Portfolio Performance & Value Experience
I-2026-GC-001: Portfolio Performance & Value Experience
Path: /products/applications/gamechanger/initiatives/I-2026-GC-001/Initiative.md
PM Owner: Sabrina
Status: discovery
Date Created: 2026-03-03
Last Updated: 2026-03-04
The Bet
We believe that hotel owners, investors, and executive decision-makers who today have no visibility into Duetto's impact on their portfolio
will engage with a self-serve performance experience anchored by a single headline ROI number, supported by five core metrics that tell a complete and defensible value story
resulting in protected LARR at renewal-risk accounts, expansion revenue from underleveraged properties, a measurable contribution to RPOS Maturity, and a new executive relationship that bypasses the revenue manager as the sole Duetto champion
because the value Duetto delivers is real, the data already exists, and the only thing standing between Duetto and a stronger renewal conversation is making that value visible to the people who write the checks.
Why This Initiative. Why Now.
Duetto's 2026 north star is $200M ARR and 70,000 hotels on the platform by 2028. The CY26 Scorecard sets the near-term bar: $90M LARR (a $18.3M gap to close), an $8M EBITDA floor, an NPS of 50+, and an RPOS Maturity score of 30 starting from zero.
This initiative is directly load-bearing on three of those five metrics.
On LARR: Enterprise accounts are Duetto's highest-ACV customers. Losing one is not a pipeline problem, it is a hole that takes quarters to fill. When renewal decisions get made without evidence of impact, the outcome depends on a CSM's relationship and a Google Slides deck built from scratch. This initiative makes the evidence continuous, on-demand, and owned by the customer, not reconstructed at renewal.
On NPS: The current NPS baseline is 45, with a target of 50+. Customer satisfaction in B2B SaaS is directly correlated with perceived value delivery. Customers who can see their ROI independently, without waiting for a QBR, are more likely to be promoters. Customers who cannot see it, even when the outcomes are strong, are more likely to be passive or detractors. The gap between delivering value and being seen to deliver value is a product problem, and it belongs on the product roadmap.
On RPOS Maturity: RPOS Maturity starts at 0. Every point on that score represents Duetto expanding beyond rooms-only revenue management into a true Revenue and Profit Operating System. This initiative is the first customer-facing surface that tells a cross-dimensional performance story. Without HotStats, it is a revenue and adoption visibility product. With HotStats, it is the first expression of RPOS that a hotel owner can actually see, profitability benchmarked against peers, cost structure vs. comp set, total performance in one view. The HotStats phase of this initiative is not a nice-to-have. It is how this initiative contributes to RPOS Maturity.
Duetto's marketing strategy frames the ambition clearly: "From the tool revenue teams love, to the platform the hospitality industry relies on." This initiative is product's answer to that brief. Revenue teams already rely on Duetto daily. Owners and investors do not yet. This closes that gap.
The Problem Worth Solving
The value story is strong. The delivery mechanism is broken.
Today, when an enterprise customer wants to understand Duetto's impact on their portfolio, here is what happens: a CSM raises a ticket in the analytics portal, which routes to one of two people in the Ops team, who manually assembles a Business Review package from multiple disconnected data sources. The Ops team receives 7 to 13 of these requests every month depending on the time of year, 84 to 156 per year. Each request breaks down as follows:
| Data Type | Time per Request |
|---|---|
| Performance Data | 40 minutes |
| Usage Data | 40 minutes |
| Rate Override Data | 40 minutes |
| System Audit | 2 to 4 hours (depends on portfolio size) |
| Total per request | 3 to 5 hours |
At 10 requests per month on average, that is 40 hours of Ops capacity consumed every month, nearly 480 hours per year, tied up in producing data that customers cannot access on their own. At peak (13 requests, 5 hours each), the monthly burden reaches 65 hours. Two people carry this entire load.
One important note on timing: an audit automation system has been in development for several months and is expected to go live at the end of April 2026. That will reduce the system audit component materially. But even with audit automation fully in place, performance data, usage data, and rate override data still consume 2 hours per request across 84 to 156 annual requests, 168 to 312 hours per year of manual work that this initiative eliminates entirely, not just reduces.
The customers receiving these packages cannot access the data themselves between delivery cycles. The customers not receiving them, the majority of the enterprise base, have no visibility at all.
Three things are broken:
1. Renewal decisions are made without the evidence. When a Duetto contract comes up for renewal, the value story has to be rebuilt from scratch. If the QBR falls off the calendar, the owner's last impression is the invoice, not the impact. Duetto has a $18.3M LARR gap to close in CY26. Protecting the enterprise base is not optional.
2. The revenue manager is the only relationship. Duetto's primary user, the revenue manager, is not typically the economic buyer. If that person leaves, is restructured out, or simply does not champion Duetto upward, the executive layer has no independent reason to care. One personnel change at a key account should not become a renewal risk.
3. The manual process does not scale, and it is not the right product. Up to 480 hours per year of Ops capacity on manual data assembly is a symptom, not a root cause. The root cause is that Duetto has no self-serve mechanism for owners to see their own performance data. As Duetto scales toward 70,000 hotels, the ticket-based model becomes an increasingly expensive and fragile way to demonstrate value, more hotels, more requests, same two people. It is also a ceiling on NPS: customers cannot become promoters of a value story they cannot see.
The Opportunity
This is not a reporting feature. It is a retention, expansion, and category-positioning initiative that happens to surface data.
Retention: Enterprise accounts with active owner engagement are less likely to churn. An owner who logs in, or receives a monthly digest, between QBR cycles has a continuous reminder of ROI. The renewal conversation starts from a position of demonstrated value, not negotiated proof.
Expansion: Owners who can see underleveraged properties, low adoption scores, configuration gaps, disabled features, have a direct, visible reason to push their revenue management teams to do more. That is an expansion conversation Duetto does not currently have a product motion for. This creates one.
RPOS Maturity: The HotStats enrichment layer, starting in Phase 2, makes this the first customer-facing RPOS surface. When an owner can see GOPPAR benchmarked against peer hotels, cost structure vs. comp set, and a displacement analysis alongside Duetto's pricing impact, Duetto stops being an RMS and starts being a Revenue and Profit Operating System. That is a category repositioning, not a feature update.
Win rate and sales motion: Marketing's OKR 3 targets a ~6% win-rate improvement through clearer messaging and enterprise proposition clarity. A publicly referenceable portfolio performance experience, with real customer outcomes, named metrics, and a defensible ROI methodology, is the kind of proof point that moves win rates at the enterprise level. Sales needs this. Marketing needs this. Product can build it.
Why the Current Process Is Not Good Enough
The manual QBR is not just slow, it is structurally incapable of doing what this initiative does.
| Dimension | Manual Process Today | Portfolio Performance Experience |
|---|---|---|
| Frequency | On request, delivered after the fact | Continuous, on-demand |
| Access | CSM-mediated via Ops ticket | Owner self-serve |
| Audience | Revenue manager / CSM | Owner, investor, C-suite |
| Story told | Usage and engagement | Business outcomes and ROI |
| Market context | None | MPI, ARI, RGI lifted from existing CommandCenter widgets (Phase 2, Advance subscribers); locked with upsell prompt for non-subscribers |
| Profitability | None | GOPPAR and benchmarks (Phase 3) |
| Action triggers | None | Inline: flag, recommend adjustments/link to fix, flag to CSM |
| Push mechanism | None | Monthly digest, trigger-based alerts |
| RPOS contribution | None | Yes (HotStats phase) |
| Ops hours required | 3 to 5 hrs per request, up to 480 hrs/year | Near zero post-launch |
The manual QBR is a workaround for a missing product. This initiative builds the product.
Industry Context: The Bar Has Been Set
Duetto's competitors are already telling outcome stories to owners. IDeaS leads its customer narrative with specific, attributed dollar outcomes: one customer reported 25% RevPAR growth in their first full year, with compound ADR growth of 7%, 18%, and 20% in subsequent years. That is the benchmark enterprise buyers are evaluating against when they ask Duetto "what's our ROI?"
Duetto has equivalent or better performance data in its systems. The Sonesta portfolio alone shows rate acceptance up 40 to 50 percentage points year-over-year, with 80 to 90% of bookings now aligning with Duetto recommendations, and 8,695 total platform hours used across the portfolio. That is a compelling value story. It is currently buried in a manual report that Sonesta's leadership cannot access on their own.
The hospitality industry benchmarks that make this story quantifiable are well-established. The Revenue Generation Index (RGI), a property's RevPAR divided by comp set average RevPAR, is the standard metric investors use to assess market position. Top-performing properties maintain RGIs of 105 to 115. Properties below 100 are losing market share regardless of absolute growth. Duetto already surfaces MPI, ARI, and RGI in CommandCenter today for Advance subscribers, with live data, clean visualisations, and YoY trend indicators. These widgets are underutilised not because the data is missing but because the people who most need to see them, owners, investors, and C-suite executives, are not CommandCenter users. Phase 2 of this initiative corrects that. For non-Advance customers, the locked metric becomes a visible, quantified reason to expand their Duetto subscription.
A study of 567 hotels using automated pricing found an average 19% revenue increase, driven by a 4% ADR gain and 14% occupancy improvement, with the most pronounced gains at properties where teams followed system recommendations rather than overriding frequently. Duetto captures exactly this signal: rate acceptance rate and override variance are clean, reliable data points that directly attribute platform value to revenue outcomes. This is as close to a causal ROI story as the industry produces without a controlled experiment.
The Metric Architecture: 5 Core Metrics + 1 Headline Number
The most effective B2B value dashboards lead with a single number that anchors the experience, supported by a small set of metrics that explain it. Metric overload is the primary failure mode, executives who see too many numbers without a clear "so what" disengage or draw the wrong conclusions. For an owner-facing experience, the threshold is low: 3 to 5 metrics that tell the ROI story on their own, with everything else in drill-down.
The Headline Number: Duetto-Aligned Revenue Uplift ($)
The anchor metric is an estimated revenue uplift from Duetto-aligned decisions, a calculated comparison between revenue on bookings that followed Duetto's recommendations versus the revenue impact of override behavior across the same period. This answers the owner question "is Duetto worth the money?" with a dollar amount, not a percentage or an index.
The data required, rate acceptance rate, booking revenue, override variance, already exists in Duetto's systems. The methodology needs to be validated with Jon Ham and Engineering in Experiment 1. But the direction is clear: Duetto knows what it recommended, it knows what happened, and it can show the difference.
The Extended Headline: Duetto-Aligned Profit Uplift ($ powered by HotStats)
For HotStats customers, the headline extends beyond revenue into profit. The Duetto-Aligned Profit Uplift surfaces an estimated GOPPAR improvement attributable to Duetto-aligned pricing decisions, answering not just "did we earn more?" but "did we keep more?"
This is a meaningfully stronger claim and a meaningfully harder calculation. Revenue uplift has a relatively direct attribution path through rate acceptance and override variance. Profit uplift requires layering HotStats cost and margin data on top and isolating Duetto's pricing contribution from other factors (cost management, mix shift, labour efficiency) that also affect GOPPAR. The methodology is unsolved and is the explicit focus of Experiment 1.5.
Two tiers of the headline, one coherent story:
| Customer Type | Headline Shown | Data Source | Phase |
|---|---|---|---|
| Duetto only | Duetto-Aligned Revenue Uplift ($) | Duetto | Phase 1 |
| Duetto + HotStats thin-file | Revenue Uplift ($) + Partial Profit Uplift ($) | Duetto + targeted HotStats data request | Phase 2/3 |
| Duetto + HotStats full | Revenue Uplift ($) + Profit Uplift ($) | Duetto + HotStats full P&L | Phase 3 |
The thin-file path is a first-class mechanism, not a fallback. For hotels that are not current HotStats customers, a targeted data request covering specific P&L fields, rather than a full onboarding, can unlock a partial Profit Uplift story. This is a lightweight data submission that surfaces enough profitability context to make the RPOS story meaningful without requiring a full HotStats relationship. It also creates a natural, low-friction entry point for HotStats customer acquisition: hotels that see partial profitability benchmarking have a visible, quantified reason to share more data. The thin-file path is both a product mechanism and a commercial one.
Metric 1: Rate Acceptance Rate (%) Lens: Revenue Performance + Platform Adoption
The percentage of Duetto's pricing recommendations followed without override, per property and portfolio-wide.
Why it matters: This is the most direct signal of whether the platform is being used as intended, and it is the clearest attribution link between Duetto and revenue outcomes. The Sonesta data shows rate acceptance up 40 to 50 percentage points YoY. Properties with high override rates are leaving money on the table, and the owner cannot see it today. High override rate is simultaneously an adoption problem, a revenue problem, and a training problem. Surfacing it creates the action.
Data reliability: High. Clean and available today.
Metric 2: ADR Growth YoY (%) Lens: Revenue Performance
Year-over-year change in Average Daily Rate, by property and portfolio rollup.
Why it matters: ADR is the most universally understood revenue metric in hospitality, it is what boards, investors, and ownership groups track as the primary measure of pricing power. It is also the primary channel through which Duetto creates value. A rising ADR trend, shown alongside rate acceptance, is the closest thing to a causal statement Duetto can make about its own impact.
Data reliability: High. Clean and available today.
Metric 3: MPI, ARI, and RGI (Market Performance Indices) Lens: Market-Relative Performance
Three comp-set and market benchmarks that together show whether a hotel is winning or losing against its competitive set across occupancy, rate, and revenue simultaneously: - MPI (Market Penetration Index): Occupancy vs. comp set. Above 100 = capturing more than fair share of occupied rooms. - ARI (Average Rate Index): ADR vs. comp set. Above 100 = pricing above the market average. - RGI (Revenue Generation Index): RevPAR vs. comp set. Above 100 = capturing more than fair share of revenue.
Why it matters: This is the metric that most embarrasses an owner when it is bad, and the one investors use to assess competitive position. Absolute RevPAR growth means nothing if the whole market grew faster. An RGI of 64 (as seen in existing CommandCenter data) tells an owner their hotel is capturing only 64% of its fair share of market revenue, regardless of whether absolute revenue is up. Industry standards place top-performing properties at RGIs of 105 to 115.
Critical discovery: these widgets already exist. MPI, ARI, and RGI are live in CommandCenter today, benchmarked against both STR comp set and Demand360 market, with YoY delta indicators and a bookings vs. same time last week signal. The visual design is already owner-readable: index bars with above/below/at-par colour coding, clear numeric scores, and trend arrows. The data flows for Advance subscribers (approximately 400 hotels). Two benchmark views are available: STR comp set (direct competitor comparison) and Demand360 market (broader market context), with a toggle between them.
This is not a build problem. It is a surface and access problem. The widgets exist. The data exists. Owners are not seeing them because CommandCenter is a revenue manager tool and owners are not in it. Phase 2 for Metric 3 is lifting these widgets into the portfolio experience and exposing them to the right audience. No new build required.
This is also direct evidence for the broader problem this initiative solves: valuable data and visualisations exist inside Duetto's platform today that the people making renewal and investment decisions have never seen.
Data reliability: High for Advance subscribers (approximately 400 hotels, data live today in CommandCenter). Locked for non-Advance customers, surfaced as an Advance upsell prompt.
Display: Portfolio view shows RGI per property with trend delta. Drill-down surfaces all three indices (MPI, ARI, RGI) with the full CommandCenter-style bar visualisation. Three benchmark views available via toggle, matching existing CommandCenter functionality:
| View | Benchmark Source | Pool Size | Owner Question Answered |
|---|---|---|---|
| STR Comp Set | Named comp set via STR | Typically 5 to 10 direct competitors | "Am I beating my closest rivals?" |
| Demand360 Comp Set | Named comp set via Demand360 | Tight direct competitor set (e.g., 8 hotels) | "Am I beating my closest rivals?" |
| Demand360 Market | Broad market via Amadeus | Full market (e.g., 390 hotels in NYC) | "Am I winning against the whole market?" |
The comp set views are the most emotionally resonant for owners. An RGI of 83 against 8 known direct competitors is a specific, hard-to-dismiss statement about competitive position. The market view provides the broader context needed for the soft-market framing (down 5% but market is down 12%). Both are already built. Bookings vs. STLW surfaced as a forward-looking complement to SPIT.
Metric 4: Platform Adoption Score Lens: Platform Adoption
A composite score per property reflecting active user engagement, feature utilization, and configuration health. Simple, scannable (0 to 100), with contributing factors visible on drill-down.
Why it matters: An owner paying for Duetto across 100 properties needs to know whether all 100 are getting value. The Sonesta portfolio shows activity concentrated in roughly 15 power users out of 100 licensed users, with a significant portion of the portfolio showing zero property-level activity over 6 months. The Adoption Score makes the invisible visible, and it is the leading indicator for the headline number: low adoption correlates with lower Duetto-aligned revenue uplift.
Data reliability: High. Can be derived from existing data today.
Metric 5: Forward Revenue at Risk, SPIT Lens: Revenue Performance (forward-looking)
A forward view of revenue volatility and opportunity across the next 6 months, flagging periods where the portfolio is under-paced vs. budget or where peak opportunity is being left on the table.
Why it matters: The other four metrics are retrospective. SPIT tells owners what is about to happen. For an investor or asset manager, forward-looking revenue visibility is the difference between reactive management and proactive strategy. It also gives the experience a reason to come back to: every time an owner opens the experience, the forward view has updated.
Data reliability: High. Clean and available today.
Metric Hierarchy Summary
| Priority | Metric | Lens | Data Reliability | Owner Resonance |
|---|---|---|---|---|
| Headline | Duetto-Aligned Revenue Uplift ($) | Revenue + Adoption | Requires validation | Highest (answers "is it worth it?") |
| 1 | Rate Acceptance Rate (%) | Revenue + Adoption | High, clean today | High (direct attribution to Duetto) |
| 2 | ADR Growth YoY (%) | Revenue | High, clean today | High (universally understood) |
| 3 | MPI, ARI, RGI (Demand360 via Advance) | Market-Relative | High for Advance subscribers (~400 hotels); locked for non-Advance | Highest (the investor metric) |
| 4 | Platform Adoption Score | Adoption | High, derivable today | Medium (operational but critical) |
| 5 | Forward Revenue at Risk (SPIT) | Revenue, forward | High, clean today | High, drives return visits |
Product upsell tiering built into the experience:
| Customer Subscription | Metrics Available | Locked with Upsell Prompt |
|---|---|---|
| GameChanger only | Metrics 1, 2, 4, 5 + Revenue Uplift headline | Metric 3 (needs Advance); Profit Uplift (needs HotStats) |
| GameChanger + Advance | All 5 metrics + Revenue Uplift headline | Profit Uplift (needs HotStats) |
| GameChanger + HotStats | All 5 metrics + Revenue Uplift + partial Profit Uplift | Metric 3 (needs Advance) |
| GameChanger + Advance + HotStats | Full experience: all metrics, both headline numbers, peer benchmarking | Nothing locked |
Every locked metric is a visible, contextualised, data-grounded reason to expand. The experience does not just demonstrate Duetto's current value. It shows owners exactly what additional value is one subscription away.
Deliberately excluded from Phase 1: GOPPAR, GOP Margin, F&B contribution, cost benchmarking, displacement analysis. These are the profitability lens, the right answer to the RPOS vision, and the metrics that close the gap between "RMS" and "Revenue and Profit Operating System." They belong in Phase 3. The open question, whether owners want market-relative performance or profitability as the primary north star, is for Experiment 0 to resolve.
The Action Loop: From Insight to Action
This is not a reporting tool. It is a decision tool. The difference is whether the user can act from it without leaving the experience.
Every data point surfaces two answers: "what does this mean?" and "what do I do about it?"
- Low adoption score on a property: Flag, recommend adjustments, link to fix, flag to CSM to schedule an enablement session
- Configuration gap (e.g., BlockBuster Walk Away Rules not enabled): Direct link to the configuration step in GameChanger, or one-click request to CSM
- RGI below 100: Surface the specific features not enabled at that property that could close the gap
- Override rate above threshold: Show the owner the estimated revenue impact of the deviation, with a prompt to share with the property's revenue manager
- Forward revenue at risk: Surface the SPIT flag with the recommended Duetto action to address it
The loop is tight: owner sees a problem, owner acts on it in the same session. No call to schedule. No ticket to raise.
The Push Mechanism: The Experience Must Come to the Owner
Building the portal is not enough. Owners will not develop a new habit of logging in unprompted. The experience must reach them.
- Monthly performance digest (email): Headline number, top 3 performing properties, top 3 needing attention, one forward SPIT flag. Designed to be read in 90 seconds.
- Trigger-based alerts (email or Slack): Configuration gap opened, property drops below RGI 100, rate acceptance falls below threshold, user inactive for 30+ days
- Renewal-cycle summary (CSM-initiated): A shareable, pre-built snapshot generated from live data, sent by the CSM before a renewal conversation, replacing the manual QBR package entirely
The digest and alert layer reduces dependence on the owner building new behavior. It also gives the CSM a data-driven reason to reach out, rather than a relationship-driven one. That is a more scalable and defensible customer success motion as Duetto grows toward 70,000 hotels.
Owner Persona Split
Not all owners ask the same question. The five-metric framework applies across personas, but weight and entry point differ. Phase 1 is designed for one primary persona, validated in Experiment 0.
| Persona | Primary Question | Key Metrics | Access Cadence |
|---|---|---|---|
| Asset Manager | Is this property performing against its investment thesis? | RGI, ADR growth, GOPPAR (Phase 3), SPIT | Quarterly, ahead of asset reviews |
| Portfolio Operator | Which properties need intervention, and why? | Adoption Score, rate acceptance, config gaps | Monthly or more frequent |
| Owner / Investor | Is the technology investment paying off? | Headline uplift number, ADR, RGI | At renewal, at QBR, via digest |
Design implication: The portfolio entry point defaults to the investor framing, headline number, high-level portfolio health. A toggle or drill-down path surfaces the operational adoption and gap detail that operators need. The experience does not open on a table of login counts.
Four Value Lenses
1. Revenue Performance
Are my hotels growing? Is Duetto driving it? - Duetto-Aligned Revenue Uplift (headline) - Rate Acceptance Rate and override variance - ADR Growth YoY - Forward Revenue at Risk via SPIT
2. Market-Relative Performance
Am I winning or losing against my market? - RGI, MPI, ARI lifted from CommandCenter (Phase 2, Advance subscribers; three benchmark views: STR Comp Set, Demand360 Comp Set, Demand360 Market; locked with Advance upsell for non-subscribers) - Soft market framing: indexed wins in down markets - Peer cluster benchmarking (Phase 2)
3. Platform Adoption
Is my team using what I'm paying for? - Platform Adoption Score per property - Active users, last active date, inactive flags - Feature enablement status, configuration gaps - Action triggers: flag, recommend adjustments/link to fix, flag to CSM
4. Profitability, HotStats Enrichment (Phase 3)
Is revenue growth flowing to the bottom line? - GOPPAR and GOP Margin - Cost structure vs. peers - Displacement analysis - Thin-file path for non-HotStats customers
Design principle: The experience works without HotStats, Lenses 1 and 3 are available to all Duetto customers from Phase 1. HotStats unlocks Lenses 2 and 4, deepens benchmarking, and contributes to the RPOS Maturity score. Non-HotStats customers see a clear, motivated unlock path.
Experiments Planned
Confidence: low-medium. The Sonesta data confirms the value story exists. We have not yet validated that owners will engage directly, which metric framing resonates, or whether the action loop meets their needs. Maximum 3 experiments before PRD.
Experiment 0: Validate Owner Engagement (Highest Priority)
Question: Will hotel owners and executives engage with a portfolio performance experience? Which metrics matter most? Does the action loop framing resonate, or do they want a report?
Key hypotheses to test: - Does the Duetto-Aligned Revenue Uplift headline anchor resonate, or do owners want a different number? - Is the primary owner KPI market-relative performance (RGI) or profitability (GOPPAR)? - Does the owner want to act from the experience, or view and share? - What access model (portal, digest, snapshot) reduces friction enough for regular use?
Method: Structured interviews with 3 to 5 owner/investor contacts (Sonesta leadership + 1 to 2 other enterprise accounts). Show a framing document and annotated wireframe screens, not a working prototype. Probe against the hypotheses above.
Success signal: 3+ of 5 owners confirm the framing matches real questions they have; at least one identifies a decision they would make differently with this data; clear preference signal on primary metric
Timeline: Week 1 to 2 Owner: Sabrina + CSM Lead
Experiment 1: Validate Data Accessibility and Headline Metric Feasibility
Question: Does the data required to power the five core metrics and the Duetto-Aligned Revenue Uplift calculation already exist in Duetto's infrastructure? What is the methodology and build lift?
Method: Engineering + analytics spike. Map each metric to its source system. Define the uplift calculation methodology. Estimate effort delta between "exists and queryable" vs. "requires new infrastructure." Validate whether Adoption Score correlates with ADR and RGI outcomes in the existing dataset.
Success signal: All five core metrics confirmed accessible; uplift methodology defined and feasible; adoption-outcome correlation directionally validated
Timeline: Week 2 to 3 Owner: Jon Ham + Engineering Lead (TBD)
Experiment 1.5: Define Profit Uplift Attribution Methodology (HotStats x Duetto)
Question: Can Duetto's pricing decisions be credibly attributed to GOPPAR improvement using HotStats data, and what is the most defensible methodology for expressing that as a Duetto-Aligned Profit Uplift number?
Why this exists as a separate experiment: Revenue uplift has a relatively direct attribution path: Duetto recommended a rate, the team accepted or overrode it, the booking happened. Profit uplift is more complex. GOPPAR is affected by cost management, labour efficiency, revenue mix, and F&B performance, not just room pricing. Claiming a profit uplift number without a sound methodology risks credibility with the very owners and investors this experience is designed to impress. This experiment defines the methodology before anything is built.
Why now (not Phase 3): The HotStats PM is an internal collaborator on this initiative, which removes the integration dependency that originally placed profit analysis in Phase 3. The methodology question can be explored immediately using existing HotStats customer data, in parallel with the Revenue Uplift work, without waiting for a product build.
Key hypotheses to test: - Do properties with higher rate acceptance show materially better GOPPAR outcomes in the HotStats dataset? (Correlation approach, fastest to validate) - Do hotels on both Duetto and HotStats show GOPPAR improvement post-Duetto implementation vs. a HotStats-only control group? (Before/after approach, more rigorous but requires sufficient sample) - Is the correlation strong enough and consistent enough across markets and property types to support a customer-facing headline number, or does it require heavy caveating that undermines its impact?
Data available: - Full P&L for existing HotStats customers (GOPPAR, GOP Margin, cost structure, F&B) - Partial P&L via thin-file data request for non-HotStats customers - Rate acceptance and override data for all Duetto customers - Overlap: Duetto customers who are also HotStats customers (confirm list with HotStats team)
Method: Analytical study using overlapping Duetto + HotStats customer dataset. Test correlation between rate acceptance rate and GOPPAR outcomes at property level. Test before/after GOPPAR trajectory at hotels that implemented Duetto while on HotStats. Define methodology, confidence level, and appropriate disclosure language for customer-facing use.
Success signal: A defensible, clearly caveated methodology exists for expressing Profit Uplift as a customer-facing number; the correlation is strong enough directionally to justify including it in the prototype; disclosure language is defined so it cannot be misread as a guaranteed ROI claim
Timeline: Week 3 to 5 (starts after Experiment 1 confirms Revenue Uplift feasibility) Owner: HotStats PM + Jon Ham
Question: Does a lo-fi prototype of the portfolio view and one property drill-down surface information owners find clear, credible, and actionable?
Method: Share prototype with one Sonesta owner-level contact and one CSM. Probe: Does the headline number feel credible? Does the portfolio view give an immediate read on the business? Does the action loop produce intent to act?
Success signal: Owner confirms they would access this independently between QBR cycles; at least one action trigger produces stated intent to act; no fundamental objection to the headline metric methodology
Timeline: Week 4 to 6 Owner: Scott Lee (prototype) + Sabrina (session design)
Experiment 3: Validate the Hotel Property Health Index (HPHI) as a Portfolio Triage Metric
Question: Do hotel owners trust and act on a composite Health Index score as a way to triage portfolio performance, or does the black-box nature of a composite undermine confidence in the number?
Why this is a separate experiment: This is a distinct methodological question that has no dependency on the prototype validation in Experiment 2. Experiment 0 through 2 establish whether owners will engage with the experience and whether the core metric set resonates. Experiment 3 tests a fundamentally different design hypothesis: whether a single composite score can replace or augment the five-metric view at the portfolio level. Running it separately keeps Experiments 0 through 2 clean and gives this question the focused attention it deserves.
Background: The Hotel Property Health Index (HPHI) is a weighted percentile-rank aggregation across 54 USALI P&L features, scoring hotels 0 to 100 within their peer cluster using PCA-implied weights. It captures overall financial and operational health across revenue positioning, cost efficiency, segment mix, and profitability in a single number. Conceptually, it solves the synthesis problem: rather than asking an owner to mentally combine rate acceptance, ADR growth, RGI, adoption score, and SPIT, HPHI does that aggregation automatically and surfaces a single, colour-coded, rankable score per property.
The trust challenge is real and specific. Composite indices succeed when users perceive the methodology as neutral and rigorous (credit scores, STR RevPAR Index, NPS). They fail when users suspect the vendor is gaming the weighting to favour their own product. An owner who sees an HPHI of 54 will ask: who set the weights? Why is this feature weighted more heavily? Is Duetto inflating this because rate acceptance is a component? The PCA-implied weighting methodology is defensible (weights are data-derived rather than subjectively assigned), but communicating that clearly to a non-technical owner is a design challenge, not just an explanation problem.
Proposed role in the experience (hypothesis to test): Rather than replacing the Revenue Uplift headline, HPHI may work best as the portfolio triage layer (the number an owner uses to rank and prioritise properties at a glance), with Duetto-Aligned Revenue Uplift as the attribution layer that explains why a property scored where it did. That structure answers two distinct owner questions: "which properties need attention?" (HPHI) and "is Duetto responsible for the outcome?" (Revenue Uplift and Profit Uplift). Neither metric has to carry the full explanatory weight alone.
