Building a $100K/Year Multi-Platform Data Pipeline — How One Enterprise Tracks 37+ US Retail Platforms Simultaneously

 

Industry

Retail Intelligence

Region

United States

Scale

37+ platforms, 200K+ SKUs

Engagement

Multi-Phase Bundled Engagement

Executive Summary

A US-based retail intelligence firm needed to track product data across 37+ commerce platforms simultaneously to power their B2B analytics product. After failed attempts to build in-house and disappointing experiences with three other vendors, they engaged Actowiz for a phased multi-platform data engagement that grew from a $54K initial project to a $105K Phase 2, with Year 2 already locked. This case study documents how a complex multi-platform engagement gets architected, priced, and delivered.

The Customer

A B2B analytics provider serving CPG brands, retail strategists, and investment researchers. Their platform aggregates pricing, product, and merchandising data across the US retail ecosystem. Founded 2019, ~30 employees, growing 60%+ YoY. Their customers include 4 of the top-10 CPG companies in the US.

The Challenge

Problem 1: Scope That Broke Conventional Vendors

Most data vendors focus on 3-5 marquee platforms (Amazon, Walmart, Target). The customer needed 37+ platforms covering: mass merchants, grocery chains, drugstores, club stores, specialty retail, marketplaces, and emerging digital natives. No vendor they evaluated could handle this breadth.

Problem 2: Schema Heterogeneity

37 platforms = 37 different page structures, anti-bot layers, product taxonomies. Without a unified schema, downstream analytics customers couldn't do anything useful with the data.

Problem 3: Failed In-House Build

The customer had spent 14 months and ~$1.4M trying to build this in-house. Their engineering team got 12 platforms working but burned out maintaining anti-bot evasion. Three platforms went dark for 6+ weeks at a time. Investors flagged the data infrastructure as concentration risk.

Problem 4: Quality Bar from Their Customers

Their CPG customers (Procter & Gamble, Unilever, Mondelez tier) had near-zero tolerance for data gaps. Daily SLA, 99.5% completeness, structured normalization. Vendor engagements that "mostly worked" weren't acceptable.

Client Feedback

"We talked to Bright Data, Oxylabs, and two other agencies. They quoted us $300K/year just for proxy infrastructure — and we'd still have to build the parsers ourselves. Actowiz quoted a fully-managed pipeline for 30% of that. We were skeptical until we saw the Phase 1 deliverable."

— VP of Engineering

The Solution — A Phased Engagement

Phase 1: 14 Foundation Platforms ($54K)

Actowiz prioritized the 14 highest-value platforms for the customer's product launch:

• Mass merchants: Walmart, Target, Costco, Sam's Club, BJ's

• Grocery: Kroger, Albertsons, Whole Foods, Publix, HEB, Wegmans

• Drug: CVS, Walgreens, Rite Aid

Engagement: 90 days from kickoff to production. Daily refresh. 50,000 SKU watchlist. Custom JSON delivery via S3.

Phase 2: 23 Additional Platforms ($105K)

Following Phase 1 success, the customer expanded scope:

• Marketplaces: Amazon, eBay, Etsy, Mercari, Wayfair

• Specialty: Best Buy, Home Depot, Lowe's, Bed Bath & Beyond, Macy's, Nordstrom, JCPenney, Kohl's

• Digital natives: Boxed, Thrive Market, Imperfect Foods, Misfits Market, Hungryroot

• Pet & specialty: Chewy, Petco, PetSmart

• Office & misc: Staples, Office Depot, Tractor Supply

• 150,000 additional SKUs added to watchlist. Same daily-refresh SLA.

Year 2: Continuous Maintenance + Expansion (Locked)

Year 2 engagement covers ongoing maintenance of all 37 platforms, plus new platform additions as the customer's product expands. Estimated value: ₹85L+/year ($100K+).

Architecture Highlights

1. Shared Proxy Infrastructure:

Actowiz amortizes proxy infrastructure across hundreds of customers. The customer's effective proxy cost is roughly 10% of what they'd pay sourcing directly from Bright Data or Oxylabs.

2. Unified Product Schema:

Despite scraping 37 different page structures, output JSON follows a single consistent schema:

• product_id (Actowiz canonical)

• platform_sku (platform-specific)

• upc / gtin (where available)

• title, brand, manufacturer (normalized)

• price, list_price, unit_price (normalized to USD per ounce)

• availability (in_stock / out_of_stock / limited)

• category_path (mapped to GS1 taxonomy)

• promo_tags, image_urls, last_seen_at

3. SLA-Driven Delivery

snapshots delivered by 6 AM ET. 99.5% completeness commitment. Automated alerting when individual platforms fall below threshold — flagged within 2 hours.

4. Versioned Data

Historical snapshots preserved for 18 months — letting the customer's product offer trend analysis to their CPG customers without rebuilding history.

Results — Year 1

37+

Platforms in production

200K+

SKUs tracked daily

99.7%

Daily SLA achievement

$159K

First-year project value

Customer Product Launch

The customer launched their flagship retail intelligence product on schedule, powered entirely by Actowiz data. Product onboarded 12 enterprise customers within first quarter — including 2 of the world's top-5 CPG brands.

Operational Efficiency

The customer's engineering team — which had been 60% allocated to data infrastructure — reallocated to product features. Their head of engineering estimates 4 engineers redeployed to higher-value work, worth ~$800K/year in productivity.

Investor Confidence

In a Series B raise the year following the Actowiz engagement, the customer raised at a 2.4x valuation step-up. Investors specifically called out "de-risked data infrastructure" as a positive signal.

Client Feedback

"Building a 37-platform pipeline in-house would have cost us a Series A. With Actowiz, we offloaded that complexity entirely and focused on what makes our product unique — the analytics layer, not the plumbing. Best ROI decision we've made in 5 years."

— CEO and Co-Founder

• Phase 1

• Scope: 14 foundation platforms, 50K SKUs

• Investment: $54,000

• Duration: 90 days

• Phase 2

• Scope: 23 additional platforms, 150K+ additional SKUs

• Investment: $105,000

• Duration: 120 days

• Year 2 Maintenance

• Scope: All 37 platforms with expansion options

• Investment: $100,000+ (locked)

• Duration: Ongoing

• Total Year 1 + 2

• Scope: 37+ platforms, 200K+ SKUs

• Investment: $259,000+

• Duration: 24 months

Why Multi-Platform Bundling Works

• Schema normalization done once benefits all 37 platforms

• Shared anti-bot R&D across customer base reduces per-platform cost

• Phased engagement de-risks customer commitment — pay-as-you-grow

• Single vendor relationship vs 37 separate vendors = 80% reduction in procurement overhead