Quantification formulas: how to put numbers behind customer value

Most sellers know they should lead with value rather than product features. The difficulty is turning that principle into a meaningful customer conversation. That’s where quantification formulas come in.

It is relatively easy to say that a solution could reduce downtime, improve engineering productivity or accelerate time to market. It can be much harder to explain how the customer could measure that impact in their own business.

This is where many value-led conversations lose momentum. The seller understands the potential benefit, but does not have a structured way to explore its financial or operational significance.

AI can help close that gap.

Not by inventing a return-on-investment figure or producing an artificial business case. Instead, AI can act as a thought partner that helps the seller build quantification formulas, identify the inputs required and prepare the questions that allow the customer to validate them.

That leads to a more credible conversation because the seller is not arriving with an answer. They are arriving with a better way to examine the problem.

What AI-generated quantification actually is

A quantification formula is simply a structured way to connect a business problem to its potential operational or financial impact.

For example, a seller preparing for a conversation with an industrial customer might explore three connected challenges:

• Unplanned downtime
• Engineering rework
• Delayed product launches

AI can help break each challenge into measurable components.

The annual cost of unplanned downtime might be explored through:

Number of incidents × average duration × estimated cost per hour

Engineering rework might be examined through:

Number of affected employees × average rework hours × loaded hourly cost

The impact of a delayed launch might be explored through:

Length of delay × expected contribution margin per period

These are not completed business cases. They are starting hypotheses.

The AI does not know the customer’s actual incident rate, labour cost or commercial margin. Those inputs must come from the customer and should be validated with the relevant operational and financial stakeholders.

The value of AI is in helping the seller identify what should be measured and how the different variables may connect.

Why this matters for sellers

A strong seller does more than identify a visible problem. They help the customer see its wider business impact, which is simplified with quantification formulas.

A customer may already know that engineering changes take too long. What they may not have considered is how those delays increase rework, disrupt production and affect launch dates.

This is where a Challenger-style conversation becomes valuable. The seller introduces a perspective the customer may not have fully considered, then works with them to test whether it is relevant.

Instead of asking, “Is engineering rework a problem?”, the seller can ask:

• How many people are typically involved when an engineering change needs to be corrected?
• How many hours are lost across engineering, manufacturing and quality?
• Does that rework ever delay production or a customer delivery?
• What happens commercially when a launch moves by a week or a month?
• Which of these costs are currently measured?

The conversation becomes more specific without becoming confrontational. It also increases the seller’s credibility because they are demonstrating commercial curiosity, not pretending to know the customer’s business better than the customer does.

Practical ways sales teams can use it

Before an initial discovery meeting, AI can turn basic account and industry context into several potential value hypotheses.

For a manufacturing customer, it might connect poor engineering change management with rework, scrap, production disruption and slower product launches. For another enterprise, it might examine manual processes, service delays or low employee productivity.

AI can then help the seller:

• Create a simple formula for each value hypothesis
• Identify the customer data needed to test it
• Prepare discovery questions for operational and executive stakeholders
• Highlight where two formulas may overlap to prevent double counting
• Separate confirmed information from assumptions requiring validation

This preparation does not replace discovery. It improves it.

The objective is not to present an impressive number during the first meeting. It is to create a shared method for investigating whether the problem is material enough to justify action.

A simple way to get started

The following CRIT prompt can be adapted for a customer, industry or opportunity:

Context

I am preparing for a value-led conversation with a potential enterprise customer. The likely challenges include unplanned downtime, engineering rework and delays to product launches. I want to explore the potential operational and financial impact without inventing customer data.

Role

Act as an experienced Challenger seller, value engineer and enterprise sales coach. Help me develop credible hypotheses while clearly distinguishing assumptions from confirmed information.

Interview

Ask me up to five questions, one at a time, to understand the customer, industry, stakeholders and available evidence.

Task

Create three simple quantification formulas covering downtime, rework and time-to-market delays. For each formula:

• Explain the business logic
• List the inputs required
• Create five discovery questions
• Identify the stakeholders who could validate the inputs
• Highlight potential overlap or double counting
• Separate known facts, assumptions and areas requiring customer validation
• Suggest one credible commercial insight I could introduce in the meeting

This gives the seller a structured starting point while keeping human judgement at the centre.

AI will not make a seller credible simply by producing a formula. Credibility comes from using that formula responsibly, asking better questions and being willing to have assumptions challenged.

The real opportunity is to move from telling customers that a solution creates value to helping them understand where that value may exist in their own organisation.

Use the prompt to prepare your next customer conversation and test the resulting value hypothesis with the customer.

About the author

Benedict Russell is a Global Partner Development Executive responsible for scaling global GTM programs across all motions. He helps shape Siemens’ digital selling and AI strategy, embedding best practices that accelerate SaaS adoption and recurring revenue. Previously, he drove partner coverage and expansion, adding 300+ partners to the Siemens ecosystem. Read Benedict’s most recent blog here.