Key hypotheses to test: - Do owners find a 0 to 100 composite score immediately interpretable, or do they distrust it without understanding the components? - Does the peer cluster framing (scored within your competitive set, not absolutely) increase or decrease confidence in the number? - When shown both HPHI and the five-metric breakdown side by side, which do owners gravitate toward as their primary signal? - Is the colour-coded triage view (red/yellow/cyan) sufficient to drive action, or do owners need to understand the underlying components before they will act? - Does knowing HotStats is the data source behind the index increase credibility, and does that hold for non-HotStats customers looking at a partial score?
Method: Concept test with 3 to 5 owner/investor contacts (can overlap with Experiment 0 participants or use a second cohort). Show two versions of the portfolio view side by side: one anchored by the five-metric core set, one anchored by the HPHI composite score. Run a structured preference and comprehension probe. Measure: comprehension speed, stated confidence in the number, and likelihood to act on it vs. dig into the components.
Success signal: Owners can correctly interpret the score directionally (higher is better, peer-relative, not absolute) without reading the methodology tooltip; at least 3 of 5 express confidence they would use it as a triage signal; no owner interprets it as a Duetto-controlled metric
What a positive result unlocks: HPHI becomes the portfolio entry point, replacing or sitting above the five-metric rollup, with the existing metrics surfaced as contributing factors on drill-down. This simplifies the portfolio view significantly and creates a proprietary, defensible benchmark that no competitor can replicate.
What a negative result means: HPHI is surfaced as an optional "deep dive" metric for analytically-oriented users rather than as the primary portfolio signal. The five-metric core set remains the default entry point. The index methodology continues to be developed and validated for a future phase.
Timeline: Runs in parallel with or immediately after Experiment 2, Week 5 to 7 Owner: Sabrina + HotStats PM (Scott Lee on visual concept if prototype needed)
Phase 1: Portfolio Performance MVP Five core metrics, headline number, portfolio view and property drill-down, action triggers, push digest. All Duetto customers. No HotStats dependency. - Headline: Duetto-Aligned Revenue Uplift ($) per property and portfolio total - Portfolio rollup: ADR YoY, Rate Acceptance Rate, Platform Adoption Score, Forward Revenue at Risk (SPIT), by property, filterable by brand/region/date - Property drill-down: override variance trend, configuration gap detail, feature adoption status, action triggers - Push layer: monthly digest email, trigger-based alerts - Access model: TBD pending Experiment 0 - CSV/Excel export - Replaces manual Business Review package for accounts in scope, eliminating an estimated 168 to 480 Ops hours per year (168 to 312 hrs post-audit automation; up to 480 hrs at current baseline)
Phase 2: Market Context Layer Surfaces existing CommandCenter MPI/ARI/RGI widgets to owners and executives for the first time. Minimal new build. Primarily a surface and access problem. Unlocks Advance upsell for non-subscribers. - RGI, MPI, ARI per property surfaced from CommandCenter (Demand360 via Advance, approximately 400 hotels live today) - Three benchmark views available via toggle: STR Comp Set, Demand360 Comp Set, and Demand360 Market, all matching existing CommandCenter functionality and data - Bookings vs. same time last week signal surfaced alongside SPIT as a complementary forward indicator - YoY delta indicators per index (matching existing CommandCenter display) - Soft market framing: indexed performance in down markets labeled and explained - Locked state with contextualised Advance upsell prompt for non-subscribers - Peer cluster benchmarking via HotStats hotel clustering model (where available)
Phase 3: Profitability and Deep Benchmarking Full HotStats integration. Contributes to RPOS Maturity score. - GOPPAR, GOP Margin, F&B contribution (HotStats customers) - Cost structure vs. peers: Rooms Cost Ratio, Labour Intensity, Utilities, POM Ratio - Displacement analysis: Group Mix trade-offs and profitability implications - Thin-file request path for non-HotStats customers - First customer-facing RPOS surface, cross-dimensional performance story from pricing through profitability
Commercial Impact
| Impact Area | Mechanism | CY26 Metric |
|---|---|---|
| LARR retention | Owners see ROI continuously; renewal conversations start from evidence, not negotiation | LARR target: $90M |
| LARR expansion | Owners see configuration gaps and underleveraged properties; natural expansion prompt | LARR gap: $18.3M to close |
| NPS | Customers who see their ROI independently are more likely to be promoters | NPS target: 50+ (baseline: 45) |
| RPOS Maturity | Phase 3 (HotStats) is first customer-facing RPOS surface | RPOS Maturity target: 30 (baseline: 0) |
| Ops capacity | 3-5 hrs/request across 7-13 requests/month eliminated post-launch; post-audit-automation floor of 168-312 hrs/year still eliminated | Up to 480 hrs/year freed |
| Win rate / sales | Referenceable ROI experience supports enterprise proposition and PMM enablement | ~6% win-rate improvement target (Marketing OKR 3) |
Success Metrics
| Metric | Target |
|---|---|
| Owner engagement in Experiment 0 | 3+ of 5 confirm framing matches real questions; clear signal on primary metric |
| Headline metric feasibility (Experiment 1) | Uplift calculation methodology defined and confirmed buildable |
| Data accessibility (Experiment 1) | All 5 core metrics mapped to source without new infrastructure |
| Adoption-outcome correlation (Experiment 1) | Directional validation that Adoption Score predicts ADR and RGI outcomes |
| Prototype validation (Experiment 2) | Owner identifies at least 1 action they would take; no objection to headline methodology |
| Ops hours eliminated | 168-312 hrs/year post-audit-automation (full 480 hrs/year at current baseline) |
| Owner return visits post-launch | Measurable unprompted access between QBR cycles |
| Monthly digest open rate | Above 40% |
| Renewal signal at Sonesta | Positive at next renewal checkpoint |
| Additional enterprise accounts adopting | 3+ within 90 days of GA |
| RPOS Maturity contribution | Confirmed as scored milestone upon Phase 3 launch |
Open Questions
- [ ] Who are the 3 to 5 owner/investor contacts accessible for Experiment 0? Does Sonesta have an owner-level stakeholder above the VP of Revenue?
