An 18-24 week cross-functional pricing and packaging redesign for $5M-$50M ARR B2B SaaS, anchored on Madhavan Ramanujam and Georg Tacke's nine-step Monetizing Innovation methodology (https://www.simon-kucher.com/en/) layered with T2D3 proprietary IP for billing migration and sales-comp redesign. Eight modules cover willingness-to-pay discovery (Van Westendorp PSM, Gabor-Granger ladder, MaxDiff, choice-based conjoint), WTP-based segmentation, product configuration and bundling (Leader/Filler/Killer architecture), pricing strategy and model selection, business case and pricing-waterfall narrative, launch and customer communication, billing-system migration with ASC 606 rev-rec, and sales enablement and comp-plan update. Targets ARPA +25%, NRR >=120%, gross margin >=78%, voluntary churn <=2%, 100% billing migration coverage, >=70% AE quota attainment, pricing-objection rate <=25%, and discount leakage <=15%. Reference methodologies: https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025 and https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. Total scope: 8 modules, 31 sections, 110 tasks, 788 hours of canonical effort with feature-gated tracks for billing-vendor selection (~130h), comp-plan redesign (~30h), and usage-based metering.
Stand up a defensible willingness-to-pay (WTP) picture by segment so every later pricing decision (tier, bundle, price, model) is anchored in evidence rather than internal opinion. Implements Ramanujam Steps 1-2: Van Westendorp PSM, Gabor-Granger ladder, MaxDiff feature ranking, choice-based conjoint for bundle simulators, and 20 qualitative WTP interviews. STOP: Standardize -> Templatize. Heaviest research module of any methodology playbook (197h).
Audit current pricing realisation, score the four failure modes (feature-shock / minivation / hidden-gem / undead) per Ramanujam, baseline T2D3 stage, brainstorm candidate value metrics, draft the research charter, and present diagnostic readout to exec team using Pain-Claim-Gain.
Audit current price list, discounts, contracts, realisation
Pull the last 12 months of invoice data, list-price card, discount approvals, and rebill events. Compute price-realisation rate (net/list) per segment, average discount, and tier mix today. This is the baseline against which every later KR is measured; without it, the discount-leakage KR (kr-discount-leakage) and ARPA-uplift KR (kr-arpa-uplift) cannot be verified. Use Monetizely's encyclopedia framework: https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. The audit also produces the top-10 discount exception list that the discount-governance workstream in M4 will eventually replace with a Green/Yellow/Red approval matrix.
Score current pricing on the 4 Ramanujam failure modes
Use the four-quadrant Ramanujam diagnostic - feature shock / minivation / hidden gem / undead - to characterise the current monetization. Drive from existing customer interviews (recent NSA / NPS verbatims), churn-reason data, and competitive teardown. The output is a one-page diagnostic that names the dominant failure mode and the quantified evidence. See https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/ for failure-mode definitions. The diagnostic determines which downstream module gets the heaviest attention - feature-shock customers need M3 bundle simplification, minivation customers need M3 hidden-gem promotion, hidden-gem customers need M4 repricing, undead customers need M3 deprecation.
Establish T2D3-stage baseline and select target stage
Confirm where the company sits on the T2D3 growth curve and which stage the redesign is supposed to unlock (typically T1 to D1 - multi-product or expansion-motion). Codify this in a one-pager so every later decision can be traced to 'this is what gets us to D1.' Reference T2D3 frame definitions at https://www.t2d3.pro and the in-product CLAUDE.md baseline. Without an explicit stage target, the M3-M4 design defaults to incrementalism rather than the structural shift the playbook is designed to enable. The stage baseline also drives the M5 business case forecast horizon and the M8 comp-plan ramp shape.
Brainstorm candidate value-metrics canvas
Run a 90-min cross-functional workshop (CPO, CFO, CRO, Eng lead) to brainstorm 6-10 candidate value metrics - per-seat, per-active-user, per-workflow, per-outcome, per-agent-run, per-record, per-API-call, per-GB processed, per-customer-of-customer (Procore-style ACV), per-revenue-share. Score each on (a) correlation with customer value, (b) measurability, (c) gameability, (d) growth alignment. Output is a ranked shortlist of 3-4 to test in the survey. Reference https://www.withorb.com/blog/saas-usage-based-pricing-examples for inspiration. The shortlist becomes the input to M1.S2 quant survey design and M3 bundle architecture.
Draft pricing research charter (budget, vendor, sample plan)
Document research goals, methods, sample-size plan (>=150/segment for Van Westendorp, >=600 total for CBC), vendor selection (Conjointly / Sawtooth / Qualtrics / in-house Surveys), budget, timeline, and decision rights. Pre-register the questions you intend to answer so post-hoc rationalisation is harder. See https://conjointly.com/blog/gabor-granger-or-van-westendorp/. The charter is the single document that prevents the research from drifting into post-hoc data fishing - if you can't answer the question 'which question are we trying to answer?' before fielding, you'll find a story in any dataset.
Build Pain-Claim-Gain diagnostic readout deck for exec team
Build the exec readout deck using Pain-Claim-Gain structure: Pain = current realisation %, NRR drift, AI-COGS compression; Claim = our redesign hypothesis; Gain = quantified KR target (ARPA +25%, NRR 120%, GM 78%). Deck reuses one-pagers from prior tasks (audit, failure-mode diagnostic, T2D3-stage baseline, value-metric canvas, research charter). 12 slides max, ends with the executive cadence commitment ask: budget for M2-M8 and exec sponsorship for cross-functional artifacts. Reference https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan for board-level pricing-narrative structure.
Build Van Westendorp PSM, Gabor-Granger ladder, MaxDiff feature block, and choice-based-conjoint bundle simulator. Recruit 450+ ICP respondents across 3 populations (current customers, evaluated-but-bought-competitor, evaluated-but-stayed-in-funnel), pilot with 50, and field full sample with <20% break-off.
Build Van Westendorp PSM block
Build the four-question Van Westendorp Price Sensitivity Meter (PSM) block in Conjointly / Sawtooth / Qualtrics: too cheap / cheap-bargain / expensive-still-consider / too-expensive. Calibrate currency, anchoring product description, and feature scope so respondents are answering about the same product across segments. Plan for 4 named output points: Point of Marginal Cheapness (PMC), Point of Marginal Expensiveness (PME), Optimal Price Point (OPP), Indifference Price Point (IPP). See https://conjointly.com/products/van-westendorp/ for output interpretation. The PSM curves become the input to M1.S4 synthesis where the four points are computed per segment and feed into the M4 list-price setting.
Build Gabor-Granger ladder block
Build the sequential price-ladder block. Show binary purchase intent at randomised price points; climb until 'no' then descend. Output: per-respondent max WTP, demand curve (% intent vs price), and revenue curve (price x intent) - the revenue-maximising price is the peak of the revenue curve. https://conjointly.com/products/gabor-granger/. The Gabor-Granger curve is the load-bearing input to M4 list-price setting (peak of revenue curve is typically 15-25% above OPP from PSM for B2B SaaS).
Author the WTP interview guide with reference-anchor + kill-the-feature exercises, recruit 20 interviewees across 3 segments, run 20 x 60-min interviews, and code transcripts on WTP signal, value driver, deal-breaker, and preferred metric.
Build WTP interview guide (reference-anchor + kill-the-feature)
Author the WTP interview guide with five sections: (1) job-to-be-done warm-up, (2) reference-anchor exercise (compare to alternatives the prospect uses), (3) Van Westendorp open answers (free-form, captures rationale), (4) 'kill the feature' trade-off - if we removed feature X to save you Y%, would you take the deal?, (5) deal-breaker probe. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The guide turns a 60-minute interview into a structured WTP capture rather than a free-form conversation that yields anecdotes but no decisions.
Recruit 20 interviewees across 3 segments
Recruit 20 interviewees: 8 current customers, 6 prospects who evaluated and bought a competitor, 6 prospects who evaluated and stayed in the funnel. Stratify by segment (SMB / Mid-Mkt / Enterprise). Calendly link + recording-consent script. https://www.userinterviews.com/blog/the-user-researchers-guide-to-gdpr for consent-language guidance. The won-lost split is critical - won-only interviews bias toward features customers like; lost-funnel interviews surface the WTP gap that explains the close-rate.
