Creating a Compelling Value Offering

Build a value claim the customer can check. Compare the real alternative, quantify benefits and costs, and use that evidence to set price and evaluate the investment.

Why this matters

Cost-based and competitor-based pricing leave the customer-value argument unresolved. Hinterhuber’s 2008 review found about 17% of surveyed firms priced mainly on customer value.

AI adds inference costs and uncertain benefits. Gartner’s June 2025 forecast cited unclear value among reasons agentic projects might be cancelled. Connect the customer’s job, alternative and economics to an offer sales can explain and finance can assess.

The value proposition canvas

Customer profile and value map

Osterwalder and Strategyzer introduced the value proposition canvas in Value Proposition Design (2014). Complete the halves separately, then compare them.

The customer profile, drawn as a circle, describes one customer segment:

• Jobs: what the customer is trying to get done. These can be functional (restore the service), social (look competent to leadership) or emotional (stop dreading the pager).
• Pains: bad outcomes, obstacles and risks around the job.
• Gains: outcomes and benefits the customer wants, from the ones they require to the ones that would surprise them.

The value map, drawn as a square, describes your offer:

• Products and services: what you actually sell.
• Pain relievers: how the offer removes or reduces specific pains.
• Gain creators: how the offer produces specific gains.

Fit means the value map addresses the segment’s important pains and gains. Strategyzer treats this as a hypothesis until customers confirm it. Test it alongside Chapter 01’s product hypothesis.

Rank jobs, pains and gains by importance. Write measurable pains and gains, then link each reliever or creator to one. Review features without a clear connection.

One canvas per buying-group role

The standard canvas assumes one customer. In enterprise software, a deal has several. Chapter 01 introduced the B2B buying group: end user, champion, economic buyer, technical evaluator, procurement. Each has different jobs, and a product that fits one role perfectly can fail on another.

Create separate canvases for users, economic buyers, reviewers and partners. Users explain adoption. Buyer gains feed the economic value model. Security concerns can block the deal, and partner value must include margin.

Economic value to the customer

Reference value plus differentiation value

Nagle and Müller, joined by Gruyaert in the 2023 edition of The Strategy and Tactics of Pricing, use economic value estimation (EVE). MIT teaches the same logic as economic value to the customer (EVC).

The buyer compares net value against the next best alternative. That comparison defines a theoretical ceiling:

EVC = reference value + differentiation value

• Reference value is the price of the customer’s next best alternative: the option they’d actually choose if you didn’t exist. It might be a competitor, an internal tool, a services contract, or people doing the work by hand.
• Differentiation value is the money value of every way you differ from that alternative. Some differences are positive (fewer hours, less downtime, more revenue). Some are negative (switching effort, training, risk that the new thing fails). Both go in the model.

At EVC, the buyer has no economic incentive to switch. Price below it, leaving some differentiation value with the customer.

MIT’s teaching note works this through with a fictional computer maker. The product was worth $6,000 more than the competitor’s over its life ($4,000 in labor, $500 in electricity, $1,500 in licenses). The competitor charged $6,800, so EVC was $12,800. The firm priced at $9,800: it kept half the differentiation value and gave the other half to the customer.

Doing it honestly

The MIT note describes a sequence that holds up well in B2B. Adapted for a PM:

1. Define the benefit, not the feature. Value comes from what changes for the customer. The note cites Xerox pricing its copiers per copy rather than per machine, because copies were the benefit.
2. Name the honest next best alternative, from the customer’s point of view. Use its real price after discounts, in the same units and over the same time horizon as yours.
3. List every source of differentiation, positive and negative.
4. Quantify each one with evidence: pilots, customer benchmarks, independent tests, the customer’s own operating data.
5. Add reference and differentiation value to get EVC.
6. Decide how much value to leave with the customer.

MIT describes a backup company that estimated EVC at twice its own price but missed competitors discounting 80% from list. Use actual alternative prices in comparable units.

How close to price to the ceiling

MIT identifies conditions supporting prices near EVC: a small share of an important end benefit’s cost, shared payment or less-scrutinized budgets. Benefit uncertainty pushes acceptable price lower.

AI benefits depend on customer data, processes and adoption. Leave more value with uncertain early buyers and use pilots, guarantees or outcome charges to establish evidence.

