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Author: Kiran Gange Artificial Intelligence (AI) transforms business pricing strategies by providing game-changing insights that boost profitability and competitiveness. This paper examines how pricing leaders can improve margin optimization, adjust to shifting market conditions, and optimize pricing strategies by incorporating AI into their daily operations. Learn about the most recent advancements, practical applications, and future possibilities of AI in B2B pricing to help your company stay ahead of the curve. Figure 1: Artificial Intelligence (AI) in Retail Market Size, 2022 - 2032 (USD Billion) Introduction:  In the current data-driven and fast-paced market climate, companies are under growing pressure to optimize their pricing strategies. Conventional pricing strategies, which frequently depend on human analysis and historical data, are insufficient to stay competitive. Artificial intelligence (AI) is becoming a more potent instrument that helps companies to increase productivity, optimize profitability, and make data-driven pricing decisions. By using cutting-edge algorithms and machine learning models to evaluate enormous volumes of data, spot trends, and suggest the best prices, artificial intelligence (AI) is completely reinventing pricing methods. ● Pricing algorithms may boost revenue from 1% to 5% and lengthen the customer life cycle by 20%, according to a comprehensive worldwide assessment carried out by the Massachusetts Institute of Technology (MIT) and the BCG Henderson Institute (BHI). ●  By 2028, it's expected that the retail industry will have invested $24.1 billion in AI-based solutions. ●Personalized suggestions, content creation, and predictive analytics are projected to be the top applications of generative AI by global marketers and advertisers in 2023. Analytics and AI for Margin Optimization :  AI is essential for improving margin optimization because it gives pricing teams instant access to information about cost structures, market dynamics, and consumer behavior. AI is able to find chances to modify prices in a way that maximizes profit margins while maintaining market share by examining these aspects. AI, for example, can identify underperforming product lines and recommend price adjustments to match competition pricing and market demand. By using a flexible approach, companies may protect their profitability while retaining a competitive advantage.   Example:  A multinational electronics firm regularly tracked market trends and competition activity using pricing analytics driven by AI. The AI system saw a trend where rivals regularly changed their rates during the busiest times of year for sales. The manufacturer increased its market share and increased its profits by 15% by aggressively adjusting its pricing approach. Figure 2: Benefits of Artificial Intelligence (AI) for Retail Business Worldwide, 2022 Leveraging AI for Everyday Pricing Operations and New Product Pricing :  AI can be used to expedite daily pricing processes in addition to long-term strategic decision-making. Routine operations like pricing monitoring, updating pricing models based on fresh data, and identifying anomalies that need attention can be automated by AI. Pricing teams may now concentrate on more strategic tasks, including introducing new goods or breaking into untapped markets, thanks to this automation.   AI can forecast the ideal launch price for new products by examining past sales data, consumer preferences, and industry trends. Businesses can make well-informed decisions that strike a balance between profitability and market acceptance by modeling several price situations.   Example: A software-as-a-service (SaaS) provider used AI to optimize subscription service pricing. Artificial Intelligence suggested tiered pricing structures based on the needs of various client categories by examining customer usage habits. This strategy led to 10% more subscription renewals and higher customer satisfaction. 2. Use Cases and Best Practices in AI-Powered Pricing from Around the World:   Global adoption of AI in a variety of industries demonstrates its adaptability and potency as a pricing strategy. These are a few noteworthy use cases: Figure 3:  AI Use Cases in Consumer Goods and Retail Industry Worldwide, 2020 Retail:  To improve markdown pricing, a top fashion retailer analyzed real-time sales data using artificial intelligence. The retailer raised sales during end-of-season discounts and eliminated surplus inventory by 20% by employing AI-driven markdown methods.   Manufacturing:  Using AI, a European supplier of auto components dynamically modified prices based on the cost of raw materials and the prices of rival companies. Despite changes in input costs, the company was nevertheless profitable thanks to its flexible pricing strategy.   E-commerce:  An Asian online marketplace used artificial intelligence (AI) to track rival prices and modify its own pricing policy. Artificial intelligence algorithms were able to identify price reductions made by rivals and automatically modify the company's prices to maintain competitiveness while safeguarding profit margins.   Example: Sephora's Beauty Insider program uses AI-powered chatbots to offer personalized beauty consultations through its "Beauty IQ" tool. This virtual assistant analyzes customer preferences and skin concerns, recommending customized product regimens. The program boasts a 25% increase in customer engagement and a 10% rise in average order value. Figure 4:  Beauty IQ tool by Sephora (Source: Sephora) Future Potential of AI in Pricing: Artificial intelligence in pricing has a bright future ahead of it. Artificial intelligence (AI) systems will become ever more proficient at comprehending intricate market dynamics and customer behavior as machine learning and natural language processing technologies progress. Real-time sentiment analysis will probably be a feature of future AI-powered pricing solutions, enabling companies to predict how customers will respond to price adjustments and modify their plans accordingly.   AI can also improve collaborative pricing models, in which retailers and suppliers collaborate to optimize pricing across the supply chain. By using a comprehensive strategy, we can increase productivity, cut expenses, and provide outcomes that benefit all parties involved.   Using AI to Optimize Your Pricing Strategy:  Businesses should adhere to these best practices in order to successfully use AI in pricing:   Invest in High-Quality Data:  AI needs data to function. Having complete, current, and correct data is essential for AI to produce insightful results.   Start Small and Scale: To gauge the effect of AI on particular product lines or market niches, start with trial initiatives. Based on demonstrated outcomes, gradually scale AI integration throughout the whole pricing strategy.   Continuous Learning: In order for AI models to adjust to shifting market conditions, they require frequent updates and recalibration. Ongoing education guarantees AI's continued efficacy in providing ideal price advice.   Work Together with Experts:  Using pricing software platforms and collaborating with AI experts can help you get the knowledge and resources you need to successfully apply AI-driven pricing strategies. Figure 5:  What Advanced Pricing Methods Improve Conclusion: With its ability to facilitate data-driven decision-making, optimize margins, and improve competitive positioning, artificial intelligence is revolutionizing the world of business-to-business pricing. Businesses can better navigate the intricacies of today's market dynamics and enhance their pricing strategies by utilizing AI. AI technology will play a more and more important role in pricing as it develops, making it a vital tool for companies looking to achieve both sustainable development and excellent pricing. Businesses that adopt AI-driven pricing strategies now will be better positioned for success in the cutthroat marketplaces of the future.