Compute Van Westendorp PMC/PME/OPP/IPP per segment, Gabor-Granger demand and revenue curves, hierarchical Bayes utility scores from MaxDiff, and combine into a segment-level WTP heatmap and value-driver matrix that feeds M2 segmentation and M3 bundle architecture.
Compute Van Westendorp PMC/PME/OPP/IPP per segment
Compute the four named points per segment (PMC, PME, OPP, IPP). Plot intersections; flag segments where PME-PMC range is too narrow (<2x ratio = pricing power weakness) or where IPP and OPP diverge (>15% = anchor mismatch). https://conjointly.com/products/van-westendorp/. The four-point output per segment is the artifact M4 list-price-setting depends on - without explicit PMC/PME/OPP/IPP per segment, list prices default to internal opinion.
Compute Gabor-Granger demand and revenue curves
Compute demand curve (% intent vs price) and revenue curve (price x demand) per segment. Identify the revenue-maximising price (peak of revenue curve) - usually 15-25% above OPP for B2B SaaS. https://conjointly.com/products/gabor-granger/. The revenue-curve peak per segment is the load-bearing input to M4 list-price setting; without it, list prices default to PSM mid-points which leave revenue on the table.
Hierarchical Bayes utility scores from MaxDiff
Run hierarchical Bayes on MaxDiff data; produce utility score per feature summing to 100 per segment. Flag features with utility >10 (must-haves), 5-10 (selectables), <5 (kill candidates). Foundation for the M3 bundle architecture. https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. The utility table is what M3 bundle-design uses to identify undead features (utility <5 across all segments) and hidden gems (high utility currently in low tier).
Sign-off section. Present WTP findings to exec team using Pain-Claim-Gain narrative and produce single-page board summary. Decision asked: green-light M2 segmentation. Two tasks - exempt from the 8-task floor per Decision 2.
Present WTP findings to Exec team (Pain-Claim-Gain)
60-min exec readout. Pain-Claim-Gain structure: Pain - quantified failure mode + benchmark gap; Claim - WTP picture suggests X repackaging; Gain - KR forecast (ARPA +25%, NRR 120%, GM 78%). Decision asked of exec: green-light M2 segmentation. https://www.getmonetizely.com/articles/saas-pricing-benchmarks-2025-how-do-your-monetization-metrics-stack-up. The readout is the gate that converts research into commitment - without explicit budget for M2-M8, the WTP work risks dying as 'interesting research' that doesn't change packaging.
Single-page board summary
Single-page board summary (no jargon): the three top WTP findings, the chosen direction, and the financial case. https://www.getmonetizely.com/articles/saas-pricing-benchmarks-2025-how-do-your-monetization-metrics-stack-up. Board pages have a different audience than exec decks - they need the answer first, the rationale second, and zero methodology jargon. The page goes in the next board pack to formally communicate that the company is investing in the redesign.
Translate WTP data into 3-4 named, sales-recognisable segments with explicit Leaders / Fillers / Killers framing per Ramanujam, so M3 packaging has a real customer-archetype anchor. Cluster analysis (k-means / Latent-Class), firmographic overlay, stability bootstrapping, sales+CS validation, CRM tagging, and exec go/no-go gate. STOP: Standardize -> Templatize.
Run k-means / Latent-Class Analysis on (WTP score, feature-utility profile, behavioural signals), overlay clusters with firmographic data (size, vertical, geo, AI maturity), and bootstrap-test cluster stability to avoid hand-tuned fragile clusters.
Run k-means / Latent-Class on WTP+utility data
Use k-means or Latent-Class Analysis on (WTP score, feature-utility profile, behavioural signals). Test 3-5 cluster solutions; pick the lowest k where silhouette score >0.5 and clusters are distinguishable on price elasticity. https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. The cluster solution is the structural input to M3 packaging - without distinguishable WTP-driven clusters, the team defaults to firmographic-only segmentation (which is what every competitor already does and yields no differentiation in pricing).
Overlay clusters with firmographic data
Map clusters to firmographic dimensions (employee count, revenue, vertical, geo, AI usage maturity). Confirms clusters aren't pure data artifacts but actually map to a sales-recognisable customer type. https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. Without a firmographic profile, the clusters can't be operationalised in CRM tagging or sales targeting - they remain a research artifact rather than a go-to-market segmentation.
Test cluster stability with bootstrapping
Bootstrap-resample 100x; report cluster stability (% of times each respondent is in the same cluster). >70% = stable. Avoids the trap of hand-tuning fragile clusters. https://en.wikipedia.org/wiki/Cluster_analysis. Stability testing is the discipline that prevents downstream M3-M8 work from being built on a cluster solution that won't survive the next data refresh - if respondents flip clusters across resamples, the segmentation is fragile and shouldn't drive packaging.
Name 3-4 segments with archetype 1-pagers (pain, JTBD, alternatives, WTP range, growth potential), classify candidate features as Leaders/Fillers/Killers per Ramanujam, and write per-segment ROI narratives that feed M8 sales enablement.
Name 3-4 segments with archetypes & user stories
Name each cluster (e.g. 'Hands-On Founder,' 'Scale-Stage Operator,' 'Enterprise Compliance Buyer'). Write a 1-pager per segment: pain, JTBD, current alternatives, WTP range, growth account size. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/ rule 2 - 'don't force one-size-fits-all'. The named archetype with a memorable handle is what makes the segmentation operational - sales reps remember 'Hands-On Founder' not 'cluster 1', and the archetype 1-pager is what they reference when qualifying.
Map Leaders/Fillers/Killers per segment per Ramanujam
For each segment, classify candidate features as Leaders (drive primary purchase decision), Fillers (nice-to-haves, neutral on decision), Killers (deal-breakers if absent). Direct application of Ramanujam's bundling logic. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The Leader/Filler/Killer mapping is the load-bearing input to M3.S1 V1 packaging tier spec - it tells the team which features must appear in the entry tier (Killers), which differentiate the top tier (Leaders), and which can be moved or dropped (Fillers).
Sign-off section. Workshop segments with sales + CS leaders for field-recognition, tag CRM accounts with new segment field (auto-rule + backfill), and exec sign-off on the 3-4 segments and Leader/Filler/Killer mapping. Three tasks - exempt from the 8-task floor per Decision 2.
Validate segments with sales + CS leaders
Workshop segments with sales + CS leaders. Check: do they recognise these archetypes? Can they place existing accounts into segments? Is the leader/filler/killer logic consistent with their experience? Catch fake-stable clusters that the data fits but the field doesn't recognise. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The field validation is the discipline that closes the gap between data-clean clustering and operational segmentation - without it, sales rejects the segments as 'corporate marketing' and reverts to their own informal categorisation.
Tag CRM accounts with new segment field
Add Segment field to Account in CRM; auto-rule based on firmographic dimensions; backfill all open opps and existing book. Pre-requisite to the M8 comp-plan reset (quotas by segment). https://help.salesforce.com/s/articleView?id=sf.fields_creating_field_about.htm. Without CRM tagging, the segmentation cannot drive comp plans, territory assignment, ABM playbooks, or CSM handoff - it stays a research artifact rather than an operational lever.
Translate segment + WTP data into a V1 packaging tier spec - what is in entry / middle / top tier, what is an add-on, and the decoy/anchor logic. Implements Ramanujam Steps 4-5. Includes feature deprecation (undead features), hidden-gem promotion, AI feature tier placement, hybrid seat+usage architecture, add-on catalog, professional-services bundling, custom-quote escalation rules, and cross-functional pre-mortem signoff. STOP: Templatize.
Decide 3 vs 4 vs 5 tiers, author V1 packaging tier spec (feature inclusion matrix, signed by CFO+CRO+CPO), set attach-rate targets per tier (35/45/20 default), and document decoy/anchor logic with mid-tier as the anchor per Ramanujam Rule 8.
Decide 3 vs 4 vs 5 tiers (incl. free / enterprise custom)
Decide 3 (good/better/best), 4 (incl free or custom enterprise), or 5 (modular). 86% of $100M+ ARR SaaS use >=3 pricing dimensions per Monetizely 2025. https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. The tier count decision determines the entire downstream packaging architecture - 3 tiers forces clean Leader-Filler-Killer separation; 4-5 tiers allows freemium and enterprise carve-outs but increases sales-conversation complexity and CPQ cost.