Value-based, cost-plus and competitive pricing

Use the three pricing approaches to answer different questions.

Appenzeller at Andreessen Horowitz estimated in 2024 that inference cost at constant capability fell roughly tenfold annually. A cost-plus AI price can therefore move rapidly. Combine three boundaries:

• Cost sets the floor. Below your fully loaded cost to serve (including inference for AI features), every sale loses money.
• The competitive reference sets the anchor. The next best alternative’s real price is what the buyer compares you against.
• Value sets the ceiling. EVC is the most a rational buyer would pay.

Price sits in the corridor between the floor and the ceiling. Where in the corridor depends on your strategy (share or margin), the buyer’s uncertainty and the lifecycle stage.

Investment value: DCF and NPV for product decisions

The investment case assesses supplier returns through discounted cash flow (DCF) and net present value (NPV). Finance and the harness can calculate them. Product must defend the inputs and counterfactual.

The idea in one paragraph

Discount future net cash flows to present value using finance’s required return, then subtract the initial investment. Gallo’s HBR refresher explains the time-value logic: positive NPV exceeds the hurdle under the model’s assumptions. Negative NPV falls short.

What a PM must understand

Seven ideas carry most of the weight:

1. The drivers are product decisions. Adoption, price, gross margin, cost to build and run, and time to first revenue all come from you.
2. The discount rate comes from finance. Riskier bets often carry a higher rate, which is fair: uncertain cash is worth less.
3. Late cash counts for less. Anything that brings revenue forward, such as faster onboarding, raises NPV more than a bigger number in year five.
4. Sensitivity beats precision. Find the two or three assumptions that flip the NPV from positive to negative, and put your evidence effort there.
5. Use scenarios, not one number. Base, downside and upside cases show the range; a single NPV to the nearest thousand dollars is false precision.
6. Model the counterfactual honestly. Clayton Christensen, Stephen Kaufman and Willy Shih argued in HBR in 2008 that DCF quietly kills innovation when the “do nothing” option is assumed to keep today’s cash flows. In most markets, doing nothing means decline, because competitors move. Compare the investment against the realistic baseline, not a flat line.
7. Staging creates option value. An investment broken into stages with go or stop gates is worth more than one all-or-nothing bet, because you can stop when early evidence is bad. Forrester’s TEI method captures the same idea as flexibility: the value of future options a first investment opens up.

Finance may also report payback period (how long until cumulative cash covers the investment), ROI (net benefit divided by cost, which ignores timing) or internal rate of return (the discount rate at which NPV equals zero). Use whichever your finance partner uses, and report NPV alongside it, because NPV respects both size and timing.

Pricing across the product life cycle

Skimming and penetration at launch

Joel Dean’s 1950 HBR article “Pricing Policies for New Products” set out the idea that a new product’s price should change as it moves from novelty toward commodity. Dean framed the launch decision as a choice between two strategies:

• Skimming: launch high, sell to the buyers who value the product most, and step the price down over time to reach the next segment.
• Penetration: launch low to win share fast, build scale or network effects, and make the market less attractive for imitators.

Skimming suits early demand that is insensitive to price, separable segments and protection from fast imitation. Penetration suits price-sensitive demand, strong scale or network effects, and competitors who will copy you quickly.

Spann, Fischer and Tellis studied 663 camera models from 79 brands in one European market. About 20% followed skimming or penetration patterns and about 60% followed market prices. Make the launch approach an explicit choice.

Growth, maturity and decline

At launch, reduce buyer risk through pilots, minimums or credits as well as price. At decline, plan switching costs, migration support and notice. Treatment of legacy customers shapes trust in the successor. Chapter 30 covers sunsetting.

Behavioral pricing: evidence and ethics

Behavioral effects can influence willingness to pay. Use the evidence with its limits.

Decoys

Joel Huber, John Payne and Christopher Puto showed in 1982 that adding an option which is clearly worse than one option, but not clearly worse than another, shifts choice toward the option it makes look good. This is the decoy or attraction effect: a “Pro” tier priced just below “Enterprise” with far fewer features makes Enterprise look like a bargain.

Frederick, Lee and Baskin’s 2014 experiments found decoys mainly in numeric tables, with weaker effects for experienced or perceptual attributes. Huber and colleagues defended replication under matching conditions. Numeric pricing tables may satisfy those conditions, which makes truthful choices and disclosure essential.

