Positioning, Messaging and the Offer

Positioning explains why a defined customer should choose your product. Messaging expresses that value for each buyer. The offer makes it purchasable, with evidence behind every external claim.

Why this matters

Products lose when buyers misunderstand their value, compare them with the wrong alternatives or cannot complete a purchase. PMs and product marketing must connect product truth, messaging and offer readiness.

You supply capabilities, best-fit customers, proof and limitations. Product marketing shapes their expression. Pricing and packaging come from Chapters 12 and 15.

AI claims need the same substantiation as other product claims, as FTC and SEC enforcement shows. Build a positioning canvas, statement, message house, offer definition and claims log before committing launch assets in Chapter 25.

Positioning, messaging and the offer

Three different things

Keep three decisions distinct:

Pragmatic Institute similarly treats positioning as an internal description of how the product solves market problems. External messages adapt that decision for buyers and personas.

April Dunford’s five components

April Dunford’s Obviously Awesome defines positioning through five dependent components, worked in order:

1. Competitive alternatives. What would customers do if your product didn’t exist? Build the list from real deals, not an analyst’s market map. In B2B the answer is often a manual process, an internal tool or “do nothing.”
2. Differentiated capabilities. What do you have that those alternatives lack? Features count, and so do a delivery model, expertise or a data advantage.
3. Value. For each capability, ask “so what?” until you reach something the customer cares about in business terms: time, money, risk, revenue. Group the answers into a few value themes, each with its proof.
4. Best-fit customers. Who cares most about that value? Describe them precisely enough that sales can spot one quickly and stop chasing the rest.
5. Market category. Choose the frame of reference that makes your value obvious to those customers. The category tells the buyer what to compare you with, which sets their assumptions about price, features and competitors before you’ve said a word.

Choose the category last. Its purpose is to make already-established strengths obvious to customers who value them.

Dunford adds three useful refinements:

• Decide the scope first. A multi-product company has to choose whether it’s positioning a single product, a suite or platform, the company, or a combination. Teams that skip this argue for weeks because they’re positioning different things.
• Position for the champion. The target persona is the deal champion: the person who builds the shortlist, brings in the other stakeholders and recommends a choice to the economic buyer. That’s often not the person who signs.
• Before launch, it’s a thesis. Dunford calls pre-launch positioning a positioning thesis. Hold it loosely until the market responds, then revise on evidence from real deals.

Write positioning to help customers choose. An investor narrative serves a different audience.

Three styles of market positioning

The category decision has three broad options. Dunford calls them styles of positioning.

• Head to head. Claim leadership of an existing category buyers already understand. Fastest when you can win on the category’s own terms; hard when an incumbent owns it.
• Big fish in a small pond. Lead a subsegment of an existing category, defined by a customer type or use case the leader serves poorly. You borrow the category’s familiarity and win where your strengths are decisive.
• Create a new category. Define a new frame because no existing one makes your value obvious. Slowest and most expensive, because the market has to learn a new term before anyone can buy.

Start with an existing category or subsegment unless you can fund category creation. Dunford notes that many startups stretch an established frame later. Eloqua is her example of a successful category creator, but this path remains expensive.

The category design debate

Play Bigger (2016), by Lochhead, Peterson, Ramadan and Maney, argues for category design: define a problem, name its category and lead it. Its current framing treats internal belief-building and external market creation as separate, necessary efforts.

Both views recognize that the frame shapes buyer judgment. Test whether a new one is worth funding:

• Is there a budget line and a shortlist? If buyers already set money aside and list vendors for something close to what you do, a subsegment of that category usually wins faster.
• Does every existing frame hide your value? If each one makes you look like a weaker version of something else, a new frame may be worth its cost.
• Can you afford to educate? A new category needs years of investment in analysts, content and events. Will the company fund it after the launch year?
• Will pragmatic buyers wait? Geoffrey Moore’s work, below, suggests mainstream buyers prefer a known category with a clear leader.

AI products make this debate live: every new capability tempts a team to coin a category.

Geoffrey Moore: the positioning statement and the beachhead

Geoffrey Moore’s Crossing the Chasm distinguishes early adopters buying promise from mainstream pragmatists buying proof. Choose a beachhead and deliver its whole product, including everything needed to solve the problem, before entering adjacent segments.

