Below is a summary of our interview with Markus Husemann-Kopetzky. You can listen to the full interview using the embedded media player below or in your favorite podcast app (e.g., Apple Podcast, Spotify and Amazon Music).
In this episode of Pricing Heroes, we sit down with Dr. Markus Husemann-Kopetzky, founder and managing director of the Price Management Institute, a boutique consultancy specializing in price management and price communication. As a senior research fellow, university lecturer, and advisor with more than a decade of experience in management and consulting roles, including over five years at Amazon as a senior manager of pricing and business analytics, Markus brings unique insights from both academic and practical perspectives. We invited him to discuss his latest report: “Data-based decision-making in retail & e-commerce,” which compiles actionable insights from a survey of more than 200 retail executives.
From Academia to Amazon: A Journey in Pricing
Markus's journey in pricing began 15 years ago with doctoral research, followed by academic publishing and consulting work. His desire to move beyond theory led him to Amazon. "After three years, I was the most senior senior pricing manager in this role and was responsible for very interesting projects across Europe together with the colleagues in Seattle," Markus recalls. After leaving an environment where all decisions are based on data and all data-driven decisions are automated, he found himself back in what he calls "the real world," where things get exciting again.
The Reality of Data-Driven Decision Making
Markus's experiences with retail executives led him to conduct comprehensive research on data-driven decision making. "I had a couple of conversations about how to set prices with executives and senior executives. And the decisions or the discussions that we had were partially disappointing. They doubted that data-based decisions are actually helpful beyond the expertise that their category managers had," he explains. This skepticism about the value of data-driven approaches prompted him to gather concrete evidence through his study of over 200 executives.
His research revealed compelling evidence for the importance of data-based decision making by identifying two distinct groups: "Data Kings" (top 25% in data-based decision making) and "Data Laggards" (bottom 25%). The findings showed that data-driven approaches deliver real competitive advantages:
- Data Kings demonstrated systematically better financial performance across four dimensions: revenue, revenue growth, profit, and profitability
- Data Kings consistently showed better financial performance relative to key competitors
A critical factor in this success was senior management's commitment. "A key distinction between both was the degree to which the senior management enforces data-based decision making," Markus notes. For Data Kings, senior management typically required data-driven decisions, while this was rarely the case for Data Laggards. This underscores that successful data-based decision making starts with leadership commitment rather than tools or specific roles.
Technical Challenges and Implementation Barriers
While 56% of companies consider data-based pricing crucial for their success, only 17% strongly agree they have sufficient technical support to achieve it. However, Markus identifies that the fundamental challenge isn't technical limitations. "The key problem is from my perspective, that the internal teams cannot make the business case very clear that it is worth investing into pricing and into AI specifically," he explains.
Only after companies commit to data-driven pricing do they encounter various technical roadblocks:
- Data fragmentation across systems (transactional data, product data, stock data, competitor pricing data)
- Promotional calendars and seasonality indicators stored in different locations
- Competitor availability data existing in separate systems
- Integration challenges across these various data sources
Markus emphasizes that while these technical challenges are significant, they become relevant only after securing commitment and investment for data-driven pricing initiatives. He also cautions against the common tendency for retailers to overestimate their internal capabilities when deciding whether to make or buy pricing solutions.
The Journey to Data-Driven Decision Making
The transition to data-based decision making typically takes 12-24 months, with Markus outlining seven key milestones:
- Start with a long or short list of options (depending on project framing)
- Select final candidate(s) for MVP phase
- Complete successful MVP phase with clear success criteria
- Execute lighthouse rollout project to create excitement
- Develop comprehensive rollout plan
- Execute successful rollout (up to 12 months)
- Establish performance management and improvement systems
He emphasizes the importance of taking small, conscious steps and maintaining agility throughout the process. "Old school waterfall approaches lead to throwing good money after bad. The agility is really key here," he states.
Success Through Data-Driven Pricing
Markus shares a compelling example of successful data-driven pricing transformation. A market-leading non-food retailer revolutionized its pricing strategy by using data to make more nuanced decisions: For Key Business Items (KBIs) where price is very important to customers, the retailer reduced prices and closed the gap to competitor prices by about 30%. However, they offset this by raising prices on items where price sensitivity was lower, resulting in an overall margin increase of 1% while becoming more competitive.
The Future of Retail Pricing
Looking ahead, Markus anticipates several key developments in retail pricing:
1. Store-Level Price Individualization – Moving beyond simple store clusters, Markus envisions more granular pricing strategies based on local market conditions. "Probably if you have a network of three and a half thousand [stores] and the composition of people around there are completely different, it might make sense to have different prices for each store," he explains. This individualization would consider regional differences in customer affluence, demographics, and store-specific buying behaviors.
2. Enhanced Price-Promotion Integration – The future will see better coordination between pricing strategies and promotional activities, particularly through personalized coupons. As Markus notes, "Coupons are a good example to individualize prices without making people feeling they're being taken advantage of." This approach uses customer affinity scores to deliver targeted promotions effectively.
3. Optimization of Marketing and Pricing Decisions – In e-commerce, Markus anticipates better coordination between pricing decisions and performance marketing investments. "You have to decide whether you bid one more or you lower your prices by one. And there is an optimal trade-off between both," he explains, highlighting the need to balance marketing costs with pricing strategies to maximize conversions.
Building Trust in AI-Driven Pricing
Markus emphasizes the importance of a gradual approach to implementing data-driven pricing solutions. He recommends starting with automation of existing rules: "Instead of framing a machine takes over, it is, 'Hey, you have so much to do. Why should you waste two hours a week with Excel?'" From there, teams can progress to more sophisticated optimization techniques while maintaining control through clear guardrails.
Recommended Resources
For pricing professionals looking to deepen their knowledge, Markus recommends:
