Dynamic Pricing in Sales

Pricing shouldn’t feel like a fight. It should feel like a fair conversation between adults who both want the relationship to last. When costs keep rising and margins start to feel thin, the worst thing we can do is spring a surprise increase and hope customers accept it. The better path is to make small, evidence-based adjustments that people can understand, and to do it with enough notice that trust grows rather than erodes. Here’s how I guide teams through it... We set a simple rule first: price reviews happen on a predictable cadence, anchored to a sensible index, and capped so there are no surprises. Then we give customers a choice. A clear Good / Better / Best set of tiers lets people pick the value that fits, and it means we stop discounting just to “make it work.” For loyal customers, we start with a grace period and then move in small, scheduled steps. It’s respectful, and it smooths cash flow for everyone. We also swap blanket discounts for an early-pay credit that protects the list price while bringing cash forward. We add a few fair boundaries so small, urgent, or high-touch work is priced to match the effort. Where costs have increased in one part of the service, we re-bundle so value is obvious and buyers are never misled. And when it’s time to talk, we keep the message short and human: here’s what changed in our input costs, here’s the adjustment we’re making, and here’s what stays the same in terms of quality and scope. If you track a few signals for 30 days, you’ll see better results like: most eligible accounts receive the scheduled uplift, the overall discount rate falls, more invoices are paid early, average revenue per customer increases, and churn and NPS hold steady. The goal is pricing that is predictable, and defensible. Think caliper, not hammer, with measured moves that protect margin and maintain customer goodwill. How do you explain price changes to customers without losing trust? ------- ➕ Follow Jonathan Maharaj FCPA for finance‑leadership clarity. 🔄 Share this insight with a decision‑maker. 📰 Get deeper breakdowns in Financial Freedom, my free newsletter: https://lnkd.in/gYHdNYzj 📆 Ready to work together? Book your Clarity Session: https://lnkd.in/gyiqCWV2

Southwest Airlines just initiated $35 checked bag fees, ending their trademarked "bags fly free" promise. Customers never love price increases, but for Southwest the reality is much worse. From a customer psychology perspective, this is more than a modest pricing change. The new fees will trigger multiple cognitive biases that drive customer defection. Customers don't see this as Southwest adding a $35 service. They see it as Southwest TAKING AWAY something they already owned. Loss aversion research shows this psychological pain is 2-3x stronger than equivalent gains. Expect vocal complaints and defection, especially from frequent flyers who built "free bags" into their mental accounting. Southwest built their entire identity on "bags fly free"—they literally TRADEMARKED it. This dramatic reversal triggers massive cognitive dissonance. When a company violates their core brand promise, customers don't just question the bags policy. They wonder what other promises might be broken next. Here's the killer: customers are anchored to Southwest as the "low-cost, customer-friendly" option. That $35 fee doesn't feel like a reasonable airline charge. It feels like highway robbery because it's compared against an anchor of $0, not competitors' similar fees. Southwest's CFO said they were "out of sync with competitors' bare-bones fare options." But he's missing the psychology: Southwest customers didn't choose them to be like everyone else. They chose them to be DIFFERENT. The paradox? By trying to optimize revenue per passenger, they're likely to trigger the exact behaviors that reduce total revenue: customer defection, negative word-of-mouth, and brand switching. This reminds me of Netflix's Qwikster disaster or New Coke's reformulation. Sometimes the financial logic is sound, but the customer psychology is catastrophic. The real question: Will Southwest's revenue optimization overcome the psychological costs of breaking a 50-year brand promise? What's your experience with companies that violated core brand promises - did the psychological damage outweigh the financial benefits? #BehavioralEconomics #CustomerPsychology #BrandStrategy #Neuromarketing

Machine learning for dynamic pricing optimization offers businesses a competitive edge by enabling them to adjust prices in real-time, ensuring they remain responsive to market demands, customer behavior, and competition, ultimately maximizing revenue and profitability. Machine learning, a subset of AI, allows systems to learn from data and improve without explicit programming, identifying patterns and making predictions from historical data. In pricing optimization, it helps set prices strategically by considering demand, competition, costs, and customer perception. Fundamental data types used include sales history, market trends, competitor pricing, customer behavior, demographics, seasonality, and search trends. Standard algorithms, such as regression, decision trees, neural networks, clustering, and reinforcement learning, are applied to predict demand shifts. Dynamic pricing then adjusts prices in real-time, boosting revenue and competitiveness. For business implementation, ML models can be integrated with existing systems like sales, ERP, and CRM, allowing for real-time price adjustments. Challenges include maintaining high data quality, investing in technology and skills, and addressing ethical and regulatory concerns regarding dynamic pricing, customer perception, and compliance. #ai #MachineLearning #Pricing #CRO #COO

