Search Results

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: Tatiani Amaral Santos In the world of pricing, we often hear about state-of-the-art software solutions, advanced analytics, and managing large, specialized teams dedicated to pricing. These are the glamorous elements of pricing strategy that many aspire to implement. But what happens when you’re the very first pricing hire at your company? What if no one truly understands what a pricing strategy even entails? What if, despite the lack of understanding, you’re expected to deliver concrete results that move the needle? Moreover, what if you're doing this without any fancy software, without a dedicated team, and without much support from other departments? It can feel like a challenge, right? I’ve been in that exact position. I was brought into a company with the mission to drive profitability, establish a sustainable pricing strategy, and—perhaps most critically—build a pricing culture from scratch. I had to gain the trust and buy-in from various departments like Sales, Product Management, Finance, and Demand Planning. The lack of a dedicated budget or sophisticated tools made the challenge even greater, but it also helped me to sharpen my focus on what really matters: delivering value quickly and effectively, using whatever resources were available. In situations like these, it’s easy to feel overwhelmed. You might feel paralyzed by the volume of work that needs to be done or by the complexity of pricing in a dynamic market environment. But here’s the key: you don’t need a huge budget, advanced software, or a large team to make a meaningful impact. Instead of waiting for all the stars to align, I realized I had to create a practical, achievable framework that could produce immediate results, without over-relying on things outside of my control. That’s when I turned to a concept that proved invaluable: Portfolio Optimization, which we eventually branded internally as Portfolio Management  to make it more relatable and digestible for other departments. The core idea behind this framework was straightforward, yet powerful. We categorized our products based on their revenue generation and margin performance, and then developed specific actions for each category. This method allowed us to focus on high-impact areas without spreading ourselves too thin across the entire portfolio. We essentially divided our product portfolio into performance-based segments. Each product was assessed and placed into one of several “buckets,” such as Best performers, Potential performers , and Underperformers . For each of these segments, we pre-defined a set of macro strategies that we could develop further with the help of key departments like Product Management, Sales, Finance, and Demand Planning. For instance, if a product was categorized as a “Potential Performer” —a product with good margin but underwhelming sales performance—we had a couple of strategic options at our disposal. These included: Increase awareness/boost marketing : This could mean collaborating with the marketing team to send out targeted newsletters to customers or launching online campaigns to increase product visibility. Increase sales/optimize distribution : For this, we might bundle the product with a high-sales item, expand distribution across different sales channels, or ensure the product is always available when needed. While the framework was simple enough to understand, the real challenge lay in the Execution . A pricing strategy, no matter how well-designed, is only as effective as its implementation across the organization. To ensure this, I made it my priority to build strong, collaborative relationships with all the key departments. Gaining buy-in from these teams wasn’t an overnight task, but it was essential for the strategy to be successful. For example, partnering with Demand Planning helped us align production schedules with our portfolio segmentation. We could allocate more production resources to high-performing products and gradually phase out the underperformers. Meanwhile, Finance  played a critical role in ensuring the portfolio optimization was reflected in discount approvals, helping maintain the balance between sales volume and profitability. Additionally, I worked closely with Product Management  to gain deeper insights into the products, especially when developing bundling strategies or figuring out which features could drive higher margins. Perhaps the most crucial partnership I built was with the Sales team . Let’s face it—salespeople are often skeptical of any pricing strategy that might complicate their work or jeopardize their deals. So, I made sure to communicate the strategy clearly, involve them in the decision-making process, and provide them with tools that would make their lives easier, not harder. By aligning all the departments around a common goal and ensuring everyone was working from the same playbook, we were able to get the Sales team fully on board. The results of this approach were not just theoretical—they were tangible. We saw measurable improvements in profitability, and slowly but surely, we began to foster a pricing culture within the company. Everyone started to understand how critical pricing was to the company’s success, and cross-department collaboration became the engine that powered our growth. If you find yourself in a similar situation, I invite you to join me at the PPS European and Global Pricing Conference  on November 21st in Berlin. There, I will share more insights and experiences from my journey. You'll see that with the right framework—no big team or expensive tools required—you can drive real, meaningful results.

