Markdown management

Lecture 5

„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

Markdowns and promotions

• 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

Nobody but a fungoid creature from another galaxy with no familiarity with earthly ways would ever pay list price for anything. —Dave Barry

• Valentine’s Day• Black Friday*• Cyber Monday• Christmas• Kohl’s, Macy’s, Saks

State of the art

• Software offered by companies such as– Manugistics– ProfitLogic – acquired by Oracle

• Situation is reminiscent of the early days of revenue management in the airline, hotel and rental car industries – the leaders are applying

• Early adopters have reported 10%-15% revenue increases – see game theory with respect to that

• For certain goods and services markdown segments the market and provides a simple method for profiting from price discrimination

• In the early days sales were associated with price wars during holidays such as Christmas and Thanksgiving between rivals such as Macy’s and Gimbels in Manhattan

• In 1950, managers at The Emporium, leading San Francisco department store were given 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.

• These were considered as mistakes in purchasing, pricing and marketing

Average discount from list price – department and specialty store. Stores with sales over $1 million before 1982 and $5

million thereafter

• On the other hand, as early as 1915, a retailing textbook also tied markdowns to many of its pros

• 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

Reasons

• Increased customer mobility• The rise of discount chains and outlets, many with

„everyday low pricing“• A self-reinforcing „vicious cycle“• An increasing interest in nonstandard items – that

have shorter shelf lives. Patterned and colored sheets have their market share go up from 12% in 1970 to 60% in 1990 against white sheets

• The use of markdown money, moving some of the risk of unsold inventory from retailers to manufacturers

• 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

• 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

The main characteristics for seller/buyer

• Inventory is fixed (seller)• The inventory must be sold by a certain out date – or its

value drops precipitously (seller)• Time of use – winter overcoat in September and in April• Fashionability – clothes, consumer electronics, video

games• Deterioration – day old bread• Obsolescence – the new version comes soon –

automobiles, consumer electronics, computer equipment

Customer segmentation and price discrimination

• Time of use – those who need it now/who are willing to wait

• Fashionability – fashionistas/ones who are willing to wait

• Deterioration – who are ok with the inferior product at a lower price

• As we have seen, segmentation and discrimination are powerful ways of increasing revenue

Lazear, 1986

• Linear price-response curve d(p)=1000-100p

• Population of 1000 buyers

• Willingness to pay is distributed uniformly $0..$10

• Marginal cost is 0• P*=$5; sales 500; total

contribution $2500

Lazear, 1986

• If he sells at $5 and $2.5, his total revenue will be $3125 – 24% increase

• Selling at $6.67 at first, and then at $3.33, he can get $3333 – 33% increase!

This customer segmentation…

• assumes global linearity of price-response curve and no anticipation of markdowns and also the fact that some customers would not have a lower wtp later 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)*

• used time of purchase and decreasing prices as a way of segmenting customers between those with a high willingness to pay and those with a low wtp.

• Furthermore, TKTS tickets require waiting in line and no guarantee, that the product will be available … and bakery goods are just not as good

• Lower willingness to pay, later on• Anticipation of markdowns– Cannibalization fraction – 0%..100%

The role of demand uncertainty

• A merchant, selling a sweater, does not know, whether it will be a top seller during the upcoming season

• As not a top seller, the market price will be $59• As a top seller, it will all be sold at $79• Thus the optimal decision is to sell for $79 at

the first period and then lower the price to $59

Another example, the Dutch flower auction

• 4 billion flowers sold in Aalsmeer annualy• Set the maximum and the minimum price• In five minutes, price drops steadily from maximum

to minimum• 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

Markdown management businesses

• Holiday items – Christmas trees, fireworks• Tours – only 10% of leisure travel in the US, a major

proportion of vacation travel in Europe and Asia*• Automobiles – seasons begin in September, these

markdowns are realized through various promotional vehicles, not through reduced list price

• Clearances and discontinuations – e.g. a new model of a washing machine is announced in three months and retailers and wholesalers have to react; most accute, when the life-cycle is short, as therein some portion of total inventory is marked down every day

Assumptions for markdown optimization

• A seller has a fixed inventory and cannot reorder• There is an out date after which the salvage value is small• There is an initial list price, which can be reduced a number

of times• Only price reductions are allowed• The objective is to maximize the total revenue, including

salvage value• 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

A deterministic model

Results

When the sales deviate from expectations – a deterministic markdown algorithm

• Solve for p1, p2 .. pt – to determine p1• Observe sales and set starting inventory x2• Solve with remaining projected demands to

determine p2• Etc…

Example

• Demand will be 70, instead of the anticipated 56

• Inventory for period 2 is 90• Use d2(p2), d3(p3) and d4(p4) to recalculate

prices for months 2, 3, 4: p2=$34, p3=$30.67, p4=$16.50

• Set p2 = $34• Etc…

A study of a similar deterministic algorithm: Heching, Gallego, and van Ryzin, 2002

