/ Retail & Consumer
Retail data at the speed of the shelf
From convenience chains to department stores, we put retail data to work: one customer record, forecasts that hold, and personalization that keeps up in real time. Every build spans the customer journey from acquisition to loyalty, and every build is measured against a revenue or cost baseline.
- 600+ locations in production
- 30% lower data latency
- 85% forecast accuracy
- 23% fewer stockouts
600+ locations, zero downtime
Real-time retail data in production.
Why retail data stalls
The shelf moves faster
than the batch.
POS, ecommerce, and loyalty each hold a piece of the customer. Forecasts run on spreadsheets, and by the time the nightly batch lands, the moment has passed. Retail decisions are real-time decisions, so the data has to be too.
30%Reduction in data latency
A governed Databricks lakehouse across 600+ locations, with real-time inventory visibility and zero downtime through the cutover.
Read the case study
/ What we build for retail
From first visit to loyal customer.
01
Customer data platform
Customer data unified across POS, ecommerce, and loyalty into one governed profile with real-time segmentation. Everything downstream, from offers to forecasts, depends on getting this record right first.
SalesforceSnowflakeBraze
Salesforce
Snowflake
Braze02
Personalization at scale
ML-driven recommendations and offers delivered in real time, measured against holdouts. Where we have run it: 35% engagement lift and 20% higher conversion, against baseline rather than a brochure.
BrazeSalesforce Marketing CloudDatabricks
Databricks03
Demand forecasting
Spreadsheet forecasts replaced with models that read sales, promotions, and seasonality. One program reached 85% forecast accuracy, cut stockouts 23%, and returned $220K+ a year in inventory savings.
DatabricksMLflowPython
MLflow
Python04
Supply chain analytics
Real-time visibility from distribution center to shelf, with predictive alerts that surface disruptions while there is still time to reroute. Works with the planning systems you already run.
KafkaSnowflakeBlue YonderKinaxis
Kafka05
Omnichannel analytics
Online, mobile, and in-store data stitched into one journey, with cross-channel attribution and store performance on the same governed numbers. One customer, one view, whichever door they came in through.
SnowflakedbtPower BI
Power BI06
Pricing optimization
Dynamic pricing and markdown optimization with competitive monitoring built in. Engagements typically land a 3 to 5% margin lift, measured against your current price book.
DatabricksPython
/ Retail systems
We speak your stack.
The systems a retail engagement actually has to integrate with. We connect what you already run instead of proposing a rip and replace.
POS Data Integration
Real-time transaction data
Loyalty Analytics
Customer lifetime value
E-commerce Platforms
Shopify, Magento, custom
Inventory Systems
SAP, Oracle, Manhattan
Marketing Platforms
Salesforce, Adobe, Braze
Supply Chain
Blue Yonder, Kinaxis
/ Results
Retail results, measured at the register.
Leading CPG & F&B leader in the USA
75%Reduction in Analytics Request Volume
How a Global CPG Leader Empowered Brand Managers with Self-Service Intelligence
Read the case study
Fortune 500 Retail Client
87%Reduction in Data Processing Time
Databricks Modernization & AI Enablement for a Leading Convenience Retail Chain
Read the case study
Fortune 500 Retail Client
67%Reduction in Reporting Cycle Time
How ACI Infotech Enabled a Retail Leader to Unlock the Power of Data
Read the case study/ How an engagement runs
Five phases. No mystery.
01
Map
Inventory the customer touchpoints and data sources, then pick the use case that moves a number you care about: stockouts, conversion, margin.
02
Design
Data foundation, identity model, and activation plan. Snowflake or Databricks underneath, your marketing stack on top.
03
Build
Pipelines and the unified customer record shipped in increments. A first governed customer view and a working use case land inside the 8 to 12 weeks we quote.
04
Prove
Lift measured against holdouts and your current baseline before anything is declared a win. Load-tested for peak before the season, not during it.
05
Scale
Roll out across banners, channels, and regions, with 24/7 operations under SLA so the platform holds through the holiday spike.
One customer record runs it all.
Personalization, forecasting, and loyalty all read from the same place: a unified customer record with identity resolution that actually holds. Building and activating that record is our MarTech and CDP practice.
MarTech & CDP/ Why ACI
Why retailers build with us
/ In production
600+ retail locations running on a lakehouse we built, with 30% lower data latency and zero downtime through the cutover.
/ Measured
Every build is measured against a revenue or cost baseline.
Forecast accuracy, engagement lift, and margin are reported, not implied.
/ Scale
Founded 2006.
1,200+ engineers across 11 global delivery hubs. 500+ enterprise projects.
/ Operations
ISO 27001 certified, with 24/7 managed operations under published SLAs.
Peak season is rehearsed, not hoped for.
From the blog
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Read/ Retail FAQ
Retail questions,
answered straight.
What data problems do retailers usually come to you with?
Three, mostly: customer data scattered across POS, ecommerce, and loyalty; demand forecasts that miss; and personalization that cannot keep up in real time. We unify the customer record first, because everything downstream depends on it.
Which platforms do you use for retail data and martech?
Snowflake or Databricks for the data foundation, Salesforce and Braze for the customer and engagement layer. We integrate them so an action in one shows up in the others without waiting on a nightly batch.
How do you handle peak traffic like Black Friday?
Auto-scaling on the pipelines and warehouses, load-tested before the season, with cost guardrails so the bill does not balloon on a quiet Tuesday. We rehearse the spike rather than hope.
Can you actually improve demand forecasting?
Yes, and it usually pays for itself in inventory. We replace spreadsheet forecasts with models that read sales, promotions, and seasonality, and we measure the lift against what you do today before claiming anything.
How long until a retail data platform shows value?
A first governed customer view and a working use case in 8 to 12 weeks. We start with the use case that moves a number you care about, not a year-long platform build.