Author V1 packaging tier spec
[Wave-2 reference: cited by ma-integration and sell-side-ma-prep] Author the V1 packaging tier spec - a single source of truth that lists every feature, who gets it in entry/mid/top tier, what the price is, and what the value-metric is. Use the WTP heatmap + leader/filler/killer matrix as inputs. Target ratios: entry 30-40%, mid 40-50%, top 10-20%; mid-to-entry price ratio 2.5-3.5x; top-to-mid 2.5-3.5x. https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. https://www.hubspot.com/company-news/announcing-upcoming-changes-to-hubspots-pricing as case study. The V1 packaging tier spec is the load-bearing artifact for M4-M8 - every later module references it.
Set attach-rate target per tier (35/45/20 default)
Set explicit target attach-rate per tier so the M5 business case has a numerator. Default starting ratios from Monetizely encyclopedia: 30-40% entry / 40-50% mid / 10-20% top. Adjust based on segment shape. https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. Without an explicit attach target, the M5 business case can't model expected ARPA - you can't compute a weighted-average ARPA without knowing the mix.
Design decoy/anchor logic (mid-tier as the anchor)
Apply decoy/anchor effect - the mid-tier should be the anchor. The top tier should look reasonable next to mid; entry should look incomplete. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/ rule 8 (customer irrationality). The decoy/anchor logic is the difference between a tier that converts and one that doesn't - if entry feels complete, customers stop reading; if mid looks like a small upgrade, ARPA stays flat. Mid-as-anchor is what drives the typical 35/45/20 attach-rate distribution.
Identify undead features to deprecate, promote hidden gems to top tier, decide AI feature tier placement (bundle vs add-on vs gate), and design hybrid seat+usage architecture if applicable. Includes the AI feature tier placement gate that fires when customer.has_ai_features == true.
Identify undead features to deprecate
From the MaxDiff utility scores, list features with utility <5 across all segments. These are undead per Ramanujam - kill candidates. Deprecation plan: stop selling, sunset by date X. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. Killing undead features is one of the highest-margin actions in the playbook - they consume engineering maintenance, sales attention, and CPQ complexity while contributing nothing to WTP.
Promote hidden-gem features into top tier
Identify features with high utility but currently in the wrong tier (free or low tier). Promote to top tier or new add-on. Use Ramanujam's 'hidden gem' framing. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. Hidden-gem promotion is the highest-leverage repackaging play - features with high WTP currently bundled into entry are giving away margin; lifting them to top tier or add-on captures their full WTP.
Decide AI feature tier placement (bundle vs add-on vs gate)
Design the add-on catalog (priority support, training, advanced AI tier, security pack, API rate-limit boost), decide professional-services bundling posture (included / add-on / partner-only), and define when sales escalates to custom quote (revenue/seat thresholds).
Design add-on catalog (priority support, training, AI tier)
Build the add-on catalog: priority support, white-glove training, advanced AI usage tier, security pack (HIPAA / SOC2 / GovCloud), API rate-limit boost. Patrick Campbell observation: 20-30% of customers will pay for priority support if priced right. https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. The add-on catalog is the high-margin lever - add-ons typically run 80%+ GM and don't cannibalise core tier upgrades.
Decide PS bundling: included vs add-on vs partner
Decide PS posture: included (bake into top tier), add-on (priced per project), or partner-only (refer to channel). Trade-off: revenue vs gross margin (PS GM ~20-30% drags blended). https://www.tsia.com/research/professional-services-research. PS is the highest-touch part of the customer journey but the lowest-margin - the choice is between revenue (sell PS at scale) and margin (push to channel partners) or hybrid (include limited PS in top tier).
Define when sales escalates to custom quote
Sign-off section. Run 90-min pre-mortem on how the packaging could fail (feature-shock returned, AI COGS exceeded, top tier unreachable, sales discounting into mid-tier), then secure cross-functional CPO+CFO+CRO+CTO formal signoff. Two tasks - exempt from the 8-task floor per Decision 2.
Pre-mortem: how could this packaging fail?
Run a 90-min pre-mortem: imagine it's 6 months post-launch and the packaging failed. What killed it? Common modes: feature-shock returned (mid-tier overstuffed), AI COGS exceeded expectations, top tier looked unreachable, sales accidentally discounted into mid-tier. Document mitigations. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. The pre-mortem is the cheapest insurance against the most common packaging failures - 90 minutes of structured imagination saves the team from repeating known anti-patterns.
Cross-functional signoff (CPO/CFO/CRO/CTO)
Formal sign-off CPO + CFO + CRO + CTO. Pre-requisite to M4 strategy work. Without explicit sign-off from all four, the packaging is built on fragile alignment - one of the four 'didn't agree' will surface in M4 / M6 / M7 / M8 as friction that derails downstream work. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/.
Codify how and how much - the pricing model (per-seat / usage / value-metric / hybrid), the strategy archetype (skim/penetrate/maximise), the 3-year glide-path, and the discount governance that protects realisation. Implements Ramanujam Steps 6-7. Includes list-price setting, localised pricing variants, annual vs monthly rules, competitive response playbook, discount approval matrix, CPQ rules config, leakage tracking dashboard, sentinel-deal review cadence. STOP: Templatize -> Optimize. Heavy Productize for the discount governance stack.
Build pricing-model decision tree (per-seat / usage / value-metric / outcome / hybrid), author trade-off memo quantifying NRR uplift vs revenue predictability vs sales motion, and lock the exec decision on the chosen model.
Build pricing-model decision tree
Build the decision tree: when does each model dominate? Per-seat - clear individual user; usage - cost-correlated with consumption; value-metric - outcome correlates with vendor success (Procore ACV); hybrid - usage adds NRR uplift to a stable subscription floor; outcome - high-trust, high-measurability case (rare in B2B). https://www.subscript.com/the-dive/what-does-usage-based-pricing-mean-for-your-b2b-saas-finance-team-a-conversation-with-kyle-poyar. The decision tree forces explicit reasoning about which model fits which segment - rather than defaulting to per-seat because that's what the team has always done.
Model trade-off memo (NRR vs predictability vs sales motion)
Memo to exec quantifying trade-offs: subscription gives revenue predictability, usage gives +28% NRR but more variability, value-metric aligns vendor + customer growth (Procore data: cohort >$100K ARR grew 20% YoY, 66% of total ARR). https://openviewpartners.com/blog/2023-pricing-data/ and https://www.procore.com/pricing. The trade-off memo is what executives reference when they need to commit to one model - the decision is non-trivial and the framing matters more than the recommendation.
Exec decision on chosen model
Exec decision recorded - the model the company is committing to. Short doc, signed. Pre-requisite to M4.S2 list-price setting (you can't price what you can't model). https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. The go decision is a structural commitment - once recorded, M4-M8 builds on it; if the model is later changed, the cost is M3+M4 redo plus whatever billing migration in M7 has shipped.
Set list prices per tier (Gabor-Granger revenue-max anchored), build per-unit usage rate card if applicable (gated on usage-based pricing), design localised pricing variants per region (gated on localised_pricing_required or target_country present), and set annual vs monthly pricing rules with 17-20% annual discount.
Set list prices per tier
Set list prices using Gabor-Granger revenue-max + PSM acceptable-range overlap. Aim for ratios 2.5-3.5x tier-to-tier. Sanity check: top tier should be reachable for 10-20% of segment without custom discounting. https://conjointly.com/products/gabor-granger/. The list price card is the most-referenced artifact post-launch - sales pitches it, customers Google it, the pricing page renders it. Getting it right is the difference between a smooth launch and a thrash that requires repricing in 6 months.
If usage-based: build per-unit rate card
If usage-based: build per-unit rate card (per-API-call, per-record, per-GB, per-token). Include volume-discount tiers and overage premium (15-25% over committed volume per Monetizely benchmark). https://www.withorb.com/blog/saas-usage-based-pricing-examples. The rate card is the load-bearing artifact for usage-based revenue - it's what billing systems implement, what sales quotes, and what auditors validate against ASC 606 in M7.
Design localised pricing variants per region
Pick skim / penetrate / maximise pricing strategy archetype per segment, author a 3-year glide-path (planned annual increases, new tiers, deprecations, model evolutions), and build the competitive response playbook for fast-follow / bundling / feature-poach scenarios.
