01 / 08 · Mozza by Cocotte · A controlled 90-day growth pilot
Mozza Has Built a Brand Worth Growing
Strong guest ratings across branches. A loyalty programme. A brand people search for by name. This is the foundation. We explore one question: can Mozza attract additional high-intent local guests, profitably, one branch at a time?
- Reputation
- Loyalty
- Brand search
- Branches
Evidence, confidence and presenter notes
Question this slide answers
Do we respect what Mozza has already built, and what is this conversation about?
Evidence
- PUBRatings of 4.5–4.9 across four branches (≈ 6.0K Google reviews, Oct 2026).Confidence: Medium.
- PUBHMA Privilege loyalty programme exists.Confidence: High.
- 3P“mozza by cocotte” ≈ 2.4K searches a month (Mangools, Oct 2026).Confidence: Medium.
Presenter notes
Thank you for the time. I will not present an audit or a list of problems. Mozza has a strong reputation and guests who search for it by name; that is the foundation of everything I will say. I have one proposition: there may be additional local demand worth capturing, branch by branch, and a small controlled pilot could test it. Everything on these slides is labelled by how sure we are. I will keep it to twenty minutes, and the only thing I will ask for today is a discovery conversation.
PUBPublic listing or page (dated)3PThird-party estimate, not analytics
02 / 08 · The opportunity
The Next Growth Opportunity Is Branch-Level Demand
Each branch serves its own mall, neighbourhood and hours. Growth may come from reaching more nearby guests when a branch has seats to fill. The question is not whether Mozza is busy, but where extra, profitable covers could fit.
Central Park
- Capacity by hour — HMA data
- Average check — HMA data
- Weekday vs weekend — HMA data
EmQuartier
- Capacity by hour — HMA data
- Average check — HMA data
- Weekday vs weekend — HMA data
Siam Paragon
- Capacity by hour — HMA data
- Average check — HMA data
- Weekday vs weekend — HMA data
Central Chidlom
- Capacity by hour — HMA data
- Average check — HMA data
- Weekday vs weekend — HMA data
Branch list to be confirmed with HMA. Nothing is filled in on purpose: these numbers can only come from HMA.
Evidence, confidence and presenter notes
Question this slide answers
Where could incremental covers realistically come from?
Evidence
- 3PSearch demand exists around the branches and is larger than brand-name demand (Keyword Planner, Bangkok, Oct 2025–Sep 2026; ranges).Confidence: Medium.
- 3PGoogle Popular Times (Central Park, Oct 2026) suggests the demand curve is not flat. It is relative to the place, one capture per day: it is not occupancy or capacity, and is used only to motivate the question.Confidence: Low.
- DATACapacity and weekday gaps are unknown until HMA shares its own figures.
Presenter notes
Mozza is a group of branches, each with its own neighbourhood and its own hours. I am not saying any branch is empty. Google's busy-time graphs are relative to the place; they do not show occupancy, so I cannot see your capacity from the outside. What I can see is that people search for Italian food around each mall. The question for you: where would an extra table be most welcome, and where would it not? That answer sits in your data, not mine.
3PThird-party estimate, not analyticsDATANeeds HMA information
03 / 08 · Where guests come from
Where High-Intent Guests Discover Italian Restaurants
Guests choosing where to eat search on Google and Maps, by area, mall and cuisine. Mozza is found by name; non-brand, local searches are a separate, larger pool. The pilot tests whether Mozza can win a share of it.
Brand searches
Guests who search for Mozza by name
Local non-brand searches
Area, mall, cuisine: guests with no name in mind
- Search or Maps
- Branch page
- Menu
- Reserve
- Visit
Search volumes are third-party estimates, not Mozza analytics.
Evidence, confidence and presenter notes
Question this slide answers
Where do new guests come from, and is there a pool Mozza does not yet draw on?
Evidence
- 3PNon-brand and mall-level volumes exceed brand volumes; one mall term sits in the 1K–10K monthly range (Keyword Planner ranges).Confidence: Medium.
- OBSOn generic “Italian restaurant Bangkok” searches other restaurants appear in the top results (one snapshot, Oct 2026; positions vary by device, location and time).Confidence: Medium.
