Introduction
Dr. Markus Husemann-Kopetzky, CEO of Price Management Institute, shared his extensive experience in pricing strategy, advising various retailers and e-commerce companies on price management. As a guest researcher at Freie Universität Berlin, Markus combines practical insights with academic expertise. The webinar was attended by representatives from leading companies such as Intersport, Verdane, MisterSpex, InternetStores, Westwing, BestSecret, Phoenix Group and ATU.
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Importance of Systematic AI Pricing Tool Selection
Markus highlighted the critical need for a systematic approach in selecting AI-based pricing software, noting that 58% of companies regret their buying decisions within 12-18 months due to impulsive and unstructured selection processes. A systematic approach mitigates this risk, ensuring better alignment with company needs and long-term satisfaction. As Markus succinctly put it, “A systematic approach helps you be safe rather than sorry.”
Steps in the AI Pricing Tool Selection Process
Project Kickoff and Team Formation
The process begins with forming a diverse internal team comprising technical, functional, and pricing experts. This team sets the foundation for a comprehensive and informed selection process.
Identifying Critical Requirements
Identifying the most critical requirements for the pricing software is crucial. This includes functional needs, technical capabilities, and integration requirements, forming the basis of the long list of potential vendors. Markus emphasized, “Your critical requirements are the backbone of your selection process.”
Long List and Pre-Screening
A long list of vendors is compiled, followed by a pre-screening process to narrow down to a shortlist of three to five vendors based on initial fit and capability.
Short List and Defining Requirements
Detailed requirements are defined, including functional, non-functional, and financial criteria. Use cases are also described to guide vendors in demonstrating their solutions effectively.
Evaluating Forecasting Models and AI Specifics
Markus emphasized the importance of understanding the forecasting models used by vendors, how they measure and optimize accuracy, and their specific use cases for AI in pricing. This ensures that the chosen solution can handle various pricing scenarios and adapt to business needs. “Understanding the demand function at the core of pricing is key to leveraging AI effectively,” he noted.
Financial Impact and Measuring Success
It’s essential to measure the financial impact of pricing decisions. Vendors should provide methods for assessing the ROI of their solutions, using techniques like A/B testing and impact analysis to validate their effectiveness. Markus pointed out, “At some point, someone will ask, ‘How much money do we make out of this?’ It’s crucial to have a clear answer.”
Use Cases and Practical Applications
Key use cases for AI pricing tools include initial pricing, long tail pricing, markdown optimization, promotional pricing, and private label pricing. Understanding how vendors address these scenarios helps in selecting the most suitable tool.
Workshop and RFI Process
Workshops and RFIs are conducted to delve deeper into vendor capabilities. Vendors present their solutions, addressing the defined use cases, and provide responses to the RFI for further evaluation. This phase is crucial for clarifying any uncertainties and ensuring that vendors can meet the specific needs of the business.
Proof of Concept (POC) Execution
Tournament vs. MVP Approach
Markus discussed two approaches for POC execution: the tournament and the MVP. The tournament involves multiple vendors competing to show their effectiveness, while the MVP focuses on one vendor, thoroughly testing their solution. “The tournament gives you the optimal decision by testing multiple options simultaneously, while the MVP approach tests the validity of your assumptions with one vendor,” he explained.
Evaluating and Rolling Out the Chosen Solution
Post-POC, the chosen solution is evaluated based on predefined criteria. A rollout plan is drafted, which could be a big bang or staged approach, and the business case for the rollout is calculated. This phase ensures that the solution is not only effective but also scalable and sustainable in the long run.
Conclusion
In conclusion, a systematic approach to selecting AI-based pricing software significantly reduces the risk of making poor investment decisions. Markus emphasized the necessity of challenging and validating AI solutions due to their complex nature. He highlighted two recommended approaches for empirical validation: the tournament, which provides the most accurate results, and the MVP, which balances decision quality with technical feasibility. By adopting these methods, businesses can ensure their pricing tools deliver superior and reliable results.
Q&A
What would be a good length for a long list and a short list of vendors? How many options should we aim to keep?
There are too many options in the market. A good long list typically consists of nine options, ideally between seven and eleven. Start with a curated list based on recommendations from your network of retailers, pricing consultants, and software vendors. A short list should include three to five vendors for in-depth evaluation.
What are the common mistakes companies make in the AI pricing tool selection process?
Common mistakes include not undertaking the project systematically and relying on opportunistic decisions. Managers often make decisions based on promises from vendors without proper evaluation. Companies should assemble a comprehensive long list, seek external insights, and ensure their pricing team is well-informed.
How long does it take to fully integrate a pricing software following these steps?
Integration time depends on the complexity of your business model, including the diversity of your assortment, sales channels, and countries involved. Typically, full integration takes between three months to one and a half years. Quick implementations can take about three months, focusing on bug fixing and edge cases, while more complex rollouts can take longer due to necessary organizational changes and technical implementations.
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