Black Friday Pricing: What Four Decades of Research Say About Profitable Discounts

Why the biggest discount is rarely the most profitable one — and how to design short, deep promotions using evidence rather than intuition.

Every November, dashboards light up. Traffic spikes, orders double, and the team celebrates a record weekend. Then the quarter closes, finance reconciles margins, returns and the quiet December, and the celebration looks premature.

This is not cynicism; it is what the research consistently finds. When CVS worked with academics to measure every promotion it ran in 2003, only about 45% of the gross sales lift turned out to be incremental for the chain, and more than half of all promotions lost money because the lower margin was not offset by extra units. Eliminating promotions in 15 of the worst-performing categories cut sales by roughly $7.8 million but raised profit by about $52.6 million (Ailawadi et al., Marketing Science 2007).

The long view is no kinder. Large econometric studies find that price promotions have virtually no permanent effect on sales, and no permanent monetary benefit for either manufacturers or retailers; retailer category margins typically fall (Pauwels, Hanssens & Siddarth, JMR 2002; Srinivasan et al., Management Science 2004).

Yet short, deep promotions are not doomed. Used precisely — on the right products, for the right customers, with honest communication and proper measurement — they can attract new customers, build traffic and clear inventory profitably. This article distils four decades of peer-reviewed research into what works, what does not, and how to plan Black Friday for profit rather than applause.

1. The arithmetic of a discount

A discount cuts profit per unit far more than it cuts price. Take a product sold at €100 with a variable cost of €60. The contribution margin is €40, or 40%. A 20% discount does not reduce that contribution by 20% — it halves it, from €40 to €20.

The break-even volume increase for any price cut follows from a standard formula found in pricing textbooks such as Nagle and Müller's The Strategy and Tactics of Pricing:

Required volume increase = −ΔP ÷ (CM + ΔP)

where ΔP is the price change (e.g. −20%) and CM is the contribution margin before the discount (e.g. 40%). For a €100 product with €60 variable cost:

• No discount: €100 price, €40 contribution per unit — 1.00× units

• 10% off: €90 price, €30 contribution — 1.33× units needed

• 20% off: €80 price, €20 contribution — 2.00× units needed

• 25% off: €75 price, €15 contribution — 2.67× units needed

• 30% off: €70 price, €10 contribution — 4.00× units needed

• 35% off: €65 price, €5 contribution — 8.00× units needed

Illustrative calculation: constant unit cost; advertising, fulfilment, payment fees and returns excluded — all of which make the hurdle higher.

At 30% off, the retailer must sell four times as many units just to stand still. That is the benchmark every Black Friday discount should be tested against: not "did sales go up?" but "did incremental contribution go up?"

Two caveats keep the numbers honest. First, they ignore what else lands in the basket, which is the whole logic of traffic-building offers (section 4). Second, they compare the promotion with a world in which those units would have sold at full price — and, as the next section shows, many of them would have.

2. Where the promotional bump really comes from

Roughly a third of a typical promotional spike is genuinely new demand. The rest is borrowed — from competitors or from your own future.

For years, marketers believed that about three-quarters of a promotion's effect came from customers switching brands. Harald van Heerde, Sachin Gupta and Dick Wittink showed that this figure described elasticities, not units sold; measured in units, brand switching accounts for at most about one-third of the bump (JMR 2003). The paper later received the American Marketing Association's O'Dell Award for long-run impact.

Using store data, van Heerde, Leeflang and Wittink split the bump into three parts that each contribute roughly a third on average (Marketing Science 2004):

• Switching — customers buy the promoted item instead of another brand or product. Gains for the brand, often little for the retailer that sells both.

• Borrowing from other periods — customers buy earlier or stock up. Sales rise now and dip later; in e-commerce this shows up as a soft early November and a weaker December.

• Category expansion — genuinely additional demand.

One nuance matters for Black Friday: the same study found that as discounts deepen, the share of category expansion tends to rise while switching and time-shifting shrink. Deep discounts are not automatically wasteful; their value depends on whether they create demand or merely move it.

The time dimension is equally sobering. Pauwels, Hanssens and Siddarth found permanent promotion effects to be virtually absent: sales settle back after an adjustment period (JMR 2002). Over the longer run, Mela, Gupta and Lehmann, using more than eight years of panel data, found that consumers become more price- and promotion-sensitive as promotion intensity rises and brand advertising falls (JMR 1997). In profit simulations, Jedidi, Mela and Gupta found increases in price promotions to be uniformly unprofitable for the brands studied (Marketing Science 1999).