- OBSThe official website came first for “mozza by cocotte” in our test of 2026-11-05 (one test, one device).Confidence: Medium.
Presenter notes
People who already know Mozza find Mozza; that is a strength. The other pool is guests who do not have a name in mind: they search for a mall, a district, or 'Italian near me', and they choose from the first few results. Volumes here are estimates from a keyword tool, not your analytics, and rankings move by device and time. I am not claiming that guests are being lost. I am suggesting that this pool is worth testing under controlled conditions.
3PThird-party estimate, not analyticsOBSOur own test (date and device stated)
04 / 08 · What we would do
Three Levers for Measurable Growth
Local discovery: make each branch easy to find and choose. Booking conversion: make reserving simple for every guest. Targeted acquisition: reach nearby high-intent guests in hours with capacity. Each lever is tested, measured, and stopped if it does not pay.
1
Discover
More branch-level discovery
2
Convert
More completed bookings
3
Acquire
More covers in the right hours
Measured by branch
Evidence, confidence and presenter notes
Question this slide answers
What would we actually do, in business terms?
Evidence
- HYPThe three levers come from a ranked judgement of opportunities, not from a measurement.Confidence: Medium.
- HYPThe size of each lever's effect is unknown until the pilot.
- DATAImplementation detail (profiles, branch pages, tracking, campaigns) is in the appendix.
Presenter notes
Three levers, in plain terms. Discovery: when a guest searches near a branch, the right branch information is easy to find. Conversion: reserving is clear and quick, including for international guests. Acquisition: a small, capped amount of media aimed at nearby guests in hours when you have seats. I cannot tell you today how much each lever is worth. That is the point of a pilot. Technical detail is in the appendix if your team wants it.
HYPHypothesis to testDATANeeds HMA information
05 / 08 · Facts and open questions
What We Observed — and What We Need to Validate
Observed: the official site came first on our brand search test; public details sometimes differ between sources. Estimated: traffic tools disagree widely. To validate with HMA: occupancy by hour, reservation sources, average check, margins. Nothing here assumes a problem.
OBSObserved (our tests)
- Official site first on the tested brand search (one test, one device)
- Some public details differ between sources
3PEstimated (third-party tools)
- Traffic tools disagree widely on Mozza's monthly visits
- Estimates are not Mozza analytics
DATATo validate with HMA data
- Occupancy by hour
- Reservation sources
- Average check and margins
Evidence, confidence and presenter notes
Question this slide answers
How much of this is fact, and how much is still a question?
Evidence
- OBSOfficial site first on the tested brand query: one test, one device.Confidence: Medium.
- OBSSome public details differ between sources: Oct 2026 review, being re-verified before the meeting.Confidence: Medium.
- 3PTwo third-party tools estimate Mozza's monthly organic visits roughly eight times apart; neither is analytics, so we do not use either as a traffic figure.Confidence: Low.
- DATAOccupancy, reservation sources and margins can only come from HMA.
Presenter notes
This slide separates three things. What we saw ourselves, in a few tests on one device. What third-party tools estimate; they disagree with each other, which tells us not to rely on them for decisions. And what only you can tell us. I am deliberately not listing problems, because I do not know that any exist. Your numbers will tell us whether there is anything worth fixing. If you recognise a figure here as wrong, please say so; I would rather be corrected than convincing.
OBSOur own test (date and device stated)3PThird-party estimate, not analyticsDATANeeds HMA information
06 / 08 · The proposal
A Controlled 90-Day Pilot
One branch, 90 days, compared with a baseline. Weeks 1–4 prepare; weeks 5–13 test; day 90 is a decision point: go, adjust or stop. Branch, hours and budget are set with HMA, and depend on capacity and measurement.
Agree and baseline (weeks 1–2)
Prepare (weeks 1–4)
Test (weeks 5–13)
Week
- Go
- Adjust
- Stop
Conditional on operational capacity and measurement feasibility.
Evidence, confidence and presenter notes
Question this slide answers
What exactly would we do, how long would it take, and how can HMA exit?
Evidence
- HYPPilot design: a test branch compared with its own baseline, plus a comparison branch where one exists. The branch is not chosen.Confidence: Medium.