Pick skim / penetrate / maximise per segment
Pick the strategy archetype per segment: skim (premium, low-volume, signal quality); penetrate (low-price, capture share, monetise later); maximise (revenue-optimal, mid-volume). Use https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan as guidance. The archetype choice drives the M5 forecast assumptions, the M6 launch comms tone, and the M8 sales pitch - skim deserves a different sales motion than penetrate.
Author 3-year pricing glide-path
Document 3-year glide-path: planned annual increases (Atlassian's 5-7.5-10% pattern), planned new tiers, planned deprecations, planned model evolutions (e.g. add usage component in year 2). https://www.adaptavist.com/blog/atlassian-cloud-price-update-and-jsm-changes-october-2024. The glide-path is what differentiates a one-shot pricing exercise from an ongoing pricing capability - it commits the team to year-2 and year-3 moves so customers, sales, and finance can plan ahead.
Competitive response playbook
Author Green/Yellow/Red discount approval matrix, configure CPQ to enforce gates physically, build dashboard for monthly discount-leakage tracking, and install monthly sentinel-deal review cadence on the 5 largest non-standard discounts.
Author discount approval matrix (Green/Yellow/Red)
[Wave-2 reference: cited by ma-integration and sell-side-ma-prep] Author the approval matrix: Green (0-15% rep approval), Yellow (16-30% Head-of-Sales approval, written justification, CAC-payback >12mo flag), Red (>30% CFO+CEO approval, gross-margin floor check), plus separate non-standard-legal-terms approval. https://www.glencoyne.com/guides/enterprise-discount-approval-matrix. Default-deny on >50% discount unless strategic exec exception. The discount matrix is the single largest realisation lever - without it, average discount drifts toward whatever the loudest AE has been allowed.
Configure CPQ to enforce matrix
Configure Salesforce CPQ / DealHub / Pricefx to hard-code approval gates from the matrix. Reps physically can't submit a >15% discount without escalation. Include automated routing email + SLA. https://dealhub.io/glossary/discount-approval/. CPQ-enforced gates are what make the discount matrix enforceable - without CPQ enforcement, the matrix is a document; with it, the matrix is a system.
Dashboard for monthly discount-leakage tracking
Build the outside-in business case and the pricing-waterfall narrative that connects list -> invoice -> net -> realised, so finance, board, and (later) M&A buyers can reason about quality of revenue. Implements Ramanujam Step 8. Includes outside-in business case model (price x volume x margin x time), ARPA+NRR forecast by quarter, cohort impact split (existing book vs new logos), pricing-waterfall narrative for board, baseline-vs-projected comparison, quality-of-revenue memo, sensitivity analysis, downside scenario, and the board readout / go-no-go gate. STOP: Optimize.
Build outside-in business case model (price x volume x margin x time) with WTP-derived elasticity, AI COGS at scale, and 6-quarter forecast. Disaggregate to ARPA+NRR by quarter, and split cohort impact (existing book vs new-logo) including 9-month migration drag.
Build outside-in business case model
Build a model with WTP-derived price elasticity, market sizing from external (G2 / Gartner) data, internal cost structure, plus AI COGS at expected scale. Output: 6-quarter forecast of ARR, ARPA, NRR, GM. Pre-test against the KR targets (ARPA +25%, NRR >=120%, GM >=78%). https://www.thesaascfo.com/your-ai-feature-is-quietly-destroying-your-gross-margin/. The business-case model is the financial backbone of the redesign - it's what the board approves on, what the M5.S3 sensitivity analysis perturbs, and what the M6 launch plan and M8 comp plan are sized to.
Forecast ARPA + NRR by quarter for 6 quarters
Disaggregated forecast: ARPA by tier, NRR by segment, expansion vs contraction split. https://www.growthunhinged.com/p/your-guide-to-the-2024-saas-benchmarks. The disaggregated forecast is what the board references quarter-to-quarter - aggregate ARPA hides the truth that one tier may be growing 40% YoY while another contracts; the per-tier and per-segment view is the operating diagnostic.
Cohort impact: existing-book vs new-logo
Model existing book vs new-logo separately. Existing book has migration/grandfathering drag - model the 9-month migration period and the <=2% expected churn. New logos see full price benefit. https://www.hubspot.com/company-news/announcing-upcoming-changes-to-hubspots-pricing. The cohort split is critical because the two cohorts behave radically differently in the first 4 quarters - aggregating them obscures the migration drag and over-promises the new-logo upside.
Author the pricing-waterfall narrative for the board (list -> invoice -> net -> realised), compute current baseline waterfall vs projected post-launch waterfall side-by-side, and write quality-of-revenue memo (concentration, multi-year %, usage %, gross margin profile) for board and future M&A buyers.
Author pricing-waterfall narrative for board
[Wave-2 reference: cited by sell-side-ma-prep for CIM appendix] Author the narrative every CFO needs: List Price -> Negotiated/Invoice Price -> Net Price (after promo/credits) -> Realised Net (after rebill, refund, sliding scale) -> blended ARPA. Each step is a leakage point. The narrative explains where revenue actually is captured and where it leaks. https://www.glencoyne.com/guides/discount-strategy-b2b-saas. The pricing-waterfall narrative is the artifact M&A buyers and board-level investors look for - it surfaces quality of revenue in a way no aggregate ARR number can.
Compute current baseline + projected post-launch waterfall
Compute the current waterfall using last 12 months of actuals, then project the post-launch waterfall using model assumptions. Side-by-side comparison shows delta at each step. https://www.glencoyne.com/guides/discount-strategy-b2b-saas. The side-by-side comparison is what makes the redesign tangible - rather than abstract KRs, the board sees 'we currently leak 22% from list to realised; the redesign cuts that to 11%.'
Sign-off section. Tornado chart on top 5 model inputs (avg discount, AI COGS, attach-rate, NRR uplift, sales-cycle delta), downside scenario (existing-book churn 5% vs target 2%, slower adoption, AI COGS 20% over plan), and board readout with green-light decision on M6 launch. Three tasks - exempt from the 8-task floor per Decision 2.
Sensitivity analysis on top 5 model inputs
Tornado chart on top 5 model inputs: average discount, AI COGS per request, attach-rate, NRR uplift, sales-cycle delta. Identifies which inputs the team must monitor most aggressively post-launch. https://en.wikipedia.org/wiki/Tornado_diagram. The tornado chart is the diagnostic that focuses post-launch monitoring - the inputs with the biggest swing on output ARR are the ones that need real-time dashboards in M6.S4 post-launch monitoring.
Downside scenario memo
Model the downside: existing-book churn comes in at 5% instead of 2%, repackaging adoption is 30 percentage points slower, AI COGS 20% above plan. What's the floor revenue? What action is taken at each trigger? https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/ rule 9. The downside scenario is what makes the board comfortable greenlighting - they need to see that the team has named the bad outcomes and pre-committed to the response.
Board readout - go/no-go decision
Land the change. Sequence internal launch (sales + CS + finance + support) before external; produce the rate-increase letter, value justification, exec save-play; minimise voluntary churn. Implements Ramanujam Step 9. Includes launch rules of engagement, 30-question internal FAQ, all-hands launch, CS renewal talk-track, grandfathering decision matrix, top-50 account action plans, co-terming runbook, save-play playbook, 3-variant rate-increase letters, segment value-justification one-pagers, pricing page redesign, public FAQ, plus 30/60/90 monitoring dashboard and voluntary-churn / pricing-objection cadence. STOP: Optimize.
Document launch rules of engagement (who-can-say-what + when), build 30-question internal FAQ, run all-hands launch with CEO+CFO+CRO, and create CS-specific talk-track for renewal conversations under new pricing.
Document who-can-say-what + when
Document: who can answer customer pricing questions, when external comms go out, embargo rules. Avoids the 04-28-pattern outage where well-meaning AE leaks pricing change before exec announcement. https://www.atlassian.com/blog/announcements. Without explicit rules of engagement, sales reps will leak the pricing change to favourite accounts ('I want to give you a heads-up'), which creates a tier of customers who feel betrayed when they realise everyone else got the same heads-up.
Internal FAQ for sales / CS / support / finance
30-question internal FAQ covering every objection, edge case, and 'what about X customer' scenario. Pre-empt sales calls back-channeling. https://blog.hubspot.com/service/price-increase. The internal FAQ is the single most-referenced artifact post-launch - it's what reps consult before customer calls and what CSMs reference in renewals. A weak FAQ generates 100x its weight in Slack DMs to PMM.
