Pain Quantification Interview
Turn vague pain into numbers the buyer owns.
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Help me quantify the cost of a prospect's problem by interviewing me about the call first. Interview me before you write anything, one question per message, and wait for each reply: (1) what problem the prospect described and in their words, (2) who is affected and how many people, (3) how often it happens, (4) how long each occurrence takes or what it costs, (5) what has been lost (deals, time, customers, penalties, overtime), (6) what workarounds exist and their cost, (7) what the prospect said is at stake, and (8) which numbers were actually stated versus guessed. Facts from the call: [CALL_FACTS]. After the interview, build a worksheet table: Cost driver | Prospect's number (stated) | My estimate (labeled as estimate) | Calculation | Annual value | Confidence | How to validate on the next call. Use formulas such as hours per week x people x hourly cost x 52, and error rate x cost per error. Then produce a one-paragraph value statement in the prospect's language, a conservative case and an expected case, three questions to confirm the numbers with the prospect, and a warning list (do not present my estimates as the prospect's facts; avoid inflating; ask them to correct the numbers). Show the arithmetic.
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See an example result (fictional data)
Prospect: operations lead at a 30-person delivery firm. Q: "How long does weekly route planning take?" A: "Two people, about 6 hours each." Hours = 12 a week. Q: "What is the loaded hourly cost?" A: "Around 28." Cost = 12 x 28 = 336 a week; x 48 weeks = 16,128 a year. Q: "What happens when a route is wrong?" A: "About 5 late deliveries a month, each costs us a 60 credit." Cost = 5 x 60 x 12 = 3,600 a year. Total quantified pain = 16,128 + 3,600 = 19,728 a year (estimate, the prospect's figures). Confirm: "So planning plus late credits is close to 20,000 a year. Does that sound right?" Record in the CRM with the source and date. Do not state a benefit until they agree the baseline.
Illustrative only, made-up data. Your results will differ and must be reviewed.