Author: Pawel Dadura , Chief Technology Officer, Revenue.AI As organizations increasingly recognize data as a strategic asset, the conversation has shifted from ‘we need data-driven operations’ to ‘how to scale them sustainably’. Yet, despite advances in data management and AI (Artificial Intelligence) tools, many businesses struggle to realize the full potential of their data to implement more effective Pricing and Revenue Management strategies. The Challenge: Bridging the Gap Between Data and Decision-Making Based on our experience working with multiple companies in Consumer-Packaged Goods (CPG), Retail, and Commodity Trading (CT), data silos are a common blocker for decision-making. The traditional workflow, where data is only understood, or worse – only accessible for a given department and isolated for the rest of the organization, creates misalignment within the company. Artificial Intelligence (AI) is advancing at an incredible pace. According to Revenue.AI ’s whitepaper , [MD1]    the rising trend of AI continuing into 2024 highlights a shift towards the democratization of AI across multiple industries. AI has been applied to resolve challenges across multiple business processes. As powerful as AI might be, however, w ithout a shift towards data literacy democratization and interdisciplinary collaboration, AI can easily fail to deliver sustained value. Apart from the typical organizational challenges, companies can face another barrier when trying to apply such technology as a solution – the mismatch between a company’s data literacy and sophistication of the tools that are being deployed. On the other hand, companies with good scaling practices spend half of their analytics budgets on adoption and yet, most organizations fail to increase or sustain adoption levels and realize value from their data. Organizations need more than AI tools - they need the tool which brings to life a strategy that empowers their people, aligns processes, and fosters a culture of data literacy growth at every level.  The Right Approach: From Silos to Data-Driven Agility Companies must transform their approach to both data and organizational structures. Here, it is critical that businesses choose and apply the right AI solution that can transform their data and help them scale efficiently. The proper AI solution will allow businesses to experience: ·  Data transparency : Front-line workers need access to real-time, digestible data that support their daily tasks and decisions. ·   Interdisciplinary collaboration : Breaking down silos to enable data exchange seamlessly between departments. Cross-functional teams must work together to solve complex problems using shared insights and common data. ·   Agile decision-making : Gut-based decisions must give a way to the culture of experimentation and data-driven decision-making. This agility encourages innovation, fosters adaptability, and empowers teams to respond to changes swiftly and accurately. In the realm of Pricing and Revenue Management, the right AI solution can help businesses achieve these goals. Right-framed AI solutions provide real-time visibility into market conditions, customer behavior, and competitive pricing, allowing companies to make more informed pricing decisions. By breaking down data silos, AI enables seamless collaboration between departments, ensuring that pricing strategies are aligned across the organization. Moreover, AI fosters agile decision-making by enabling continuous experimentation with pricing models, adjusting them dynamically based on demand, seasonality, and other key factors. This approach not only maximizes revenue potential but also allows businesses to stay competitive in rapidly changing markets. What can businesses look for in AI solution? Data literacy enablement Scaling AI in an organization is not solely a technological challenge - it’s an educational one. The right AI solution would enable companies to increase data literacy across the workforce, providing products that are business value focused, and reducing barriers to the use of analytics for faster decision making. People enablement necessity AI transformation requires a workforce that is continuously learning and adapting. Yet, a significant portion of employees are either resistant to training or lack opportunities to build the skills needed to work alongside AI. According to OECD data, nearly 50% of adults neither train nor want to train, and around 60% do not participate in training in a given year. The best AI solutions should provide training tailored specifically for the company workforce itself. People learn best through experiences. The AI solution should offer assistance through Intelligent Center of Excellence (ICE) to help users maximize the value of those experiences and accompany users at every stage of growth. An example of Kolb’s learning process application with AI-enabled Intelligent Automation (Source: Revenue.AI ) The Future: Sustainable Growth via AI-Supported Culture AI is no longer a futuristic concept - it's transforming industries and processes. But for organizations to truly benefit from it, they need to find the suitable AI tool that fosters a data-driven culture and follows business-proven roadmap. This should begin with AI education, targeting quick wins, and gradually evolve to building specialized teams and developing a comprehensive AI and data strategy. The question is no longer "Should we embrace AI?" – it’s "How fast can we transform?"