Author: Rajeeb Chowdhury The global e-Commerce logistics market is expected to reach USD 1.9 trillion by 2030 and reflecting a CAGR growth of 23.5% during the period 2021 – 2030. (source: www.alliedmarketresearch.com ). The wide spread surge of B2C e-Commerce websites and platforms has significantly boosted the demand for domestic and cross-border e-commerce logistics globally. We have witnessed similar pace in the growth of number of e-commerce logistics players both locally within countries and across various regions globally. Interestingly, we have also observed gradually growth in the number of leading global and regional e-retailers who are entering the logistics space and offering integrated solutions which includes both on-line shopping and last mile delivery. This sector is going through fast paced transformation driven by continuously evolving customer needs and enormous possibilities with the adoption of various technological solutions. The logistics companies and e-retailers are also faced with enormous challenge – on one hand to offer very competitive prices for the delivery of their goods to the end-consumers and on the other hand continue to invest in devicing innovative processes and solutions to stay afloat in the market. Profitability challenges facing most companies: During this cut-throat competitive journey, a number of companies have also gone bankrupt and perished as they lacked the competitive edge in successfully pursuing the most appropriate strategies for realizing profitable growth. While achieving ‘economies of scale’ has been the dominant philosophy across most market segments, a number of companies under-estimated the importance of other key values drivers which are equally significant in driving profit realization. It is imperative that business leaders in this sector pursue a more holistic approach in translating customer value proposition into optimal profit realization. This requires a careful assessment of key value drivers, but more importantly successfully implementing some of the simple but effective business principles within an organization and in relationship to the target customers and suppliers. Workshop objective: This workshop will provide an holistic cross-functional view on how B2C logistics companies and e-retailers could optimize growth and profitability in the context of the ongoing market trends and innovation. The workshop participants will have the opportunity to analyse and learn some of the proven industry best practices pursued by leading B2C logistics players. The 3 key strategic levers for profitable growth in B2C industry growth will be evaluated based on a few examples of best practices currently being implemented by leading companies. The 3 key levers covered during this workshop are: 1. volume growth 2. cost optimization and 3. price levers. Introduction to the speaker – Rajeeb Chowdhury Rajeeb is a dynamic and results-driven leader, currently serving as the Chief Strategy & Transformation Officer at AJEX, a leading Middle East specialist in B2C and last mile solutions. With over 30 years of experience across diverse industries, Rajeeb has a proven track record of driving growth and profitability through strategic innovation and business transformation. Rajeeb’s expertise spans business turnaround, change management, strategy design, and process re-engineering. Previously, he has worked with DHL Express for 20 years on various senior management roles and also as a price professional as Global Head of Strategic Pricing and Regional VP of Pricing & Yield – Asia Pacific and Emerging Markets.

