price is becoming a ceiling, with most sales taking place at a discount More and more customers won’t buy an item if it isn’t on sale
3. Markdowns and promotions
4. The point of mark-down management is to find the timing and magnitude of price reductions that move the inventory while maximizing revenue For many years, retailers relied on their judgment or simple rules of thumb to determine markdowns
5. Nobody but a fungoid creature from another galaxy with no familiarity with earthly ways would ever pay list price for anything.   —Dave Barry Valentine’sDay Black Friday* CyberMonday Christmas Kohl’s, Macy’s, Saks
6. Stateofthe art Softwareofferedbycompaniessuchas Manugistics ProfitLogic – acquiredbyOracle Situation is reminiscent of the early days of revenue management in the airline, hoteland rental car industries– the leaders are applying Earlyadoptershavereported 10%-15% revenueincreases – see gametheorywithrespecttothat
7. Forcertaingoods and servicesmarkdownsegmentsthe market and provides a simplemethodfor profiting frompricediscrimination
8. IntheearlydayssaleswereassociatedwithpricewarsduringholidayssuchasChristmas and ThanksgivingbetweenrivalssuchasMacy’s and Gimbelsin Manhattan In 1950, managers at TheEmporium, leadingSan Francisco departmentstoreweregiven a memo stating High markdowns benefit no one. Not the store. Not the manufacturer. Not the customer. The store loses in value of assets or inventory. The manufacturer loses in future sales and by loss of prestige of his product. The customer is getting merchandise that is not up to standard, at a low price it is true, but, remember—she would rather have fresh, new merchandise that she can be proud of and with real value at regular price than pay less for questionable merchandise. Thesewereconsideredasmistakesinpurchasing, pricing and marketing
9. Averagediscountfrom list price – department and specialtystore. Storeswithsalesover $1 millionbefore 1982 and $5 millionthereafter On the other hand, as early as 1915, a retailing textbook also tied markdowns to many of its pros
10. Sometime in the 1970s, purchasing goods at a discount went from being an exception to being the rule (National Retail Federation, 1998) states that 72% of all fashion items sold in 1998 were sold at a discount
11. Reasons Increasedcustomermobility Theriseofdiscountchains and outlets, manywith „everydaylowpricing“ A self-reinforcing „viciouscycle“ Anincreasinginterestinnonstandarditems – thathaveshortershelflives. Patterned and coloredsheetshavetheir market sharegoupfrom 12% in 1970 to 60% in 1990 againstwhitesheets Theuseofmarkdownmoney, movingsomeofthe risk ofunsoldinventoryfromretailerstomanufacturers
12. Discounting is still relatively rare in prescription drugs, movie tickets and various staple food and home items Thus for those goods, even small price decreases can have a big impact on sales Consensus is that retail discounting is likely to continue and increase – in this notoriously thin-margin business this can make a difference The „everyday low price“ retailers, such as Wal-Mart, discount rarely
13. Clothing items will at first be discounted, and in the end sent to an outlet store or to a jobber – for 20% or less of its original price For the TV sets, which were depicted on a graph as promotions, they also have to be marked down occasionally – when a new model comes out
14. Themaincharacteristicsforseller/buyer Inventoryisfixed (seller) Theinventory must besoldby a certainoutdate – oritsvalue drops precipitously (seller) Time of use – winterovercoatin September and inApril Fashionability – clothes, consumerelectronics, video games Deterioration – dayoldbread Obsolescence – thenewversioncomes soon – automobiles, consumerelectronics, computerequipment
15. Customersegmentation and pricediscrimination Time of use – thosewho need itnow/who are willingtowait Fashionability – fashionistas/oneswho are willingtowait Deterioration – who are okwiththeinferiorproduct at a lowerprice Aswehave seen, segmentation and discrimination are powerfulwaysofincreasingrevenue
16. Lazear, 1986 Linearprice-responsecurve d(p)=1000-100p Populationof 1000 buyers Willingnesstopayisdistributeduniformly $0..$10 Marginalcostis 0 P*=$5; sales 500; totalcontribution $2500
17. Lazear, 1986 Ifhesells at $5 and $2.5, histotalrevenuewillbe $3125 – 24% increase Selling at $6.67 at first, and then at $3.33, hecanget $3333 – 33% increase!
