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Retail media isn't an ad problem. It's a data problem

Your network was built to report on the past - not to drive the next decision. Transactions, web behaviour, and loyalty data sit in separate systems, connected too late to matter. KPI Partners fixes the data foundation first - so you can prove value, personalise in the moment, and run AI you can trust. 

First-Party Data Clean Rooms Agentic AI New-to-Brand Audience Activation Identity Resolution Brand Measurement Closed-Loop Attribution Incrementality Real-Time Personalization
Everyone sees the revenue number. Nobody can prove what drove it. 
 
Most networks still can't answer the one question that matters to a brand: did this placement win a genuinely new
customer, or cannibalize someone who would have bought anyway? That's not a media question. It's a data question. 
 
 
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Five gaps quietly costing

networks right now 

 

The organisations winning in retail media aren't the ones with the flashiest ad tech. They're the ones who quietly fixed their data plumbing first - before agents, before AI, before clean rooms. 

 

  1. Signals Trapped in Separate Systems

    Transactions in one platform, on-site behavior in another, loyalty data in a file someone emails on Monday. The first-party advantage exists on paper, but nobody is connecting it in time.

    | No model can run on infrastructure your own teams can't change.

  2. Incrementality You Can't Prove

    A brand spends big on sponsored placements and asks at review: did this grow sales, or reach people already buying us? The data exists, but reconciling it takes weeks, by which point the budget has moved elsewhere.

    | Without clean incrementality, you defend spend instead of proving value.

  3. Personalization Stuck at "Customers Also Bought"

    Every retailer claims personalization; few go beyond look-alike basics. Knowing a shopper buys premium oil on weekends, browses protein snacks on weekday evenings, and hasn't reordered in 18 days means nothing if the datasets aren't connected for a live decision.

    | Real personalization is a real-time decision, not a weekly batch.

  4. Batch Thinking in a Real-Time Game

    Offers and audiences decided overnight arrive a day late and a context short. Agentic AI purpose-built agents on platforms like Google's Vertex AI, can close the loop continuously, but only as a live process, not a batch job.

    | The decision has to happen now, or it isn't worth making.

  5. Fast AI on a Fragmented Foundation

    Clean rooms, agents, and models are only as good as the data feeding them. Run them on a fragmented foundation and you get confident, wrong decisions - faster.

    | Before agents, before AI, before clean rooms: can you actually trust your data?

We fix the data plumbing first - then the rest works 
 
KPI Partners is a data, analytics, and AI consultancy with deep cloud alliances. For retail media, we sequence the work so each layer compounds: connect the data, resolve identity, decide in real time, and govern it all, through best-of-breed data products and accelerators. 
 
 

Bring transactions, on-site behavior, and loyalty into one governed, cloud-native foundation, so the signals that prove value finally live in one analyzable place.

  • POS, e-commerce, loyalty & CDP integration

  • Cloud lakehouse on BigQuery, Databricks, Snowflake, or Fabric

  • Pipeline automation and real-time ingestion

  • Legacy EDW and ETL modernisation

  • Governed, reusable data products

Use cases a connected foundation unlocks 

Each one depends on the same thing - transactions, behavior, and loyalty connected

in time to drive a decision. Fix that, and these stop being aspirations. 

 

 

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Proving Incrementality to CPG Brands

A brand spends ₹5Cr on placements and asks whether it grew sales or reached existing buyers. With the right foundation, test-vs-control and new-to-brand analysis runs automatically — not in weeks of manual work. 

 

requires → unified, governed data

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Personalization That Actually Works

Know that a shopper who buys premium oil on weekends, browses protein snacks on weekday evenings, and hasn't reordered in 18 days is the right person to see a specific placement right now — not next week. 

 

requires → consolidated claims data

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Closed-Loop Attribution

Tie exposure to outcome across on-site and offline purchase, cleanly and repeatably, so every placement's true contribution is visible, the closed loop the pitch deck promised.

