Restaurant M&A Data Scraping Case Study — Evaluating a Saudi F&B Group's Acquisition Readiness
How a GCC PE firm used independent restaurant M&A data scraping across 3 platforms to verify a 28-outlet Saudi F&B group and underwrite a $62M acquisition with confidence.
Client overview
Who the client is
The client is a GCC-focused private equity firm evaluating a 28-outlet Saudi F&B group for acquisition. Before underwriting a $62M deal, the firm needed reliable, independently-built restaurant M&A data intelligence covering the target's outlet-level performance — not just the seller's representations. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Verify the target group's 28-outlet footprint independently
- Track outlet-level performance signals across 36 months
- Identify outlets with strong, weak, or declining performance
- Quantify the group's competitive positioning in each market
- Replace seller representations with independent merchant-level data
- Underwrite the acquisition with diligence-grade evidence
The challenge
Seller representations were strong; independent verification was missing
The Saudi F&B group's investment memorandum painted a confident picture: 28 outlets, strong unit economics, healthy growth trajectory. But sellers' representations are exactly that — representations. The PE firm needed an independently-built view of every outlet's performance signals: review velocity, menu evolution, pricing positioning, and competitive context. Without that, the $62M underwriting was effectively a faith-based decision.
The solution
A 28-outlet Saudi F&B diligence panel
FoodDataScrape built a continuous Hungerstation data scraping pipeline (plus Jahez and Mrsool coverage) focused on the target group's 28 outlets across Saudi Arabia, with 36-month historical backfill and per-outlet performance analytics. The build went live in four weeks.
Verify outlet footprint
We confirmed all 28 outlets existed, were operating, and matched the seller's representation.
Reconstruct 36-month history
Historical performance signals (review velocity, menu changes, pricing, ratings) were backfilled for every outlet.
Competitive context
Each outlet's local competitive landscape was mapped to assess defensibility and growth headroom.
The AI layer
How does AI-assisted M&A diligence work?
AI-assisted restaurant M&A diligence combines food delivery data scraping with classification models that score per-outlet performance signals — producing independent, defensible outlet-level evidence on a target group's acquisition readiness.
On top of the raw feed, an AI diligence-scoring layer turned platform data into restaurant M&A intelligence: it scored each outlet's performance trajectory, identified outlets with declining signals (potential discount-able acquisitions), surfaced outlets with strong unit economics, and produced a diligence-ready report. The firm received this as a one-time diligence package plus optional post-close monitoring.
- Verified all 28 outlets independently — no phantom locations
- Identified 4 outlets with declining review velocity over 12 months
- Surfaced 19 outlets with strong defensible competitive positions
- Flagged 5 outlets with menu-thrash patterns suggesting operational instability
Data captured
What data we captured
The pipeline captured a full restaurant M&A data intelligence view:
| source | method | fields |
|---|---|---|
| Hungerstation | Hungerstation data scraping | outlets · menu · price · reviews |
| Jahez | Jahez data extraction | outlets · ratings · velocity |
| Mrsool | Mrsool data scraping | outlets · operations · status |
BEFORE VS AFTER
Before vs after comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Outlet verification | Seller representation only | All 28 independently verified |
| Per-outlet performance | Aggregate metrics | Outlet-level diligence scoring |
| History depth | 12-month seller data | 36-month independent backfill |
| Competitive context | Generic market overview | Local competitive density per outlet |
| Diligence quality | Seller-dependent | Independent merchant-level evidence |
| Investment confidence | Faith-based | Data-anchored underwriting |
ROI impact
From Assumption to Measurable ROI
Closed with confidence based on independent merchant-level diligence.
Every outlet in the target's representation independently confirmed.
Three full years of per-outlet performance signals backfilled.
Pipeline delivered fast enough to inform a live deal timeline.
The diligence pipeline turned a faith-based $62M underwriting into a defensible, evidence-anchored acquisition — and was followed by a post-close monitoring engagement to track the integrated group's performance.
Client testimonial
In the client's words
"Seller representations are the seller's view. We needed our own view, independent and merchant-level. The Hungerstation data plus Jahez and Mrsool gave us per-outlet visibility we could defend to the investment committee."
— Investment Director, GCC PE firm (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in food delivery data scraping across the GCC
- Hungerstation, Jahez & Mrsool coverage out of the box
- AI-assisted per-outlet diligence scoring
- 36-month historical backfill across the target group
- Compliance-aware sourcing and dedicated KSA analyst support
- Live in four weeks with a free proof-of-concept first
Questions
Frequently asked questions
It combines food delivery data scraping across all major regional platforms with AI diligence scoring that produces independent, defensible outlet-level evidence on a target group's acquisition readiness.
Hungerstation data scraping, Jahez data extraction, and Mrsool — covering the dominant Saudi food delivery ecosystem for comprehensive merchant visibility.
Cross-platform matching confirmed each of the 28 outlets in the seller's representation existed, was operating, and had the operational signals consistent with the seller's narrative — no phantom locations.
A defensible $62M acquisition underwriting, independent verification of all 28 outlets, and a continuing post-close monitoring engagement tracking the integrated group's performance.
Yes — the same M&A diligence pipeline can be deployed for restaurant acquisitions in any market with delivery-platform visibility — UAE, India, SEA, Europe, US, and others.
Yes — we use compliance-aware sourcing across all GCC markets and delivery platforms.
Need restaurant M&A diligence data for your deal?
Tell us your target group and markets. We'll scope a diligence-grade tracking pipeline and show sample output in a short demo.
