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Optimizing pricing predictions using Artificial Intelligence (AI)
Transforming product pricing at IBM
Predicting the optimal price of a product is a critical part of any product sales strategy. But it can often be a daunting task, particularly when there are hundreds of products to evaluate alongside critical data such as customer buying histories, product histories, industry trends and the inflation rates. Adding to the complexity, often such data is incomplete or rapidly changing.
IBM sellers and Business Partners faced challenges in estimating optimal pricing for products they were selling. Because of limited access to critical historical data on product sales, industry trends and negotiation history, they had to overwhelmingly rely on personal experiences with lengthy manual assessments, not scalable when making pricing decisions for thousands of customers and hundreds of products. These manual assessments also led to very lengthy approval processes, affecting their ability to effectively close deals on time and with a high degree of confidence.
Additionally, it was difficult for sellers to integrate sales and product data with data from disparate third party and legacy systems. Variations in geographic, business and market trend data also impacted the accuracy of their predictions. This made it difficult for IBM to compete with companies with fewer but similar offerings and faster approval times in the market.
To resolve this business challenge, the CIO Pricing Systems IT team, IBM’s Chief Analytics Office and IBM research co-developed the Cognitive Pricing Analytics solution to recommend optimal price predictions to aid IBM sellers and Business Partners selling IBM Hardware and Software products.
The solution will recommend optimal pricing for each individual customer based on their geographic location and buying patterns. The Cognitive Pricing Analytics solution design takes these parameters and others into account by using Machine Learning (ML) models and years of transactional data across IBM’s lines of business, to determine the optimal price. Using this data, each Cognitive Pricing Analytics model determines the factors contributing toward a given quote’s price sensitivity and uses this value to recommend the optimal price.
Cognitive Pricing Analytics is a data driven AI-based pricing model to improve the sales pricing and negotiation process and is designed to integrate seamlessly into the sales workstreams.
“When IBM sellers and Business Partners alike apply for Cognitive Pricing Analytics pricing prediction, they will have an optimized, approved quote within minutes, auto approved based on relevant business rules,” explains Sachin Shinde, CIO Data Scientist, IBM. The recommended optimal price, combined with the greater accuracy in the pricing prediction, delivers great business value. The pricing team benefits from reducing the number of approval layers by using the optimal price which enables faster approvals. IBM is evaluating expanding the use of Cognitive Pricing Analytics across all business units.
The Cognitive Pricing Analytics team is further enhancing the solution with key AI and Data Science tools and with governance capabilities, to continuously monitor the pricing models for fairness, with explainability.
- Planned additions include IBM Watson Studio for collaborative model development, with dashboards to gain clear visual understanding of the data. Watson Studio’s AutoAI functionality automatically evaluates multiple built-in models to determine the best ones to apply to any specific data, and quickly recommends the top models.
- Watson Studio pipeline will be leveraged to build a visual pipeline structure for model deployment in the run-time environment and monitoring through IBM Watson OpenScale.
- Governance capabilities are embedded into Watson OpenScale, including the Fairness 360 kit. The models deployed and monitored through OpenScale are also monitored for risk through IBM OpenPages GRC (Governance Risk and Compliance), thus establishing end to end governance, from development to deployment.
“This is very much an AI solution, leveraging our flagship and standard data science platform. The Cognitive Pricing Analytics team completed a proof of concept to further modernize the solution with key AI and Data Science tools and to continuously monitor the models for fairness and explainability for AI governance. Next step is to take advantage of the IBM watsonx.governance capabilities.”, explains Suj Perepa, IBM Distinguished Engineer, CTO Data & AI.
The Chief Information Officer (CIO) Organization leads IBM’s internal IT strategy and is responsible for delivering, securing, modernizing and supporting the IT solutions that IBM employees, clients and partners use to do their jobs every day. The CIO strategy encompasses creating an adaptive IT platform that makes IT easier to access across the enterprise, accelerates problem-solving and serves as an innovation engine for IBM, catalyzing business growth.
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Produced in the United States of America. February 2024.
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