DBA 8050 Module 4 Final Project: A Narrative Literature Analysis of Markdown Pricing for Perishable Seasonal Stock Example

Selling a Season Before It Wilts: A Narrative Analysis of Markdown Pricing Research for Perishable Garden Stock

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Doctor of Business Administration, Thomas Edison State University

DBA-8050: Seminar in Marketing and Entrepreneurship

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Month Day, Year

Selling a Season Before It Wilts: A Narrative Analysis of Markdown Pricing Research for Perishable Garden Stock

Introduction

Each spring our five garden centers buy about 40,000 flats of annuals and 25,000 perennials in containers. Demand peaks in May, slows in June and nearly stops in July, while unsold plants decline in appearance every week. For years the chain has held one half-price sale after the Fourth of July and discarded what remains in August. In 2024 about 11 percent of spring plant stock was sold at half price and another 7 percent was thrown away. This analysis asks what research on markdown pricing says about selling a fixed, perishable stock over a short season, and what that research implies for a better policy.

Scope and Approach

A narrative analysis gathers and interprets a body of research rather than pooling results statistically. I began with the assigned reading by Reiner and Natter (2007), followed its references to foundational pricing models and searched Business Source Complete and Google Scholar for studies of markdown, clearance and dynamic pricing, adding work on how buyers respond to planned price cuts. I kept studies that addressed a fixed stock sold over a limited period and set aside work on airline and hotel capacity except where it explained a principle. The analysis is organized by theme: models, retail practice, buyer response and gaps.

Foundations: Pricing a Fixed Stock Over a Season

The basic problem was framed formally in a model of a seller with a fixed inventory, a finite selling horizon and uncertain demand that falls as price rises (Gallego & van Ryzin, 1994). Their analysis showed that the best price depends on two things at each moment: how much stock remains and how much time is left. A seller with too much stock late in the season should lower prices, and one running short can hold them. They also showed that a simple fixed price, chosen well, often captures much of the revenue that continuous adjustment would earn, which matters to firms that cannot change prices constantly.

Elmaghraby and Keskinocak (2003) reviewed the growing literature on dynamic pricing with inventory and sorted it by whether inventory can be replenished during the season, whether demand depends on past prices and whether customers are myopic or strategic. Garden plants fall clearly in one cell of their map: stock is mostly bought before the season, it cannot be reordered cheaply after June, and its value falls with time. That places our problem in the markdown branch of the literature, where prices move in one direction, downward, as the season runs out.

Revenue management offers the general principle behind these models. Kimes (1989) described yield management as a tool for firms whose capacity is fixed and perishable, with demand that varies and can be segmented, and argued that such firms gain by matching price to the time a sale is made. Unsold spring plants behave much like an empty hotel room on a past date: once the season ends, their value is close to nothing.

Markdown Practice in Retail Chains

Research on actual retailers adds the conditions that models simplify. Smith and Achabal (1998) studied clearance pricing in retail chains and showed that demand for seasonal goods depends not only on price and time but also on the inventory on display: as assortments thin and the best items sell, the remaining stock sells more slowly at any price. Their model set markdowns and inventory policies together and was applied in practice by chains planning clearance of fashion goods. For a garden center the finding has a clear parallel, since a bench of picked-over, root-bound perennials in July sells worse than the same varieties did in full bloom in May.

Reiner and Natter (2007) took the analysis into a market where product value falls quickly: mobile phones in Austria. Using market data, they estimated how demand responded to price over each model's life and compared alternative markdown strategies against one another. Their encompassing view, which considered the timing, size and number of price cuts together, supports a lesson that runs through the field: a markdown policy should be judged as a whole path of prices, not as a single sale, and the right path depends on how fast the product's appeal declines. Phones lose appeal as newer models arrive; plants lose it as they age on the bench and as the planting season closes. In both cases, waiting too long to cut prices leaves stock that no discount can move.

