Identify Key Data Points for Account Research Based on Value Proposition

The file

---
name: identify-key-data-points-for-account-research-based-on-value-propositi
description: >-
  The account research data points that matter for your value proposition, so reps research what proves fit and skip the rest. Adapted from Christian Krause.
  Task type: Analyze. Built for Sales.
  Use when the request sounds like "Identify Key Data Points for Account Research Based on Value Proposition".
  Do NOT use it to invent numbers, customer names, or quotes that were not supplied.
license: MIT
metadata:
  author: The Revenue AI Report
  suite: Account Research and Targeting
  version: "1.0"
  source: https://www.therevenueaireport.com/prompts/identify-key-data-points-for-account-research-based-on-value-propositi
---

# Identify Key Data Points for Account Research Based on Value Proposition

The account research data points that matter for your value proposition, so reps research what proves fit and skip the rest. Adapted from Christian Krause.

One job only: the account research data points that matter for your value proposition, so reps research what proves fit and skip the rest. Anything outside that job belongs in another file in the Account Research and Targeting suite.

## When to use this skill

- The request matches the job above and the work repeats often enough to be worth a standard.
- You have, or can ask for, the inputs listed below.
- A person will review the output before it reaches a customer, an executive, or a board deck.

## When not to use it

- The answer needs a number, a benchmark, or a quote nobody supplied. Ask for it instead of producing one.
- The task is a different job in the same suite. Open that file rather than stretching this one.
- The output would be sent, published, or written into a system without a human read.

## Inputs to collect first

- value proposition
- data point 1
- justification 1
- data point 2
- answer

Ask for anything missing before running. Do not fill a gap with a plausible guess.

## Process

1. Confirm the job, the audience, and the decision the output feeds.
2. Collect the inputs above. Name anything still missing out loud.
3. Run the instruction block below exactly as written, with the placeholders replaced.
4. Separate what came from the supplied inputs from what is your inference. Label the inference.
5. Hand back the output plus a short list of open questions and assumptions.

## Instruction block

Run this text with the placeholders filled in.

```text
Your task is to help identify the most important data points to cover in account research, based on a given value proposition. A value proposition outlines the key benefits and unique selling points of a product or service. Researching relevant data points about potential customers can help assess how well the value proposition aligns with their needs and priorities.

Here are the steps to follow:

1. Read the provided <value_proposition> carefully and thoroughly to understand the key aspects and unique selling points being highlighted.

2. In the following scratchpad, think through the main components and themes of the value proposition. Break it down into its core elements and identify what would be most important to research for potential customers.

<scratchpad>
[AI should analyze the value proposition and outline the key aspects here]
</scratchpad>

3. Based on your analysis in the scratchpad, list out the most important data points that should be researched about potential customer accounts. Present these as a numbered list.

4. For each data point, provide a brief justification explaining how it relates to and supports assessing the value proposition for that account. Your justifications should directly reference specific components of the value proposition.

The overall structure should be:

<data_point_1>
[Data point description]
</data_point_1>
<justification_1>
[Justification tying the data point to the value proposition]
</justification_1>

<data_point_2>
...

5. Focus on identifying data points that are directly relevant to evaluating fit with the value proposition. Do not include generic data points that would apply to any sales situation.

When you have completed the list of data points and justifications, provide your response inside <answer> tags.
```

## Decision rules

- Supplied evidence beats inference. If the two disagree, say so and keep the evidence.
- Unknown is a valid answer. Write "unknown" rather than estimating.
- Keep one recommendation per output. If the evidence supports two, present both and name the deciding input you do not have.
- Match the reading level of a working operator. Short sentences. Explain any term the reader may not use daily.

## Quality checks

- Every number, name, date, and quote traces to an input the user supplied.
- No vendor claim is repeated as fact without naming the source.
- Assumptions are listed separately from findings.
- The output answers the original request and nothing else.
- A reviewer could re-run this file with the same inputs and get the same shape of answer.

## Guardrails

- Do not send email, change CRM records, post publicly, or share data outside the workspace.
- Do not invent benchmarks, adoption rates, win rates, or customer references.
- Flag anything that needs a human decision instead of deciding it.

## Source

- Prompt page: https://www.therevenueaireport.com/prompts/identify-key-data-points-for-account-research-based-on-value-propositi
- Suite: https://www.therevenueaireport.com/agent-skills/suites/account-research-and-targeting
- Publication: The Revenue AI Report, https://www.therevenueaireport.com