For a platform PM, a beachhead is a segment plus a use case. “Enterprises” isn’t one. “SRE teams of 20 to 100 engineers with a heavy incident load, for incident diagnosis” might be.

Moore also popularized the positioning statement, a fill-in structure that Chapter 02 borrowed for vision work. In outline:

• For [target customers]
• who [have this need or problem],
• [product] is a [market category]
• that [delivers this key benefit].
• Unlike [the primary competitive alternative],
• our product [has this primary difference].

Write the statement after the analysis. Its category and alternative must match real buyer choices. Aspirations and admired competitors cannot substitute for evidence.

From positioning to pitch

Dunford’s Sales Pitch builds an eight-step B2B conversation around positioning and market insight. PMs supply discovery evidence and proof from pilots, customer outcomes and evaluations (Chapters 01 and 31).

Separate value, the business reason to choose you, from objection handling, the requirements that can disqualify you. Champions need the value case. Security, IT, legal and procurement need credible answers about adoption, integration and risk.

Messaging frameworks and the message house

James Shaw’s Product Marketing Alliance framework links a value proposition to persona pain points, benefits, product detail and proof. Review it with product and apply it consistently across channels.

Many teams organize messaging as a message house, a common practitioner tool with no single recognized originator:

• The roof is one umbrella message: the positioning, expressed in customer language.
• Three pillars carry the supporting messages, usually one per value theme from the positioning.
• Proof points sit under each pillar: data, customer evidence, third-party validation.
• The foundation holds the facts that make the claims credible: company scale, security and compliance facts and, for AI products, the trust and governance facts.

Map value themes to pillars, evidence to proof points and adoption requirements to the foundation. Log every external proof point before use.

Build one house per offer and one variant per persona. The champion’s variant leads with value; the security reviewer’s leads with the foundation.

The offer: what you actually sell

The offer defines what customers can order. A functioning product still needs a quotable offer.

An offer definition specifies:

• SKUs. The orderable items in the price book, each with an owner and a unit.
• Editions and tiers. Which capabilities are in which edition, and the upgrade path between them.
• Bundles and add-ons. What’s sold only together, what’s sold separately and what’s sold both ways. Chapter 12 covers the economics of bundling.
• Unit of charge. Per seat, per environment, per usage unit or per outcome, with the exact definition of the unit. For outcome pricing, the definition is a contract term.
• Terms. Contract length, minimum commitments, overage, true-up, renewal and price protection.
• Eligibility. Who may buy which edition, in which regions, through which channels (direct, reseller, managed service provider) and with what prerequisites.
• Entitlements and limits. What the customer gets: quotas, support level, data retention and service levels.

Turn Chapter 15’s pricing and packaging decisions into an offer sales, finance, legal and partners can execute. Make the best-fit edition easy to identify and buy.

Offer readiness: quote to cash

Test the full quote-to-cash path before launch: configuration, pricing, quote, contract, order, payment, billing, revenue recognition and renewal. Zuora’s stage model helps identify gaps in quoting and metering new units.

IFRS 15, issued in 2014 and effective from 2018, and US counterpart ASC 606 require revenue accounting to consider contract promises and variable pricing. IFRS 15’s five steps are contract identification, performance obligations, transaction price, allocation and recognition as obligations are satisfied. Bundles can contain promises with different timing. Outcome pricing introduces variable consideration. Have finance review the offer before announcement.

Legal readiness covers the contract terms, the data processing terms and, for AI products, a set of AI-specific terms: whether customer data is used for training, acceptable use, what the product does autonomously and what needs human approval, and how errors are handled and credited. Chapter 33 covers the regulatory side.

Claims substantiation

Every message carries claims, and objective claims need evidence. The FTC’s 1984 Policy Statement Regarding Advertising Substantiation sets out the principle: an advertiser must have a reasonable basis for objective claims before making them. How much evidence counts as reasonable depends on the type of claim, the product, the consequences of a false claim, the benefit of a truthful one, the cost of developing the evidence and what experts in the field would consider reasonable. A claim that states its own level of support (“tests show,” “proven in pilots”) needs at least that level of support.

“Cuts diagnosis time in half” is an express, objective, quantified claim. Before it appears in a deck, a web page or a keynote, someone must hold dated evidence for it from a population that matches the customers it addresses. The claims substantiation log holds that evidence. Rules differ by jurisdiction and counsel decides what’s sufficient; the log gives them something to review.