All-hands launch event with execs
30-min all-hands: CEO + CFO + CRO present. Pain-Claim-Gain pitch internal-first. Pricing model, business case, why-now. Decisive moment: every employee leaves with the same story. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The all-hands is the cultural commitment moment - employees who don't hear the rationale from the CEO will fill the gap with their own narrative, often less generous than the actual rationale.
CS talk-track for renewal conversations
CS-specific talk-track for renewal conversations under new pricing. Pain-Claim-Gain at the customer level: their realised value, the pricing change, the gain (new features unlocked / better support / locked-in for X months). https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. The CS talk-track is the make-or-break for kr-existing-book-churn - CSMs handle 80% of renewal conversations and the talk-track is what they reference 30 minutes before each call.
Build grandfathering decision matrix (revenue-at-risk x strategic-value), bespoke action plan for top 50 accounts (named exec sponsor each), co-terming runbook for aligning multi-contract customers, and save-play playbook for at-risk accounts targeting <2% voluntary churn.
Build grandfathering decision matrix
2x2 matrix: revenue-at-risk (low/high) x strategic value (low/high). High-rev x high-strategic = grandfather indefinitely; high-rev x low-strategic = negotiate co-terming + multi-year for grandfather; low-rev x low-strategic = move to new pricing immediately. https://www.adaptavist.com/blog/atlassian-cloud-price-update-and-jsm-changes-october-2024. The matrix prevents the team from grandfathering by squeaky-wheel - it forces a structured decision per account rather than ad-hoc concessions to whoever pushes hardest.
Bespoke action plan for top 50 accounts
Bespoke account-by-account migration plan for the top 50 accounts (revenue concentration). Each gets named exec sponsor, talking points, target migration date, fallback grandfather position, save-play trigger. https://blog.hubspot.com/service/price-increase. The top-50 are 60-80% of ARR for typical SaaS - their migration outcomes determine whether the redesign hits or misses kr-existing-book-churn.
Annual-contract co-terming runbook
Author 3-variant rate-increase letter (top-50 strategic / mid-book / SMB self-serve), per-segment value-justification one-pagers, redesign public pricing page with new tier spec and ROI calculator, and ship public-facing FAQ + comparison page.
Author rate-increase letter (3 cohort variants)
Author 3 letter variants (top-50 strategic / mid-book / SMB self-serve). Each follows Pain-Claim-Gain: acknowledge change, show added value, offer protection (annual lock-in option). 30-60 day notice per https://www.withorb.com/blog/how-to-announce-a-price-increase. The rate-increase letter is the single piece of customer communication most likely to trigger churn - it deserves disproportionate care. The 3-variant approach acknowledges that what works for a top-50 strategic account is not what works for an SMB self-serve buyer.
Value-justification one-pager per segment
Per-segment value-justification one-pager: $ saved / $ generated by the product, named for the customer's domain. Sent with the rate-increase letter. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The value-justification one-pager is what shifts the customer's emotional response from 'they're raising my price' to 'they're raising my price but here's why they're worth it.' Without it, the rate-increase letter triggers pure-price comparison.
Cadence section. 30/60/90 monitoring dashboard on ARPA / NRR / GRR / discount / win-rate / sales-cycle / pricing-objection-rate, voluntary-churn alerts wired to CRO Slack with cohort tagging, and pricing-objection-rate tracking via Gong/Chorus tags. Three tasks - exempt from the 8-task floor per Decision 2.
30/60/90 monitoring dashboard
Dashboard tracking ARPA, NRR, GRR, average discount, win-rate, sales-cycle, pricing-objection-rate at days 30/60/90 post-launch. Compare to baseline. Trigger: any KR drifts >10% off plan -> exec review. https://www.benchmarkit.ai/2025benchmarks. The 30/60/90 dashboard is the operating mechanism that converts the M5 forecast into accountability - without it, the team learns at quarter-end whether the redesign worked rather than at day-30 when corrective action is still cheap.
Voluntary-churn monitoring vs target <=2%
Specifically watch voluntary churn from existing book vs new logos. Cohort-tag every churn event with 'repricing' reason. Alert if >2% logo churn. https://optif.ai/learn/questions/b2b-saas-net-revenue-retention-benchmark/. Voluntary-churn from repricing is the single biggest risk to the redesign - it's the only KR that can decisively kill the playbook outcome (ARPA gains are wiped out if 5%+ of book churns).
Track pricing-objection rate via Gong / Chorus tags
T2D3 proprietary IP module. Implement the new pricing in the billing system without disrupting cash, recognise revenue correctly under ASC 606, and migrate the existing book over a defined 9-month window. Includes billing vendor decision (Stripe Billing / Chargebee / Maxio / Sequence / build), canonical billing data model, usage-meter instrumentation, billing-migration runbook, proration and dunning configuration, end-to-end staging tests, 9-month migration wave plan and execution, weekly reconciliation, ASC 606 rev-rec memo, rev-rec system config, and audit-package prep. STOP: Productize. Entire module gated when the customer already has a target billing vendor in production. Canonical 130h (~65% of M7 fires only when customer needs vendor change).
Decide billing vendor (Stripe Billing / Chargebee / Maxio / Sequence / build) using vendor-selection matrix, design canonical billing data model (Customer / Subscription / Plan / Price-versioned / UsageRecord / Invoice / CreditNote / RefundEvent), and instrument usage events in product if usage-based (gated on customer.usage_based_pricing). Entire module gated on customer.has_billing_vendor == false || billing_vendor == 'build' || billing_vendor != target_billing_vendor.
Decide billing vendor (Stripe Billing / Chargebee / Maxio / build)
Vendor matrix: Stripe Billing (best for hybrid sub+usage, dev-friendly, US-centric); Chargebee (best for global SaaS with complex localisation); Maxio (best for enterprise with deep ASC 606 needs); Sequence (modern usage-first); build (only if you have Engineer capacity to amortise). Score against your spec from M3/M4. https://www.outseta.com/posts/best-saas-billing-platforms. The vendor decision is one of the highest-stakes calls in the redesign - it locks the team into 3-5 years of platform amortisation cost and determines billing flexibility for every future pricing change.
Design canonical billing data model
Design the canonical billing data model: Customer, Subscription, Plan, Price (versioned!), UsageRecord, Invoice, CreditNote, RefundEvent. Versioning is critical for grandfathering: each Subscription points to a specific Plan version. https://docs.stripe.com/billing/subscriptions/migrate-subscriptions. The data model is the foundation that determines whether the team can support grandfathering, mid-cycle upgrades, and clean migrations in M7.S3 - get the versioning wrong and the migration runbook becomes a series of one-off SQL fixes.
Instrument usage events in product (if usage-based)
Add usage events at every value-metric event in product (per-API call, per-record, per-token). Idempotent, durable, replay-able. Streaming pipe to billing system every 5 min. https://www.withorb.com/blog/saas-usage-based-pricing-examples. Usage instrumentation is the single longest-running engineering task in the playbook - it requires changes in every product surface that emits the value metric, and getting it wrong creates either revenue leakage (under-counting) or customer disputes (over-counting).
Author billing-migration runbook (T2D3 IP, vendor-specific scripts), configure proration + change-of-plan rules, configure dunning flow (failed-payment retries through day 45), and run end-to-end test in staging with 10 representative accounts covering all change scenarios.
Author billing-migration runbook (T2D3 IP)
[Wave-2 reference: cited by ma-integration (target-co billing consolidation)] Author the comprehensive runbook: prerequisites checklist, data export from old system, customer mapping, payment-method portability, billing-cycle preservation, proration handling, validation queries, rollback plan, freeze window, cut-over date. Include vendor-specific scripts for Stripe / Chargebee / Maxio. https://www.chargebee.com/blog/stripe-billing-migration/ and https://docs.stripe.com/billing/subscriptions/migrate-subscriptions. The migration runbook is T2D3's load-bearing IP for this playbook - it's what differentiates a 9-month migration that works from a series of weekend cutovers that produce billing disputes.