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: Mike Gorham As pricing professionals, we have all heard the saying "price is the most important lever". Recent CIL analysis found that in the UK, price has 6.7x the impact of volume growth, with older, international studies have estimating it closer to 10x. Yet, many companies still take the most important decision in their business effectively blind. To give a couple of examples from the tech sector: · In 2023, 93% of managed service providers set pricing without supporting research or analysis. · In 2022, SaaS companies reported spending an average of just six hours to set their pricing. Addressing Pricing Optimisation Concerns Many businesses simply lack an effective framework for pricing. They under-value pricing research because they lack experience with it, and they fail to recognize the biased and incomplete nature of information gathered in sales conversations. This leads to rushed decisions taken with insufficient information. A Framework for Enhancing Data-driven Pricing Strategies A practical framework can help businesses refine their pricing approach, make data-driven decisions, and unlock hidden value for sustainable growth.   By following these steps, executives can build confidence in their pricing, take early action for maximum impact, leverage expert insights, and maintain control over pricing for sustained success. Build confidence in price positioning A successful pricing strategy begins with a thorough evaluation of pricing power and growth potential. By leveraging market data early on, businesses can gain the insights needed to ensure their pricing is competitive and well-aligned with market demand. This data-driven approach enables informed, confident decisions that maximise profitability and unlock greater value. Develop a Clear Understanding of Value Effective pricing starts with a solid understanding of what customers truly value. Conducting accurate, unbiased customer research is critical to capturing insights into customer perceptions, market positioning, and the unique value your product or service offers. This helps ensure that pricing reflects the true worth of your offerings and resonates with the market. This research ensures that pricing decisions are based on real data rather than assumptions, leading to more precise and effective pricing strategies. Leverage External Expertise Realising a company’s full pricing potential requires focused effort. Management teams, despite their talent and motivation, will often lack the dedicated resources and specialised skills needed for comprehensive pricing market research and commercial excellence initiatives. When internal resources are limited, partnering with external experts can fast-track the pricing strategy process. External specialists bring deep insights and experience, allowing businesses to refine their pricing models more effectively. This collaboration not only optimises pricing decisions but also helps build critical skills within the company, ensuring long-term capability in pricing management. Maintain Pricing Control for Sustained Success Effective pricing control hinges on robust data and well-defined processes. By investing in data platforms that provide insights into customer profitability and pricing performance, businesses can manage both new and existing accounts with greater accuracy. This level of control allows for more precise adjustments and helps ensure that pricing strategies remain adaptable as the market evolves. To achieve long-term success, your systems and processes must be designed to support scalable and repeatable pricing strategies, enabling sustained growth and profitability.   Mastering strategic pricing is essential for any business seeking long-term profitability and growth. By taking a data-driven approach and leveraging the right expertise, businesses can ensure their pricing strategies are both effective and sustainable. Through careful evaluation of pricing power, understanding customer value, and maintaining strong control over pricing systems, companies can unlock hidden value, improve margins, and stay competitive. The steps outlined above provide a clear, actionable framework for executives looking to make pricing a key driver of their success. Implementing these strategies will not only enhance profitability but also build a more resilient and adaptable business model for the future.   Mike Gorham, CIL’s Pricing Director, will lead a breakout session on “Sustainable profit growth: Comprehensive pricing model insights” at the PPS Europe conference in Berlin, November 19-22. His presentation will be Friday at 14:20. Mike Gorham leads the Pricing practice at CIL, an international strategy consultancy. He specialises in pricing, strategic marketing, and sales effectiveness. Mike combines strategic insights, data analytics, and practical sales experience to deliver holistic and sustainable pricing improvements for clients across various sectors and sizes. He holds a BSc from the London School of Economics and an MBA from INSEAD. [1]   Strategic Pricing: A methodical approach | CIL Management Consultants [2]   https://syncromsp.com/download-msp-pricing-models/ [3]   https://www.paddle.com/blog/frequency-pricing-change