Who owns what: PM and product marketing

Pragmatic Institute’s framework covers 37 activities across seven categories without fixed role assignments. Its usual division places market understanding and strategy with product, and positioning, launch and enablement with product marketing. Agree a workable split:

The PM owns the product truth: what it does, for whom, proven how and what isn’t ready. Product marketing owns the words and the field. Decide positioning together, with sales in the room.

How to apply it

Allow four to six weeks alongside other work. Complete the evidence and offer package before Chapter 25’s launch commitments.

Step 1: Prepare the evidence (week 1).

1. Decide the scope with product marketing (product, suite, company or a combination) and write it down.
2. Name the champion persona from your buying group (Chapter 01). The positioning is for them.
3. Gather the evidence: win/loss notes from the last two to four quarters, discovery snapshots, pilot results, usage data, eval results for AI features and the competitors that actually reached shortlists. Have your AI harness pull and summarize it through its connectors, and check the sources it cites.
4. Pick five to ten customers who get the most from the product. Their patterns are your best-fit signal.

Step 2: Run the positioning session (week 2).

5. Bring together product, product marketing, a sales leader who closes deals in the target segment, customer success and a product leader who can decide. Keep it under ten people.
6. Work the five components in order on the positioning canvas. Don’t let the group skip to the category.
7. Choose the positioning style and record why the other two were rejected.
8. If the product hasn’t launched, mark the canvas as a thesis and set a review after the first 20 or so qualified deals.

Step 3: Compress and express it (week 3).

9. Write the positioning statement and test it on three sales reps and two friendly customers. Can they repeat it in their own words? Does the “Unlike” line name what they’d really do instead?
10. With product marketing, build the message house and a variant per buying-group role.
11. Log every proof point and every quantified, comparative or availability claim in the claims substantiation log. Anything without evidence stays out of external material.

Step 4: Define the offer and make it ready (weeks 3 to 5).

12. Write the offer definition from the Chapter 15 packaging and pricing decisions.
13. Walk it through each quote-to-cash stage with sales operations, billing, revenue accounting and legal. Give each gap an owner and a date.
14. Label every capability as generally available, preview or roadmap, the same way in messaging and in the offer.

Step 5: Approve and hand over (weeks 5 to 6).

15. Get the claims log signed off by legal and product marketing, and the readiness check by finance and sales operations.
16. Hand the package to launch planning (Chapter 25) and sales enablement (Chapter 27), and record the positioning decision in your decision log.
17. At the review date, test the positioning against real deal outcomes. Revise it on evidence, not on the latest competitor announcement.

The AI lens

Positioning AI-powered products

Dunford’s 2026 AI guidance keeps the positioning principles while accounting for faster shifts in products, rivals and buyer expectations:

• Offer a point of view and a path. Buyers want a credible view of where the market is going and a path from today’s needs to it. Future vision helps only when it’s tied to present value; otherwise it stalls deals.
• Position against today’s shortlist. Build the product for tomorrow’s competitors, but position against the ones prospects consider now, using customer evidence (advisory boards, win/loss) rather than investor or peer narratives.
• Don’t reposition on every model release. If a new capability strengthens a value theme you already own, update the proof and keep the positioning. Rebuild only when you can deliver value you couldn’t before, or new competitors enter. Value changes more slowly than features.

Four implications follow for AI products.

Don’t make “AI” the category. “AI-powered” and “agentic” describe how the product works, and every competitor uses them. Position on the job and the outcome. Calling a chatbot or a script an “agent” is the same overstatement as calling a rules engine “AI,” and it carries the same substantiation risk.

Make the autonomy level explicit. State what the product suggests, what it does with human approval and what it does on its own, under which customer controls. That precision is both a trust message and a substantiation requirement.

Availability is a claim. In April 2025, the National Advertising Division of BBB National Programs found that Apple’s unqualified “Available Now” heading for Apple Intelligence implied features were available at launch when they weren’t, and that its footnotes weren’t clear or close enough to the claim. Apple said it would follow the recommendations. Label every capability as available, preview or roadmap at the point of the claim.

Accuracy and autonomy numbers are claims. The enforcement record is consistent.

The FTC set aside its Rytr order in December 2025, citing the administration’s AI Action Plan, while retaining its commitment to pursue unlawful AI uses. Keep substantiation discipline for your own product claims.