What this means in practice: never evaluate a promotion by the lift of the discounted SKU. Look at the category, the whole basket, and the weeks after the event.

3. Why retailers cut prices when demand is highest

Black Friday looks paradoxical: prices fall exactly when demand peaks. Economics explains it — the discount is a weapon in the fight for traffic, not a way to sell more of the discounted item.

Judith Chevalier, Anil Kashyap and Peter Rossi analysed seven and a half years of prices at a large supermarket chain. Prices of products tended to fall during their seasonal demand peaks, mostly because the retailer cut its own margin. The pattern fits "loss-leader" models, in which retailers advertise low prices on popular items to win the shopping trip (American Economic Review 2003).

Elizabeth Warner and Robert Barsky documented frequent markdowns in the intense pre-Christmas shopping period and at weekends. Their explanation: when shopping intensity is high, consumers compare more offers, so demand at the store level becomes more price-sensitive (Quarterly Journal of Economics 1995).

And Black Friday is more than a bargain hunt. Interviews by Thomas and Peters describe it as a collective consumption ritual, planned in advance and shared with family and friends (International Journal of Retail & Distribution Management 2011). The visibility and credibility of the offers matter, not just their depth.

The implication: the strategic question is not "how much should we take off everything?" but "which offers will win the visit and the basket, and from whom?"

4. Ten evidence-based Black Friday strategies

The common thread is precision: the right discount, on the right products, for the right customers, communicated credibly. Each strategy below states the mechanism, the evidence and how it translates to e-commerce. Where the evidence comes from grocery stores or catalogues rather than online retail, that is flagged.

Strategy 1: Discount a few traffic drivers deeply, not everything a little

A handful of well-known products at genuinely sharp prices wins the visit; the margin is earned on the rest of the basket.

Beyond the loss-leader evidence above, Gauri, Ratchford, Pancras and Talukdar studied 24 grocery stores over 55 weeks. Featured promotions built store traffic, especially in high-penetration, frequently bought categories, and promotions on branded items worked better than on unbranded ones (Journal of Retailing 2017). Two caveats from the same work: these promotions tended to attract smaller-basket shoppers, and broad discounting within one category showed diminishing returns (ScienceDaily summary).

Credibility also erodes with volume. In catalogue experiments, Eric Anderson and Duncan Simester found that "sale" signs lose effectiveness as more products carry them, and total category demand peaked when some, but not all, items were marked (Marketing Science 2001).

In practice: split the assortment into traffic drivers (popular, easily compared items with sharp prices and limited stock), complements (accessories, add-ons with healthy margins, surfaced in the cart) and margin protectors (items with strong organic demand that need little or no discount). Then check that basket contribution actually covers the margin given up on the drivers.

Strategy 2: Run fewer, deeper events instead of constant discounting

Customers learn a retailer's price rhythm. If there is a 15% sale every other weekend, the sale price becomes the real price.

Matthew Osborne estimated a dynamic model of consumer stockpiling on scanner data for a storable category. In counterfactual simulations, deeper discounts were more effective per dollar of promotion spend than more frequent ones, and combining deeper discounts with lower frequency was more effective still (Japanese Economic Review 2018). This is one category and a simulation, not a field test, but it points the same way as Mela et al. on price sensitivity and as operations research on strategic consumers: when shoppers anticipate markdowns, retailers end up offering smaller discounts and earning less (Cachon & Swinney, Management Science 2009).

In practice: treat promotional frequency as a strategic variable. One distinctive Black Friday beats a "Black Month" followed by Cyber Week, pre-Christmas and January sales that teach customers never to pay full price.

Strategy 3: Treat new and established customers differently

The same discount builds one relationship and erodes another.

In three large field experiments with a catalogue retailer of durable goods, Anderson and Simester found that deeper discounts increased future purchases by first-time customers but reduced future purchases by established customers. Pricing on short-run elasticity therefore sets prices too low for established customers and too high for prospects (Marketing Science 2004).

In practice: evaluate acquisition offers on incremental customer lifetime value, not first-order margin; give existing customers early access, loyalty benefits or category-expanding offers rather than the deepest across-the-board cut. Inactive customers are a separate test cell — the experiments do not establish an optimal reactivation discount.

