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Data and AI Solutions for Retail and CPG

Forecast demand precisely, personalize every interaction, optimize price, promotion and inventory, and prepare your catalog and customer data for AI-driven commerce.

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Win Shoppers with Connected Commerce Data

Retailers and consumer packaged goods brands operate on thin margins and fast-changing demand. Shoppers move between stores, apps, marketplaces, social channels and increasingly AI assistants that research and even buy on their behalf. Every interaction produces data, from point-of-sale transactions and loyalty profiles to clickstreams, retail media impressions and supply chain events. The winners use that data to put the right product in the right place at the right price, and to give each shopper a relevant experience.

Hanoi Technology helps retailers and brands build the data and AI capabilities behind those decisions. We unify customer, product and inventory data, build forecasting, pricing and personalization models, support retail media and clean room collaboration, and prepare product content and APIs for agentic commerce. Solutions are designed to run at peak season scale and to be adopted by merchants, planners and store teams, not just data scientists.

The Challenge

The Data Challenges Facing Retailers and Consumer Brands

Channels, data sources and shopper expectations are multiplying faster than legacy systems can adapt.

Volatile, Fragmented Demand

Promotions, weather, social trends and competitor moves shift demand quickly, and sales are split across stores, eCommerce, marketplaces and quick commerce. Forecasts built on aggregated history miss local and channel-level patterns.

Inventory Imbalances

Stores run out of popular items while slow sellers pile up in distribution centers. Without unified, accurate inventory visibility, omnichannel promises such as buy online, pick up in store become unreliable.

Customer Data Spread Across Systems

Loyalty, POS, eCommerce, marketing and service platforms each hold a partial view of the shopper. Privacy regulations and the decline of third-party cookies make first-party data essential, but it is rarely unified.

Product Data Not Ready for AI Commerce

AI shopping assistants, marketplaces and retail search depend on rich, structured product attributes. Inconsistent catalogs, missing attributes and thin descriptions make products harder to find and recommend.

Business Value

What Modern Data and AI Deliver for Retail and CPG

More Accurate Forecasts

Machine learning forecasts by item, location, channel and day incorporate promotions, events and weather. Planners trust the numbers and focus on exceptions.

Optimized Inventory and Fewer Stockouts

Replenishment and allocation models balance availability and working capital. Shoppers find what they want, and markdowns fall.

Relevant, Personalized Experiences

Recommendations, search and offers adapt to each shopper’s behavior and context. Conversion and basket size improve across channels.

Smarter Pricing and Promotions

Elasticity and promotion lift models show which offers drive incremental profit. Trade and promotional spend goes where it works.

New Retail Media Revenue

Audience, measurement and clean room capabilities let retailers monetize first-party data. Brands gain closed-loop measurement of their media spend.

Catalogs Ready for AI Commerce

Enriched, structured product data improves discoverability in site search, marketplaces and AI assistants. Products are represented accurately wherever shoppers look.

Every Decision Is a Data Decision

From what to buy and where to place it, to what price to charge and which offer to show, retail is a chain of decisions repeated millions of times a day. Small improvements compound quickly at that scale. We help retailers and brands improve those decisions systematically, with models that merchants and planners understand and trust.

Our Services

Our Retail and CPG Solutions

Six solution areas covering shopper, product, price and supply chain.

Unified Commerce Data Platform

We consolidate POS, eCommerce, loyalty, inventory, product and supply chain data into a governed cloud platform with consistent customer and product identifiers.

  • Retail data model and pipelines
  • Customer identity resolution
  • Real-time inventory data services

Demand Forecasting and Replenishment

We build item-location-day forecasts that include promotions, events, weather and channel shifts, and connect them to replenishment and allocation.

  • Machine learning forecasting models
  • Replenishment and allocation optimization
  • Forecast accuracy and exception dashboards

Personalization and Customer Analytics

We develop recommendation, search ranking, segmentation and lifetime value models and activate them in web, app, email and store channels.

  • Recommendation and next-best-offer models
  • Customer segmentation and CLV
  • Activation through CDP and marketing tools

Pricing, Promotion and Revenue Growth Management

We model price elasticity, promotion lift and cannibalization, and build tools for merchants and CPG revenue management teams to plan and evaluate offers.

  • Elasticity and promotion lift models
  • Promotion post-event analysis
  • Price and trade spend optimization tools

Retail Media and Data Collaboration

We build audience segmentation, campaign measurement and privacy-preserving clean room collaboration between retailers and brands.

  • Audience and segment builder
  • Closed-loop measurement and incrementality
  • Clean room setup and governance

Product Content and AI Commerce Readiness

We enrich and standardize product attributes with generative AI and human review, improve site search and expose structured product data and APIs for marketplaces and AI agents.