Tie marketing numbers to evals. The cleanest way to keep AI claims honest is to source every accuracy or autonomy number from the same eval sets and production metrics the product team trusts (Chapter 31). If the number in the keynote can’t be traced to an eval run with a date and a scope, it doesn’t go in the keynote.

Doing the work with AI

An AI harness with connectors to your CRM, call recordings, win/loss notes, support tickets and product analytics helps most with the evidence.

• Mining alternatives and wins. Ask which alternatives appeared in deals over the last few quarters, how often, and why customers chose you. That replaces the team’s memory of “who we usually see” with a list grounded in deals. Insist on citations to specific opportunities and calls.
• Testing the canvas. Ask it to argue against your positioning: which capabilities the alternatives share, which value themes lack proof, which best-fit traits don’t match won deals.
• Drafting persona variants. Models draft variants per persona and channel quickly, and drift from the house just as quickly. Check each against the house and the claims log.
• Auditing claims. Give it the launch material and the claims log, and ask it to list every objective claim and match it to a log row. Unmatched claims are your work list.
• Checking offer readiness. Ask it to walk the offer through each quote-to-cash stage and list each team’s open questions.

The rule from Chapter 01 still applies: AI can summarize what customers said, but it can’t tell you what they’d say or decide your positioning. Synthetic buyers can find confusing phrasing; they can’t show that a message works. Keep customer data in an approved tool.

The Beacon case: positioning Beacon Agents for Northlight Live

Scope and champion. Northlight would like a suite story across Observe, Desk and Beacon. You and product marketing position Beacon Agents as a product instead, because staying tool-agnostic opens the larger market. The Observe data advantage becomes a differentiated capability for Observe customers, not the frame. The positioning is written for Priya, the SRE lead, who builds the shortlist and recommends to Marcus.

Competitive alternatives. The harness pulls every Beacon opportunity from the last four quarters and lists the alternatives named in each. For most prospects the main alternative is humans plus runbooks plus chat. Then come AIOps features bundled into large observability platforms (the two Chapter 01 switchers left for one, on price and procurement simplicity), incident-management tools adding AI and ops-agent startups. The “AI SRE” startups the agency wanted to position against rarely reach shortlists.

Differentiated capabilities and value. The team runs the “so what?” exercise on each capability.

Three value themes survive: fewer and shorter 3 a.m. wake-ups for the on-call team; faster diagnosis and lower MTTR, which is Marcus’s CFO story; and trust and control that security can approve.

Best-fit customers. From the Chapter 01 hypothesis and the won deals: SRE and platform teams of 20 to 100 engineers at enterprises with a heavy incident load and an existing correlation footprint.

The category argument. The agency wants to create a category, “autonomous operations.” You run the test. Buyers already have budget and shortlists for incident management. Inside incident response, diagnosis with cited evidence stands out, so the existing frame doesn’t hide Beacon’s value. Northlight hasn’t committed to a multi-year category campaign. And “autonomous” overstates a product whose autonomy is limited to allowlisted actions. You choose big fish in a small pond: AI incident response for SRE teams, a subsegment of incident management.

The positioning statement. For SRE and platform teams who lose sleep and hours to incident diagnosis, Beacon is an AI incident response agent that finds the likely root cause with cited evidence and runs approved fixes. Unlike runbooks and chat war rooms, Beacon gives the on-call engineer a diagnosis in minutes, and only acts with human approval or within limits the customer sets.

Three sales reps repeat it back accurately, and a design-partner customer recognizes their own process in the “Unlike” line.

The message house. The roof, “Give your on-call team their nights back,” leads with the burnout insight, because that’s what Priya feels. The second pillar carries Marcus’s MTTR story. Sam’s requirements (audit logs, scoped credentials, a kill switch, documentation for EU AI Act obligations) sit in the foundation: they won’t win the deal, but missing any of them loses it. Dana’s variant leads with “what broke and what do I do”; Leo’s leads with multi-tenant views and per-customer billing.

The claims log. You and the product marketing manager log every claim in the draft deck and web page. Several change.