Author: Cath Brands, Chief Marketing & Innovation Officer, Flintfox  In today's unpredictable market, pricing professionals are no longer in the background; they’re at the forefront, maintaining stability as well as driving business growth. But meeting these expectations isn’t easy in a highly volatile market and when working under the constraints of legacy systems. Our recent global survey of over 1,000 pricing professionals revealed a startling fact: 86% of respondents believe they lost out on profit over the past three years because they couldn't adjust prices quickly enough. On average, that's a staggering $420,000 per year down the drain. The Three Key Challenges Holding Businesses Back Slow Reaction to Market Changes : 94% of pricing professionals worry about how fast they can implement price changes in response to shifting market conditions. Complex Pricing Across Channels and Regions : 71% of pricing professionals struggle with multi-channel pricing, while 73% find regional pricing strategies difficult to manage. System Limitations : Over a third of companies say their system can't support their pricing strategy, and nearly half struggle to manage promotions and rebates effectively. Building a Resilient Pricing Strategy To overcome these challenges, businesses need to ask themselves critical questions: Are you reacting fast enough to market changes? Are you losing value through discounts and promotions? Is your pricing consistent across channels and regions? Are you optimizing pricing based on customer segments? Are your pricing tools supporting your strategy? Interrogating these five areas can reveal where your pricing strategy might be falling short and where technology can help bridge the gap. The Role of Technology in Modern Pricing To understand how technology can support your pricing strategy, we first need to understand the role of each tool. Think of your pricing strategy as a house. Your ERP system forms the foundation while price-setting tools build the framework. Price execution ensures everything flows smoothly, like the wiring and plumbing in a home. Finally, price management acts as the roof, protecting your entire pricing structure. By understanding where each aspect of pricing technology fits within your pricing strategy and the interplay between each, you can identify the gaps or areas for improvement. And with the right technology in place, your pricing strategy can become more agile, responsive, and profitable, ready to face the challenges of today's volatile market. I'll be diving deeper into these topics and sharing actionable insights at the PPS Fall Pricing Conference in Vegas on Thursday 24 October. Join me to discover how you can maximize value through technology-powered pricing strategies.

Author: Brian Sherry AI Empowered Pricing It is without a doubt that artificial intelligence (AI) has become a more important topic in many companies following the launch of ChatGPT in late November 2022. With every passing day, artificial intelligence is gaining more attention, especially when it comes to its use in the business environment and its role in optimizing pricing strategies. Driven by this rising interest in data science for AI-based pricing solutions, the market for price management and optimization software has been experiencing rapid growth and is now projected to reach $2.7 billion by 2028. One of the main factors driving AI's growth in pricing is its wide range of use cases, which can help address many of the complex pricing challenges that B2B companies face today. A Complex B2B Pricing Challenge Many mid-sized B2B (business-to-business) companies, particularly in sectors like manufacturing, wholesale, and IT services, can process hundreds to thousands of quotes each month. One significant challenge B2B companies face today is the ability to provide their sales teams with accurate and timely price guidance, for example: What prices do sales quote when a customer comes with a price request or wants to negotiate new contract pricing and how can we control this? How can we make this an iterative process that ensures our pricing guidance remains data driven, consistent, profitable, and accurate over time? Overcoming these challenges is essential for pricing departments if they want to improve their win rates, accelerate their sales cycles, enhance their negotiations, and elevate the overall customer experience. AI Driven Price Guidance Solution Nowadays to solve these complex challenges many B2B companies are now using advanced analytics tools and AI to automate their data collection and transactional analysis. AI driven price guidance solutions are now enabling pricing functions to provide their sales teams with structured pricing recommendations and boundaries, ensuring that pricing decisions are aligned with their company’s overall strategy. By leveraging large datasets, advanced machine learning algorithms, and continuous improvement processes, businesses can now better understand their customers’ willingness to pay, allowing them to better optimize their price guidance further. The real value of these AI based solutions is further enhanced by their ability to continuously collect new data on sales and customer behavior, enabling the continuous learning and improvement of any price guidance modelling taking place. Powering Up Your Price Guidance Solution Another crucial point that is worth remembering is that price guidance solutions get even more powerful when used with other tools. For example, price guidance and Net Price Index (NPI) distribution analysis are powerful tools when used together. A NPI distribution analysis involves evaluating the distribution of net prices (actual transaction prices after discounts) across different deals and customer segments. Price Guidance + AI Conclusion Price guidance is a key pricing use case for AI, delivering significant benefits in accuracy, efficiency, profitability, and competitiveness. For example, according to research by McKinsey & Company, B2B companies that leverage advanced analytics and automation for their pricing and quoting processes can handle higher volumes more efficiently (+49%) and with greater accuracy (+30%). In today’s volatile world an AI driven price guidance solution is now a crucial tool for businesses to ensure consistent, competitive, and profitable pricing guidance. By providing structured, data-based pricing recommendations, it helps your sales teams navigate complex pricing landscapes, improve negotiation efficiency, and align pricing strategies with overall business goals