Anchoring

Tversky and Kahneman described anchoring in 1974. Ariely, Loewenstein and Prelec’s 2003 willingness-to-pay study used social-security digits as anchors. Above-median numbers produced valuations 57%–107% higher, while relative product rankings mostly held. Initial figures can influence absolute values.

In enterprise sales, the anchor is the first price or the first ROI figure in the conversation. That’s an argument for opening with a real enterprise tier and a well-evidenced value model, and against inventing a list price you never intend to charge.

Charm pricing

Eric Anderson and Duncan Simester ran three field experiments with a mail-order retailer and found that prices ending in 9 increased demand in all three, more so for new items. The authors suggest such endings work best when customers have limited information. Enterprise buyers with procurement teams are the high-information case, so charm pricing belongs on a self-serve plan, not in a negotiated contract.

Bundling

William Adams and Janet Yellen’s 1976 analysis distinguished separate pricing (components only), pure bundling (the package only) and mixed bundling (both). Because buyers value components differently, a bundle can sell to buyers who wouldn’t pay the standalone price of either part. Suites, platform editions and AI add-ons are all bundling decisions; Chapter 15 covers packaging in depth.

The ethical line

Behavioral effects are tools, and regulators have started drawing lines around their use. The US Federal Trade Commission’s 2022 staff report on dark patterns sorted deceptive designs into four groups: designs that create false beliefs, that hide or delay material information, that lead to unauthorized charges, and that obscure privacy choices. Its examples include countdown timers on offers that aren’t really time-limited and drip pricing, where mandatory fees appear only late in the purchase. The report cites a ticketing study in which buyers who weren’t shown fees upfront spent about 20% more and were 14% more likely to complete the purchase. In the EU, Article 6a of the Price Indication Directive, applying since May 2022, requires that announced price reductions be genuine.

Those rules mostly target consumer markets, but surprise usage overages, AI consumption charges buried in an order form and “launch week only” discounts that quietly continue are the enterprise versions of the same tactics.

ROI and business value models for B2B buyers

Total cost of ownership

Total cost of ownership (TCO) includes acquisition, implementation, integration, training, administration, operation and eventual retirement. Compare the full relationship cost.

AI products add costs that older TCO models miss: usage and overage charges, the time people spend reviewing and approving AI output, data preparation, evaluation, change management and keeping integrations working as connected systems change. If your value model ignores these, the buyer’s finance team will add them for you, usually pessimistically.

The TEI pattern

Forrester’s Total Economic Impact (TEI) combines benefits, costs, flexibility and risk adjustments. Studies usually model a composite organization from customer interviews. Vendor-commissioned studies must disclose commissioning under Forrester policy.

That structure, including the disclosure, is a good template for any vendor-built ROI claim. Risk-adjust every benefit. Say who built the model. Separate what was measured from what was modeled.

From value model to customer ROI calculator

The internal EVC model estimates the price ceiling. A customer ROI calculator lets buyers test it with editable inputs. Cite defaults, include full TCO, ramp benefits, adjust risk and report net benefit, payback and NPV at the customer’s discount rate.

After sale, measure the same value drivers. Renewal discussions should compare the original claim with realized outcomes.

How to apply it

This method builds the value offering for a new product or a major capability. Plan on four to six weeks alongside discovery, and revisit it at every lifecycle stage.

1. Draw a canvas for each buying-group role. Start from the Chapter 01 personas. Rank jobs, pains and gains in measurable form, tag every reliever and creator, and mark which rankings come from interviews and which are guesses.
2. Name the next best alternative per segment. Ask customers what they’d do if your product disappeared, and record its real cost after discounts, in your units and time horizon. Often the alternative is people and spreadsheets, and its cost is time.
3. Build the EVC model with three customers. Quantify positive and negative drivers with the customer’s own numbers, risk-adjust the uncertain ones, and log a source for every number.
4. Set the price corridor. Take the fully loaded cost to serve (with inference) from finance as the floor and risk-adjusted EVC as the ceiling. Write down where you’ll sit and why.
5. Write the investment case. Give the harness your drivers and finance’s discount rate; ask for NPV, sensitivities and three scenarios against a realistic do-nothing baseline. Propose stage gates.
6. Spec the customer ROI calculator, and agree with customer success how its drivers will be measured after the sale.
7. Write the lifecycle pricing plan: stage, objective, price relative to EVC, value metric, discount guardrails and the trigger for the next stage.
8. Run the ethics review on every pricing surface: pricing pages, proposals and order forms.
9. Close the loop. Each quarter, compare realized value from product data with the value you claimed. Where it falls short, fix the product or the claim.

The AI lens

When the product is AI-powered

AI changes the value offering in five ways.

The charge model. Bessemer Venture Partners’ 2026 AI pricing playbook describes five models: per seat, by usage (tokens, calls), per workflow completed, per outcome achieved, and hybrids. Seat pricing breaks when the product does work that seats used to represent. Stripe’s guide to pricing AI products separates the value metric (what the customer gets) from the charge metric (what you bill on): a resolved ticket is the value; a conversation, an action or a credit may be the charge.

Define the outcome first. Bessemer recommends outcome pricing where results are measurable, performance is reliable and compute variance is manageable. Zendesk announced autonomous-resolution pricing in August 2024. Intercom Fin lists $0.99 per outcome with confirmation, no-further-help or workflow definitions and a monthly minimum. Agentforce combines action credits, conversations and user licenses. Define billable events, rejected or reversed results, reopened cases, disputes and harm credits before setting the rate.

Margin is a constraint again. Bessemer characterizes AI companies’ gross margins as commonly 50% to 60%, against 80% to 90% for traditional software. Price on value; guard on cost. Check the price against the AI cost model (Chapter 31) at today’s inference cost and at the volume you expect a year out, because agentic workloads use more tokens per task even as the price per token falls.

The reference value moves to labor. Bessemer notes that AI spending often comes from labor budgets, not only IT budgets. When an agent does work a person used to do, the honest next best alternative may be that person’s time. That raises EVC, and it raises scrutiny, because the buyer will check the claim against real hours.

Uncertainty is the main discount. Buyers discount AI value because they aren’t sure they’ll realize it. Pilots with agreed success measures, a minimum with outcome-based charges above it, visible quality reporting and spend alerts reduce that uncertainty more cheaply than a lower price does. A surprise overage is the enterprise version of drip pricing.

Doing the work with AI

Use the harness for analysis while supplying the evidence and owning the assumptions.

Where the harness helps. Drafting canvases from interview snapshots; pulling value drivers and customers’ own numbers out of discovery transcripts; building the EVC, NPV and ROI models from stated assumptions; running sensitivities and scenarios; reviewing a pricing page against the ethics test. With connectors to the CRM, product analytics and billing, it can also compare claimed with realized value across the customer base each quarter.

Supply sourced inputs. Customer downtime cost, finance’s discount rate and the actual alternative need evidence. Mark gaps instead of accepting model-generated benchmarks.

Keep the math checkable. Ask the harness to show each calculation in words next to its result, and recompute one line yourself. Value models are read by finance teams who will.

The Beacon case: building the value offering for Beacon Agents

The canvases. You draw one per role. Dana needs to know what broke and what to do in under five minutes. Priya’s top pains are alert floods and fear that the agent will make an incident worse. Marcus wants headcount leverage and an ROI case for his CFO. Sam’s pains are privileged production access, audit and AI regulatory documentation. Leo’s main gain, for the MSP channel, is margin. Mapping pain relievers shows a gap: apart from the approval gate, nothing addresses Priya’s fear of the agent acting wrongly. The credit policy below becomes a pain reliever as well as a pricing term.

The next best alternative. Most prospects already pay for people, runbooks and chat. Others have bundled AIOps at no incremental software price. With reference value near zero, Beacon must justify the price through differentiation value.

The EVC model. You build it with one design-partner customer, using their numbers. They have about 1,200 significant incidents a year, and the pilot target is 60% resolved with Beacon’s help, as defined in Chapter 31: diagnosis accepted, no reopening within 24 hours.

Downtime is the biggest driver and the least certain, so it gets the largest haircut. The wrong-diagnosis line comes straight from the hallucinated-rollback storyline: you’d rather name it than have Sam’s team raise it in procurement.

The price corridor. Finance supplies the cost to serve, and the AI cost model from Chapter 31 sets a 70% margin floor. With reference value near zero, EVC for this customer is about $500,000 risk-adjusted. Because the buyer’s uncertainty is high at launch, you propose capturing roughly a quarter of it: a $60,000 platform fee plus $100 per resolved incident, about $132,000 a year at 720 incidents. That’s about 26% of risk-adjusted EVC and about 14% of the unadjusted figure. In Marcus’s terms: about $592,000 of risk-adjusted benefit against about $224,000 of total cost including setup, oversight and risk, a net benefit near $368,000.

The outcome definition and credit policy. Following the discipline of public outcome definitions, you specify what counts as a resolved incident, what’s excluded (diagnoses responders rejected, incidents reopened within 24 hours, actions rolled back), and a credit when Beacon’s action makes an incident worse. A monthly minimum protects Beacon’s cost base; outcome charges above it scale with value.

The investment case. Finance sets a 12% discount rate for the Beacon Agents program. Your illustrative model has a $6M upfront investment and incremental gross profit of $0.5M, $1.5M, $2.5M and $3.0M over four years. Against a flat do-nothing baseline, NPV is about −$0.7M, and the program looks like a mistake. But the realistic baseline isn’t flat. Observability platforms are bundling AIOps, and the two switchers from Chapter 01 left for exactly that reason. Modeling the erosion Agents would prevent (an illustrative $0.3M, $0.8M, $1.4M and $2.0M a year) turns NPV to about +$2.5M. The sensitivities that flip the sign are the resolved-incident rate, the per-incident price and inference cost per incident. You propose stage gates on each: a 60% diagnosis acceptance rate in the pilot, at least 70% gross margin at launch volume, and renewal of the first design partners.

The lifecycle plan. One portfolio, four stages:

• Beacon Classic (decline): price held for existing contracts, migration credits toward Agents, and an announced end-of-life date.
• Correlate (maturity): folded into the platform fee and offered as a bundle with Northlight Observe.
• Diagnose (growth): good, better, best tiers with discount guardrails.
• Agents (introduction): neither a pure skim nor a penetration play. A list price anchored to EVC, with risk reduced through design-partner pilots, the credit policy and the minimum, rather than through a low headline price.

The pricing page. Before Northlight Live, marketing proposes a “Pro” tier designed to make Enterprise look cheap, and a countdown banner for “launch week only” pricing that would quietly continue afterward. You reject both under the ethics test. The page keeps three real tiers, shows Enterprise as a genuine anchor, and states the per-incident charge and the minimum next to the platform fee, so no usage cost appears for the first time on an invoice.

The ROI calculator. You spec a calculator for Marcus built from the same drivers, with his inputs editable and every default cited. The disclosure says it was built by Northlight and that results are modeled, not measured. Customer success agrees to report the same three drivers from product data in every quarterly business review, so the ROI claim becomes a tracked number before the first renewal.

Product 360 artifacts

# Value proposition canvas · [Product] · [Role] · v[n] · [Date]

**Segment:** [segment]   **Role in the deal:** End user / Champion / Economic buyer / Technical evaluator / Procurement / Partner
**Owner:** [PM]   **Evidence:** [number of interviews, pilots, data sources]   **Status:** Hypothesis / Partly confirmed / Confirmed

## Customer profile
### Jobs (ranked)
| Rank | Job | Type (functional / social / emotional) | Evidence |
|---|---|---|---|
| 1 | | | |

### Pains (ranked, measurable)
| Rank | Pain | Measurable form (how much, how often) | Evidence |
|---|---|---|---|
| P1 | | | |

### Gains (ranked, measurable)
| Rank | Gain | Measurable form | Required / expected / desired / unexpected | Evidence |
|---|---|---|---|---|
| G1 | | | | |

## Value map
### Products and services
-

### Pain relievers
| Pain reliever | Addresses (pain ID) | Proof available today |
|---|---|---|
| | | |

### Gain creators
| Gain creator | Addresses (gain ID) | Proof available today |
|---|---|---|
| | | |

## Fit
- Top pains and gains with no reliever or creator:
- Relievers or creators that address nothing ranked:
- Gains that feed the economic value model (economic buyer canvas only):