- DATAFeasibility depends on HMA's capacity data and on what reservation reporting can show.
Presenter notes
Small and reversible. One branch, ninety days, and a comparison against what the branch was doing before. The first month is preparation with no media spend. At day ninety we decide together: go, adjust, or stop. I would only start if two conditions are true: the branch has seats to fill in the hours we target, and we can measure reservations well enough to judge the result. If either is false, I would tell you and not start.
HYPHypothesis to testDATANeeds HMA information
07 / 08 · The measure
How We Will Judge Success
Success means incremental contribution after media, commissions and relevant operating costs, not clicks or rankings. Results are compared with a baseline, with thresholds agreed in advance. If the data cannot show a clear benefit, we say so and stop.
- Incremental revenue
- −Media
- −Commissions
- −Relevant operating costs
- =Incremental contribution
Supporting metrics, in order
- Interactions
- Booking clicks
- Confirmed reservations
- Seated covers
Evidence, confidence and presenter notes
Question this slide answers
What number decides whether this was worth it?
Evidence
- HYPMethod: incremental, capacity-capped, net of costs. No outcome is promised.Confidence: High.
- DATAWhether the pilot can show measurable incremental revenue depends on the pilot design and on data quality.
Presenter notes
I measure incremental contribution after costs: media, any booking-platform commissions, and the operating costs that extra covers really bring. Not clicks, not rankings. Incremental means what would not have happened anyway, so we compare with a baseline and, where possible, another branch. Thresholds are agreed before we start, so nobody is tempted to move the goalposts. I do not promise a result. If the data cannot show a clear benefit, I will say so, and we stop with no hard feelings.
HYPHypothesis to testDATANeeds HMA information
08 / 08 · The ask
The Next Step: 20-Minute Discovery Meeting
Twenty minutes to hear your priorities, which branch and hours matter most, and what information already exists. Then we decide together whether a pilot is worth scoping. We ask for no data access before that conversation. Contact: Wadhah Belhassen.
- Discovery meeting
- Scope (only if worthwhile)
- 90-day pilot
Evidence, confidence and presenter notes
Question this slide answers
What is the smallest decision we are asking for today?
Evidence
- PUBContact details are the ones already published on the Strategy page.
Presenter notes
My ask is small: twenty minutes, whenever suits you or your team. I would like to hear which branches and hours matter to you, what you already track, and where you would not want me to go. I am not asking for any access or data before that. If it makes sense afterwards, I will scope a pilot with you. If it does not, I will have learned something, and so will you. Thank you.
PUBPublic listing or page (dated)
Appendix
Supporting detail, shared after the discovery meeting. Technical and measurement detail lives here, not on the slides.
A. Evidence register
Every statement used on the slides, with its label, source and confidence.
| Statement used | Label | Source / date | Confidence | Slide |
|---|---|---|---|---|
| Official site first for “mozza by cocotte” (Bangkok test) | Our own test, one device | 2026-11-05 | Medium | 3, 5 |
| Brand search ≈ 2.4K a month | Third-party estimate | Mangools, Oct 2026 | Medium | 1 |
| Ratings 4.5–4.9, ≈ 6.0K reviews, four branches | Public listings | Oct 2026 | Medium | 1 |
| HMA Privilege programme | Public page | Current | High | 1 |
| Non-brand and mall-level search demand (ranges) | Third-party estimate | Keyword Planner, Oct 2025–Sep 2026 | Medium | 3 |
| Other restaurants in top results on generic Italian searches | Our own snapshot | Oct 2026 | Medium | 3 |
| Popular Times (Central Park): relative peaks | Third-party estimate, not occupancy | Oct 2026 | Low as occupancy evidence | 2 (motivation only) |
| Two tools estimate monthly organic visits ≈ 8× apart (Semrush ≈ 2.8K, Ahrefs ≈ 354–380) | Third-party estimates | Nov / Oct 2026 | Low as a traffic figure | 5 |
| Some public details differ between sources | Our own review | Oct 2026 (being re-verified) | Medium | 5 |
| Complete branch list | Needs HMA information | — | Open | 2 |
| Occupancy, average check, margin, reservation sources | Needs HMA information | — | Open | 2, 5, 7 |
B. Measurement methodology
Implementation detail, only relevant once HMA agrees to scope a pilot.