Author: Dean Tindall Often when it comes to setting pricing for products businesses can be faced with a large number of unknowns. New products, new markets, new competitors, even new prices can mean that the information which companies hold on actual market responses to previous price changes is sub-optimal. You could conduct in-market pricing tests to vary your price and capture sales data from real paying customers, but this can be an expensive and risky route. Plus, what your competitors do during your in-market test could skew your pricing experiment. You could analyse past sales data to calibrate the optimal price if we have a database with a history of prices and sales volume for your product and competitors. But it is not that simple. Your existing data might not have enough independent price changes to stabilize the kinds of predictive models needed to pinpoint optimal price points and optimize revenue or profit.  When faced with sub-optimal internal data and a series of unknowns, it can often be useful to de-risk any decisions made on the pricing of products by undertaking market research, and specifically using survey-based pricing techniques. In our session we will talk about the pros and cons of four of the main techniques used by practitioners today. Survey Research Methods for Measuring Price Sensitivity  Survey research enables you to evaluate a range of products at different prices, measuring the price sensitivity for consumers and key market segments—before you go to market.  However, pricing tasks in your survey need to be realistic, and your respondents need to be representative of the target market in order to gain accurate data about how price impacts their likely choices.   Consider the following popular traditional pricing research methods that have appeared over the years. Monadic Price Experiments    In Monadic Price Experiments respondents are presented with a product, or a series of products, at one of the price points you are interested in testing, and asked to state how likely they would be to purchase that product at that price. Different pricing scenarios are presented to different respondents, and we can identify how price drove a difference in their reported behaviours. This isolates the reactions you are seeing to respondents who only saw a single price point for your product. By combining the reactions to each of the pricing scenarios, we can draw a demand curve and identify any price sensitivities which may exist. Van Westendorp’s Price Sensitivity Meter VWD analysis involves asking four key questions about your product. Introducing them to the product, before asking them to state when they thought the price would be “Too Cheap,” “A Bargain,” “Expensive” and “Too Expensive.”  The cumulative frequencies of these price points are charted across all respondents, with the resulting chart yielding what is said to be an acceptable range of pricing, alongside specific points of interest such as the “Indifference Price Point” and the “Optimal Price Point.” Gabor-Granger Pricing Method This approach to pricing research involves asking respondents if they would be likely to buy a product at a given price. If they say “yes,” then we ask the question again at a higher price. If they say “no,” then we ask the question again at a lower price. The initial price point can be set at the mid-point of the pricing range or seeded randomly to avoid anchoring effects. Gabor-Granger techniques allow you to easily create a demand curve based on respondent reactions to the varying prices which were shown. Choice Based Conjoint Analysis Conjoint analysis is an advanced technique that creates questions which systematically vary a range of products and prices before presenting them to respondents. Here respondents can pick which product they would be most likely to choose in each of the carefully rotated scenarios. Based on how respondents react to price and other feature changes, you are able to generate a model that reveals the price sensitivity and willingness to pay which each respondent exhibited across the tasks. This model can then be used to create a “market simulator” which can help you to understand what the level of demand which is likely to be generated across a range of pricing and competitive scenarios. Understanding the strengths and weaknesses of each of these approaches is vital to ensuring that you are capturing data which is going to a net benefit to any pricing decision you make.

Author: Phillip Michael In the fast-paced commercial world we inhabit, the frequency and complexity of negotiations have escalated dramatically. This surge necessitates a robust system capable of managing hundreds of negotiations effectively, ensuring that each one aligns with the overarching strategic goals of the organization. To thrive in this environment, companies must cultivate a commercial management approach that is agile, innovative, and grounded in an entrepreneurial mindset.   The Core of a Holistic Approach: People, Data, Governance To navigate the complexities of today's commercial challenges, organizations must embrace a three-pronged strategy: People, Data, and Governance. This comprehensive approach not only addresses the immediate needs of a business but also lays the foundation for sustained success in an ever-changing market landscape. People: Cultivating a Negotiation Mindset Central to any commercial strategy are the individuals who bring it to life. Developing a negotiation mindset across your organization is paramount. This mindset acknowledges the importance of maximizing negotiation outcomes while striving for mutual gains. It recognizes that negotiation is not devoid of conflict; rather, it is about managing conflict intelligently to achieve the best possible outcome for all parties involved. Encouraging this perspective requires a cultural shift—one that values strategic preparation, empathy, and the ability to navigate the delicate balance between assertiveness and cooperation. Data: Empowering Decisions with Machine Learning In our data-driven age, leveraging information effectively can provide a significant competitive edge. By employing machine learning algorithms, organizations can analyze patterns from past negotiations, market trends, and pricing dynamics to inform future strategies. This approach ensures that decisions are based on comprehensive insights, allowing businesses to anticipate and adapt to market changes with precision. Governance: Fostering Psychological Safety and Avoiding Narrow Framing Governance structures play a crucial role in creating an environment where team members feel psychologically safe to express ideas, take calculated risks, and learn from setbacks. This atmosphere of psychological safety is critical for fostering innovation and agility in a commercial organisation. Moreover, it's essential to be aware of the pitfalls of many biases like for example narrow framing—a common mistake that can lead to anxious behavior and suboptimal decision-making. By broadening our perspective and considering a wider range of outcomes and strategies, we can avoid the traps of common biases and make more informed, confident decisions. Implementing the Strategy for Commercial Success Implementing this holistic strategy requires unwavering commitment from every level of the organization. It starts with a clear vision from leadership and permeates through to every team member. Training programs, data analytics tools, and governance models must all be aligned to support the development of a negotiation mindset, the application of data-driven strategies, and the cultivation of an environment that champions psychological safety and broad thinking.   Conclusion As the commercial landscape continues to evolve at an unprecedented pace, traditional negotiation and pricing strategies are no longer sufficient. By embracing a holistic approach that integrates people, data, and governance, companies can create a lasting commercial culture that is prepared to face the challenges of today and tomorrow. This transformation goes beyond improving the bottom line; it redefines the very way business is conducted in an increasingly complex and unpredictable world.