You're afraid to raise your prices because you think you'll lose clients. Here's the counterintuitive truth: You might lose some clients, and that's actually strategic. I worked with a professional speaker who raised her minimum speaking fee. She lost 25% of her revenue initially. But here's what happened next. That same price increase saved her 40% of her time by eliminating lower-paying engagements below her new threshold. What did she do with those reclaimed hours? She wrote a book proposal. She developed a signature workshop series. She built relationships with higher-tier event planners. Within 18 months, her revenue was 30% higher than before the price increase. The best clients who truly value your work will stick with you. The ones who leave either can't afford your current level of expertise or weren't aligned with where you're heading anyway. Here's the practical strategy that makes this work: Give existing clients 6-12 months advance notice of your price increase. Grandfather them in at current rates until that date. Why this timeline works: Six months gives them enough time to budget for the change without feeling blindsided. It preserves your current relationship while you're building new work. And it positions the increase as inevitable growth, not a sudden cash grab. The real insight? This isn't just about raising prices. It's about strategically choosing which clients you keep as you level up your business. 🛟 Save this post if you're ready to get paid what you're actually worth. ➡️ Follow Dorie Clark for more strategies on building a business that values your expertise.

Delta Air Lines is piloting AI-driven dynamic pricing on a portion of its fares, with plans to expand the program substantially by year's end. Framed as a modernization of pricing strategy, this shift warrants a deeper examination of how algorithmic systems are shaping access and at what cost. Dynamic pricing is often described as demand responsive. But in execution, it frequently introduces volatility that obscures fairness. Similar approaches in retail have led to disproportionate price increases in lower income communities, raising concern that these systems are less responsive to human need than to data correlations detached from context. Several issues demand scrutiny: - Bias and disparity: Pricing algorithms can reproduce regional, racial, and economic inequities, particularly when data reflects underlying structural imbalances. - Loss of predictability: Consumers face fluctuating costs without the tools to understand or anticipate those changes, making budgeting and planning increasingly difficult. - Opaque logic: There is little transparency around how these models are developed, what inputs are prioritized, or what safeguards exist to ensure equitable outcomes. Delta reports that early results are "amazingly favorable." Without clarity on who benefits, how outcomes are defined, or which metrics are being used, these claims raise more questions than they resolve. This initiative signals a broader transformation in how corporations deploy AI across consumer-facing systems. These models are increasingly designed to maximize extraction without transparency or accountability. The consequences are rarely confined to the checkout screen. They affect who has access, who carries the burden, and who is excluded from the benefits of technological progress. This also applies to healthcare. As AI becomes more embedded in clinical decision making, triage, and resource allocation, the same concerns apply. We acknowledge that a lot of policies and stands in the field have been adopted from aviation. Hello, Universal Protocol! An algorithm that controls pricing today could soon influence how risk is scored or how treatment urgency is determined. Without safeguards, these systems risk distorting clinical judgment and widening disparities in care. What begins in commerce often finds its way into health systems, especially when the underlying logic is left unchallenged. In addition to technical efficiency and "optimization," we need governance frameworks that prioritize equity, transparency, and accountability across every domain touched by AI. "It's not about what it is, it's about what it can become."- Dr. Seuss #healthcareonlinkedin #aiethics #consumerrights #aiinaviation

Your FMCG pricing team spent weeks building that price architecture. An AI just changed it in milliseconds. Welcome to 2026. This is what's actually happening right now in FMCG pricing: Dynamic pricing has evolved into a primary offensive weapon; algorithms monitor competitor stock levels and price changes in mere milliseconds. If your competitor drops the price of a hero product:baby formula, shaving cream, laundry detergent; their A I has already matched or undercut you before your pricing team has opened their laptop. AI software spending in FMCG is expected to hit $4.3 billion by 2026. FMCG businesses taking a data-driven approach to revenue growth management are witnessing sales growth of 3% to 5%. At Unilever scale; that's billions. But AI pricing isn't just about margins. It's creating an entirely new competitive battleground. Agentic bots now lurk in the background, flagging consumers browsing competitor sites and serving them a one-time discount code in real time. This allows brands to lower prices for specific consumers without undermining their wider market price. Read that again. Your brand has a public price. And a private price. Determined in real time by an algorithm. For each individual consumer, the price on the shelf is becoming fiction. And the consumer knows it. FMCG consumers are becoming increasingly hypervigilant of shrinkflation and algorithmic pricing and when prices fluctuate wildly, trust erodes fast #FMCG #CPG #AIpricing #RevenueGrowthManagement #BrandStrategy #Pricing