How Buyers Respond to Markdowns

The models above treat buyers as reacting only to today's price. Later work asked what happens when buyers learn the pattern. Aviv and Pazgal (2008) modeled forward-looking customers who time their purchases to expected discounts and showed that ignoring such behavior can lead a seller to overestimate the gains from markdowns. Cachon and Swinney (2009) showed that strategic waiting reduces a retailer's profit and that the ability to restock quickly in response to demand can reduce the incentive to wait. For our chain the risk is concrete: experienced gardeners know about the Fourth of July sale, and some plan their purchases for it. A predictable date trains them to wait.

Buyers also judge whether prices are fair. Kahneman et al. (1986) found that people consider price increases driven by higher demand unfair, while they accept reductions and increases tied to higher costs. Markdowns are therefore safe from the fairness objection, but a policy that marks plants down and then raises prices for a late spring heat wave would not be. This asymmetry supports a one-way markdown path for seasonal stock.

Gaps in the Research

Three gaps limit how far the literature transfers to our case. First, most markdown studies concern fashion apparel, electronics or other durable goods whose quality does not physically decline on the shelf; living plants do, so demand falls both with time and with condition. Second, weather drives garden demand from week to week, and models that treat demand as following a stable pattern underplay that uncertainty. Third, research on strategic buyers rests largely on formal models, with less field evidence on how many shoppers actually wait and in which product categories. These gaps argue for a cautious trial rather than a full model.

Implications for the Garden Centers

The research points to four changes. First, replace the single half-price sale with a two-step path: a 25 percent cut on any variety whose sell-through falls below 60 percent by June 15, and a 50 percent cut when a plant's condition grade drops, judged weekly by the nursery manager. Second, consolidate benches as stock falls so displays stay full, following Smith and Achabal (1998). Third, stop advertising a fixed sale date, so buyers who wait gain less from waiting. Fourth, never raise a plant's price once it has been cut. I propose running the policy in two stores in 2025 and comparing their full-price share, markdown share and waste with the other three. If waste and margin improve without a fall in May sales, the policy would extend to all five stores.

Conclusion

Markdown research offers a coherent account of how to sell a fixed stock as time runs out: let remaining inventory and remaining time set the price, plan the whole path of cuts, keep displays full and expect some buyers to learn the pattern. Its main gaps, perishable quality and weather, are exactly where garden stock differs from phones and fashion, which is why the next step is a measured trial rather than a model. The literature does not hand our chain a formula, but it shows why one late sale has been leaving money on the bench.

References

Aviv, Y., & Pazgal, A. (2008). Optimal pricing of seasonal products in the presence of forward-looking consumers. Manufacturing & Service Operations Management, 10(3), 339-359. https://doi.org/10.1287/msom.1070.0183

Cachon, G. P., & Swinney, R. (2009). Purchasing, pricing, and quick response in the presence of strategic consumers. Management Science, 55(3), 497-511. https://doi.org/10.1287/mnsc.1080.0948

Elmaghraby, W., & Keskinocak, P. (2003). Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Science, 49(10), 1287-1309. https://doi.org/10.1287/mnsc.49.10.1287.17315

Gallego, G., & van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Science, 40(8), 999-1020. https://doi.org/10.1287/mnsc.40.8.999

Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1986). Fairness as a constraint on profit seeking: Entitlements in the market. American Economic Review, 76(4), 728-741.

Kimes, S. E. (1989). Yield management: A tool for capacity-constrained service firms. Journal of Operations Management, 8(4), 348-363. https://doi.org/10.1016/0272-6963(89)90035-1

Reiner, G., & Natter, M. (2007). An encompassing view on markdown pricing strategies: An analysis of the Austrian mobile phone market. OR Spectrum, 29(1), 173-192. https://doi.org/10.1007/s00291-005-0027-y

Smith, S. A., & Achabal, D. D. (1998). Clearance pricing and inventory policies for retail chains. Management Science, 44(3), 285-300. https://doi.org/10.1287/mnsc.44.3.285