## Untested assumptions
1. [Assumption] · Test: [how] · Signal: [what would falsify it]

# EVC model · [Product] · [Segment] · [Customer or composite] · [Date]

**Owner:** [PM]   **Built with:** [customer names and roles, or "composite of n customers"]   **Time horizon:** [e.g. 12 months]   **Units:** [per year / per seat / per incident]

## 1. Benefit (not feature)
What changes for the customer, in one sentence:

## 2. Next best alternative
- What the customer would do without us:
- Its real price after discounts, in our units and horizon: $[ ]   Source:
- **Reference value:** $[ ]

## 3. Differentiation drivers
| Driver | + / − | Calculation in words | Inputs (owner) | Annual value | Evidence and source | Confidence (H/M/L) | Risk haircut % | Risk-adjusted value |
|---|---|---|---|---|---|---|---|---|
| | + | | | | | | | |
| | − | | | | | | | |
| **Total differentiation value** | | | | | | | | |

## 4. EVC
- EVC (unadjusted) = reference value + differentiation value: $[ ]
- EVC (risk-adjusted): $[ ]

## 5. Price corridor
- Floor: fully loaded cost to serve, including inference: $[ ]   Source: finance
- Ceiling: risk-adjusted EVC: $[ ]
- Proposed price: $[ ]   Share of risk-adjusted EVC captured: [ ]%
- Why this share (lifecycle stage, buyer uncertainty, strategy):

## 6. Assumption log
| # | Assumption | Value used | Source | Date checked | Owner |
|---|---|---|---|---|---|
| 1 | | | | | |

## 7. Review
Next review date, and the product data that will confirm or correct each driver:

# Customer ROI calculator spec · [Product] · v[n] · [Date]

**Owner:** [PM]   **Partners:** product marketing, sales engineering, customer success, finance
**Audience:** [economic buyer role] and [champion role]
**Disclosure line (shown on every output):** Built by [company]. Results are modeled from the inputs shown, not measured at your organization. [If any inputs come from a commissioned study, say so.]

## Value drivers
| Driver | Calculation in words | Input the customer owns | Default value | Source of default | Editable? |
|---|---|---|---|---|---|
| | | | | | Yes |

## Cost side (full TCO)
| Cost | Calculation in words | Default | Source |
|---|---|---|---|
| Subscription or platform fee | | | |
| Usage, outcome or overage charges | | | |
| Implementation and integration | | | |
| Internal admin and oversight time | | | |
| Training and change management | | | |
| (AI products) Review and approval time, data preparation | | | |

## Time to value
- Ramp assumption by month or quarter:
- Month in which full value is assumed:

## Risk adjustment
| Driver | Default haircut % | Why | Customer can change? |
|---|---|---|---|
| | | | |

## Outputs
- Net benefit (year 1 and three-year)
- ROI
- Payback in months
- NPV at the customer's own discount rate (customer enters the rate)
- A sensitivity view: the two inputs that move the result most

## Rules
- No default without a cited source. Missing evidence shows as "Customer to supply."
- Every calculation is visible in plain words, not hidden.
- The customer can export inputs and assumptions.

## After the sale
| Driver | How it is measured in product | Report | Cadence | Owner |
|---|---|---|---|---|
| | | Quarterly business review | Quarterly | Customer success |

# Investment case · [Initiative] · [Date]

**Owner:** [PM]   **Finance partner:** [name]   **Decision maker:** [name]   **Decision needed by:** [date]

## Decision asked
[One sentence: approve $[ ] for [scope] through [first gate].]

## Options
| Option | Description | Baseline assumption |
|---|---|---|
| Do nothing | | [Realistic baseline, including erosion if competitors move] |
| Option A | | |
| Option B (staged) | | |

## Key assumptions
| Driver | Value | Evidence | Confidence (H/M/L) |
|---|---|---|---|
| Adoption (customers, ramp) | | | |
| Price and value metric | | | |
| Gross margin (including inference) | | | |
| Cost to build | | | |
| Cost to run | | | |
| Time to first revenue | | | |

## Cash flow summary (by year, versus do-nothing baseline)
| | Year 0 | Year 1 | Year 2 | Year 3 | Year 4 |
|---|---|---|---|---|---|
| Incremental revenue | | | | | |
| Incremental gross profit | | | | | |
| Investment | | | | | |
| Erosion avoided versus baseline | | | | | |
| Net cash flow | | | | | |

