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Case-Study-Actowiz-Solutions-–-Price-Clustering-&-Discount-Mapping-on-GoPuff-Using-AI-Algorithms-(UK)

Introduction

In the UK’s ultra-fast delivery space, GoPuff has emerged as a major player, offering essentials, groceries, and convenience items within minutes. But behind GoPuff’s success lies a complex and dynamic pricing engine—with frequent price changes, regional discounting, and hyper-targeted offers.

Actowiz Solutions partnered with top FMCG brands to decode GoPuff’s pricing logic through AI-powered price clustering, discount mapping, and pattern recognition—uncovering the hidden algorithms shaping retail decisions in the UK’s quick commerce space.

Client Objectives

  • Track SKU pricing across UK cities like London, Manchester, and Birmingham
  • Identify price clusters by product category and region
  • Map discount patterns over time (flash sales, weekend deals, etc.)
  • Benchmark GoPuff's pricing against local and retail market standards
  • Provide actionable insights to plan promotions, pricing strategies, and product launches

Key Challenges

The-Client
  • Price changes occurred frequently, sometimes hourly, making manual tracking impractical
  • Regional pricing created city-level differences across SKUs
  • Flash discounts lacked warning or duration details
  • SKU naming conventions varied, requiring normalization for clustering
  • Brands lacked tools to group similar pricing patterns

Actowiz’s Data Collection & AI Modeling Approach

The-Client
GoPuff Scraping Infrastructure

Actowiz built real-time web scrapers to extract:

  • SKU Name & Brand
  • Original Price & Offer Price
  • Discount %
  • Category & Subcategory
  • Product ID
  • Region (Postcode-based or city-tagged)
  • Timestamp of each scrape

Sample Data Structure (London Zone)

Timestamp Product Name Price Discount Category Region
2025-06-15 12pm Red Bull 250ml £1.65 15% Beverages London SE1
2025-06-15 12pm Pepsi Max 500ml £1.10 0% Beverages London E1
2025-06-15 12pm Coca-Cola 330ml x4 £2.99 25% Beverages London SW6

AI Models Used

  • K-Means Clustering – Grouped SKUs into clusters based on price similarity by region
  • DBSCAN – Identified irregular pricing behavior and outliers
  • Time-Series Discount Detection – Tracked weekly/monthly price dip patterns
  • Discount Elasticity Modeling – Analyzed customer interest spikes vs. discount levels
  • Price Anomaly Detection – Alerted for steep, unscheduled price changes

Pricing Clusters Identified

Top Pricing Clusters in London:
Cluster Avg Price Range Categories Dominant Zone Sample
A £0.99–£1.50 Snacks, Soft Drinks SE1, NW1
B £2.00–£3.50 Household, Hygiene E1, SW6
C £5.00+ Alcohol, Baby Care, FMCG W1, EC3

Insight: Snacks and beverages maintained stable pricing, while clusters C (premium SKUs) showed highest volatility.

Discount Pattern Mapping

Weekly Discount Patterns (Birmingham Example):
  • Monday–Tuesday: Minimal offers, mostly full-price
  • Wednesday–Thursday: Category-specific discounts (e.g., personal care)
  • Friday–Sunday: Surge in promotions across beverages, frozen foods, essentials

Top Flash Discount Trends:

SKU Discount Spike Days Peak Discount (%) Duration
Red Bull 4-Pack Friday, Sunday 25% 6–12 hours
Doritos Nacho 150g Saturday 18% 4 hours
Andrex Tissue Sunday 30% 8 hours

Actowiz Dashboard Features

Feature Description
Real-Time Price Cluster Heatmap Visual map of average pricing tiers across regions
Discount Calendar Visualizes day-wise discount activity across product categories
Elasticity Curve Generator Shows impact of discount % on price engagement rates
Flash Sale Detection Alerts for sudden discounts in targeted categories
SKU Benchmarking Tool Compare GoPuff SKU vs. Sainsbury’s, Tesco, Amazon UK

Geographic Zones Covered

The-Client
  • London (SE1, SW6, E1, W1, NW1, EC3)
  • Manchester (M1–M20 postal clusters)
  • Birmingham (B1–B33 regions)
  • Bristol, Liverpool, Leeds
  • 1200+ regional SKUs monitored daily

Impact for FMCG Clients

Results After 45 Days:
KPI Before Actowiz After Actowiz
SKU Pricing Visibility Manual checks 24/7 automated
Discount Planning Accuracy ~45% 88%
Campaign ROI (targeted by cluster) - +29% uplift
Flash Discount Awareness Time >12 hrs late Real-time alert
Price Outlier Response Time 2–3 days <1 hour

Use Case Examples

  • Beverage Brand: Adjusted weekend promotions in sync with GoPuff's flash deals in Manchester
  • Household Product Supplier: Used Actowiz price clustering to launch region-specific bundle offers in London
  • Snack Brand: Detected inconsistent pricing across GoPuff’s zones and aligned with competitor positioning

Client Testimonial

“Actowiz gave us pricing clarity on GoPuff that we never had. Their AI models help us plan offers smartly—by location, by category, and even by day.”

– Senior Pricing Manager, UK-based FMCG Brand

Next Steps

  • Extend model to include Amazon Fresh UK and Ocado Express
  • Predict next discount cycle triggers using historical clustering
  • Add basket-level analytics by scraping cart behavior
  • Offer Slack/Teams alerts for category-specific price shifts

Conclusion

GoPuff’s pricing engine is dynamic, regional, and strategically unpredictable—but not for brands using Actowiz Solutions. With AI clustering, discount mapping, and near-a real-time tracking, FMCG companies can finally respond with data—not guesswork.

This case proves how AI + scraping delivers actionable competitive intelligence in today’s digital grocery battlefield—across the UK, one postcode at a time.