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Retail verified Verified Outcome TRL 9

AI-Driven Personalization Engine (Deep Brew)

domain Client: The world's largest coffeehouse chain handshake Provider: Internal / Microsoft Azure schedule Deploy: Q1 2022 (Maturity)
92 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

To drive frequency and ticket size, the retailer built an AI engine ('Deep Brew') that personalizes the mobile app experience for millions of users. It uses contextual data (weather, time of day, store inventory, user history) to recommend specific products and customize offers in real-time.

rate_review Analyst Verdict

"A leading example of AI-driven loyalty. By shifting from generic marketing to 1:1 personalization, the entity successfully increased the 'habit' factor. The tight integration with inventory (not recommending out-of-stock items) is a critical operational bridge often missed by peers."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

With millions of daily transactions, generic marketing was losing effectiveness. Customers were overwhelmed by menu choices. The brand needed a way to increase spend-per-visit (upsell) without slowing down the ordering process or annoying users with irrelevant offers.

psychology The Solution

Deep Brew runs on Azure RL (Reinforcement Learning). It treats every app session as a unique context. If it's a hot day, it suggests cold brew. If the store is out of oat milk, it suppresses oat milk lattes. It optimizes for the 'next best action' to maximize lifetime value.

settings_suggest Technical & Deployment Specs

Integrations
Mobile App, POS, Inventory
Deployment Model
Public Cloud
Data Classification
Consumer Behavioral
Estimated TCO / ROI
Medium
POC Summary (2017-01-01 to 2019-01-01)

"Internal R&D project expanded to global platform."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Privacy Fatigue Medium Transparent use of data; value exchange (free stars) for data access.
Model Bias Low Focus on product preference rather than demographic profiling.

trending_up Impact Trajectory

Audited value realization curve

Personalization of >30 million active app users Verified Outcome
Primary KPIReduction in menu search time
Audit CycleIncrease in average ticket size via upsell

policy Compliance & Gov

  • Standards: CCPA
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Consumer Behavioral

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for The world's largest coffeehouse chain without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-814
Evidence: Direct SQL Logs
Public
public

2. Verified Asset

Outcome: Verified
Ref ID: #COI-814

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