Strategy 4: Protect customers who bought just before the sale

Some of the costliest effects of Black Friday land on people who paid full price a few weeks earlier.

In a 28-month randomised field experiment with over 50,000 customers, Anderson and Simester showed that customers who saw a lower price for something they had recently bought made fewer subsequent purchases. The effect was strongest among the most valuable customers and persisted throughout the measurement period, amounting to an apparent boycott (Quarterly Journal of Economics 2010).

Online, there is a second, more mechanical cost: returns. Gijsenberg, Bijmolt and Hirche analysed purchases and returns at a large European online retailer. Discounts at the time of purchase were associated with lower return rates, but price cuts made after purchase, while the return window was still open, raised returns once they exceeded about 25%, in a non-linear way. For discounts above 40% (20% for customers with prior discount-driven returns), the harm from post-purchase cuts outweighed the benefit of at-purchase discounts (Journal of Retailing 2025).

In practice: offer price protection or a credit to recent buyers, avoid deep cuts on items many customers bought in the preceding return window, and include returns in the campaign P&L.

Strategy 5: Target offers, but never reward waiting

Personalised discounts can work spectacularly in the short term and teach customers the wrong lesson.

In a randomised experiment with Alibaba involving more than 100 million customers and 11,000 retailers, discounts on items left in shopping carts doubled sales of the promoted products (Zhang et al., Management Science 2020). In the following month, treated customers engaged more, but they also behaved more strategically — adding more viewed items to carts, possibly in anticipation of future promotions, and paying lower prices on later purchases. These effects spilled over to sellers who had never offered the promotion (abstract). According to Washington University's summary, the results persuaded the company to end the cart promotions (WashU).

In practice: an automatic "abandon your cart and get 10% off" rule is a lesson in waiting. Tie targeted offers to eligibility, products or campaign goals, and measure them with randomised control groups over the following weeks, not just on redemption day.

Strategy 6: Set discount depth SKU by SKU

A flat "25% off the category" ignores the fact that products differ in elasticity, margin, competitive exposure and stock.

Two field-validated studies from online retail show the payoff of product-level pricing. At the flash-sale retailer Rue La La, Ferreira, Lee and Simchi-Levi built a machine-learning demand model and a price optimiser; in a field experiment, recommended price increases on medium- and high-priced items did not reduce sales, and revenue in the test group rose by about 9.7% (90% confidence interval 2.3%–17.8%) (Manufacturing & Service Operations Management 2016). Even a business built on discounts was underpricing some products. Fisher, Gallino and Li combined randomised prices with competitors' prices and stockouts to build a best-response pricing policy for an online retailer; a five-week controlled experiment produced an 11% revenue increase while keeping margin above the retailer's target (Management Science 2018).

Neither study compares Black Friday discount levels directly, and neither supports blindly undercutting competitors. What they support is estimating demand response per product and pricing under explicit margin constraints.

In practice: for each SKU, evaluate several candidate prices on expected contribution — (promotional price − variable cost) × expected units — and adjust for cannibalisation, basket effects, stock, competitor prices and availability, supplier funding and the demand you will pull forward from December.

Strategy 7: Use basket-level offers — but test the architecture

"€20 off orders over €150" works differently from "20% off, up to €20", even when the maximum saving is identical.

In seven preregistered studies including a field study, Yi, Hardisty, Griffin and Allard found that when the spending trigger is low relative to what customers typically spend, threshold promotions lead to higher purchase intentions and conversion than equivalent capped promotions — but potentially smaller purchase amounts. When the trigger is high, the preference reverses and capped offers are seen as fairer (Journal of Consumer Research 2026).

Shipping thresholds are a related trap. Lewis, Singh and Fay found that free shipping and free shipping above a threshold were very effective at generating sales for an online retailer, yet the lost shipping revenue and the lack of response from several segments were large enough to make such promotions unprofitable (Marketing Science 2006).

In practice: set thresholds relative to your actual order-value distribution, and judge them on incremental contribution per visitor, not on average order value. A higher AOV can coexist with lower profit.

Strategy 8: Add value instead of cutting price where you can

A free gift, a bundle or extra quantity can deliver the sense of a good deal without lowering the price customers remember.

