Chatting with ChatGPT: A perspective on Value-based pricing

I was chatting with ChatGPT about designing a model for value-based pricing. The problem we’re trying to solve is moving away from placing the value on the “hour.” With the adaption of AI delivering extreme productivity gains, tasks take less time. Continuing to place the value on the time it takes to complete a task will certainly further commoditize the actual value of professional services work. My working thesis is that if we start to place the value on real things, we can improve how we price.

Chat and I decided our model will look at these variables:

1. Client’s Objectives and Outcomes: Understanding the specific goals and desired outcomes your client wants to achieve by engaging your services. This could include factors like increased revenue, cost savings, improved efficiency, or market share growth.

2. Industry and Market Dynamics: Consider the industry your client operates in and the market conditions they face. Evaluate the competitive landscape, market demand, and any specific challenges or opportunities that may impact the value your services can provide.

3. Expertise and Unique Selling Proposition: Assess the expertise, skills, and unique advantages you bring to the table. Consider factors like your experience, track record, specialized knowledge, or innovative approaches that differentiate you from competitors.

4. Time and Effort Required: Although value-based pricing is not solely dependent on time, it’s still important to estimate the time and effort needed to deliver the desired outcomes. This helps you ensure that the pricing aligns with the resources you’ll invest.

5. Risk and Opportunity Cost: Evaluate any risks involved in delivering the desired outcomes and the opportunity cost associated with choosing your services over alternatives. This can help justify the value you bring and the price you set.

6. Client’s Budget and Willingness to Pay: Understand the client’s budget and their willingness to invest in achieving their objectives. This can help you gauge the range within which you can set your pricing.

We then used this scenario:

1. Client’s Objective: Driving Revenue of $10mm, conservatively
2. Industry: Retail, Connected Customer Experience Assignment.
3. Expertise: Over 20 years of experience in retail and digital innovation.
4. Team Size and Duration: A cross-functional team of 8 for 6 months.
5. Potential Overrun: This team is relatively new at working together. Considering the team’s relative newness, account for a 2-month learning curve.
6. Client Budget: $2.5 million.

So here’s our example calculation:

1. Estimate the value: Assess the potential revenue impact your project can generate based on industry benchmarks, market research, and past experiences. Let’s say your analysis suggests a potential revenue increase of $10 million.

2. Determine the percentage of value captured: Based on discussions with the client, agree on the percentage of value you will capture through your services. For example, if you agree on capturing 20% of the potential revenue increase, it would be $2 million.

3. Consider the additional time required: Account for the 2-month learning curve and adjust the project duration to 8 months.

4. Calculate the monthly fee: Divide the value captured ($2 million) by the adjusted project duration (8 months), resulting in a monthly fee of $250,000.

5. Validate against the budget: Confirm that the monthly fee ($250,000) multiplied by the adjusted project duration (8 months) does not exceed the client’s budget of $2.5. million. In this case, the total fee would be $2 million, well within the client’s budget.

Benefits

• Focused, dedicated team
• Realistic expectations for the team
• True client partnership to come up with a price that feels fair

It’s not perfect, but I need a lab for this experiment. I have the start of a new pricing model.