Technology-Driven Pricing Solutions

Despite pricing being the most powerful business lever for growing Operating Profits, many mid-market companies still rely on static, cost-plus formulas to generate prices, missing key opportunities to drive higher profits on both ends (leaving money on the table and missed sales opportunities). Price optimization is built on advanced analytics, including AI and machine learning, to set prices that maximize profitability while aligning with broader business objectives (i.e., balance revenues with gross profit $). It leverages transactional and market data to deeply understand customer behavior and adapt to changing inputs (i.e., competitor prices, inventory levels, seasonality, etc.). Whether you’re in manufacturing, distribution, or retail, some form of an insights-driven, dynamic, and automated pricing strategy is essential for profitable growth. In the below article (see comments), we explore foundational pricing methodologies such as dynamic pricing, value-based pricing, and competitor-based pricing: 1. Dynamic Pricing: Adjust prices in real-time (or near real-time) based on competitor actions, inventory levels, market trends, and financial goals. Amazon’s dynamic model exemplifies how real-time adjustments can balance a low-price reputation with margin optimization. 2. Value-Based Pricing: Set prices on perceived customer value rather than costs or competitors. This ensures your pricing reflects the unique differential value you provide. A simple approach is assigning a competitive price index premium based on detailed customer research. 3. Competitor-Based Pricing: Position products strategically by considering competitors’ real-time prices. Techniques like premium pricing, price matching, and loss leader pricing help assign the right comp-pricing strategy to each customer or product segment. Successful price optimization requires avoiding pitfalls. Overcomplicating pricing models can lead to inefficiencies and erode trust among commercial teams—we’ve seen this too often. Relying on opaque “black-box” AI systems can also cause a loss of control and transparency. The key is balancing sophistication with simplicity, ensuring strategies are effective and embraced by the sales team. Building or insourcing your price optimization capabilities offers significant advantages. It aligns your pricing with business goals, provides greater decision control, and strengthens long-term pricing acumen. You can create a robust, customized pricing engine tailored to your unique needs by fostering collaboration across teams and continuously refining your models. Mid-market companies have a unique opportunity to elevate price optimization from a tertiary (or non-existent) concern to a core business function. Achieving this requires a deliberate, thoughtful approach that leverages advanced analytics, your internal/external data assets, and a collaborative approach with your Finance/Pricing and Commercial teams. #revenue_growth_analytics

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

Static paywalls are leaving money on the table; intelligent pricing is how publishers reclaim it. Fixed paywalls block access and revenue potential. Relying on static pricing risks falling behind competitors like Schibsted, which saw a 19% increase in average revenue per user (ARPU) after adopting dynamic pricing (INMA, “Dynamic Paywalls Gain Momentum”, 2023). Traditional paywalls offer the same deal to every user, but not all readers are the same. Behaviour, loyalty, and content value vary, and a one-size-fits-all approach ignores these critical factors. This rigidity limits revenue yield and risks losing high-value audiences to more agile publishers. How AI-Driven Paywalls Maximise Revenue Yield Dynamic pricing, powered by AI, allows publishers to adjust subscription offers based on real-time user behaviour and perceived content value. Here’s how: ✅ Behavioural Targeting:  The Dallas Morning News increased conversions by 28% by offering discounts to frequent readers and trials to casual visitors (INMA, 2023). ✅ Content Valuation: The Financial Times uses dynamic pricing to align fees with content value, a strategy that contributed to a 14% YoY digital subscription growth (FT Group Annual Report, 2023). ✅Predictive Adjustments: Amedia reduced bounce rates by 18% using AI-driven exit-intent discounts (Reuters Institute, “Journalism, Media, and Technology Trends”, 2023). Instead of setting prices in stone, publishers use intelligent signals to flexibly match user willingness to pay, unlocking hidden revenue pockets. Three Practical Steps to Smarter Paywall Monetisation ✓ Audit Current Paywall Performance: Identify weak points like high drop-off rates or low conversion on high-value articles. ✓ Implement AI Segmentation: Use machine learning models to predict engagement and optimise when and how offers are shown. ✓ Define Dynamic Pricing Rules: Allow prices to shift based on real-time behaviour, content consumption trends, and traffic patterns. AI-driven dynamic paywalls aren’t about squeezing users—they're about aligning subscription offers with actual user value and intent. Early adopters have seen 20–35% higher conversion rates and up to 15% lift in average revenue per user (ARPU).Static pricing is no longer sustainable for publishers aiming to maximise revenue yield. Intelligent pricing strategies are the future. Here are key takeaways: 1. Static paywalls limit potential revenue growth. 2. AI-driven paywalls tailor offers based on user behaviour and content value. 3. Dynamic pricing improves both conversion rates and ARPU. 4. Publishers must audit, segment, and dynamically adjust pricing strategies to stay competitive. It’s time to audit your pricing model. If it can't adapt, your revenue won't, either. Is your paywall strategy optimised to maximise revenue yield in 2025? Share your thoughts with me in the comment section. #AIMonetization #DigitalPublishing #PaywallStrategy #SubscriptionRevenue #PublisherRevenue

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

Outcome-based pricing is reshaping the dynamics between AI vendors and clients in enterprise AI. Rather than conventional flat fees or usage-based rates, providers now tie their compensation directly to measurable business outcomes. AI vendors are adopting a new approach by pricing their solutions based on specific achievements like successful ticket resolutions in customer service automation, revenue generated from sales optimization systems, or cost savings in inventory management AI. The more value delivered, the greater the provider's earnings. This innovative pricing strategy cultivates a more harmonized relationship between vendors and clients, reducing risks for enterprises embracing AI. Companies pay based on proven results, moving away from speculative benefits. Furthermore, it encourages AI providers to prioritize genuine business impact over superficial features. However, implementing outcome-based pricing requires a precise definition of success metrics, attribution methods, and payment structures. Both parties must agree on how outcomes will be evaluated and verified. The pricing model should also consider external factors beyond the AI's control that may impact the results. Despite the intricacies involved, outcome-based pricing is gaining traction as organizations demand accountability from their AI investments. This shift signifies a move from selling AI capabilities to ensuring tangible business outcomes. Share your experiences with these pricing models - have you witnessed them effectively driving results in practical applications