• “The AI SRE that never sleeps” and “replaces your on-call engineer” are cut. Both imply professional substitution, the pattern in the DoNotPay order, and customers don’t want it.
• “Cuts diagnosis time in half” was the Chapter 01 solution hypothesis, not a result. It stays out until the design-partner pilots report, and then it states the population (design partners) and the period.
• “Autonomous resolution” becomes “resolves allowlisted low-risk incidents without waiting for a human,” with the rate’s denominator written down because of the Presto order.
• “Accurate root cause” gets an eval-backed number with a date and scope, or no number. The claim has to hold up if the customer who rolled back the wrong service after a hallucinated root cause reads it.
• The keynote script labels each capability GA or preview, matching the offer definition.

Because Northlight is public, the log gets a column for investor-relations review of any claim that will also appear in an earnings call or filing.

The offer definition. The offer follows the packaging in Chapter 15 and the pricing in Chapter 12: Correlate and Diagnose sit in the platform fee in three tiers, and Agents is the outcome layer, a per-resolved-incident charge with an annual commitment and a monthly minimum. Audit logs, scoped credentials and the kill switch ship in every tier that includes Agents. The EU AI Act documentation pack and the multi-tenant console are add-ons. The Chapter 15 resolved-incident definition becomes a contract term, with its exclusions, dispute window and a credit when Beacon’s action makes an incident worse. Only customers with the security controls configured may buy Agents, and the MSP offer gives Leo per-tenant metering and invoicing at a wholesale rate.

Offer readiness. The quote-to-cash walk finds four gaps. Quoting has no SKU for a resolved-incident unit. Billing can’t yet meter resolved incidents from product telemetry, so engineering adds a billable event carrying the dispute fields finance asked for. Revenue accounting treats the per-incident charge as variable consideration and wants pilot data to estimate it. Legal adds AI terms: no training on customer data without consent, the human-approval commitment and the autonomy allowlist as a customer-controlled setting. MSP partner billing and the EU documentation pack won’t be ready in time, so Agents launches direct-only, with the MSP offer labeled “coming” and dated.

The result. Northlight Live announces “AI incident response for SRE teams,” with keynote numbers linked to approved claims. Review the thesis after the first 20 qualified Agents deals, including whether the burnout message resonates with economic buyers.

Product 360 artifacts

# Positioning canvas · [Product or offer] · v[n] · [Date]

**Owner:** [PM] and [PMM]   **Decision maker:** [Name]   **Status:** Thesis / Validated / Under review
**Scope:** Single product / Suite or platform / Company / Combination: [which]
**Champion persona:** [role, from the buying group]   **Review date:** [date or after N qualified deals]
**Evidence used:** [win/loss for which period, number of discovery interviews, pilots, usage data, evals]

## 1. Competitive alternatives (what customers would do if we didn't exist)
| Alternative | How often it appears in deals (source) | Where it falls short for best-fit customers |
|---|---|---|
| [Status quo or manual process] | | |
| [Competitor or category] | | |
| [Do nothing] | | |

## 2. Differentiated capabilities (what we have that the alternatives lack)
| Capability | Which alternatives lack it | How we know (evidence) |
|---|---|---|
| | | |

## 3. Value themes ("so what?" for each capability, grouped into two to four themes)
| Value theme | Capabilities that deliver it | Proof we have today | Proof still needed |
|---|---|---|---|
| | | | |

## 4. Best-fit customers
- Characteristics that make a customer care a lot about this value: [size, situation, tech stack, trigger events]
- Characteristics that predict a poor fit: [ ]
- Example best-fit customers (from won deals): [ ]

## 5. Market category
- Style chosen: Head to head / Big fish in a small pond / New category
- Category or subsegment: [name]
- Why this frame makes our value obvious: [ ]
- Budget line and shortlist test: [does the buyer already budget and shortlist for this?]
- Styles rejected and why: [ ]

## Optional: relevant trend
- Trend: [ ]   How it links to present value: [ ]   Risk of sounding like hype: [ ]

## Decision
- Decided on [date] by [names]. Logged in decision log as [ID].

# Positioning statement · [Product or offer] · v[n] · [Date]

**Source canvas:** [link]   **Owner:** [PM and PMM]   **Status:** Draft / Approved

**For** [best-fit customers, from canvas section 4]
**who** [need or problem, in their words],
**[Product] is a** [category or subsegment, from canvas section 5]
**that** [primary value theme, as a benefit].
**Unlike** [the primary competitive alternative, usually the one that appears most in deals],
**[Product]** [primary differentiation, from canvas section 2].