Hardesty and Bearden found that bonus packs are a viable alternative to price discounts at small and moderate promotion levels, with less damage to the brand, while percentage price discounts were preferred when the benefit was large (Journal of Retailing 2003). Darke and Chung found that discounts can trigger negative quality inferences, whereas free-gift framing maintained quality perceptions and increased perceived deal value (Journal of Retailing 2005). Economic theory has long shown that offering both individual items and a bundle (mixed bundling) can extract more value than selling items separately (Adams & Yellen, QJE 1976), though how much it pays in practice depends on demand.

In practice: pair a hero product with high-margin complements or services. In the EU, do not inflate the "worth" of the bundle with list prices nobody pays — that is exactly the kind of claim regulators scrutinise.

Strategy 9: Frame the discount for the price level — and skip "up to"

Identical savings feel different depending on how they are expressed.

Chen, Monroe and Lou found that for a high-priced product, a reduction framed in money looked more significant than the same reduction in percent, and the opposite held for a low-priced product (Journal of Retailing 1998). A set of three experiments found amount-off framing improved value perceptions and purchase intentions for products priced above $100 (Journal of Business Research 2016), and a 2022 meta-analysis in Psychology & Marketing confirms the amount-off advantage is conditional rather than universal (Yuan et al.). Jonah Berger popularised this as the "Rule of 100" in Contagious — a useful heuristic, not a law.

The frame also shapes what customers expect afterwards: deep discounts framed as percentages led to higher post-promotion price expectations and choice than the same discounts framed in money (DelVecchio, Krishnan & Smith, Journal of Marketing 2007).

Avoid vague "up to 70% off" banners. In display-advertising experiments, Lambrecht and Tucker found such tensile claims on average reduced clicks and, among those who clicked, add-to-cart rates; they did better only where consumers had little price knowledge (London Business School).

In practice: show money saved on expensive items, percentages on cheap ones, and test both. State specific discounts rather than maximums.

Strategy 10: Use urgency honestly — and treat it as a hypothesis

Time limits are the signature of Black Friday, but online shoppers are more sceptical than in-store ones.

Classic research found that restrictions such as purchase or time limits can signal value and raise sales (Inman, Peter & Raghubir, Journal of Consumer Research 1997); the effect was strongest among less deliberative shoppers. Online, the picture is weaker. Hmurovic, Lamberton and Goldsmith found that the positive effects of time-scarcity promotions seen offline may not hold online, apparently because they trigger consumers' awareness of persuasion. Justifying the deadline with a reason outside the retailer's control helped, but online time-limited offers still did not consistently beat identical offers without a deadline (Journal of Marketing Research 2023).

Fake urgency is a separate matter. A crawl of about 11,000 shopping sites found 393 countdown timers, 157 of them deceptive — the "deadline" reset or the offer continued after it expired (Mathur et al., ACM CSCW 2019).

In practice: Black Friday itself is a credible external reason for a deadline. Test duration and deadline messaging separately (same price, with and without a visible true end time), and never run a timer that lies.

5. What not to do

Most Black Friday damage is invisible on the night of the sale. It shows up weeks or months later, in margins, returns and customer behaviour.

1. Discounting the whole store. It dilutes the signal of your genuinely good offers (Anderson & Simester 2001) and gives away margin on units that would have sold anyway (Ailawadi et al. 2007).

2. Turning one event into a season. Constant promotions raise price sensitivity and train customers to wait (Mela et al. 1997; Cachon & Swinney 2009).

3. Ignoring last month's buyers. Visible price drops after purchase cost loyalty (Anderson & Simester 2010) and, online, trigger returns (Gijsenberg et al. 2025).

4. Inflating the reference price. A comparison price powerfully raises perceived value and reduces search — which is exactly why it is open to abuse (Compeau & Grewal, Journal of Public Policy & Marketing 1998). In the EU it is now also unlawful (section 6), and it backfires: a preregistered experiment found that showing a lower 30-day prior price significantly reduced purchase intent (Barrafrem & Tinghög, Journal of Consumer Policy 2025).

5. Faking scarcity. Deceptive timers and invented stock counters are documented dark patterns (Mathur et al. 2019) and an enforcement target.

6. Measuring revenue instead of incremental profit. A comparison with last weekend or last year's Black Friday is not a counterfactual. Without a control group, you cannot tell lift from borrowing.

There is a consumer-side warning too. Every year since at least 2021, the UK consumer organisation Which? has tracked Black Friday deals for a full year around the event. In its 2024 analysis of 175 home and tech products from eight major retailers, 94% were the same price or cheaper at some other time in the six months before or after, and 56% were cheaper (Which? 2025). That is descriptive evidence, not a controlled study, but it shows how fragile Black Friday credibility has become — and why retailers that make genuine, verifiable offers can stand out.

6. The European constraint: the 30-day rule

In the EU, a Black Friday discount must be calculated from the lowest price you charged in the previous 30 days. That turns the promotional calendar into a pricing decision. (This section describes the rules; it is not legal advice.)

• Article 6a of the Price Indication Directive, introduced by the Omnibus Directive (EU) 2019/2161, requires any announced price reduction to indicate the prior price — the lowest price applied in a period of at least 30 days before the reduction. Its purpose is to stop artificially inflated reference prices. The European Commission's December 2021 guidance (OJ C 526) explains how to apply it, including to successive campaigns (European Commission).

• The Court of Justice settled the calculation in Aldi Süd (C-330/23, 26 September 2024): an advertised reduction such as "−30%" must be computed from that 30-day prior price, not merely shown next to it (Deloitte Legal summary).

• National rules differ. Poland, for example, applied the rules from 1 January 2023, extended them to services, did not adopt the optional exception for progressively deepening reductions, and its consumer authority UOKiK expects the explicit wording "lowest price in the 30 days before the reduction"; a crossed-out price alone is not enough (Hogan Lovells). UOKiK has opened proceedings against several retailers over how they display the 30-day price (UOKiK).

Three strategic consequences follow:

1. Plan the sequence 60–90 days ahead. An early-November promotion lowers the reference price for Black Friday; the Black Friday price then becomes the reference for Christmas offers within the 30-day window.

2. Be careful with "Black Month" ladders. Where no progressive-reduction exception applies, each deeper step is measured against a lower base, so the advertised percentage shrinks just as you need it to grow.

3. Non-price value gains relevance. Bundles, gifts and loyalty benefits sit outside the simple crossed-out-price logic (though misleading "worth" claims remain prohibited), and they have research support of their own (Strategy 8).

Compliance is not a pricing strategy, but it is a hard constraint on one — and pricing systems need reliable price histories to meet it.

7. A profit-first plan for Black Friday

Start with objectives and economics, not with the discount percentage. Seven steps turn the evidence above into a plan.

1. Define the objective — and quantify it. Incremental contribution, profitable new customers, inventory clearance or category penetration call for different rules. A clearance campaign and an acquisition campaign should not share one discount grid.

2. Give every product a role. Classify SKUs as traffic drivers, complements, margin protectors or clearance items, using contribution margin, price elasticity, competitive exposure, stock cover and cross-sell potential.

3. Set a discount range per SKU. Evaluate candidate prices on expected contribution, stock consumption and substitution; respect minimum margins, supplier terms and channel rules. Where you lack a demand model, run price tests before the event.

4. Design offers by segment. New, established and lapsed customers get different incentives; recent buyers get protection. Make sure no rule rewards delaying a purchase.

5. Engineer the message. Choose money-off or percent-off by price level, state specific discounts, use real deadlines with a real reason, and test threshold versus capped basket offers.

6. Measure against a counterfactual. The core metric is incremental contribution: contribution with the promotion minus expected contribution without it, net of product cost, fulfilment, shipping subsidies, payment fees, media, expected returns and cannibalisation. Hold out a randomised control group wherever you can.

7. Keep measuring after the event. Track the post-promotion dip, repeat purchase, returns and inventory over a window matched to the category's purchase cycle.

Checklist before sign-off:

• What volume increase does each planned discount require to break even, and is it realistic?

• Which three to ten products are our traffic drivers, and what basket do we expect around them?

• How do we treat new customers, loyal customers and customers who bought in the last 30 days?

• How much December demand are we pulling forward?

• Is every reference price the genuine 30-day lowest price, and is every deadline true?

• Where is our control group, and who owns the post-event analysis?

Conclusion: the best Black Friday strategy is not the biggest discount

The research does not say "don't discount". It says that a discount is an investment whose return depends on where the extra sales come from, who receives the offer, how it is communicated and what happens after the weekend ends.