18. Thiscustomersegmentation… assumesgloballinearityofprice-responsecurve and no anticipationofmarkdowns and alsothefactthatsomecustomerswouldnothave a lowerwtplater on there are two such cases that are commonly practiced, however: baked goods and broadway tickets (afterwards sold through a TKTS outlet at Times Square)* usedtimeofpurchase and decreasingpricesas a wayofsegmentingcustomersbetweenthosewitha highwillingnesstopay and thosewith a lowwtp. Furthermore, TKTS ticketsrequirewaitinginline and no guarantee, thattheproductwillbeavailable … and bakerygoods are just notasgood
19. Lowerwillingnesstopay, later on Anticipationofmarkdowns Cannibalizationfraction – 0%..100%
20. Theroleofdemanduncertainty A merchant, selling a sweater, doesnotknow, whetheritwillbe a top seller duringtheupcomingseason Asnot a top seller, the market pricewillbe $59 As a top seller, itwill all besold at $79 Thustheoptimaldecisionisto sell for $79 at thefirstperiod and thenlowerthepriceto $59
21. Anotherexample, theDutchflowerauction 4 billionflowerssoldinAalsmeerannualy Setthemaximum and theminimumprice Infiveminutes, price drops steadilyfrommaximumtominimum Each potential buyer has a PURCHASE button, he can press Both for selling sweaters and flower sellers seek to extract the highest price for their constrained and perishable supply in the face of demand uncertainty
22. Markdownmanagementbusinesses Holidayitems – Christmastrees, fireworks Tours – only 10% of leisure travel in the US, a major proportion of vacation travel in Europe and Asia* Automobiles – seasonsbeginin September, thesemarkdowns are realizedthroughvariouspromotionalvehicles, notthroughreduced list price Clearances and discontinuations – e.g. a newmodelof a washingmachineisannouncedinthreemonths and retailers and wholesalershavetoreact; mostaccute, whenthelife-cycleisshort, asthereinsomeportionoftotalinventoryismarkeddowneveryday
23. Assumptionsformarkdownoptimization A seller has a fixedinventory and cannotreorder Thereisanoutdateafterwhichthesalvagevalueissmall Thereisaninitial list price, whichcanbereduced a number oftimes Onlypricereductions are allowed Theobjectiveistomaximizethetotalrevenue, includingsalvagevalue Marginal costs are zero – costs are sunk ---------------- The marked-down good being sold is inferior, as it might no longer be fashionable, or simply not be available
24. A deterministicmodel
25. Results
26. Whenthesalesdeviatefromexpectations – a deterministicmarkdownalgorithm Solve for p1, p2 .. pt– todetermine p1 Observesales and setstartinginventory x2 Solve withremainingprojecteddemandstodetermine p2 Etc…
27. Example Demandwillbe 70, insteadoftheanticipated 56 Inventoryforperiod 2 is 90 Use d2(p2), d3(p3) and d4(p4) torecalculatepricesformonths 2, 3, 4: p2=$34, p3=$30.67, p4=$16.50 Set p2 = $34 Etc…
28. A studyof a similardeterministicalgorithm: Heching, Gallego, and van Ryzin, 2002 60 fashiongoods at a women’sspecialtyapparelretailinthe US Constraints: discounthadtobe at least 20% Initialdiscountnotsoonerthanafter 4 weeks Increasedtotalrevenue – 4.8% – relativeto standard storepractice Mostcommon – smallmarkdownssoonerthantheusualstorepolicy
29. A simpleapproachtoincludinguncertainty Thecurrentmarkdownopportunityisthe last Salvageprice r (maybezero) = $5 D(p) is a randomvariable, whosedistributiondepends on p D(p) – uniformdistributionbetween 0 and a-bp, a=200, b=10 Inventory x=60