## Persona check
| Persona | Does the statement speak to them? | What they would add or object to |
|---|---|---|
| Champion | | |
| Economic buyer | | |
| Technical evaluator or security | | |

## Repeat-back test
| Tester (sales rep or customer) | Their version in their own words | Accurate? |
|---|---|---|
| | | |

## Claims in this statement
- [List each objective claim and its claims-log row ID]

# Messaging framework · [Product or offer] · v[n] · [Date]

**Positioning source:** [canvas and statement links]   **Owner:** [PMM]   **Accuracy reviewer:** [PM]

## Message house
**Roof (umbrella message, one sentence, customer language):** [ ]

| | Pillar 1 | Pillar 2 | Pillar 3 |
|---|---|---|---|
| Value theme (from canvas) | | | |
| Supporting message | | | |
| Proof points (claims-log IDs) | | | |
| Main persona | | | |

**Foundation (credibility, trust and objection handling):** [security, compliance, data use terms, support, integrations, company facts, AI governance facts]

## Persona variants ([Product] messaging for [persona])
| Persona | Role in deal | Pain in their words | Lead pillar | Core message | Proof they trust | Objections to handle |
|---|---|---|---|---|---|---|
| Champion | | | | | | |
| Economic buyer | | | | | | |
| End user | | | | | | |
| Technical evaluator or security | | | Foundation | | | |
| Partner | | | | | | |

## Availability labels
| Capability mentioned | Label (GA / Preview / Roadmap) | Matches offer definition? |
|---|---|---|
| | | |

## Words we use and words we avoid
- Use: [ ]
- Avoid: [for example, "replaces your [professional]", "fully autonomous", unqualified "available now"]

# Offer definition · [Offer name] · v[n] · [Date]

**Owner:** [PM]   **Packaging and pricing source:** [Chapter 15 decision, link]   **Status:** Draft / In readiness / Approved / Live

## What is sold
| SKU | Name | Edition or add-on | Unit of charge | List price | Owner |
|---|---|---|---|---|---|
| | | | | | |

## Editions and entitlements
| Capability or entitlement | Edition A | Edition B | Edition C | Add-on |
|---|---|---|---|---|
| | | | | |

Limits and quotas: [ ]   Support level: [ ]   Data retention: [ ]   Service levels: [ ]

## Unit definition (required for usage or outcome pricing)
- Exact definition of one unit: [ ]
- Who measures it and from which data: [ ]
- Exclusions: [ ]
- Dispute window and process: [ ]
- Credits when the product makes things worse: [ ]

## Bundles
- Sold only together: [ ]   Sold separately: [ ]   Both ways: [ ]

## Terms
- Contract length: [ ]   Minimum commitment: [ ]   Overage and true-up: [ ]
- Renewal and price protection: [ ]   Discount guardrails: [ ]

## Eligibility
- Who may buy each edition (prerequisites): [ ]
- Regions: [ ]   Channels: Direct / Reseller / MSP / Marketplace
- Partner-specific terms (for example, per-customer billing): [ ]

## Availability
| Capability | GA / Preview / Roadmap | Date | Shown in messaging as |
|---|---|---|---|
| | | | |

## AI-specific terms (AI products)
- Customer data used for training: [never / with consent / terms]
- What the product does autonomously, with approval, or only suggests: [ ]
- Customer controls (allowlists, kill switch): [ ]
- Third-party models or services relied on: [ ]

# Offer readiness check · [Offer name] · [Date]

**Launch date:** [ ]   **Owner:** [PM]   **Go / no-go decision maker:** [Name]

| Stage | Question | Owner | Status (Ready / Gap / N/A) | Gap and fix date |
|---|---|---|---|---|
| Offer configuration | Is every SKU, edition and add-on configured, with rules for valid combinations? | Sales operations | | |
| Pricing | Are list prices, discount guardrails and approval levels set? | Pricing or finance | | |
| Quoting | Can a rep produce a correct quote for each edition, region and channel without help? | Sales operations | | |
| Contract management | Are the terms, unit definitions, AI terms and data processing terms approved? | Legal | | |
| Orders | Can orders be booked and provisioned, including entitlements and limits? | Operations | | |
| Billing | Can billing measure and charge every unit of charge, with the data needed for disputes? | Billing and engineering | | |
| Payment and collections | Are payment terms and collections processes ready, including for partners? | Finance | | |
| Revenue recognition | Has revenue accounting identified the separate promises and any variable consideration? | Revenue accounting | | |
| Renewal | Is the renewal, upgrade and migration path defined? | Customer success | | |
| Partners | Can partners quote, order and bill this offer, or is it labeled direct-only for now? | Partner team | | |
| Claims | Is every external claim in the claims log and approved? | PMM and legal | | |