Some promotions create demand; many merely move it between products, retailers and weeks. Deep discounts can win new customers and quietly teach loyal ones to wait. Targeted offers can double short-term sales and change shopping behaviour for the worse. Urgency can help, but online shoppers see through arbitrary deadlines. And in Europe, the 30-day rule makes honesty a legal requirement as well as a commercial one.

The question that matters is not how much Black Friday sells. It is how much additional profit those sales create — and how much of it is still there in January.

If you want to put the methods from this post into practice, I cover them in detail in three books:

• Practical Pricing Strategies for eCommerce: pricing and offer management for your own online store. It starts the series: how to set prices, plan promotions and protect margin.

• Practical Amazon Offer Management (2026 Edition): price, promotions, inventory and compliance for Amazon sellers in the US and Europe. It covers fee-based price floors, the Buy Box, Automate Pricing and deal planning.

• Practical Shopify Offer Management (2026 Edition): pricing, promotions and assortment strategy for Shopify stores. It covers three price floors, price testing, discounts that comply with the EU 30-day rule, and inventory turnover.

👉 All three books are available on Amazon in paperback and Kindle.

Methodological note and references

The studies cited cover grocery and drugstore chains, mail-order catalogues and online retailers. Applications to Black Friday are managerial interpretations unless a study explicitly tested a promotional event online. Numerical examples are illustrative calculations, not research results. Consumer-organisation data (Which?) is descriptive, not causal. Legal content describes the rules as of November 2026 and is not legal advice.

Peer-reviewed research

• Adams, W.J. & Yellen, J.L. (1976). Commodity Bundling and the Burden of Monopoly. Quarterly Journal of Economics, 90(3), 475–498.

• Ailawadi, K.L., Harlam, B.A., César, J. & Trounce, D. (2007). Practice Prize Report — Quantifying and Improving Promotion Effectiveness at CVS. Marketing Science, 26(4), 566–575.

• Anderson, E.T. & Simester, D.I. (2001). Are Sale Signs Less Effective When More Products Have Them? Marketing Science, 20(2), 121–142.

• Anderson, E.T. & Simester, D.I. (2004). Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies. Marketing Science, 23(1), 4–20.

• Anderson, E.T. & Simester, D.I. (2010). Price Stickiness and Customer Antagonism. Quarterly Journal of Economics, 125(2), 729–765.

• Barrafrem, K. & Tinghög, G. (2025). The Effect of the Omnibus Pricing Directive on Consumer Purchase Intentions. Journal of Consumer Policy.

• Cachon, G.P. & Swinney, R. (2009). Purchasing, Pricing, and Quick Response in the Presence of Strategic Consumers. Management Science, 55(3), 497–511.

• Chen, S.F.S., Monroe, K.B. & Lou, Y.C. (1998). The Effects of Framing Price Promotion Messages on Consumers' Perceptions and Purchase Intentions. Journal of Retailing, 74(3), 353–372.

• Chevalier, J.A., Kashyap, A.K. & Rossi, P.E. (2003). Why Don't Prices Rise during Periods of Peak Demand? Evidence from Scanner Data. American Economic Review, 93(1), 15–37.

• Compeau, L.D. & Grewal, D. (1998). Comparative Price Advertising: An Integrative Review. Journal of Public Policy & Marketing, 17(2), 257–273.

• Darke, P.R. & Chung, C.M.Y. (2005). Effects of Pricing and Promotion on Consumer Perceptions: It Depends on How You Frame It. Journal of Retailing, 81(1), 35–47.

• DelVecchio, D., Krishnan, H.S. & Smith, D.C. (2007). Cents or Percent? The Effects of Promotion Framing on Price Expectations and Choice. Journal of Marketing, 71(3), 158–170.

• Ferreira, K.J., Lee, B.H.A. & Simchi-Levi, D. (2016). Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing & Service Operations Management, 18(1), 69–88.

• Fisher, M., Gallino, S. & Li, J. (2018). Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments. Management Science, 64(6), 2496–2514.

• Gauri, D.K., Ratchford, B., Pancras, J. & Talukdar, D. (2017). An Empirical Analysis of the Impact of Promotional Discounts on Store Performance. Journal of Retailing, 93(3), 283–303.

• Gijsenberg, M.J., Bijmolt, T.H.A. & Hirche, C.F. (2025). Promoting Product Returns? The Impact of At-Purchase and Post-Purchase Discounts on Customers' Return Behavior. Journal of Retailing, 101(3), 473–492.