Configure proration + change-of-plan rules
Configure proration rules: upgrade mid-period (charge prorated upgrade fee + new full period), downgrade mid-period (credit unused, apply at next renewal), add-on mid-period, seat-add mid-period. Edge cases for usage-based: in-period meter resets. https://www.maxio.com/blog/saas-revenue-recognition-asc-606. Proration is the second-most-disputed billing surface (behind usage metering) - getting it wrong creates a steady stream of customer disputes that drag CSM bandwidth and corrode trust.
Build 9-month migration wave plan (Wave 1 SMB, Wave 2 mid-market, Wave 3 enterprise excl. top-50, Wave 4 strategic), execute weekly migration cadence with save-play triggers, and run weekly reconciliation between old and new billing with <=0.5% drift threshold per cohort.
9-month migration wave plan (cohort-by-cohort)
9-month wave plan: Wave 1 (m1-2) - self-serve / SMB (lowest risk, biggest dataset for learning); Wave 2 (m3-5) - mid-market; Wave 3 (m6-7) - enterprise excluding top-50; Wave 4 (m8-9) - top-50 strategic + grandfathered exceptions. https://www.adaptavist.com/blog/atlassian-cloud-price-update-and-jsm-changes-october-2024 (Atlassian server-to-cloud forced migration as analogue). The wave plan sequences risk - SMB customers are the cheapest dataset to learn on; top-50 strategic customers come last when the team has 6 months of operational learning to draw on.
Execute migration waves, weekly cadence
Run weekly migration cadence over 9 months. Each week: identify cohort, send 30-day notice, execute migration, validate invoice reconciles, post adjustment journal entry if needed. Plus the 'save-play' trigger if customer pushes back. ~24h aggregate executive oversight (the actual transactional work is automated). https://docs.stripe.com/billing/subscriptions/migrate-subscriptions. The execution phase is where M6 customer-comms and M7 billing logic come together in the real world - the 9-month duration is what gives the team room to absorb edge cases without compounding them.
Author ASC 606 rev-rec memo applying the 5-step model to the new value metric, configure rev-rec system (NetSuite ARM / Maxio / Zuora RevPro), and prep audit walkthrough package (memo + sample contracts + journal entries + reconciliation reports) to pre-empt year-end auditor questions.
ASC 606 rev-rec memo for new value metric
Author a memo applying ASC 606's 5-step model to the new pricing: Identify contract -> identify performance obligations -> determine transaction price -> allocate transaction price -> recognise revenue. Special treatment for usage-based ('as-invoiced' practical expedient where applicable). https://www.maxio.com/blog/saas-revenue-recognition-asc-606 and https://kpmg.com/kpmg-us/content/dam/kpmg/frv/pdf/2024/revenue-software-saas-1.pdf. The ASC 606 memo is the audit-defence artifact - without it, the auditor's first conversation about the new pricing model becomes a weeks-long discovery exercise rather than a documented walkthrough.
Configure rev-rec system (NetSuite / Maxio / Zuora)
Configure rev-rec system (NetSuite ARM / Maxio / Zuora RevPro) to pick up new pricing. Test with 10 sample contracts. https://ordwaylabs.com/blog/revenue-recognition-for-usage-based-pricing/. The rev-rec system config is what operationalises the ASC 606 memo - the memo describes the principle; the config implements it in the system that posts to GL.
T2D3 proprietary IP module. Reset sales comp around the new value metric, train AEs/SDRs/CSMs, update CRM and CPQ, install discount-controlled deal-inspection cadence. Includes AE / SDR / CSM comp-plan redesign with ramp+accelerator+SPIF math, comp-plan modeling, CRM field updates, MEDDPICC field updates, CPQ tier rules, sales pitch + ROI calculator, sales certification program, competitive battlecards, deal-inspection rubric, weekly cadence, and 90-day scorecard. STOP: Productize. Comp-plan redesign track gated when customer keeps existing comp plan.
Redesign AE comp plan (quotas, ramp, accelerators, new-packaging SPIFs), SDR comp plan (sales-qualified meetings + segment SPIFs), CSM comp plan (NRR-tied commission + expansion bonuses), and model expected payouts at 80/100/120/150% attainment vs OTE budget. Track gated on customer.activates_comp_plan_redesign.
Redesign AE comp plan (quotas, ramp, accelerators, SPIFs)
[Wave-2 reference: cited by ma-integration for sales-team integration] Redesign AE comp around new value metric. Components: base $92K-$218K + commission at 11-14% of ACV + accelerators kicking in at 100% attainment (e.g. 1.5x from 100-150%, 2x from 150%+) + new-packaging SPIF (extra commission for selling the new top tier in first 6 months) + 3-6 month ramp (50% Q1, 75% Q2). Set quota at 4-6x OTE. https://www.everstage.com/sales-compensation/saas-sales-compensation-benchmarks and https://www.thesaascfo.com/how-to-create-a-sales-compensation-plan-for-saas/. The AE comp plan is the load-bearing IP of M8 - it determines whether the new packaging is sold the way the redesign intends.
Redesign SDR comp plan (sales-qualified meetings + segment SPIFs)
SDR plan: base + per-SQM commission + segment-specific SPIFs (extra for top-tier prospects). Goal: align outbound effort to highest-ARPA segments. https://www.cobloom.com/careers-blog/how-to-design-a-winning-saas-sales-compensation-plan. The SDR comp plan is what bridges segmentation into pipeline mix - if SDRs are paid the same per meeting regardless of segment, they'll book the easiest segments rather than the highest-ARPA ones.
Redesign CSM comp plan (NRR-tied, expansion bonuses)
CSM plan: base + commission tied to NRR + expansion bonuses + retention bonuses. Critical lever for kr-nrr-target. https://www.visdum.com/blog/4-proven-saas-sales-compensation-plan-examples-that-boost-revenue. The CSM comp plan is what turns NRR from a metric the team measures into a metric the team owns - if CSMs aren't comp'd on NRR, the metric drifts toward whatever is easy to defend in QBRs rather than what actually moves expansion.
Model comp plan: payouts at 80/100/120% attainment
Model expected comp payout at 80% / 100% / 120% / 150% attainment vs OTE budget. Sanity-check: total comp <= 30% of new ARR per CFO benchmark. https://www.everstage.com/sales-compensation/saas-sales-compensation-benchmarks. The modeling step is what catches the comp plan that's mathematically impossible to fund - if 120% attainment costs 35% of ARR, the comp plan is over-promising and will trigger CFO clawbacks.
Update CRM fields for new value metric (auto-populate from product where possible), update MEDDPICC fields aligned to new value metric, and configure CPQ tier rules + bundle logic + add-on attach + escalation triggers (gated on customer.has_cpq).
Update CRM fields for new value metric
Add CRM fields for the new value metric (e.g. expected per-workflow run rate, predicted seats, AI usage tier). Auto-populate where possible from product data. Foundation for forecast accuracy. https://www.qobra.co/uk/blog/saas-sales-compensation. The CRM fields are what wire the new value metric into pipeline forecast and rep accountability - without them, AEs sell tier names but no one tracks the metric the customer is committing to.
Update MEDDPICC fields aligned to new value metric
Update Metrics field in MEDDPICC: now tied to the new value metric. AE must capture customer's expected workflow volume / seat count / outcome tied to the new pricing model. Cross-reference with sales-methodology playbook (separate Wave 1 playbook). https://en.wikipedia.org/wiki/MEDDPICC. The MEDDPICC alignment is what closes the loop between qualification and pricing - 'Metrics' is no longer a vague 'they care about ROI' but a quantified workflow volume that maps directly to the value metric.
Configure CPQ tier rules + bundle logic
Update master sales pitch deck and slide deck (Pain-Claim-Gain by segment), build per-segment ROI calculator, run sales certification program (presentation + quiz + 30-min role-play graded against rubric), and update competitive battlecards mapping our packaging vs top-3 competitors.
Update sales pitch + slide deck
Update master pitch deck: Pain-Claim-Gain by segment using new packaging. Lead with top 2-3 value drivers (Ramanujam rule 7). https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. The pitch deck is what reps ship to prospects in the first 30 minutes of a call - if it leads with feature lists rather than PCG, the conversation defaults to feature comparison and the differentiated tier story is lost.
Build ROI calculator (per segment)
Build ROI calculator per segment (similar to LinkedIn Sales Navigator's Forrester TEI: 312% ROI / sub-6mo payback). Web-based + sales-handoff PDF. https://www.salestechscout.com/article/linkedin-sales-navigator-cost-guide-2025. The ROI calculator is the de-friction tool that converts skeptical prospects into champions - it gives the buyer a defensible case to take to their CFO, who would otherwise reject the pricing on instinct.
