Personalized Pricing Offers

Fascinating paper from researchers at Pandora that explores how revenue can be optimized across subscriptions and advertising by personalizing ad load. While personalized pricing for digital products is a well-explored topic, the optimization of "implicit prices" like ad load is relatively fertile ground for research. This paper explores how a firm that monetizes with both ads and subscriptions can utilize ad load as a mechanism for maximizing revenue. The paper's authors conduct a large-scale field experiment in which 7MM Pandora users are separated into seven experimental conditions based on "pods" of ad load: FxL, or number of ad breaks per hour * number of ads per break (eg., 3x2, or 3 ad breaks per hour comprised of 2 ads each). Note that these levels of ad load only reflect *intended* ad load, and not realized, since Pandora can't control whether any given impression is filled. The paper's authors then implement a set of neural-network-based structural models, trained on the variation revealed through the experiment, to test how different users respond to the varying levels of ad load. This personalization policy: 1) estimates individual-level counterfactual subscription uplift 2) estimates individual-level ad-revenue uplift and 3) assigns heavier loads to users for whom the predicted subscription uplift outweighs the loss in ad-supported listening and ad revenue. The paper finds that, while increased ad load is associated with decreased ad-supported listening hours, the personalization strategy is associated with a 7% increase in subscription profits while ad revenue remains constant. This is associated with a roughly 2% decline in consumer welfare, disproportionately felt by users with a high willingness to pay. The authors determine that achieving this same revenue result without a personalization policy would require a roughly 22% increase in universal ad load. Note that the authors highlight one limitation of this approach: impressions remain roughly fixed in the immediate term, so personalizing ad load in this way merely shifts impressions across users, holding the total number of ads served fixed. Link to paper below

Inflation often forces businesses into a dilemma—raise prices and risk losing customers, or keep prices stable and shrink margins. But what if data could help strike the perfect balance? 🚀 Challenge: Flipkart, one of India’s largest e-commerce platforms, noticed fluctuating customer retention rates and declining repeat purchases, especially during inflationary periods. Traditional deep-discount campaigns led to short-term sales spikes but failed to build long-term customer loyalty. 🔎 Solution: Data-Driven Discounting Strategy Flipkart’s analytics team uncovered a key insight: Small, frequent discounts (e.g., 5-10% on repeat purchases) led to higher engagement. Personalized offers based on purchase history encouraged repeat buys. A/B testing revealed that customers preferred consistency over occasional deep discounts. 💡 Implementation: Using AI-driven dynamic pricing, Flipkart rolled out: ✅ Tiered discounts for loyal customers. ✅ AI-powered coupon recommendations. ✅ Targeted email campaigns promoting small, time-sensitive discounts. 📈 Results: After three months of testing, Flipkart saw: ✔️ 17% increase in repeat purchases ✔️ 12% uplift in customer retention ✔️ Higher profit margins vs. deep discounting 🎯 Key Takeaway: In an inflationary environment, data-driven pricing isn't just about maximizing revenue—it’s about customer psychology. Businesses that personalize their offers and optimize discounts intelligently can boost retention while protecting margins. 𝑾𝒉𝒂𝒕 𝒑𝒓𝒊𝒄𝒊𝒏𝒈 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒊𝒆𝒔 𝒉𝒂𝒗𝒆 𝒘𝒐𝒓𝒌𝒆𝒅 𝒇𝒐𝒓 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒊𝒏 𝒄𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒊𝒏𝒈 𝒕𝒊𝒎𝒆𝒔? #datadrivendecisionmaking #DataAnalytics #DiscountStrategy #BusinessStrategies

AI knows LOTS about you. And it's about to set the prices YOU, personally, pay... One of the early movers in AI pricing is Delta Airlines. They plan to expand AI-personalized pricing from 3% to 20% of tickets by year's end. Their president told investors: "We will have a price that's available on that flight, on that time, to you, the individual." Customer Reaction: "Wait, WHAT?" Translation: The algorithm has calculated how much you're likely to pay. Profit-wise, it's working. It's producing "amazingly favorable unit revenues." But what about the customers on the other side of these transactions? Seems like a zero-sum game. Delta's AI knows you. Your credit score. Purchase history. Loyalty status. That discount you almost clicked. How many times you checked the price. Whether you're on an iPhone or Android. Lots more. Here's the psychology they're missing: We're hardwired for fairness. Nobel winner Daniel Kahneman showed people will actually reject profitable deals if they feel unfair. They'll even pay extra to punish companies they perceive as predatory. When customers find out they paid more because AI analyzed their "willingness to pay," trust dies. This isn't yield management where everyone understands prices vary by timing and open capacity. This is weaponized information asymmetry that makes used car dealers look transparent. (More on that in my Forbes CMO Network article, linked in comments.) The irony? Short-term revenue gains could trigger long-term loyalty collapse. Customers who feel manipulated don't just leave. They tell everyone why they left. What's your take: Is AI-personalized pricing the future of commerce or a trust-destroying mistake? Is there a right way to do this? #CustomerPsychology #AIpricing #CustomerExperience #PricingStrategy