Set and forget is not a pricing strategy ! Price--> Design--> Build We know that's what everyone says, but thats an oversimplification of what the entire process should look like. The assumption your pricing was correct in the pre-design phase and doesn't need change is dangerous, dangerous, dangerous !! I have seen too many physical and software products change drastically between initial design to final delivery. Product owners will typically assume that pricing still holds. You have to change that philosophy. In the real world we need a lot more iteration in price: Step 1: Initial Price: This stage you quantify the value and set an initial target price. This is a combination of internal/external research, some value quantification and pricing knowledge. Step 2: Design: With that price info, the product team designs a product that hits product and profitability targets. This is also where you need to keep track of the product margins. Often product will go design a better product at the expense of higher cost, and margins suffer before launch. Step 3: Reprice: Now that we know the new design constraints that impact the profitability, this stage gives you the opportunity to reprice the product based on the design. If substantial value has been added, price should go up. Do not fall into the 'lets over deliver on value and keep price same' trap. Step 4: Build: Now with that new price info and product roadmap the product goes through the build stage. Step 5: Pre launch reprice : Now significant time may have passed since last price review. The market for the product, the economy etc may have changed. This stage can assist in making last changes before product goes out. Good time to also establish guardrails for price performance, discount strategy, or sales strategy. Step 6: Launch: Goes without saying the product is out in the real world. Great way to capture feedback. Also a stage where performance is measured against the price guardrails. Step 7: Reprice 3: Based on sales feedback, you start charting next steps. Selling too slow, you may need discount or reprice. Selling too fast, it may be overdelivering on price vs value. Pricing metric may need change. Fx may have changed. This is the price adjustment stage, should be annual or semi annual. You can incorporate these steps into new product introduction framework or annual or semi annual pricing strategy process, either ways it will help establish good pricing principles in the org. I know of many products that once designed were never repriced years into its life.. Surely things must have changed all those years... Think of Pricing as a lifecycle !! -------------------------- We are in #Pricingtribe.

As VP Sales, I helped Syte go from $1M-10M ARR in 24 months. Here are the 5 changes we made to our enterprise sales playbook to 3x ACV to $150K: 1. Product Positioning/Pricing Shift Your product CANNOT be priced 3x more in the same market—not unless it's positioned differently. So I decided to roll up my sleeves, take calls, and experiment with positioning our multi-products as a suite with a single pricing model. The result? Not only did that lead to bigger deals, but customers started seeing us as a more strategic partner. Don’t look at biz models as just tactics—they play a big role in how you’re perceived. 2. From 'Selling Products' To ’Transformations' 'Selling a product' means you’re focused on product-level pain (e.g. Hidden stakeholders in deals). 'Selling a transformation' means you're focused on your prospects' key priorities (e.g. Ensuring GTM effectively moves up-market ahead of next fundraising). Of course, both levels matter, but the question is... Is your champion telling a 5, 6, or 7-fig ACV story? 3. Coached SEs To Build Advanced ROI Our e-commerce buyers lived and breathed data—if we couldn’t talk their numbers game, we’d lose. We also needed to prove the money they were bleeding by not fixing the problem. Our AEs initially struggled to hit CFO-level standards, so we pulled in our Head of Analytics and built a playbook on articulating ROI. Once our SEs learned it, they could walk a CFO through real-world math in minutes. That credibility was a game-changer for large deals. 4. Tripled The Steps Of Our POC Playbook Our tech was mind-blowing in live demos, and we knew that’s where we’d outshine competitors—if done right. But a chaotic POC can do more harm than good. So we reworked the entire playbook: No Mutual Action Plan? No POC. Every AE was required to run a kickoff call defining scope, success criteria, resources, and—ideally—exec-level commitment. Slowing things down up front led to faster, more decisive closes later. 5. Made ‘Challenger Sales’ Our Go-To This was a highly complex motion. We had to sell against an ‘easy’ status quo. AEs who were only teaching about our product couldn’t create a compelling case for 6–7fig funding. We decided: every rep must adopt a Challenger mindset—teach buyers about problems they didn’t know they had, offer a brand new perspective about how to solve them, and reframe the solution. That consistency kicked open bigger budgets and higher ACVs across the team. —— Being a startup sales leader is hard. It sometimes feels impossible. But that’s why you shouldn’t play it safe. Take the bold bets. Run risky experiments. Challenge assumptions. Get comfortable being uncomfortable. That’s where the magic happens. P.S. We built Aligned to help manage the deal complexity of Enterprise Sales. A 100% FREE Deal Room used by 30,000 sellers. Try it here: https://lnkd.in/d_49kHZE