30. Simpleprobabilisticmarkdownpricingalgorithm – dynamicprogramming Weestimatedemandasifwe are goingtoholdthepriceconstantfor all remainingperiods E.g. D1($5) – normaldistributionmean 50, variance 25; D2($5) – normaldistributionmean 30, variance 10 Thus D^($5) – mean 80, variance 35 Wecalculatethepricefortheupcomingperiodasifitwerethefinalmarkdownperiod Aftersettingpriceforperiod 1, dothesamecalculationswith D2 only and againsetprices, ifthereweremoretimeperiods, repeat; finally sell forsalvagevalue
31. Results: Bitran, Caldentey, and Mondschein, 1998 WastestedforsixitemsineightstoresoftheChileanfashionretailchainFalabella and comparedagainstsalesforthe 1995 autumn-winter 12% improvement Cannotdirectlycomparetothe 4.8% ofthedeterministicmodel, asbasesmightbedifferentaswellastheenvironment
32. Estimatingmarkdownsensitivity (increasedsalesduetoadditionaldiscount) Levy and Woo, 1999 Techniquesformeasuringpricesensitivity – suchaspricetesting Systemsthatallowtrackinghistoricsales and discountsbyitem and category
33. Calculatebaselineproductlifecycleforecast Aggregatingsalesoffashiongoodsforperiodsinwhich no markdownshavebeentaken Attribute all salesinexcessofthebaselineproductlifecycletothemarkdowns Fitanappropriateprice-responsecurve Weeks 32-39 – the 30% markdown at week 32; 40-47 – the 50% markdown; 48-52 – the 70% markdown
34. Establishing a priceresponsecurve 0% markdown 1219 sales and 30% markdown 4253 – twopointsthroughwhichwecanfitthepriceresponsecurve
35. Markdownelasticity (4253-1219)/1219=249% increaseinsalesfrom a 30% decreaseinprice – themarkdownelasticityis 249/30=8.3 Wemayexpectsuchhighlevels, notfoundinelasticityregardingthe list price
36. Markdownmanagementdidnotgetoffthegroundbeforethemid 1990s Prior to 1990, therequiredEnterpriseResourcePlanning (ERP) and CustomerRelationshipManagement (CRM) softwarewasnotinplace Mostretailersmanagedmarkdownusingpromotionsbudget – $100M inventory and $15M promotionsbudget In a decentralizedfashion, buyerswereinchargeofthelifecycleoffashionmerchandise
37. A moregeneralcycle By mid 2000s, the retail expansion of mid-nineties was over, and not that many new stores were opened The 800-pound gorilla ofretailing: Wal-Marthadarrived Thefirstwidelypublishedsuccesswas at ShopKo – 141 discountstoresunderShopKo and 229 smallerPamidastores, applyingSpotlightSolutionssystem 300 programsshowed a 14% increase, accordingtoWallStreetJournal FollowersincludeTheGap, JC Penney, HomeDepot, Bloomingdale’s, Sears, Circuit City
38. Onebasicconclusionfromoptimalmarkdowns, istotakeearlier, smallerdiscounts, thanbeforewasconsideredfeasible Theeffectofoptimizationhasadditionallybeenthetailoredmarkdownschedules – e.g. earlierthesamediscountwasgivento a specificsweaterbothin Boston and in LA E.g. CanadianNorthernGroupRetail, usingProfitlogic „regionalneeds, weatherpatterns and othertrends“, e.g. nationwideswimsuitdiscountin September – okfor Boston, Chicago and NY, whereas tourist seasonwas just beginninginOrlando and Miami
39. Cumbersomebusinessrules Firstmarkdownafter 4 weeks, and at least 15% All LizClaibornesweaters, must bemarkeddownbythesame % @ sametime Oneitem – max 4 markdowns Everymarkdown must discountby at least 10% morethanprevious 5% units, e.g. 13% and 22% not allowed Finalprice at least 25% oftheoriginal Oneweekspanminimum All storesinoneregion must havethesamestrategy Outletstores must alsohaveenoughto sell, as a result
40. Businessrules Are constraints on thebasicoptimizationproblem Many are INT constraints, that are hardtoimplement Constraintscanmakeourmethods too complextobe solved by standard methods Thusthe need tousecustomalgorithmslikesimulatedannealing and geneticalgorithms