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MethodSalesRunning the dealStarter

Build an objection-handling playbook

The top objections with root cause, response framework, proof and follow-up questions.

This is a practical, copy-paste process for deals stall on the same objections.. Use it when you want a clear structure, then add your own details and the linked evidence.

Published by Deepstory Research. Reviewed Sep 2026.

How to use this method

  1. 1. Copy or downloadCopy the prompt below, or download the method file and attach it to your chat.
  2. 2. Paste into any AIChatGPT, Claude, Gemini or Copilot. Replace each [PLACEHOLDER] with your details, or let the AI ask you.
  3. 3. Add evidenceAttach the evidence files listed on this page so the AI works from sourced numbers, not guesses.

The prompt

Paste it as-is. The AI will ask for anything still in [BRACKETS].

Download .md
# Build an objection-handling playbook
Method by Deepstory Research: deepstoryresearch.com/method/objection-handling-playbook

You are a sales enablement lead. Follow the method below step by step. Goal: The top objections with root cause, response framework, proof and follow-up questions.

## My inputs
- Product and price: [PRODUCT_AND_PRICE]  (example: $20k/year analytics platform)
- Common objections: [COMMON_OBJECTIONS]  (example: too expensive, we built it in-house, not now)
- Proof assets: [PROOF_ASSETS]  (example: case studies, ROI data)

Before you start: if any input above is still in [BRACKETS] or missing, ask me for it in one short numbered list and wait. Do not guess. If I attached files (data, briefs, Deepstory evidence files), treat them as the primary source.

## Steps to follow
1. List 8–12 objections, grouped (price, timing, trust, need, competitor).
2. For each: what is really being said, a response (acknowledge, clarify, respond, confirm), proof and a question back.
3. Add a talk track for competitor comparisons.

## Deliver the output in this structure
- Objection table
- Talk tracks
- Proof library mapping

## Quality rules
- Responses must be honest; never disparage competitors with unverified claims.
- Label every number as FACT (with source and date), ESTIMATE (show the calculation) or ASSUMPTION.
- If a Deepstory evidence file is attached, use its figures as the reference values and cite its source references; tag claims that are not from it as [UNVERIFIED].
- End with: the 3 assumptions that matter most, what would change the answer, and the single best next step.

What you need to give the AI

InputPlaceholderExample
Product and price[PRODUCT_AND_PRICE]$20k/year analytics platform
Common objections[COMMON_OBJECTIONS]too expensive, we built it in-house, not now
Proof assets[PROOF_ASSETS]case studies, ROI data

Use it when

  • Deals stall on the same objections.
  • Onboarding new sellers.

What you get

  • Objection table
  • Talk tracks
  • Proof library mapping

The procedure, step by step

Read this before you run it, so you can check the AI actually followed the method.

  1. 1

    List 8–12 objections, grouped (price, timing, trust, need, competitor).

  2. 2

    For each: what is really being said, a response (acknowledge, clarify, respond, confirm), proof and a question back.

  3. 3

    Add a talk track for competitor comparisons.

Quality checks built into the prompt

  • • Responses must be honest; never disparage competitors with unverified claims.
  • • Every number labelled FACT, ESTIMATE or ASSUMPTION; claims outside attached evidence tagged [UNVERIFIED].

Evidence to use with this method

Methods tell the AI how to think. Evidence gives it sourced numbers to think with. Download a context file and attach it with the prompt.

Run next

Methods that chain well after this one.

Deepstory methods are process guidance for AI tools. Review AI output before relying on it; legal, tax, medical and investment decisions need a qualified professional.