 

 

requires → cross-line data join

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Self-Serve Brand Measurement

Give brands a trusted, always-current view of their campaign performance and incrementality, turning quarterly defence meetings into a standing strategic relationship.

 

requires → modern BI & semantics

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Audience Activation & Clean Rooms

Collaborate with brands on first-party data in a privacy-safe clean room,  building and activating audiences without exposing raw customer data. 

 

requires → governed semantic layer

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Real-Time Campaign Optimization

Let agentic AI continuously reallocate placement and audience based on live signals, closing the loop as a process rather than a Monday-morning report. 

 

requires → lineage & controls

From fragmented signals to provable value.

The following are illustrative scenarios that reflect common retail media engagements.

They are masked and do not depict specific named clients. 

 

Best-of-breed data products and accelerators for retail media

A suite of migration and analytics accelerators for all your modernization needs - connecting retail signals onto the cloud platforms that real-time decisioning and agentic AI run on, and pre-building the measurement and personalization analytics on top.

 

 

Retail source systems, data warehouses, and ETL - connected and modernized, with logic and lineage preserved.

Source - migrate from

POS / Transaction Systems E-commerce (Adobe / Shopify / SAP CAR) Loyalty Platforms Teradata SSIS Oracle Retail Clickstream / Web Analytics CDP (Segment / Tealium) SQL Server Legacy EDW Informatica Azure Data Factory

Target - migrate to

Google BigQuery Vertex AI Databricks Snowflake Microsoft Fabric dbt

Legacy reporting modernized, plus the activation and clean-room targets retail media depends on.

Source - migrate from

OBIEE / OAC IBM Cognos MicroStrategy SSRS Spreadsheet & Manual Reporting Tableau

Target - migrate to

Power BI Snowflake Data Clean Rooms Looker Google Ads Data Hub LiveRamp Copilots / Gemini

Case Studies & Insights

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Williams-Sonoma - KPI Retail Analytics on Azure and Tableau
Read Case Study
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Chanel - Leading Fashion Retailer Improves Revenue Forecasting Using Cognos
Read Case Study
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Pharmaceutical Retailer Launches Payroll Fraud Analytics using the Data Products Accelerator
Read Case Study
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How KPI Partners Deployed a Reasoning Agent for Global Retail Intelligence
Read case Study

Before agents, before AI  

can you trust your data?

 

Two fast ways to find out where you stand.

 

Whether you're scaling a retail media network or trying to make personalization real, KPI Partners offers focused, low-risk engagements built for the messy reality of retail data.

 

 

Retail Media Data Readiness Assessment (2 Weeks)
A clear-eyed look at how your transaction, web, and loyalty data connect today, where the attribution gap sits, and a roadmap to a real-time foundation.
 
 
Incrementality Proof of Value
We run a real test-vs-control, new-to-brand analysis on your own data - proving on a single brand or category what an automated foundation would deliver at scale.
 
 
Clean-Room & Identity Foundation Review 
Assess your first-party data, identity resolution, and privacy posture for clean-room collaboration and audience activation.
 
 
Agentic AI Readiness 
Determine whether your data foundation can safely support real-time agents on Vertex AI, before you build on it.
 

Standard Enterprise

AI Engagement Model

 

AI Engagement Model

 

Data Platform Migration for Modern Analytics

 

KPI DataBridge Suite is designed to help enterprises modernize their data and analytics infrastructure across:
 
BI Modernization 🔗
Data Platform Migration 🔗
Data Products 🔗

 

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Explore Real-World
Data Platform Migration Case Studies

 


 

BI Platform Migration

GenAI Accelerators

Databricks Accelerators

4 Infrastructure Optimizer
5 Databricks Unity Catalog Enablement
Description Text is Optional
6 Data Quality Validator for Databricks
Description Text is Optional

Snowflake Accelerators

2 Oracle / SQL Server to Snowflake Migration
3 Snowflake Cost Optimization App
4 Snowflake Security Automation App
5 Snowflake Data Governance App
Description Text is Optional
6 Data Quality Validator for Snowflake
Description Text is Optional
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