Sales certification program (cert quiz + role-play)
90-min cert program: presentation + quiz (>=85% pass) + 30-min role-play graded against rubric. Required before AE can quote new pricing externally. https://www.everstage.com/sales-compensation/saas-sales-compensation-benchmarks. The certification gate is what makes the launch safe - it ensures every rep who quotes the new pricing has a documented baseline competence rather than learning by doing on customer calls.
Cadence section. Build deal-inspection rubric tied to new value metric (pricing-model fit, MEDDPICC completeness, discount zone, save-play trigger), install weekly 60-min manager cadence between manager and each AE, and ship the first-90-day post-launch scorecard with named owners + remediation playbooks per KR. Three tasks - exempt from the 8-task floor per Decision 2.
Build deal-inspection rubric tied to new value metric
Build rubric for manager weekly deal inspection: pricing-model fit (correct tier?), MEDDPICC completeness, value-metric quantified, discount in green/yellow/red zone, save-play triggered if at risk. https://www.glencoyne.com/guides/discount-strategy-b2b-saas. The rubric is what makes the weekly cadence efficient - without it, manager-AE 1:1s degenerate into status updates rather than coachable inspection.
Install weekly deal-inspection cadence
Install weekly 60-min cadence between manager and each AE: top 5 deals reviewed against rubric. Drives forecast accuracy and discount discipline. https://www.glencoyne.com/guides/discount-strategy-b2b-saas. The cadence is the operating mechanism that turns the rubric into accountability - rubrics alone don't change behavior; the weekly review does.
First-90-day post-launch scorecard
Build 90-day scorecard: ARPA, NRR, GRR, win-rate, discount-leakage, voluntary churn, comp attainment, pricing-objection-rate. Each KR red/yellow/green with named owner + remediation playbook. Reviewed at exec meeting day 30/60/90. https://www.benchmarkit.ai/2025benchmarks. The 90-day scorecard is the structural close of the playbook - it converts research-and-build into measured outcome and triggers the M5 downside-scenario remediation playbooks if any KR drifts red.
Design MaxDiff feature-importance block (15+ features)
Use MaxDiff (best-worst scaling) on 15-20 candidate features to produce utility scores summing to 100 per segment. Hierarchical Bayes estimation, 12-18 sets per respondent. Identifies which features belong in entry vs middle vs top tier - the input to the bundle architecture in M3. https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. Critical methodology choice: avoid simple importance ratings (everything looks like a 7/10), use forced trade-offs that simulate the real bundle decision a buyer faces.
Design choice-based-conjoint bundle simulator
For complex 4-5-attribute bundles (price, seats, usage cap, support, AI usage), set up choice-based conjoint with 600+ respondents. Output: per-attribute utility, simulator that lets the team test any new bundle's market share. Required if the redesign considers value-metric pricing. https://conjointly.com/. The CBC simulator is the load-bearing input to M3 bundle design - it lets the team simulate 'what if mid-tier loses feature X?' before committing in the V1 packaging tier spec.
Recruit >=150 ICP respondents per segment (3 populations)
Recruit >=150 respondents per segment across the three WTP populations Patrick Campbell recommends - current customers, prospects who know you but didn't buy, prospects who never heard of you. Use customer DB + Pollfish / dscout / Respondent / G2 panel. Quota by ICP firmographic. https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. The three-population approach is what separates a usable WTP picture from a 'preaching to the converted' artifact - current customers anchor on what they already pay; non-buyers and never-heard-of yield true elasticity.
Pilot survey, refine question logic
Soft-launch with first 50 respondents, examine break-off, time-on-task, internal validity (PSM curves cross properly, Gabor-Granger declines monotonically, MaxDiff utilities sum to 100). Refine before full-field. Pilot is the cheapest insurance against fielding 600 respondents on a broken survey - identify question wording that confuses respondents, attribute levels that produce non-monotonic curves, and timing issues that drive break-off. https://www.surveymonkey.com/market-research/resources/gabor-granger-vs-van-westendorp/.
Field full survey, monitor break-off rate <20%
Field full survey to >=450 respondents. Monitor daily for break-off, panel fraud (red-herring traps), straight-lining, and impossibly fast completes. Hold open until >=150 completes per segment. https://www.surveymonkey.com/market-research/resources/gabor-granger-vs-van-westendorp/. Daily monitoring is essential - a single day of unmonitored bot-panel infiltration can pollute the dataset and force a re-field. The clean dataset feeds M1.S4 synthesis (PSM curves, revenue curves, utility scores, WTP heatmap).
Run 20 x 60-min WTP interviews
Run 20 x 60-min interviews using the guide. 30 hours = 20 x 1h interview + 10h prep / scheduling overhead. Record with consent, transcribe via Otter / AssemblyAI. Surfaces qualitative rationale that quant can't capture (why they'd accept a per-outcome model but reject per-seat). https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. The interviews capture the 'why' behind the WTP curves that PSM and Gabor-Granger only describe quantitatively.
Code transcripts on WTP, value drivers, deal-breakers
Tag transcripts using a structured taxonomy: WTP signal (anchor, decoy reference, certainty cue), value driver (efficiency, growth, risk, status), deal-breaker (legal, technical, political), preferred metric (per-seat, per-outcome, etc.). Output: heatmap of tags by segment, fed into M2 segmentation. The structured coding turns 20 unstructured transcripts into a quantifiable input to clustering - without it, the qualitative work risks being treated as anecdote rather than evidence. https://dovetail.com/blog/qualitative-research-coding-guide.
Build segment WTP heatmap and value-driver matrix
Combine PSM + Gabor-Granger + MaxDiff outputs into a single Google Sheet heatmap: rows = features, cols = segments, cells = weighted utility x WTP signal. Becomes the input to M2 segmentation and M3 bundle architecture. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The heatmap is the single artifact that consolidates 7 prior tasks into one decision-grade picture - everyone reading the playbook can answer 'which features drive WTP for which segments?' in 30 seconds.
Build segment-specific ROI narrative
Write per-segment ROI narrative - Pain-Claim-Gain. Becomes input to sales enablement (M8) and external comms (M6). https://www.getmonetizely.com/articles/the-saas-pricing-metrics-encyclopedia-every-metric-you-need-to-know-2025. The segment ROI narrative is the bridge between WTP research and sales conversations - sales reps don't quote utility scores, they tell ROI stories. Each narrative names the customer's pain, the claim our product makes, and the dollar gain (or risk reduction) the customer should expect.
Exec go/no-go on segment definitions
Exec sign-off on the 3-4 segments and Leader/Filler/Killer mapping. Pre-requisite to M3 packaging. https://www.skmurphy.com/blog/2016/11/16/nine-rules-from-monetizing-innovation/. The go/no-go gate is a structural decision point - once execs approve the segments, M3-M8 builds on them; if execs reject, the team has to redo M2 segmentation rather than discover the mismatch when M3 packaging hits a wall.
Decide whether AI features are (a) bundled into mid+top tier (Notion 2025 model) - when adoption >30%; (b) priced as add-on with token bucket; (c) gated behind value metric. Critical for kr-gross-margin. https://www.getmonetizely.com/articles/the-hidden-cogs-of-ai-why-your-pricing-model-might-be-doomed and https://www.cnbc.com/2025/09/18/notion-launches-ai-agent-as-it-crosses-500-million-in-annual-revenue.html. AI tier placement is the single largest GM lever in the redesign for AI-heavy products - bundling AI into a flat-fee tier with no usage cap is what destroys gross margin (GitHub Copilot loses $20-80/user/month per leaked WSJ data).
Design hybrid seat + usage architecture if applicable
If the chosen model is hybrid (the Procore / Snowflake / Datadog pattern), define the seat vs usage split. Typical: 60/40 commitment-to-usage (per Monetizely 2025 dev-tools benchmark) with 15-25% overage premium. https://www.withorb.com/blog/saas-usage-based-pricing-examples. Hybrid is the dominant architecture for B2B SaaS that wants both revenue predictability and NRR uplift - seats give a stable subscription floor; usage adds the +28% NRR multiplier (per OpenView 2023).