- KPI ladder (never blended): Maps / Google Business Profile interactions → booking clicks → confirmed reservations (provider-reported) → seated covers and revenue (point of sale) → modelled store visits (supportive only, if the account is eligible).
- Incrementality: a pre-period baseline of 4–8 weeks; difference-in-differences against the branch's own baseline and, where one exists, a comparison branch; optionally a within-branch comparison of targeted vs untargeted slots; a brand-ad on/off holdout only if brand ads are ever tested.
- Tracking implementation (only after HMA agrees to scope): GA4 key events (reservation start/click, directions, call, WhatsApp, LINE); GTM container review; UTM on outbound booking links; Google Business Profile performance export; provider reservation exports by source and branch; covers and revenue by hour.
- Technical SEO and local checks (supporting, not primary): Google Business Profile completeness per branch, branch landing pages, Restaurant structured data, consistent name-address-phone details, readable HTML menus, language versions.
- Formula: incremental confirmed reservations × show rate × party size = incremental covers (capped by available seats in the targeted slots); × spend per cover × contribution margin = incremental contribution before costs; net incremental contribution = that − media − provider commissions − relevant incremental operating costs.
- Attribution limits: no exact branch or source attribution is promised until the booking providers' source fields are known.
- Example values (spend per guest, party size) are never shown as Mozza data.
C. Required HMA data
Requested at or after the discovery meeting. Nothing is requested before it. Aggregated counts only; no guest personal data.
- Tier 1 (to scope a pilot): (1) branches in scope; (2) average check and party size per branch; (3) margin definition and any commissions; (4) covers or occupancy by hour, weekday vs weekend, 8–12 weeks; (5) reservations by source and no-show rate; (6) current marketing spend by channel and branch; (7) which analytics and booking reports exist, and who owns them.
- Tier 2 (once scope is agreed): read-only access to Search Console, GA4 / GTM and Google Business Profile insights; reservation-platform exports.
D. Risks and assumptions
| # | Risk / assumption | Mitigation |
|---|---|---|
| 1 | Branches may be near capacity in the targeted hours (no room for incremental covers) | Choose branch and hours from HMA's own data; do not start if capacity is not there |
| 2 | Reservation reporting may not show source or branch | Use baseline plus a comparison branch; judge on confirmed reservations and covers; state the limit |
| 3 | Incrementality may be low (guests would have booked anyway) | Judge on incremental, not attributed, results; holdout where appropriate |
| 4 | Third-party tools can mislead (traffic estimates ≈ 8× apart; competitor lists are algorithmic) | Not used for decisions; replaced by HMA's first-party data |
| 5 | Small sample over 90 days | Pre-agreed minimum event counts; “Adjust” extends 30 days |
| 6 | Seasonality or events distort the read | Compare with baseline and a comparison branch; record events |
| 7 | Platform or provider constraints (booking flow, content approvals) | Agree scope with HMA's process; no platform migration in the pilot |
| 8 | Our own checks are single-device tests | Repeat and archive before any claim |
| A | Assumes HMA wants to explore additional covers on weekdays | Confirm in the meeting |
E. Pilot decision criteria
Form only. Thresholds are set with HMA in weeks 1–2.
| Gate | Rule |
|---|---|
| Start | Capacity exists in the targeted hours AND at least basic reservation counts by branch are available AND HMA approves branch, hours and budget cap. Otherwise: do not start. |
| Go (scale to more branches) | Net incremental contribution after media, commissions and relevant operating costs ≥ the threshold agreed in weeks 1–2, in slots with spare capacity, with data quality passed. |
| Adjust (extend up to 30 days) | Positive early signals but too few events or incomplete data; fixes agreed. |
| Stop | Net incremental contribution below threshold after the agreed learning period, or no measurable difference from baseline, or data cannot support a conclusion; spend stops at once. |
The stop rule and budget cap are written down before launch. Platform migration is not part of the pilot.
Independent growth proposal by Wadhah Belhassen. Not an official Mozza / HMA document. Concept demo; third-party figures are estimates, not Mozza analytics.