Author: Satchin Gowrea Often, when we think of Revenue Management, price as the most effective profit lever to deliver significantly higher returns comes to mind.  Why?  Are we victims of our own success?  Is this labelling of our own conjuring somehow?  It should be about revenue growth, not just pricing growth.    How do we evolve in a strategic manner while optimising price growth? And at what pace to align with business strategy?  In this breakout session, I will take you through a three year culinary journey to share what Revenue Management has been cooking at Iron Mountain.  I run my team like cooking, not making a big kitchen.  In the same way a great cook can turn out an amazing meal in any kitchen, we serve and delight the business with our lovely dishes that appeal to the leadership team’s palate.    Think of it like street food - bang on with the flavour - and evolving to keep up with changing tastes.  We have evolved, we had to. I am grateful for the opportunity to share with you where we started in year one to ensure pricing upsides do not remain ‘on paper’, the transition in year two and the coming together in year three of both price growth and commercial growth.    How do you achieve price growth with your largest accounts? How do you support deals with cross-sell and upsell, how do you pivot to a more structured process?  We will have a fun time going through these salient topics.    Whether you have already embarked on that journey or not and whether you have travelled a little or a lot, you should be able to get something out of the breakout session.  Even better, if you can apply some or all of it.   I would love to see all of you at my breakout presentation at the 2024 European & Global Pricing Workshops & Conference in Berlin, November 19-22. I will be speaking on Friday at 16:20.   About: Satchin Gowrea is a Revenue Management Director at Iron Mountain and has over 13 years of experience in Revenue Management.  He currently manages Global Industries, Warehousing & Logistics, Media Archival Services, India and leads the team responsible for driving commercial growth.  Prior to joining Iron Mountain, he held pricing leadership roles at British Telecom and Equifax.  Satchin is a Chartered Accountant with audit, deal advisory and SOX experience, holds an LLB (Hons), and an MBA from Brown University.

Author: Jose Mendoza  Image this: you walk into your favorite store, excited to pick up your go-to product, only to find the price has changed—again. Is it a sale? A mistake? Or is this just the new reality of shopping? Welcome to the era of dynamic pricing, where prices are constantly adjusted based on real-time data and algorithms. In today’s fast-paced retail world, sticking with static pricing models can leave your business behind. Embracing dynamic pricing not only keeps you competitive but also helps you meet ever-changing customer expectations. Let’s dive into the different dynamic pricing frameworks and discover practical insights to help you find the best strategy for your business. Why Dynamic Pricing Matters More Than Ever Retail has evolved beyond simply offering the best products—it’s now about offering them at the right price, at the right time. Dynamic pricing allows retailers to adjust prices in response to market demands, inventory levels, and individual customer behavior, keeping them a step ahead in a highly competitive landscape.   The Shift from Static to Dynamic Traditional pricing models often fall short in capturing subtle market shifts. Dynamic pricing, however, provides the flexibility and responsiveness retailers need to thrive. Here’s how it benefits your business: Stay Competitive:  Quickly respond to market changes and competitor moves. Optimize Revenue:  Adjust prices to match what customers are willing to pay. Enhance Customer Experience:  Offer personalized pricing that resonates with shoppers.   Exploring Key Dynamic Pricing Frameworks: Finding Your Perfect Match Understanding different dynamic pricing models can help you choose one that fits your business goals. Rule-Based Pricing This model adjusts prices based on set rules, like time of day, stock levels, or competitor pricing—a bit like cooking with a recipe. Pros:  Easy to implement and understand; offers control over pricing. Cons:  Can be too rigid in fast-changing markets; may become complex with numerous products. Recommendation:  Great for beginners or smaller businesses. Start simple and add more variables as you gain confidence.   2. Demand-Based Pricing Prices fluctuate based on real-time demand—think surge pricing on ride-share apps during rush hour. Pros:  Increases revenue during high demand; helps clear inventory during slow periods. Cons:  Frequent changes can frustrate customers; relies on accurate demand forecasting.   3. Competitive Pricing Adjust prices based on competitors—a classic “keeping up with the Joneses” approach. Pros:  Keeps your offerings competitively priced; attracts bargain hunters. Cons:  Risk of price wars; may overshadow your unique value.   4. Price Optimization Algorithms This model uses data to analyze customer behavior, market trends, and stock levels to find the ideal price. Pros:  Data-driven decisions boost accuracy; balances revenue and profit goals. Cons:  Requires technical know-how; higher setup costs.   5. AI and Machine Learning-Based Pricing Using artificial intelligence to predict market trends and customer preferences allows highly personalized pricing strategies. Pros:  Learns and improves over time; offers tailored pricing experiences. Cons:  Can be opaque; raises questions about pricing fairness.   Overcoming Challenges in Dynamic Pricing Dynamic pricing comes with its challenges, but with the right approach, you can navigate these hurdles effectively: Data Quality and Availability Challenge:  Poor data quality can lead to bad pricing decisions. Solution:  Invest in solid data management systems and regularly audit data for accuracy.   Customer Trust and Perception Challenge:  Frequent price changes can make customers wary. Solution:  Be transparent about price adjustments and consider offering price guarantees or loyalty programs.   Ethical Considerations Challenge:  Pricing algorithms might unintentionally result in unfair pricing. Solution:  Set clear ethical guidelines and regularly review your algorithms for biases.   Choosing the Right Framework for Your Business There’s no one-size-fits-all approach to dynamic pricing. Consider these factors when selecting your model: Business Size and Resources:  Smaller businesses might find rule-based pricing more manageable, while larger operations can benefit from AI-driven models. Market Dynamics:  Keep a close eye on competitors in highly competitive markets. Customer Base:  Understand how price-sensitive your customers are and adjust your strategy accordingly. Key Takeaways: Stay Flexible:  Adapt your pricing strategy as your business and market evolve. Data Matters:  High-quality data is essential for effective pricing decisions. Collaborate:  Working across departments can enhance your pricing strategy Want to Learn More? Interested in transforming your retail business with dynamic pricing? Join us at the Professional Pricing Society (PPS) Conference in Las Vegas from October 22 to 25, 2024. Explore these frameworks in-depth, hear success stories, and learn about effective implementation strategies.

