PlaybookPrompts

Build a talk track for a pricing conversation gone sideways

Sales & Outreach pricingnegotiationtalk-track

When a prospect reacts badly to pricing—sticker shock, competitor comparison, or a surprise discount request—reps often wing it. This prompt prepares a structured verbal response for the three most likely reactions.

Prompt
You are a sales coach. Build a talk track for a rep who has just shared pricing with a prospect and received a negative reaction.

Deal context:
- Our product: {{OUR_PRODUCT}}
- Our price shared with prospect: {{PRICE_SHARED}}
- Prospect's company type and size: {{PROSPECT_PROFILE}}
- Known value delivered / ROI case (if any): {{VALUE_CASE}}
- Discount authority the rep actually has: {{DISCOUNT_AUTHORITY}}

For each of the three reaction types below, write a talk track:

Reaction A — Sticker shock ('That's way more than we expected')
Reaction B — Competitor anchor ('Competitor X quoted us half that')
Reaction C — Discount pressure ('We need 20% off or this won't get approved')

For each reaction:
1. A 1-2 sentence empathy acknowledgment (not 'I understand', find a different opener)
2. A reframe that connects price to the specific value case
3. A clarifying question to uncover what's really driving the reaction
4. What the rep should NOT say (one specific trap to avoid)
5. When to escalate or loop in a manager versus handling solo

Note: If {{DISCOUNT_AUTHORITY}} is zero, Reaction C track must address that honestly rather than stalling.
Variables to fill in
  • {{OUR_PRODUCT}}
  • {{PRICE_SHARED}}
  • {{PROSPECT_PROFILE}}
  • {{VALUE_CASE}}
  • {{DISCOUNT_AUTHORITY}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{OUR_PRODUCT}}{{PRICE_SHARED}}{{PROSPECT_PROFILE}}{{VALUE_CASE}}{{DISCOUNT_AUTHORITY}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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Pair this prompt with a tool

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