- [ ] What is the right access model for a non-GameChanger user? New role, standalone portal, or shareable snapshot? (Experiment 0 answer)
- [ ] Is the primary owner KPI market-relative performance (RGI) or profitability (GOPPAR)? (Experiment 0 answer)
- [ ] What is the methodology for Duetto-Aligned Revenue Uplift, and can it be calculated from existing data? (Experiment 1 answer)
- [ ] Does higher Adoption Score correlate with better ADR and RGI outcomes in the existing Duetto dataset? (Experiment 1 validation)
- [ ] How many Duetto customers are also HotStats customers? What is the overlap, and is the sample large enough to run the GOPPAR correlation study in Experiment 1.5?
- [ ] Is the correlation between rate acceptance and GOPPAR strong enough and consistent enough across markets and property types to support a customer-facing Profit Uplift number? (Experiment 1.5 answer)
- [ ] What specific P&L fields are needed for the thin-file data request to power partial Profit Uplift? What is the minimum viable dataset?
- [ ] What disclosure language is required for the Profit Uplift number to be customer-facing without being misread as a guaranteed ROI claim? (Legal/compliance input needed)
- [ ] What HotStats market coverage exists for Sonesta's portfolio geographies (primarily Americas), and what is the data latency?
- [ ] Does the HotStats HPHI hotel clustering model already produce the peer grouping needed for Phase 2, or is new work required?
- [ ] How is RPOS Maturity scored, and does Phase 3 of this initiative qualify as a scored milestone? (Confirm with Kartik)
- [ ] What is Sonesta's renewal timeline, and does it create a hard delivery deadline for Phase 1?
- [ ] Which team charter does this initiative roll up to, and does the HotStats collaboration require a formal cross-team agreement?
Stakeholders
| Role | Name |
|---|---|
| PM Owner | Sabrina |
| CPO | Kartik |
| CTO | Bob |
| Prototype Specialist | Scott Lee |
| Product Analytics | Jon Ham |
| CSM Lead | TBD |
| Engineering Lead | TBD |
| HotStats PM | Sabrina (internal collaborator, Experiment 1.5 owner) |
| HotStats CS Director | Neil Farren (aware, companion initiative owner when ready) |
Related Initiatives
HotStats CS Business Review Automation (Companion Initiative, TBD)
Neil Farren, Director of Customer Success at HotStats, has identified a parallel need: the ability for the HotStats CS team to generate Business Reviews for HotStats customers using Claude, mirroring the same process efficiency goal this initiative addresses for Duetto's CS team.
This is intentionally scoped as a separate initiative rather than absorbed into this one. The problem structure is the same (manual Business Review assembly consuming CS and Ops time), but the data environment, customer base, and ownership are distinct. Coupling the two would blur the problem statement and introduce a technical dependency (Claude's connectivity to HotStats's internal tools) that could complicate this initiative's timeline.
Shared work between this initiative and the companion: - Profit Uplift attribution methodology (Experiment 1.5): findings apply to both - HPHI trust and comprehension validation (Experiment 3): findings apply to both - Thin-file data request path: relevant to both customer bases
Open question for the companion initiative: Claude needs to be connected to HotStats's internal tools to automate Business Review data assembly. If those tools have an accessible API or can be connected via an MCP server, the automation is achievable. If the data lives in disconnected internal systems, a data infrastructure step is required before Claude can be the assembly mechanism. Neil's team should scope the connectivity question before committing to the build approach.
Status: Not yet in APEX. Neil Farren to initiate when ready.
Kill Criteria
This initiative will be killed or pivoted if:
- Experiment 0 reveals owners will not engage directly, they want the story delivered by CSMs, not self-served
- Experiment 0 reveals the five-metric core set does not match how owners think about portfolio performance
- Experiment 1 reveals the Duetto-Aligned Revenue Uplift calculation requires modeling infrastructure that materially changes the effort estimate
- Experiment 1.5 reveals that the correlation between rate acceptance and GOPPAR is too weak or inconsistent to support a customer-facing Profit Uplift number without misleading owners
- Experiment 2 reveals the action loop does not produce stated intent to act from an owner-level user
- Experiment 3 reveals owners distrust the composite Health Index score and cannot interpret it without reading the methodology. In that case, HPHI is deprioritised as a portfolio entry point and the five-metric core set remains the default