Author: Stacey Adams To achieve long-term profit growth and provide value to customers, organizations must adopt proactive pricing and quoting programs that consider market dynamics, competition, and customer behavior. However, one fundamental factor that significantly impacts profitability is the cost of the products and services being offered. For manufacturing organizations, calculating costs accurately can be challenging due to various factors that affect the final result. From fluctuations in material prices to disruptions in the supply chain and inaccurate cost allocation methods, neglecting costs during the quoting and pricing process can lead to unprofitable deals and products. So, What’s the Relationship Between Profit and Price? Let's start with the basic profit equation: Profit = Revenue - Cost. Regardless of the industry, customer base, or sales channel, every organization generates revenue by selling products or services and incurs costs to make those products available. In the pricing equation, Price = Cost of One Unit + Expected Return or Markup, it is clear that the cost plays a crucial role. Without confidence in your cost calculations, you may find yourself either losing potential deals or leaving money on the table. But what does it mean to truly know your costs? Calculating costs involves considering more than just direct material and labor expenses. Post-production, delivery, service, quality control, logistics, and other overhead costs all contribute to the final cost of a product. Additionally, costs are not static numbers; they can be influenced by market dynamics, competition, and customer behavior. Without a comprehensive understanding of the cost of each product you sell, you risk pricing yourself out of the market or missing out on potential profits. Accurate cost data is essential for pricing professionals as it helps identify cost-saving opportunities, adjust pricing, and optimize production processes to maximize profitability. While you may not have full control over the cost of each unit, successful pricing and quoting professionals recognize the critical importance of cost in their decision-making. Categorizing Costs and Identifying Key Drivers Costs occur at every stage of your company's value chain. To effectively manage costs, organizations need to identify the true drivers behind them. For manufacturers, direct costs associated with production are significant, but there are many other cost considerations to take into account. The Cost and Profitability Framework, developed by 3C Software, defines three areas of cost to help organizations understand how costs are generated and help teams understand their impact.   Cost to Source:  This includes calculating the costs of materials and resources required to manufacture products. It involves determining the inbound landed costs of materials, subcontractor or vendor manufacturing costs, target costs, and should-cost estimates for new products. Cost to Make:  This encompasses the costs associated with manufacturing products, such as manufacturing product costs, bill of materials and routing costs, allocations, rate building, and inventory costs. Cost to Deliver:  This includes the costs associated with moving products and servicing customers. It encompasses post-manufacturing costs, supply chain expenses, outbound transportation and reverse logistics costs, and all the selling and support costs related to your products. These different cost categories combined offer a comprehensive view of costs for the organization and the foundation for all types of analysis. One area quoting and pricing teams find interesting is cost-based quoting - the ability to calculate detailed cost estimates for customer quotes. Cost-Based Quoting: Reducing the Risk of Inaccurate Quotes For quoting teams in manufacturing, calculating costs is crucial. In scenarios where customers place custom orders, the cost-based quoting process becomes essential because each order is unique, and there are no existing bills-of-materials (BOMs), routings, or established processes. The first stage of cost-based quoting involves inventing the product. This entails understanding the customer's specifications, creating BOMs and routings, and estimating direct costs. While this step may appear straightforward, it is actually quite intricate. Customer specifications in spreadsheets, drawings, specifications, and other documents are reviewed, and relevant data is captured and shared with the product or cost engineering teams. The initial product specifications are then confirmed, and the process moves to the next stage. The second stage focuses on calculating costs and determining prices. In most cases, three steps are involved in arriving at the final selling price communicated to the customer: ·       Computation of material, production labor, and overhead costs. ·       Summing and amortizing specific non-recurring costs, such as equipment, tooling, engineering, design, or other required expenses. ·       Assigning freight and distribution costs, selling, general, and administrative expenses, royalties, required profit margins, and others to the product. The final stage involves communicating pricing decisions to the customer and monitoring the ongoing performance of the quote. One important factor to note is that cost-based quoting is not a linear process. Throughout the three stages, there is constant communication and collaboration among stakeholders, including customers, commercial teams, quoting teams, product and cost engineering teams, and procurement. During negotiations, and even when the deal is complete, market changes can impact the costs of the quoted product. For example, commodity or raw material prices may change between