Marketing Operations Jan 2, 2013

Buy­ing and Sell­ing Sea­son­al Goods

Decod­ing the dual dilem­mas faced by cus­tomers and retailers

CSA-Printstock via iStock

Based on the research of

Gonca P. Soysal

Lakshman Krishnamurthi

When the leaves start to change col­ors in autumn, they sig­nal to fash­ion-mind­ed shop­pers to begin their search for the per­fect new fall coat. But for some buy­ers, price trumps being the first to wear this year’s trends; and it is these con­sumers who ulti­mate­ly wait, before part­ing with their cash, for retail­ers to slash prices. Retail­ers, of course, for their part want as many shop­pers as pos­si­ble to make their pur­chas­es before prices are decreased.

When it comes to buy­ing and sell­ing goods with a lim­it­ed life cycle — such as fash­ion appar­el, con­cert tick­ets, and hol­i­day mer­chan­dise — shop­pers and retail­ers face dif­fer­ent dilem­mas. Shop­pers must decide whether to buy ear­ly in the sea­son at a high­er price or lat­er in the sea­son after a mark­down. Buy­ing lat­er pos­es a trade-off because while the prod­uct will be cheap­er, shop­pers will have less time to use it. Mean­while, sell­ers must decide how much to order, how to get the shop­pers to buy at high­er prices, and when and by how much to mark prod­ucts down.

Lak­sh­man Krish­na­murthi, a pro­fes­sor of mar­ket­ing at the Kel­logg School of Man­age­ment, sought to bet­ter under­stand these dual dilem­mas by study­ing actu­al sales data from a nation­al spe­cial­ty appar­el retail­er. Krish­na­murthi, along with his stu­dent Gonca Soysal, now a pro­fes­sor at the Uni­ver­si­ty of Texas at Dal­las, gained some key insights by ana­lyz­ing two years’ worth of data on the sales and inven­to­ry lev­els of hun­dreds of prod­ucts, such as coats. They designed a struc­tur­al mod­el that accounts for a shopper’s expec­ta­tions and buy­ing behav­ior based on pat­terns that emerged from the data. Their mod­el assumes that con­sumers know the cur­rent prices of prod­ucts, and have a sense of the stock­ing lev­els, let­ting them form expec­ta­tions about future prices and availability.

About the Mod­el
Krish­na­murthi and Soysal treat­ed each type of coat as a sep­a­rate mar­ket — for exam­ple, some­one look­ing to buy a leisure coat is not like­ly to buy a coat suit­able for office work instead. The retail­er whose data they mined, whose coats are ini­tial­ly priced between $100 and $350, has an estab­lished his­to­ry of offer­ing at least two to three mark­downs as each sea­son pro­gress­es, with sea­sons vary­ing from 11 to 30 weeks long. The first mark­down is the largest (38 per­cent of retail price on aver­age) and has the biggest effect in terms of cre­at­ing a spike in demand. The sec­ond and third mark­downs are not as large and always have small­er effects on sales.

When coats sold at the full retail price, the retail­er moved 43 per­cent of their inven­to­ry, which amount­ed to 57 per­cent of total rev­enue. Anoth­er 45 per­cent of the inven­to­ry, or 36 per­cent of rev­enue, sold after the prices were marked down to between 40 and 80 per­cent of the full retail price. (The retail­er typ­i­cal­ly applied deep­er mark­downs on coats with high­er ini­tial prices.)

The mod­el revealed some key char­ac­ter­is­tics about the typ­i­cal con­sumers patron­iz­ing the retail­er in the study. We dis­cov­ered that there were two kinds of buy­ers. First, there were fash­ion-sen­si­tive buy­ers, and these make up about 80 per­cent of the sales,” Krish­na­murthi says. They are very impor­tant to the retailer’s prof­its. Then there were price-sen­si­tive buy­ers, and while these make up only 20 per­cent of the sales, they are piv­otal to soak­ing up excess inven­to­ry.” They also learned that the com­po­si­tion of the mar­ket changed as the sea­son pro­gressed, from the fash­ion-sen­si­tive buy­ers ear­ly in the sea­son to the more price-sen­si­tive buy­ers lat­er in the season.Consumers were assumed to exit the mar­ket after mak­ing their purchase.