## Results
- Discount rate (from finance): [ ]%
- NPV: $[ ]   Payback: [ ] months   IRR (if finance uses it): [ ]%
- NPV against a flat baseline, for comparison: $[ ]

## Sensitivity (top three drivers)
| Driver | Value at which NPV reaches zero | How far that is from the base case |
|---|---|---|
| | | |

## Scenarios
| Scenario | Key differences | NPV |
|---|---|---|
| Downside | | |
| Base | | |
| Upside | | |

## Stage gates and kill criteria
| Gate | Date | Evidence required to continue | Kill or pivot if |
|---|---|---|---|
| | | | |

## Value not in the numbers
- Strategic options this creates (flexibility):
- Risks not modeled:

# Lifecycle pricing plan · [Product or portfolio] · [Date]

**Owner:** [PM]   **Partners:** pricing or monetization, finance, sales, customer success

| Product or capability | Stage (intro / growth / maturity / decline) | Evidence for stage | Pricing objective | Price relative to EVC | Packaging and value metric | Discount guardrails | Trigger for next stage | Customer communication | Risks |
|---|---|---|---|---|---|---|---|---|---|
| | | | | | | | | | |

## Launch choice (introduction stage only)
- Skimming / penetration / market-based, and why:
- How buyer uncertainty is reduced (pilot, guarantee, minimum, credits):

## Decline and sunset (decline stage only)
- Price for the remaining base:
- Migration offer toward the successor:
- End-of-life date and notice period:
- Contract commitments to honor:

## Review
Review at each quarterly business review, and whenever a stage trigger fires.

# Pricing ethics review · [Pricing page, proposal or order form] · [Date]

**Reviewer:** [PM]   **Second reviewer:** [legal or product marketing]

| Check | Pass / Fail | Notes |
|---|---|---|
| Every option shown is real and purchasable (no decoy that cannot be bought) | | |
| Any reference or "was" price is a price we actually charged | | |
| Any deadline or scarcity claim is true and will be honored | | |
| The full price, including usage, overage and minimums, is shown before commitment | | |
| AI consumption charges are explained in plain words with an example bill | | |
| Spend visibility, alerts or caps exist for usage-based charges | | |
| Cancellation or downgrade terms are as easy to find as the purchase path | | |
| A customer who learned how this was designed would not feel tricked | | |

## Decision
Ship / Fix and re-review. Fixes required:

Prompts

Goal: Get a first canvas for each buying-group role, grounded in what customers actually said.
Paste in: interview snapshots, call notes or win/loss notes, and your persona set. Use only tools approved for customer data, and remove personal identifiers first.

From the material provided for [product], draft a value proposition canvas for each of these roles: [end user, champion, economic buyer, technical evaluator, partner].

For each role:
1. List jobs, pains and gains, ranked by how often and how strongly they appear in the material. Write each pain and gain in a measurable form where the material allows.
2. Cite the source document for every item. If the material doesn't support an item, leave it out.
3. Map our current products and features to pain relievers and gain creators, tagging each to the pain or gain ID it addresses.
4. List the top-ranked pains and gains with no reliever or creator, and any features that address nothing ranked.

Mark every canvas "Hypothesis: to be confirmed with customers."

Check the output: open three citations and confirm the paraphrase matches. Look hardest at the economic buyer's gains, since they feed the value model.

Goal: Turn differentiation into money and find the price ceiling for one segment.
Paste in: the economic buyer's canvas, notes from value conversations with [n] customers, the next best alternative and its real price, and any pilot data. This includes customer data; use an approved tool.

Build an economic value to the customer (EVC) model for [product] in [segment], over [time horizon].

1. State the benefit in one sentence, in customer terms.
2. Confirm the next best alternative and its real price after discounts, in our units. If my evidence doesn't support the price, say so.
3. List every differentiation driver, positive and negative. Include switching effort, oversight time and the risk of the product failing.
4. For each driver, give the calculation in words, the inputs, the source of each input and a confidence rating. Use supplied numbers. Mark missing values as "Needs customer input."
5. Propose a risk haircut per driver based on confidence, and show unadjusted and risk-adjusted EVC.
6. Show each calculation next to its result.

Check the output: recompute the largest driver yourself. Confirm that no input appears without a source, and that the negative drivers aren't missing or understated.