• Hardesty, D.M. & Bearden, W.O. (2003). Consumer Evaluations of Different Promotion Types and Price Presentations. Journal of Retailing, 79(1), 17–25.

• Hmurovic, J., Lamberton, C. & Goldsmith, K. (2023). Examining the Efficacy of Time Scarcity Marketing Promotions in Online Retail. Journal of Marketing Research, 60(2), 299–328.

• Inman, J.J., Peter, A.C. & Raghubir, P. (1997). Framing the Deal: The Role of Restrictions in Accentuating Deal Value. Journal of Consumer Research, 24(1), 68–79.

• Jedidi, K., Mela, C.F. & Gupta, S. (1999). Managing Advertising and Promotion for Long-Run Profitability. Marketing Science, 18(1), 1–22.

• Lambrecht, A. & Tucker, C. (2017). Tensile Promotions in Display Advertising. Working paper, London Business School / SSRN.

• Lewis, M., Singh, V. & Fay, S. (2006). An Empirical Study of the Impact of Nonlinear Shipping and Handling Fees on Purchase Incidence and Expenditure Decisions. Marketing Science, 25(1), 51–64.

• Mathur, A. et al. (2019). Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW).

• Mela, C.F., Gupta, S. & Lehmann, D.R. (1997). The Long-Term Impact of Promotion and Advertising on Consumer Brand Choice. Journal of Marketing Research, 34(2), 248–261.

• Osborne, M. (2018). Frequency versus Depth: How Changing the Temporal Process of Promotions Impacts Demand for a Storable Good. Japanese Economic Review, 69(3), 258–283.

• Pauwels, K., Hanssens, D.M. & Siddarth, S. (2002). The Long-Term Effects of Price Promotions on Category Incidence, Brand Choice, and Purchase Quantity. Journal of Marketing Research, 39(4), 421–439.

• Srinivasan, S., Pauwels, K., Hanssens, D.M. & Dekimpe, M.G. (2004). Do Promotions Benefit Manufacturers, Retailers, or Both? Management Science, 50(5), 617–629.

• Thomas, J.B. & Peters, C. (2011). An Exploratory Investigation of Black Friday Consumption Rituals. International Journal of Retail & Distribution Management, 39(7), 522–537.

• van Heerde, H.J., Gupta, S. & Wittink, D.R. (2003). Is 75% of the Sales Promotion Bump Due to Brand Switching? No, Only 33% Is. Journal of Marketing Research, 40(4), 481–491.

• van Heerde, H.J., Leeflang, P.S.H. & Wittink, D.R. (2004). Decomposing the Sales Promotion Bump with Store Data. Marketing Science, 23(3), 317–334.

• Warner, E.J. & Barsky, R.B. (1995). The Timing and Magnitude of Retail Store Markdowns: Evidence from Weekends and Holidays. Quarterly Journal of Economics, 110(2), 321–352.

• Yi, S., Hardisty, D.J., Griffin, D.W. & Allard, T. (2026). Promotion Architecture: A Deal Fairness Model of Restricted Price Promotions. Journal of Consumer Research, 53(4), 369–389.

• Yuan, Q., Li, J., Jiang, Y. & Liu, C. (2022). When Do Amount-Off Discounts Result in More Positive Consumer Responses? Meta-Analytic Evidence. Psychology & Marketing, 39(1), 46–58.

• Zhang, D.J., Dai, H., Dong, L. et al. (2020). The Long-term and Spillover Effects of Price Promotions on Retailing Platforms: Evidence from a Large Randomized Experiment on Alibaba. Management Science, 66(6), 2589–2609.

• Amount off versus percentage off — when does it matter? (2016). Journal of Business Research, 69(3), 1022–1027.

Books, regulatory and descriptive sources

• Berger, J. (2013). Contagious: Why Things Catch On. Simon & Schuster.

• Nagle, T.T. & Müller, G. The Strategy and Tactics of Pricing. Routledge.

• European Commission (2021). Guidance on the interpretation and application of Article 6a of Directive 98/6/EC, OJ C 526.

• Court of Justice of the EU, Case C-330/23, Verbraucherzentrale Baden-Württemberg v Aldi Süd, 26 September 2024.

• Which? (2025). Most Black Friday deals the same price or cheaper at other times of the year.