✈️ Personalization: The New Currency in Airline Sales & Revenue Management The old game of segmentation is over. In 2025, the winners will be airlines that treat every traveler as a segment of one—not just a booking reference. 🔹 Emirates: uses Skywards data to predict upgrade acceptance rates—driving surprise upgrades and chauffeur-drive offers exactly when travelers are most likely to say “yes.” 🔹 Singapore Airlines (KrisFlyer): AI-driven loyalty personalization. If your redemption history shows a preference for upgrades rather than free flights, KrisFlyer nudges you with tailored promotions that match your exact behavior. 🔹 Delta Air Lines: True “in-trip retailing.” Their app pushes real-time offers—Wi-Fi, lounge, same-day upgrades—optimized for business travelers who value productivity on the move. 🔹 Saudia: Moving towards next-generation retailing with Amadeus Nevio Order and the AI-powered Travel Companion. This shift enables dynamic fares, tailored ancillaries, and real-time service bundles—paving the way for Offer/Order-based retailing aligned with IATA’s NDC & ONE Order vision. 🔹 flyadeal: Even LCCs can personalize. Dynamic pricing of seats, bags, and meals ensures every passenger sees the right offer at the right time—proof that personalization is not just for premium carriers. --- 💡 Why personalization matters in Sales & RM ✔ Real-time fare optimization = higher revenue ✔ Ancillaries delivered at the right time = higher conversion ✔ Stronger emotional bonds = loyal advocates who stay 👉 The message is clear: airlines that personalize will grow. Those that don’t will fall behind. 🔍 Which airline do you think is truly leading the personalization game today—and why? Share your thoughts 👇 #AirlineSales #RevenueManagement #AirlineRetailing #NDC #Aviation

Personalised Pricing in Retail: Balancing Engagement and Fairness 🛍️ In recent years, many retailers have used personalised points offers to build deeper connections with their customers through their loyalty programs. The rationale is simple: tailoring offers to individual shopping habits has a greater impact on customer behaviour whilst also reducing the cost of the incentive. Importantly, it ensures that retailers don't reward customers who would have purchased anyway. 👍 The Shift to Personalised Pricing 💡 A small number of retailers now offer personalised discount promotions or even personalised prices. Cash discounts provide immediate savings, appealing to customers facing tough economic times. Direct discounts are also attractive to the significant segment of customers who prefer instant benefits over points, and may not even participate in points offers. An early pioneer was UK retailer Waitrose & Partners with its "Pick Your Own Offers" scheme, letting shoppers choose from a curated range of discount offers tailored to their shopping habits. Tesco's Bold Experiment with "Your Clubcard Prices" 🚀 Tesco has recently hit the headlines with its "Your Clubcard Prices" trial, where some members will receive targeted, multi-use discounts each week. Dubbed "personalised prices," I assume this initiative will use customer data to drive loyalty by offering personalised discounts on customers' favourite products. Some Pros and Cons of Personalised Prices Pros: • Increased Engagement: Analysing purchase history lets retailers offer deals that boost loyalty and repeat visits. • Efficient Marketing Spend: Targeted offers reduce waste and are more cost-effective than blanket discounts. Cons: • Perceived Unfairness: If customers see others receiving better deals, it may spark dissatisfaction and claims of price discrimination. • Technology Challenges: Dynamic pricing requires both advanced analytics and the technology needed to deliver personalised discounts across all channels, including in-store tills. Conclusion 🌟 Personalised pricing offers an opportunity to both boost engagement and optimise promotional investment. However, pulling this off will depend on balancing these benefits with fairness concerns and robust systems. Personally, I believe the benefits of personalised pricing will be too significant to ignore, though transparent communication and sensitivity to customer sentiment will be vital. Kudos to those retailers like Tesco that are leading the way and experimenting with this new approach to personalised engagement.

A recent headline CNN article (link in the commnents) shed light on a fascinating and sometimes contentious topic: price discrimination and dynamic pricing. This time it was about dedicated promotions at Starbucks These strategies, increasingly powered by AI and machine learning, are transforming how businesses engage with their customers. Here’s how you can harness these tools effectively and ethically. Understanding Price Discrimination and Dynamic Pricing 🤔 Price discrimination involves charging different prices to different customers for the same product. Dynamic pricing adjusts prices in real-time based on demand and other factors. Both strategies aim to align prices with customers' willingness to pay. Practical Tips for Effective Implementation 🛠️ 1 Leverage Customer Data 📊: - Utilize data from loyalty programs and past purchases to understand buying patterns. - Use machine learning to predict customer behavior and price sensitivity. 2 Segment Your Market 🗂️: - Traditional segmentation techniques still apply. - Use AI to create micro-segments for precise targeting. 3 Personalize Offers and Pricing 🎁: - Offer discounts to price-sensitive customers or bundle deals for high-value customers. - Train AI models to recognize when promotions are unnecessary to avoid revenue loss. 4 Test and Iterate 🔄: - Implement A/B testing to determine effective pricing strategies. - Use predictive analytics to anticipate the impact of price changes on sales. 5 Maintain Transparency 🧐: - Clearly communicate the reasons behind price differences to build customer trust. - Use feedback to refine pricing strategies and enhance customer experience. Avoiding Common Pitfalls ⚠️ 1 Over-reliance on Technology 🤖: - Regularly review and adjust AI models to align with business goals and customer expectations. 2 Ignoring Customer Perception 👥: - Be mindful of how customers perceive price differences to avoid dissatisfaction. 3 Inadequate Data Management 🗃️: - Ensure data is clean, up-to-date, and comprehensive to support accurate predictions. Starbucks is just another company using AI for personalized promotions, driving incremental sales without unnecessary discounts. Recenly I worked for food delivery app to adjust prices dynamically, ensuring competitive pricing and effective inventory management. Dynamic pricing and price discrimination is going mainstream... So, is Price Discrimination a hit or miss for your business? Share your experiences and thoughts! Is it time to shift more agressively to price discrimination solutions? 🛠️💰 ----- 📢 Curious about navigating the dynamic world of pricing and staying ahead of the curve? Hit the 🔔 icon and follow me to receive timely updates on pricing strategies, industry trends, and more!