**Decision:** Go / Go with scope reduced (what is labeled "coming" and when) / No-go

# Claims substantiation log · [Product or launch] · [Date]

**Owner:** [PM] (evidence)   **Wording owner:** [PMM]   **Approver:** [Legal]   **Investor relations review (public companies):** [Name]

| ID | Claim text (exact wording) | Where used | Type: express or implied; quantified, comparative or availability | Evidence (link) | Population and period the evidence covers | Matches the audience of the claim? | Evidence owner | Status (Draft / Approved / Rewrite / Cut) | Review date |
|---|---|---|---|---|---|---|---|---|---|
| C-01 | | | | | | | | | |

## Rules
- No objective claim appears externally without an Approved row.
- A claim that states its own level of support ("tested," "proven," "in pilots") needs at least that level of support.
- Evidence must exist when the claim is made, be retained and cover the same population the claim addresses.
- AI accuracy and autonomy numbers trace to a named eval set or production metric, with date and scope, and state the denominator.
- Claims that the product replaces a professional, or that it acts without human involvement, get extra scrutiny.
- Availability claims match the offer definition's GA, preview and roadmap labels.
- Re-review every row when the model, eval set or product version behind it changes.

Prompts

Goal: Ground alternatives and value themes in documented deals.
Paste in (or connect the harness to): CRM opportunities, win/loss notes and sales call summaries for [product] over the last [two to four] quarters. This is confidential customer data: use an approved tool and remove personal identifiers.

From the material provided, answer:
1. Which competitive alternatives appeared in deals (including status quo, internal tools and "do nothing")? For each, give the number of deals it appeared in and cite the opportunity IDs.
2. In won deals, what reasons did customers give for choosing us? Group them into themes and quote the customer's words with source references.
3. In lost deals, what did the winner have that we lacked? Separate missing value from missing objection-handling items (security, integrations, support, terms).
4. Which customer characteristics are most common in won deals and rare in lost ones?
5. Which competitors do we talk about internally that rarely appear in deals?

Use reasons recorded in the material. Mark fields with missing evidence.

Check the output: open five cited opportunities and confirm the alternatives and reasons are recorded there. Treat any theme that rests on one or two deals as a lead to investigate, not a finding.

Goal: Find the weakest parts of your positioning before the market does.
Paste in: your completed positioning canvas and the evidence summary behind it.

Act as a skeptical sales leader and a skeptical buyer reviewing this positioning for [product].
1. For each differentiated capability, name any listed alternative that also has it, or that a buyer would believe has it.
2. For each value theme, say whether the proof provided would convince a buyer, and what proof is missing.
3. Check that the best-fit customer description would let a rep qualify or disqualify a deal in one call. Rewrite it if not.
4. Apply the budget-line-and-shortlist test to the chosen category. Argue for the strongest alternative style (head to head, subsegment, new category).
5. Identify any part that describes what we hope to deliver rather than what the product does today.

Give a candid critique based on documented competitor capabilities. Mark uncertain capabilities for verification.

Check the output: verify every competitor statement yourself before acting on it. Bring the two strongest objections to the positioning session.

Goal: Compress the canvas into a positioning statement that sales can repeat.
Paste in: the approved positioning canvas.

Using this canvas, write three versions of a positioning statement for [product] in this structure: For [best-fit customers] who [need], [product] is a [category] that [key benefit]. Unlike [primary alternative], [product] [primary differentiation].
- Version A: closest to the canvas wording.
- Version B: in the champion's language, using words from the customer quotes in the canvas.
- Version C: shortest possible without losing the alternative or the differentiation.

Then, for each version, list every objective claim it makes and the evidence in the canvas that supports it. Flag claims with no evidence.

Check the output: make sure the "Unlike" line names the alternative that appears most in deals, not the competitor you most want to beat. Test the chosen version by asking three reps to repeat it in their own words.

Goal: Get consistent messaging variants for each buying-group role.
Paste in: the message house, the persona set from Chapter 01 and the claims log.