Most conventional cost-effectiveness models in pharmacy assume drug prices never change – they are static. That’s a problem. In the real world, prices fall dramatically after patent loss. --- New #research from Center for the Evaluation of Value and Risk in Health (CEVR) and National Pharmaceutical Council shows just how far off conventional cost-effectiveness analyses (CEAs) can be when they ignore real-world #DrugPricing dynamics. The study compared static CEAs to dynamic models that incorporated drug price changes over time, which is particularly crucial to account for price changes after loss of exclusivity. --- The result: static CEAs consistently overstated #DrugCosts, skewing the cost-effectiveness ratio by 27% to 82%, depending on the treatment type. Key takeaways: -Chronically administered treatments are most affected. Their price drops after losing exclusivity were the single biggest factor in shifting cost-effectiveness. -One-time treatments are less sensitive to price changes, but they are highly influenced by baseline patient age and discount rates. -Dynamic models offer a more realistic view of opportunity costs and better reflect the #pharmacoeconomics of patented drugs over time. --- Why does this matter? Conventional CEAs may currently be penalizing certain #pharmacy therapies, especially those treating chronic conditions, by ignoring the competitive market forces that eventually drive prices down. This may distort resource allocation, payer negotiations, and long-term pricing strategy. Models should reflect how prices behave in the real world, and not assume a fixed price indefinitely. --- How are you accounting for future price shifts in your trends and projections? Most drug prices act pretty similarly after patent loss. It may be worth creating some patent loss assumptions to incorporate into your models if you aren't already.

𝗛𝗼𝘄 𝘁𝗼 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗴𝘂𝗲𝘀𝘀𝘄𝗼𝗿𝗸) When it comes to deciding product’s pricing strategies, most of the PMs have 2 approaches: → Guessing work → Get overwhelmed by over 25 pricing strategies available in the market It makes the hard thing (pricing) even harder to decide and execute. But let me share a simple 3 step framework that would work for almost all the product pricing strategies. 1. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗱𝗮𝘁𝗮 - The first step is to dive into the data. - Study competitor pricing, identify key profit margins, and identify customer segments that are most profitable for you at the current stage. - Look for insights that reveal how your product is perceived in the market. 👉 For instance, when Swiggy ventured into subscription models, it experimented with its Swiggy Super plan. By analyzing customer data, it found that users preferred free delivery perks. This insight allowed them to create a pricing model that not only increased subscriptions but also improved overall order volumes. ✅ So, pricing should always be a dynamic process. Don’t rely on a “set and forget” approach. Continuously engage with your pricing team and adjust based on market shifts and customer behavior. 2. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘃𝗮𝗹𝘂𝗲 - Don’t focus solely on maximizing profits or sales volumes, think about the value your product delivers. Consumers today are willing to pay a premium for products they feel add significant value. 👉 Consider Tata Nexon EV, one of India's leading electric vehicles. Despite higher upfront costs compared to traditional fuel cars, it offers long-term savings and environmental benefits, which customers perceive as valuable and they are buying it. ✅ As a product manager, your job is to understand what drives consumer decision-making. Are they paying for premium features, better service, or convenience? The more you emphasize value, the stronger your pricing strategy will be. 3. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 - Once you understand your costs and customer segments, develop three pricing strategies - conservative, aggressive, and a middle ground. - Think of it as a Goldilocks approach: one option may be too extreme, another too safe, but the third might hit the sweet spot. - This gives your business a range of options to test and optimize. 👉 Take Netflix India as an example. When it introduced the low-cost mobile-only plan, it allowed the company to penetrate deeper into the price-sensitive Indian market. By offering different pricing tiers, Netflix was able to serve both premium and budget-conscious users. 𝗜𝗻 𝗮 𝗻𝘂𝘁𝘀𝗵𝗲𝗹𝗹: Pricing is all about understanding what your customers are willing to invest in terms of time, energy, and money. What's your go-to strategy for product pricing?