Goal: Find the assumptions that decide whether the investment is worth making.
Paste in: the investment case draft, the driver assumptions with their evidence, and the discount rate from finance.

Act as a skeptical finance partner reviewing this investment case for [initiative].

1. Recompute NPV and payback from the assumptions given, showing each year's discounted cash flow in words and numbers.
2. Run sensitivity on every driver and list the three that move NPV most. For each, give the value at which NPV reaches zero.
3. Build downside, base and upside scenarios and explain what differs in each.
4. Critique the do-nothing baseline. If it's flat, describe what a realistic baseline would look like given these competitive facts: [facts], and recompute NPV against it.
5. Propose stage gates with measurable kill criteria.

Label any additional assumption as "added by reviewer."

Check the output: recompute one year's discounted cash flow by hand. Make sure any change to the baseline is backed by evidence you can show, not by optimism.

Goal: Produce a buyer-facing ROI calculator spec that a finance team will trust.
Paste in: the EVC model, the TCO components you know, the ramp assumptions and the evidence for each default.

Write a spec for a customer ROI calculator for [product], for [economic buyer role].

Include: value drivers with the calculation in words and the input the customer owns; full TCO including internal time; a ramp; a visible risk haircut per driver; outputs (net benefit, ROI, payback months, NPV at the customer's discount rate); a disclosure line saying who built it and that results are modeled; and a plan for measuring the same drivers in product after the sale.

Flag every default that lacks a cited source. Flag any benefit that would be counted twice.

Check the output: read it as the buyer's CFO would. If any default would embarrass you when the customer asks "where's that from?", replace it with "Customer to supply."

Goal: Make the pricing stage and the next move explicit for each product in a portfolio.
Paste in: the list of products or capabilities, revenue and growth trends, competitive moves, EVC estimates and current discounting data.

For each product in [portfolio]:
1. Propose its lifecycle stage and cite the evidence (growth, competitive pressure, discount trends, successor products).
2. State the pricing objective for that stage and the price relative to EVC.
3. Recommend packaging moves, discount guardrails and the trigger that would move it to the next stage.
4. For any product in introduction, compare skimming, penetration and market-based launch pricing for our situation, and recommend one with reasons.
5. For any product in decline, propose a migration offer and a sunset communication plan that protects trust.

Check the output: challenge any stage assignment that rests on a single data point. Confirm with sales that the discount guardrails are enforceable.

Goal: Catch decoys, false urgency and hidden charges before customers do.
Paste in: the pricing page copy or screenshot text, the proposal or order form, and the actual price book.

Review this pricing surface for [product] against these checks:
1. Is every option real and purchasable? Flag any tier that seems designed only to make another tier look better.
2. Are all reference prices, discounts and deadlines true and honored as stated?
3. Is the full price, including usage, overage and minimums, visible before commitment?
4. Are AI consumption charges explained with a worked example bill?
5. Is cancelling or downgrading as easy to find as buying?
6. Would a customer who learned how this page was designed feel tricked?

For each failure, quote the text, explain the problem and propose replacement copy.

Check the output: compare every price and deadline the model accepted against the price book yourself. The model can't know whether a "limited time" offer will in fact end.

Anti-patterns

Checklist

• Each buying-group role has a value proposition canvas with ranked, measurable pains and gains and tagged relievers and creators.
• The next best alternative is named per segment, with its real price after discounts and the source.
• The EVC model includes positive and negative differentiation, a source for every input and risk-adjusted totals.
• The price corridor is documented: cost floor from finance (including inference), risk-adjusted EVC ceiling and the share of value captured, with reasons.
• The investment case shows NPV against a realistic do-nothing baseline, the top three sensitivities, three scenarios and stage gates.
• A customer ROI calculator spec exists, with editable inputs, cited defaults, full TCO, ramp, risk haircuts and a disclosure line.
• The same value drivers are measured in product after the sale and reported in business reviews.
• Each product has a lifecycle stage, a pricing objective and a trigger for the next stage.
• Launch pricing is an explicit choice (skimming, penetration or market-based) with the reason written down.
• For AI products, the outcome definition, exclusions and credit policy are written before the price is set, and the price is checked against the AI cost model.
• Every pricing surface has passed the ethics review.
• No number in any value artifact came from the harness without a named source.