For each persona below, write a messaging variant for [product]: the pain in their words, the lead pillar, a two-sentence core message, three supporting points and the objections to handle.

Rules:
- Use only proof points that have an Approved row in the claims log, and cite the row ID next to each.
- Lead with value for the champion and the economic buyer. Lead with the foundation (security, compliance, control) for the technical evaluator.
- Use capabilities, numbers and customer names only from the message house or claims log.
- Label any capability that is preview or roadmap.

[personas]
[message house]
[claims log]

Check the output: compare each variant to the house. Delete any sentence that introduces a new claim, and send the variants to one real person in each role (or the account team that knows them) before using them.

Goal: Catch AI-washing and unsupported claims before anything ships.
Paste in: the launch deck, web copy, keynote script or press release, plus the claims log and the offer definition's availability labels.

Review this material for [product]. List every objective claim, including implied ones, in a table with: exact text, location, type (express or implied; quantified, comparative, availability, or "replaces a person or professional"), the matching claims-log row ID, and a status:
- Supported: an Approved log row covers it, for the same audience and population.
- Mismatch: a log row exists but the wording overstates it, or the population differs.
- Unsupported: no log row.
- Availability risk: describes as available something labeled preview or roadmap.

Then suggest a rewrite for every claim that isn't Supported, staying within the evidence. Pay special attention to accuracy and autonomy numbers, words like "autonomous," "eliminates" or "without human," and any implication that third-party technology is our own.

Check the output: read the full material yourself for implied claims the model missed, especially in images, demo scripts and headlines. Send the Mismatch and Unsupported rows to legal and product marketing.

Goal: Find offer-readiness gaps while there's still time to fix them.
Paste in: the offer definition, the launch date and any known constraints of your quoting, billing and revenue systems (described in words).

Walk this offer through each quote-to-cash stage: offer configuration, pricing, quoting, contract management, orders, billing, payment and collections, revenue recognition and renewal. For each stage:
1. List the questions the owning team will need answered to support this offer.
2. Identify anything in the offer definition that is ambiguous for that team (for example, an undefined unit of charge, a missing exclusion or an unclear eligibility rule).
3. Flag where the offer may create separate promises in one contract, or variable pricing that finance must estimate.
4. Note what partners or resellers would need that direct sales doesn't.

Finish with a list of the five gaps most likely to delay the launch, each with a suggested owner.

Check the output: review the list with sales operations, billing and revenue accounting. The model can't see your systems; the owning teams decide what's really a gap.

Goal: Avoid repositioning on every model release, and catch the release that really needs it.
Paste in: the current positioning canvas, a description of the new capability or model change, and any new competitors that have appeared in deals.

Assess whether this change requires new positioning for [product]:
1. Does it strengthen a value theme we already own? If so, propose updated proof points and an updated explanation, and keep the positioning.
2. Does it let us deliver value we couldn't before? Describe the new value and who would care.
3. Does it bring us into competition with new alternatives? Name them, and say whether they appear in deals yet.
4. Recommend: update proof only, revisit one component of the canvas, or rerun the full positioning exercise. Explain why.

Check the output: confirm any new-competitor claim against real deal data. If the recommendation is a full rerun, take it to the positioning team rather than acting on the model's draft.

Anti-patterns

Checklist

• The positioning scope (product, suite, company) and the champion persona are decided and written down.
• The competitive alternatives come from real deals, including the status quo, with sources.
• Each differentiated capability maps to a value theme, and each value theme has proof or a plan to get it.
• Best-fit customers are described precisely enough for a rep to qualify a deal.
• The category was chosen last, with the positioning style and rejected alternatives recorded.
• The positioning statement passes the repeat-back test with sales reps and at least one customer.
• The message house has a roof, three pillars, proof points and a foundation (where objection handling sits), with a variant for each buying-group role.
• The offer definition specifies SKUs, editions, bundles, unit of charge, terms, eligibility and entitlements.
• Usage or outcome units are defined as contract terms, with exclusions, disputes and credits.
• Every quote-to-cash stage is ready or has a dated fix, and finance has reviewed revenue recognition.
• Every external claim has an Approved row in the claims log, with evidence matching the claim’s population.
• AI accuracy and autonomy numbers trace to a named eval set or production metric, with the denominator stated.
• Every capability is labeled available, preview or roadmap, consistently across messaging and the offer.
• A review date is set for the positioning thesis after launch.