Define when AE escalates from list-tier to custom quote (>$100K ARR; >500 seats; specific compliance ask). Becomes input to discount governance (M4) and CPQ logic (M8). https://www.glencoyne.com/guides/enterprise-discount-approval-matrix. Without explicit escalation rules, every AE develops their own informal threshold and pricing realisation degrades silently - the trigger rules are the upstream input to the Green/Yellow/Red discount approval matrix.
[Wave-2 reference: cited by country-expansion] If >=15% of book is outside-home (Patrick Campbell rule), design localised pricing variants per region. Cosmetic localisation +40% ARPU; market localisation = 2x faster intl growth. Decide which countries get full PPP-based localisation vs. cosmetic currency conversion. https://www.paddle.com/blog/saas-localized-pricing. Localised pricing is the highest-leverage intl-growth play - charging USD list to a UK SMB undermarkets to a willing-to-pay GBP buyer who would have paid more in their home currency.
Annual vs monthly pricing rules
Set annual-vs-monthly discount rules: typical 17-20% off list for annual (LinkedIn Sales Nav, Atlassian benchmark). Multi-year rules. Auto-renew + cap on annual increase per existing-customer terms. https://www.salestechscout.com/article/linkedin-sales-navigator-cost-guide-2025. The annual discount is a load-bearing tradeoff - too small and customers don't shift to annual (worse cash-flow + churn); too large and ARPA suffers. The 17-20% benchmark is the SaaS-wide consensus.
Playbook for 'we move; what they do': Fast-follow (drops their price 5-15%), bundling response (re-bundles to fight tier comparison), feature-poach response (announces our key feature). Plus our counter-moves. https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The competitive playbook is the structured response to the predictable counter-moves - without it, the team scrambles when the competitor reacts and that scramble is when discount discipline breaks.
Build dashboard tracking month-over-month: average discount %, price realisation %, exception count by tier, dollar-leakage vs target. Goal: <=2% leakage, achieved by ~85% realisation. https://www.getmonetizely.com/articles/pricing-governance-establishing-effective-policies-for-discounts-and-exceptions. The dashboard is the operating mechanism that closes the loop - without monthly leakage tracking, the discount matrix erodes silently as exceptions accumulate.
Set up monthly sentinel-deal review (largest 5 discounts)
Monthly 30-min review of the 5 largest non-standard discounts. Looking for pattern abuse, account-team capture, or systemic policy hole. Rotate sentinel set with CFO. https://www.glencoyne.com/guides/discount-strategy-b2b-saas. The cadence is what turns the dashboard into action - dashboards alone don't fix policy holes; the monthly review does.
Quality-of-revenue memo (concentration, multi-year %, usage %)
Memo to board characterising the quality of post-launch revenue: concentration (top-10 customer %), multi-year %, usage % (more elastic), gross-margin profile. Becomes input to any future M&A discussion. https://baincapitalventures.com/insight/gross-margin-is-a-bs-metric/. Quality of revenue is the diligence framing that determines exit multiple - a $50M ARR business with 60% multi-year and 25% usage-based commands a higher multiple than a $50M ARR with 20% multi-year and 0% usage.
30-min board readout: model summary, waterfall, sensitivities, downside. Decision: green-light M6 launch. Pain-Claim-Gain narrative. https://www.getmonetizely.com/articles/saas-pricing-benchmarks-2025-how-do-your-monetization-metrics-stack-up. The board readout is the structural gate that converts research into commitment - without an explicit board green-light, M6-M8 risks under-resourcing or being delayed by the next quarterly priority shift.
Runbook for co-terming: aligning all of a customer's contracts to the same renewal date so the price-change conversation happens once, not three times. Includes proration logic, billing system steps, communication templates. https://www.maxio.com/blog/saas-revenue-recognition-asc-606. Co-terming is what takes a 3-conversation renewal cycle and converts it to 1 - critical for enterprise customers with multiple contracts (eg main app + add-ons + renewals at different dates).
Save-play playbook for at-risk accounts
Decision tree for at-risk accounts: trigger (customer requests price hold / threatens churn) -> response tree (offer 1-yr lock, partial grandfather, additional services, exec save call). Each branch has a script. Goal: <2% voluntary churn from repricing. https://blog.hubspot.com/service/price-increase. The save-play playbook is what converts panicked CSM escalations into structured saves - without it, the CSM defaults to whatever discount the account team can negotiate, which corrodes the discount discipline.
Redesign pricing page on website
Redesign public pricing page using new tier spec, decoy/anchor logic, and ROI calculator. ~12h includes dev + QA + A/B test setup. Reference HubSpot's Mar 2024 pricing-page changeover (https://www.hubspot.com/company-news/announcing-upcoming-changes-to-hubspots-pricing). The pricing page is what 70%+ of new logos see before talking to sales - it has to communicate the tier story in 30 seconds, render correctly on mobile, and not over-promise (every clarifying conversation it forces is conversion-rate destroyed).
Public FAQ + comparison page
Public-facing FAQ on pricing. 8-12 most-asked questions with clear, friendly answers. Reduces support load and pricing-objection rate. https://blog.hubspot.com/service/price-increase. The public FAQ is the SEO-friendly artifact that captures pricing-related search intent and pre-empts the most common sales-call objections, freeing AEs to focus on qualification rather than pricing 101.
Tag Gong / Chorus calls; track % of lost-deal reasons that cite price as #1 objection. Target <=25%. Sentiment trend; surface in weekly RevOps standup. https://www.everstage.com/sales-compensation/saas-sales-compensation-benchmarks. The pricing-objection rate is the leading indicator of whether the new tier story is landing - if reps are losing on price > 25% of the time, the issue is not the price, it's the value justification.
Configure dunning flow (failed-payment retries)
Configure dunning: failed payment -> retry on day 1, 3, 7, 14, 21 -> suspend service on day 28 -> cancel on day 45. Includes email templates per stage. Critical: misconfigured dunning is one of the top 3 causes of involuntary churn (10-15% of total churn per ProfitWell research). https://www.intercom.com/blog/podcasts/profitwells-patrick-campbell-on-the-art-and-science-of-pricing/. Dunning is the most-overlooked revenue lever in B2B SaaS - 10-15% of churn is involuntary and recoverable with a properly tuned dunning flow.
End-to-end test in staging w/ 10 sample accounts
E2E test with 10 representative accounts covering: monthly->annual switch, tier upgrade mid-period, add-on attach, seat-add, currency switch, dunning trigger, cancellation. Sign-off by CFO. https://docs.stripe.com/billing/subscriptions/migrate-subscriptions. The E2E test is the gate before production migration - if any of the 10 sample scenarios fails, the production migration is held until the underlying logic is fixed.
Reconciliation: old vs new billing, <=0.5% drift
Weekly reconciliation: invoices issued in old system vs new, ASC 606 revenue impact, refund / credit-note issuances, ARR delta. Drift threshold: <=0.5% per cohort. https://www.maxio.com/blog/saas-revenue-recognition-asc-606. Reconciliation is the audit-grade safety net - it catches the silent revenue leaks (rounding errors, currency conversion drift, mid-cycle proration mistakes) that aggregate to material amounts over 9 months.
Audit-package prep (year-end auditor walkthrough)
Prep audit walkthrough package: rev-rec memo + sample contracts + journal entries + reconciliation reports. Pre-empts year-end auditor questions. https://kpmg.com/kpmg-us/content/dam/kpmg/frv/pdf/2024/revenue-software-saas-1.pdf. The audit package converts what would be a stressful year-end discovery exercise into a structured walkthrough where the auditor's questions are answered before they're asked.
Configure CPQ (Salesforce CPQ / DealHub / Pricefx) with new tier rules + bundle logic + add-on attach + escalation triggers from enterprise-custom-quote-rules. https://dealhub.io/glossary/discount-approval/. The CPQ config is the system enforcement of the M3 tier spec + M4 discount matrix - without it, reps build quotes manually and the redesign drifts back to ad-hoc pricing within 90 days.
Update competitive battlecards for new tier story
Update battlecards for new tier story. Map our packaging to the top 3 competitors' packaging; identify wedge cases (where we win on tier comparison) and risk cases (where they're cheaper for entry-level). https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan. The battlecards are the structured response for the moment in every deal where the prospect says 'how do you compare to X?' - without them, the rep improvises and competitor strengths get under-weighted.
