Disc Good Better Best Options

---
name: disc-good-better-best-options
description: Builds three real options for one engagement that differ in scope and outcome, each priced from your own price list, with what each leaves out and the assumptions each rests on, and no decoy. Use for "run disc-good-better-best-options", "give them three options", "good better best", "options instead of one price", "build tiers from my price list", "they said no to my one price", "what would a smaller version look like", part of the Claude for Winning Proposals Pack by Polar Bear.
---

# Good-Better-Best Options

## When To Use
A single price gets a yes or no, and too often a no. Use this once the problem is confirmed and you know what you would do, to give the client a choice between three offers you would each be glad to deliver.

## When Not To Use
If you are still deciding how to charge (fixed, retainer, value-based), start with Fee Structure Choice. If the client asked for everything and you need to cut one scope down to fit, MoSCoW Scope is the better tool; if only one honest answer exists, offer one option and say so.

## Inputs
- Your problem statement or issue tree, and the client's stated outcomes and budget if they gave one
- Your price list, day rates or past fees for similar work; I never set a price
- What you would and would not take on, and whether you offer a "do part with your own AI" route
If you have none of this, I start from the problem in your words and leave every price as [your price], marked as a first draft.

## Approach
Tiered options are a practitioner convention: three offers that differ in what the client gets, not in how much was shaved off. The trap is the decoy effect described by Huber, Payne and Puto in the Journal of Consumer Research (1982): an option clearly worse than another on every count, added only to make its neighbour look good. Clients feel that, and it costs trust at the moment you need it most. So every option here must pass one test: you would be happy to deliver it at that price. Works in any plain chat.

## Workflow
1. Ask at most three questions: what outcome does the client care about most, in their words; what is the smallest piece of work that still moves it; and do you offer a route where they do part of it with their own AI while you do the rest?
2. Name the axis the options climb. Pick one or two from: outcome reached (first move, all moves, adoption), depth (diagnosis, plan, plan and delivery), or who does the work (you, shared with their team, mostly them with their AI and your review). An option that only adds hours is not a new option.
3. Draft the three. Good is the smallest version that still solves something real; Better is the one you would recommend; Best changes the outcome, for example faster, or with you staying through adoption. Write in one sentence what each does that the one below it does not. If you cannot, merge them.
4. Price each from your list only. Show the fee by phase or deliverable where you have the figures, and write [your price] wherever you do not. If you ask what the market charges, I will say I do not know and ask for the price you would defend in the room.
5. List what each option leaves out and the assumptions it rests on: client time, access to data, number of reviewers, revision rounds, start date. Assumptions are what protect you in month three.
6. Run the decoy test. Compare every pair across outcome, scope, client effort and price; if one option loses on every count to another, it is a decoy. Remove it or redesign it until someone would rationally choose it.
7. Mark your recommendation and the reason, tied to what the client said matters most. The client chooses; you decide what you put in front of them.

## Output Format
```markdown
# Three Options
Client problem, in their words: [from your notes]
| | Good | Better | Best |
|---|---|---|---|
| Outcome the client gets | [outcome] | [outcome] | [outcome] |
| Scope | [what is done] | [what is done] | [what is done] |
| What it leaves out | [left out] | [left out] | [left out] |
| Client effort | [time, people, data] | [time, people, data] | [time, people, data] |
| Rests on these assumptions | [assumptions] | [assumptions] | [assumptions] |
| Adds over the one below | n/a | [one sentence] | [one sentence] |
| Price | [your price] | [your price] | [your price] |
## Decoy check
| Pair compared | Does one lose on every count? | Change made |
|---|---|---|
| [Good vs Better] | [yes or no] | [none or what changed] |
## Recommended option
[Option and the reason, tied to the client's stated priority]
## Decision
[Your name] approves the three options and every price by [date], before they go into the proposal; the client chooses.
```

## Done When
- Each option differs in outcome or scope, and the difference fits in one sentence.
- Every price comes from your list or reads [your price].
- Every pair has passed the decoy test, and what each option leaves out is written down.
- You have said you would deliver each option gladly at its price.

## Quality Bar
- Options climb by what the client gets, never by padding the same work with hours.
- The smallest option still solves something real; it is not a punishment for a small budget.
- A "do part with your own AI" option is offered honestly where you offer it, never priced to fail.
- Assumptions are specific enough that a finance lead could check them.
- Every price is yours; no decoy options.

## Next
Run disc-fee-structure (Fee Structure Choice) to choose how each option is charged.

## About the makers

This pack is made by Polar Bear, a consultancy built by ex-McKinsey founders with a dream to make AI work for People, not instead of them. We help our clients build people systems and AI-first ways of working, and we run our own company on Claude. If your team has outgrown the self-serve version, message Pauline (linkedin.com/in/paulinebertry).