accepting an order and producing it. With a cost-based quoting approach, you can evaluate the impact of those changes to ensure you meet your profit targets. Similar situations can arise when creating new products or modifying existing product lines to maintain or gain market share. A deep understanding of the costs associated with ongoing production is critical for determining pricing. How Cost Accounting Can Help Cost accounting analysis has the potential to uncover areas where a company can reduce internal costs, thereby minimizing the necessity for substantial price increases for clients. This approach facilitates the discovery of common ground and the development of win-win solutions that benefit both parties. Improved cost data can provide valuable insights for negotiations and broaden your pricing capabilities beyond mere price increases. For instance, your team could suggest alternative pricing models like volume discounts, usage-based tiered pricing, or longer-term contracts with fixed prices to address client concerns. In any situation, having access to precise cost information will only enhance your ability to maximize profits. Collaboration & Transparency: The Profit Multipliers The objective of strategic pricing is to maximize the amount that customers are willing to pay. While cost may become less significant from this perspective, its critical role in generating substantial profits should not be disregarded. Collaboration among teams cultivates initiatives to save costs, unifies everyone towards common objectives, and enhances transparency in pricing decision-making. A comprehensive understanding of costs allows for shaping customers' perceptions of value, managing discounts to safeguard profitability, and effectively communicating price increases in response to market conditions. Conclusion Cost accounting provides invaluable insights to pricing professionals, supporting informed decision-making, and optimizing pricing strategies. Often overlooked, cost accounting provides accurate data to benchmark and analyze competitors, facilitate strategic planning and forecasting, justify pricing decisions to stakeholders, and enable data-driven decision-making. In summary, effective pricing programs require a solid understanding of costs. By categorizing costs, identifying key drivers, and leveraging cost accounting insights, organizations can achieve profitable quoting and pricing strategies, maximize profitability, and succeed in the long run.

Author: Steven Goldstein, CPP, Director Pricing / Finance, PLZ Corporation As pricing professionals, we are tasked with driving pricing excellence.  Often this means that our organizations want us to increase pricing to ultimately drive more profits to the bottom line.  In the simplest form, this means a price increase.  This approach may work in the short term, but this does nothing to drive pricing excellence in your organization.   Look for “low hanging fruit”.  During an inflationary period, market conditions help craft a story that not only can your sales team grasp, but your customers are prepared for too.   As part of raising prices, you need to back up the price increase with publicly available data that your teams can share with their customers.  For example, data from the US Bureau of Labor Statistics is a free source of data that anyone can access. Analyze your data and look for losers.  Years ago, I was employed by a pricing software company and was working on a project at a company in the automotive space.  That company had hundreds of parts that were losing money with each sale. I hate to say it, but this scenario was not unique to this company and happened to some extent at every company that I have engaged with.   Look at your discounts, rebates and other incentives. Ask yourself and your partners on the sales team if you really need to be treating your smallest customers like you treat your largest customers?   For example, does the customer that spends $15,000 with your company deserve the same extended payment terms as the customer that buys over $1 million a year? Look at your rebates. Should you continue to give rebates to customers that have not shown any growth or potential for growth?  Neither of these examples should be taken as an “absolute”. Work with your sales partners to understand what is driving these behaviors.  Are there market standards?  Is the customer poised to grow?   Tools. Process and Governance No pricing action is sustainable without the infrastructure to support the changes.  Development of these tools and processes can be done in-house or with the support of outside subject matter experts. For either approach, make sure you scale accordingly.  Do not buy the Ferrari, if your team is just learning how to drive. Take time to document processes. This will allow the team to understand what actions and which team members need to be engaged during the change process. You may want to consider brining in a partner to help in this step. These partners can provide knowledge and experience that you may not have internally. After developing the process, determine which areas can be improved with tools.   These tools may be developed in-house or may come from a 3rd party provider. These tools should become part of the process, If using a 3rd party tool, you may need to adjust what you do to fit the tool seamlessly into your process.    The final piece is governance. Governance should not be seen as a roadblock to doing your job.  It should be seen as guidelines to make sure that the job is done correctly and aligns with the goals of your organization.  It creates a methodology to ensure that the right checks and balances are in place when something may have a material impact on your business.

