The surge in access to detailed marketing data and analytic tools has elevated pricing to the forefront of corporate strategy and the driving force of strategic revenue growth. Emerging roles like the Chief Growth Officer (CGO) and Chief Revenue Officer (CRO) focus predominantly on pricing decisions. For most companies, improving pricing strategy represents both a strategic goal and a strategic challenge. Most struggle with the same basic questions: How do we formulate a pricing strategy? What should we charge? Which data and methods should we be using to make pricing decisions? Who should be in charge of pricing decision-making?
McKinsey predicts “65 to 85 percent of [B2B] organizations expect to adopt gen AI or agentic AI in pricing over the next one to three years, up from just 10 to 30 percent today.”[1] But in practice, most firms still use ad hoc rules-of-thumb. The simplicity of these rules comes at a substantial cost to a firm’s potential profitability. Most rules fail to align pricing with consumers’ perceived value and underlying willingness-to-pay. Many firms also struggle with the delegation of pricing responsibility within the organization, exacerbating the use of rules-of-thumb that fail to deliver effective pricing decisions and monetize. The fact your company made money last year does NOT confirm the optimization of your prices: you may be leaving substantial money on the table.
This course blends marketing analytic frameworks, marketing strategy, microeconomic theory and customer data (evidence!!!) to formulate actionable pricing strategies. Students learn how to coordinate pricing decisions with the rest of the marketing value proposition. Numerous pricing structures are developed in the course, along with their microeconomic foundations. Students learn the underlying theory for each pricing structure, along with the practical considerations for implementation. Students will use generative AI to implement quantitative and analytic pricing solutions that leverage customer data.
The course combines cases and analytic assignments to teach students how to design and execute pricing strategies. Students work with different forms of data and corresponding analytic methods, including the use of generative AI. The examples span a wide array of business contexts including services, B2B, B2C and international.
The course includes 5 group homework assignments (4 graded), 2 group case write-ups and 1 individual case write-up. The individual assignment is due during week 3. If you register late for the course, you must still submit this first assignment before the week 3 class. Failure to submit this assignment will result in a grade of zero (no exceptions).
For the group assignments and group case write-ups, students must form a group of 4-5 people. All group members must be registered for the same section (no exceptions and please do not contact me about exceptions). It is much easier to work on these assignments as a team so you can trouble-shoot methods and brainstorm how to incorporate findings into a pricing decision or recommendation. To ensure that your team functions effectively, the first group assignment (due in week 3) involves (i) reading an insightful article about team work at Google; and (ii) drafting a one-page, jointly-written charter for your group in this course.
Some of the group homework assignments require students to run a regression and/or to use optimization tools. Two review sessions have been scheduled with one of the course TAs to assist those students who are unfamiliar with these techniques. These sessions will cover regression and optimization, along with the use of R and the use of generative AI to help you prompt the creation of source code. Students with no prior experience in basic coding will be expected to learn these tools outside of lecture time.
Auditing is not permitted for this course except during week 1 for students on the waiting list.
[1] B2B pricing: Navigating the next phase of the AI revolution”, Brian Elliott, Nicolas Magnette, and Shamik Bandyopadhyay, Matt Cherry and Nidhi Bagri, McKinsey & Company, April 7, 2026.