• 60 fashion goods at a women’s specialty apparel retail in the US

• Constraints: – discount had to be at least 20%– Initial discount not sooner than after 4 weeks

• Increased total revenue – 4.8% – relative to standard store practice

• Most common – small markdowns sooner than the usual store policy

A simple approach to including uncertainty

• The current markdown opportunity is the last• Salvage price r (may be zero) = $5• D(p) is a random variable, whose distribution

depends on p• D(p) – uniform distribution between 0 and a-

bp, a=200, b=10• Inventory x=60

Simple probabilistic markdown pricing algorithm – dynamic programming

• We estimate demand as if we are going to hold the price constant for all remaining periods

• E.g. D1($5) – normal distribution mean 50, variance 25; D2($5) – normal distribution mean 30, variance 10

• Thus D^($5) – mean 80, variance 35• We calculate the price for the upcoming period as if it

were the final markdown period• After setting price for period 1, do the same calculations

with D2 only and again set prices, if there were more time periods, repeat; finally sell for salvage value

Results: Bitran, Caldentey, and Mondschein, 1998

• Was tested for six items in eight stores of the Chilean fashion retail chain Falabella and compared against sales for the 1995 autumn-winter

• 12% improvement• Cannot directly compare to the 4.8% of the

deterministic model, as bases might be different as well as the environment

Estimating markdown sensitivity (increased sales due to additional discount) Levy and Woo, 1999

• Techniques for measuring price sensitivity – such as price testing

• Systems that allow tracking historic sales and discounts by item and category

• Calculate baseline product life cycle forecast– Aggregating sales of fashion goods for periods in

which no markdowns have been taken• Attribute all sales in excess of the baseline

product life cycle to the markdowns– Fit an appropriate price-response curve

• Weeks 32-39 – the 30% markdown at week 32; 40-47 – the 50% markdown; 48-52 – the 70% markdown

Establishing a price response curve

• 0% markdown 1219 sales and 30% markdown 4253 – two points through which we can fit the price response curve

Markdown elasticity

• (4253-1219)/1219=249% increase in sales from a 30% decrease in price – the markdown elasticity is 249/30=8.3

• We may expect such high levels, not found in elasticity regarding the list price

Markdown management did not get off the ground before the mid 1990s

• Prior to 1990, the required Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) software was not in place

• Most retailers managed markdown using promotions budget – $100M inventory and $15M promotions budget

• In a decentralized fashion, buyers were in charge of the lifecycle of fashion merchandise

A more general cycle

• By mid 2000s, the retail expansion of mid-nineties was over, and not that many new stores were opened

• The 800-pound gorilla of retailing: Wal-Mart had arrived• The first widely published success was at ShopKo – 141

discount stores under ShopKo and 229 smaller Pamida stores, applying Spotlight Solutions system

• 300 programs showed a 14% increase, according to Wall Street Journal

• Followers include The Gap, JC Penney, Home Depot, Bloomingdale’s, Sears, Circuit City

• One basic conclusion from optimal markdowns, is to take earlier, smaller discounts, than before was considered feasible

• The effect of optimization has additionally been the tailored markdown schedules – e.g. earlier the same discount was given to a specific sweater both in Boston and in LA

• E.g. Canadian Northern Group Retail, using Profitlogic „regional needs, weather patterns and other trends“, e.g. nationwide swimsuit discount in September – ok for Boston, Chicago and NY, whereas tourist season was just beginning in Orlando and Miami

Cumbersome business rules

• First markdown after 4 weeks, and at least 15%• All Liz Claiborne sweaters, must be marked down by the same

% @ same time• One item – max 4 markdowns• Every markdown must discount by at least 10% more than

previous• 5% units, e.g. 13% and 22% not allowed• Final price at least 25% of the original• One week span minimum• All stores in one region must have the same strategy• Outlet stores must also have enough to sell, as a result

Business rules

• Are constraints on the basic optimization problem

• Many are INT constraints, that are hard to implement

• Constraints can make our methods too complex to be solved by standard methods

• Thus the need to use custom algorithms like simulated annealing and genetic algorithms