Author: Kaavya Muralidhar In the last few years, inflation has had a significant impact on businesses, making pricing particularly challenging. At the same time, the possibility of an economic downturn creates an opposing pressure and makes the prospect of maintaining profitability and growth even more elusive. Businesses are faced with difficult choices, compounded by resistance of increasing prices. In this article, I discuss recommended principles and focus areas that enable managing profitability through pricing during inflationary periods. Focus on Value Drivers over Historical Precedent Consider pricing a heavy duty conveyor belt with the ability for inclines sold to a shipping warehouse in Minnesota, with an expedited delivery, while costs have decreased 8% and competitors offer 10% discounts. A reliance on precedent may involve segmenting all historical data to identify those historical transactions that were most similar. For example, perhaps lightweight conveyor belts with inclines were sold in California four times earlier this year with standard delivery. Therefore, a new price may be evolved based on that precedent – perhaps trying to maintain the same margin or incorporating pre-defined add-ons for changed configurations. With this process, you’d be missing out! You maybe have sold the same type of product in California before, but what about the expedited delivery process or product differentiation? How should they contribute to price? it’s crucial to get a detailed, in-depth view into what actually drives value across your business, and what may be different in this particular case. With detailed insight of your value-drivers, which can be derived through data analysis and AI explainability, you may observe that while expedited shipping drives a 4% price change in other states, perhaps in California, it may drive an 8% change. Additionally, you may see that the niche you have carved in this state for high quality Heavy Duty conveyor belts has really paid off, with the differential value offering you real pricing power to keep prices higher even as competitors decrease price, at least in the short term. But how do you know when to explore a new strategy, and whether it’s working or not? Focus on Experimentation over Replication Several price strategies assume that staying within the range of what has worked in the past is the best approach for the future. Any history-based pricing analysis can only yield insights that applied in the past. For example, a cost plus method may stick to offering a certain margin above rising costs, without considering willingness to pay at higher prices, even as capital costs continue to rise. A competitor-benchmarking based strategy may assume that the value offered by your specific offering does not change under different circumstances. These assumptions do not fare well in a fast-moving market, especially the unprecedented conditions following the COVID-19 pandemic. While it may seem self-evident new circumstances call for new strategies, the judgement calls that need to be made accurately for successful execution create high risk. How do you know what the right price strategy is, when you are in conditions that have never been seen before? The key is to incorporate risk-adjusted price exploration, paired with an adaptive learning loop. With risk-adjusted price exploration, you are able to analyze different price points for the purpose of learning, while still keeping within a confidence bound of what you know to mitigate risk. The linchpin of this strategy lies in learning from and adapting to the result of these price explorations – what does it tell you about elasticity and willingness to pay in this new world? An adaptive learning loop incorporated into your pricing enables these changes to happen quickly and iteratively, minimizing the likelihood of getting stuck in a local maxima, or falling behind the times. This is crucial in being able to explore, learn, and adapt in unfamiliar market terrain. Focus on Fluidity and Personalization over Rigidity Even when the painstaking work is done to incorporate experimentation and analytics into decision making, the most costly challenge may lie in price execution. When using tools like Excel, by the time pricing analysts can simulate, analyze scenarios, apply changes to price lists, matrixes, or business rules, and step through a cumbersome process to upload these new prices to ERPs and CRMs, it is likely that the conditions have changed again and new data is available, requiring a restart from step one. The challenge and cost of manual pricing may lead to another short-sighted trade-off: making a broad-stroke, sweeping price change across the business in an effort to catch up to an unexpected and major environmental change. If anything, this could incur more risk than not changing prices at all, by damaging customer relationships and eroding trust both within your company, and outside. Price sensitivity may vary greatly across each customer, across each product, depending on whether it is highly central to that customer’s business, and even the role that product plays in a given quote or contract. The ability to be both fast and personalized comes from having systems that provide automatic in-built triggers for price changes, nuanced customer willingness-to-pay assessment based on historical data analysis, and auditable approval workflows. In short, having the automation systems take over accurate execution enables your teams to focus on strategic long-term decision making. A well-executed pricing strategy during periods of inflation can make or break profitability for a business. Each of the above principles has a key role to play in ensuring success. A focus on value drivers enables to make personalized decisions that don’t leave money on the table. Leveraging feedback loops enables businesses to continue tuning, learning, and updating. Finally, empowering teams with tools to move fast and execute with speed enables to make the above happen efficiently and accurately

Author: Chuck Davenport It is easy to see the importance of sustainability and to make commitments that align with ESG targets. Execution of intentions is much more difficult. Over 2/3 of Fortune 500 companies have made timeline-based commitments to various ESG goals. However, 40% of those are falling behind in their progress and several high-profile ones have abandoned some of their key objectives altogether, often because there is not a clear path to value for stakeholders.  Many early success stories are consumer-focused ESG offers. In a recent global study of over 23,000 consumers, respondents reported a willingness to pay a premium for sustainable products - on average, a premium of 12%.  This premium should trickle up the value chain, enabling every supplier to receive its commensurate share, but there are many cases where this is not happening. Without a new mindset around pricing and value capture for ESG-enabled products, companies risk missing viable opportunities for this important, purpose-driven business segment. The path to long-term success in ESG for B2B businesses is not radically different from any business, but there are three important nuances that many companies overlook – context of the business ecosystem, consideration of new currencies of value, and the consequences of expected S-curves and E-curves.  As companies launch initiatives, they quickly learn that unilateral efforts are hard to execute – the ecosystem is critical to success. Also, carving out a unique value using a broader range of value currencies enables stronger monetization. Finally, planning, communication, and executing against realistic expectations of progression of customer adoption and production efficiency enhance the viability of these important initiatives. I would love to see all of you at my keynote presentation in the spring PPS conference in Chicago April 23-26.  I will be speaking on Friday at 11:15. About: Chuck Davenport is a Pricing Expert Partner at Bain and Company.  He has over 27 years of experience in Pricing and Profitability Management, both as a business executive and as a consultant and leads Bain’s efforts in Pricing for ESG and Dynamic Pricing.  He has also published, spoken, and lectured extensively on the subject of Pricing to MBA student at schools such as Emory, UNC Chapel Hill, Duke, Yale, Cornell, and University of Rochester.  Chuck holds an Electrical Engineering degree from Georgia Tech and an MBA from Emory University.