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: Angie Jackson, CPP - PPS Director of Marketing and Key Accounts So, if you’ve committed to earning your Certified Pricing Professional   designation, you’ve taken the six online and/or Workshop prerequisite courses, you’ll have a fundamental understanding of the values-based pricing both from a mathematical/scientific perspective and when there’s some art involved (we don’t get to price in a vacuum, for example). You’ve passed the quizzes. You’ve learned from experts that live and breathe this discipline every day, including success stories and some failures as wise words of caution. Now it’s time to prove you know your stuff when it comes to price setting and execution, including segmentation and promotions and the like. But how does one approach the CPP exam? Quick caveat: I am not a pricer by nature. I have a marketing and sales background, both leveraging pricing, if somewhat, indirectly. But I was committed to going through the CPP journey to demonstrate pricing mastery, understand personally what our member experience is, and because, well, if I can do it, any one can. Here’s what I did to wrangle this bad boy: Study and take it quickly as you can. I took the test 7 days from beginning to study (8 days total including the exam). The first entire day, I created a study guide (see #2) to set myself up for success and create a pricing playbook for myself to use forevermore. I front loaded all my regular work so I could focus and deep dive into the content. I did little else for those 8 days besides study and master the concepts in the study guide. I read my notes before closing my laptop on the 7th day, and then read them again before starting the test. Insider tip:  Don’t drag it out. Just do it.   Create a Word document to serve as your master study guide. I spent my entire first day of studying pulling all the editorial content from the PDFs associated with each study module into a Word document, so it was searchable for the exam since the exam is open notes. As I went through the 11 (there are really 12) study modules via the videos and Dr. Tim Smith’s   engaging and thorough commentary, I made notes of things he said for later reference, and I pulled in the charts and graphs via screenshots and labeled the graphs with typed words so I could find them later when searching the document. Literally, on the exam, if I had a question about ‘Economic Value to Customer’, for example, I searched the document so I could find every mention of it to give me confidence when answering the question. Insider tip:  I also created a one pager with all the formulas so I could keep that right next to me as I went. Make an exam plan or use mine. With the test being 4 hours long, 110 questions, and I needed an 83%, I thought through the timing and test execution so I could make sure I was on pace to complete the exam while marking questions I was unsure about to double check them, if I had time. Here was my plan: I can miss 18 questions. 1 hour mark: 28 questions 2 hours mark: 56 3 hours mark: 84 Insider tip:  I’d guess the exam is about 65% math. That’s not a fact; that’s an estimation. And I had plenty of time. I completed the test in 2 hours and 45 minutes.   No need to do the study modules in order but ensure you have mastery of the quiz questions on each. When it came to the study modules themselves, they are about 75 minutes long each. I did two per day, stopping to take notes as I went and make sure I have complete mastery of the questions at the end of the modules  (except for one, see below). If I didn’t get a question right, I went back to the section Tim suggested and rewatched it, so I could execute the question correctly.   I started them order, but I knew the heavy quantitative sections were going to be more difficult for me, and therefore take more time. I decided to skip those—price segmentation and conjoint analysis to do those last. That was a key for me because: 1. I didn’t get bogged down studying those and could keep my studying momentum going. I studied those two on the last day, so the math execution was fresh in my mind. It seemed to me there were disproportionately more questions on those two sections, but maybe that was just me because I found them the most time consuming. Also, the segmentation and conjoint questions build on themselves, so if you missed one conjoint question, for example, then you could miss them all. And we can only miss 18 questions. Also, the test is randomized so the easiest question probably won’t be first. You may be asked a conjoint question first that you have to perform a few more calculations to answer. Insider tip: Conjoint wasn’t difficult for me, but I hear it’s a little scary to some people. Don’t be scared. Just take the practice test, and you’ll be able to do it . Nothing, absolutely nothing, is on the exam that isn’t stated or taught in the study module. The test was easier than I expected because I was well prepared for the exam through the study modules.   Don’t over think the exam. I’m an overthinker, as I’m sure many of us are. Do not overthink the exam questions. The most obvious answer is probably the right one. If you know Tim, he’s not interested in tricking us. The test is multiple choice, and often two of the answers are obviously wrong. But you’ll often have to choose from two answers that, upon first glance, seem correct. If you made your complete study guide, search the document for everywhere he talked about that topic, and you’ll be able to determine which is the best choice. Insider tip:  I’d guess 75%+ of the exam are questions directly derived from the study module quizzes. Have I said make sure you can demonstrate mastery of the study modules?   PPS CPPs grow their businesses with evidence-based profitability strategies and techniques. It’s an honor to join this elite group of over 2,000 distinguished pricing professionals (and growing every day). If you have any questions around the CPP exam or any other aspects of PPS, don’t hesitate to reach out.  Good luck and happy studying!

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.