What Is a Retail­er to Do?
These find­ings lead to a key ques­tion for the retail­er: How do you man­age prices to max­i­mize over­all rev­enue? Offer­ing a mark­down too ear­ly in the sea­son or slash­ing prices by too much can harm sales. Con­verse­ly, wait­ing until the product’s util­i­ty has dimin­ished too severe­ly may dis­cour­age price-sen­si­tive shop­pers from snap­ping up the remain­ing inventory.

By run­ning a series of coun­ter­fac­tu­al exper­i­ments, Krish­na­murthi and Soysal dis­cov­ered a coun­ter­in­tu­itive result. They showed that retail­ers could increase their prof­its by induc­ing scarci­ty to con­vert some late-sea­son price-sen­si­tive buy­ers to ear­ly sea­son buy­ers. By slight­ly reduc­ing their inven­to­ry — there­by cre­at­ing a sense of urgency for shop­pers to buy ear­li­er in the sea­son — the retail­er would be able to increase the num­ber of sales tak­ing place at high­er prices. This occurs because shop­pers who are knowl­edge­able about how much stock remains will fear that the prod­uct may not be avail­able if they wait too long to make their purchase.

By slight­ly reduc­ing their inven­to­ry — there­by cre­at­ing a sense of urgency for shop­pers to buy ear­li­er in the sea­son — the retail­er would be able to increase the num­ber of sales tak­ing place at high­er prices.”

When we reduced stock by just 5 per­cent, it increased prof­its by 4.5 per­cent,” Krish­na­murthi says. But reduc­ing stock by 10 per­cent yield­ed only a 3-per­cent gain in prof­its. Ulti­mate­ly, each retail­er will have to exper­i­ment with stock­ing lev­els to see what lev­el of reduc­tion max­i­mizes their own profits.”

Addi­tion­al exper­i­men­tal sce­nar­ios revealed that the retail­er was bet­ter off offer­ing small­er mark­downs ear­li­er in the sea­son rather than large mark­downs late in the sea­son. The exper­i­ments showed that when small mark­downs were offered ear­li­er, strate­gic buy­ers caused, on aver­age, a 9 per­cent dip in sales rev­enue if there was a risk of stock run­ning out. But this dip was as high as 35 per­cent of the sales rev­enue if large mark­downs were offered a lit­tle lat­er in the sea­son and there was no risk of stock run­ning out. Again, Krish­na­murthi points out that indi­vid­ual retail­ers will have to exper­i­ment with their own num­bers to see what per­cent­age mark­down works best for them.

Krish­na­murthi says this research may be unique with­in the rev­enue man­age­ment lit­er­a­ture that deals with demand and pric­ing strate­gies for sea­son­al goods because it devel­ops a real­is­tic demand mod­el that accounts for vari­abil­i­ty among con­sumers and their behav­iors. It is also unique, he says, in that it is, to his knowl­edge, the first empir­i­cal­ly based mod­el that takes into account the impact of lim­it­ed prod­uct avail­abil­i­ty on con­sumer deci­sion making.

The find­ings can be applied beyond the fash­ion indus­try, Krish­na­murthi says, to any prod­uct that is sold over a short life cycle. With the roles of buy­ers and sell­ers now decod­ed, it just may be a seller’s mar­ket in the fall.

Relat­ed read­ing on Kel­logg Insight

The Out­let Mall: Dump­ing ground, can­ni­bal chan­nel, or smart strategy?

Ratio­nal Retail Pric­ing: Demand-based pric­ing ver­sus past-price dependence

See­ing Prof­it Despite Mis­un­der­stood Pric­ing Strat­e­gy: Impli­ca­tions in real-world mar­ket situations

About the Writer

T. DeLene Beeland is a science writer based in Asheville, NC.

About the Research

Soysal, Gonca P., and Lakshman Krishnamurthi. 2012. “Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis.” Marketing Science 31(2): 293–316.

Read the original

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