  • Attribute extraction and enrichment
  • Semantic and vector product search
  • Product data feeds and APIs for AI channels

Explore

Services for Retail and CPG

Our Approach

How We Engage with Retailers and Brands

A commercial, outcome-led approach built around merchants, planners and marketers.

  1. 01

    Commercial and Data Diagnostic

    We review the decision in scope, such as forecasting, pricing or personalization, the systems and data involved and current performance, with business and IT teams.

    Deliverables: Opportunity and data assessment · Baseline performance metrics

  2. 02

    Value Case and Test Design

    We quantify the expected impact and design a controlled test, such as a set of pilot stores, categories or customer segments, to prove it.

    Deliverables: Value case · Test and control design

  3. 03

    Data Foundation for the Use Case

    We build the pipelines and data models required, prioritizing reusable components such as product, location and customer dimensions.

    Deliverables: Curated data sets · Reusable data model

  4. 04

    Model Build and Controlled Pilot

    We develop models in sprints, run them in the pilot scope and measure results against control groups with merchants and planners.

    Deliverables: Pilot results report · Model documentation

  5. 05

    Scale, Integrate and Optimize

    We integrate the solution with planning, commerce and marketing systems, roll it out across categories or regions and keep improving it based on monitored performance.

    Deliverables: Production integration · Monitoring and continuous improvement plan

Use Cases

Retail and CPG Use Cases

Concrete scenarios across store, digital and brand operations.

Fresh Food Forecasting

A grocer forecasts daily demand for fresh items by store, accounting for weather, holidays and promotions. Ordering is adjusted automatically, reducing waste while keeping shelves full.

Personalized Homepage and Search

An online retailer ranks products and search results based on each shopper’s browsing and purchase history. Shoppers find relevant items faster and conversion improves.

Promotion Effectiveness for a CPG Brand

A brand analyzes retailer sell-out data to measure the true lift and cannibalization of past promotions. Trade spend shifts to mechanics and retailers that deliver incremental volume.

Retail Media Audience Targeting

A retailer builds purchase-based audiences that brands can target on its site and offsite channels. Brands receive closed-loop sales measurement through a privacy-preserving clean room.

Catalog Enrichment for Marketplaces

Generative AI extracts attributes from supplier descriptions and images and fills gaps in a large catalog, with merchandisers reviewing changes. Products become easier to filter, search and recommend.

Store Inventory Accuracy

Analytics compare expected and observed sales patterns to flag likely phantom inventory. Store teams count suspect items first, improving availability and online order fulfillment.

Technology

Platforms and Standards We Work With in Retail and CPG

We build on cloud data platforms, commerce systems and retail data standards.

  • GS1 Standards
  • GS1 Digital Link
  • Databricks
  • Snowflake
  • Google BigQuery
  • Snowflake Data Clean Rooms
  • AWS Clean Rooms
  • Shopify integrations
  • Salesforce Commerce Cloud integrations
  • SAP integrations
  • Bloomreach
  • Segment
  • dbt
  • Apache Kafka
  • Power BI
  • Looker

Platforms

Deliver Retail and CPG on Your Preferred Platform

Why Hanoi Technology

Why Organizations Choose Hanoi Technology

Senior, Cross-Functional Teams

Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.

Proven Delivery

More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.

Radical Transparency

A written update every week and a demo of working software every sprint, so you always know where things stand.

Cost-Effective Global Delivery

Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.

+32%conversion rate

Success Story · E-commerce and retail

Personalized shopping experience for a US fashion retailer

A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.

Read the Full Story

More Success Stories

Food delivery

+58%total orders

AI dispatch and live tracking for a food delivery startup

A fast-growing urban food delivery startup was losing customers to slow deliveries at peak times. We built AI order dispatching and connected restaurants, drivers and customers in real time.

SaaS

75%of tickets fully automated

AI support assistant and smart escalation for a global SaaS company

A project-management SaaS provider receiving thousands of support requests a day needed faster answers without growing the support team. We built an AI assistant grounded in its knowledge base, with escalation to human agents.

Explore All Success Stories

Resources

Featured Resources

Industry trends and practical guidance to help you get more value from data, AI and technology.

Make Every Retail Decision Smarter

Book a free consultation to discuss forecasting, personalization, pricing or retail media priorities with our retail and CPG team. We reply to every enquiry within 24 hours.

Book a Free Consultation

Retail and CPG FAQ

How much historical data do we need for demand forecasting?

Ideally two or more years of daily or weekly sales by item and location, plus promotion calendars, prices and stock levels, so models can learn seasonality and promotional effects. Less history can work for stable categories, and techniques such as hierarchical forecasting help new items and stores borrow patterns from similar ones. We start with a data assessment and back-test models against your current forecasts to show the improvement before go-live.

Can you integrate with our ERP, POS and commerce platforms?

Yes. We integrate with common ERP, point-of-sale, order management, eCommerce, loyalty and marketing platforms through APIs, change data capture, event streams or file feeds. Model outputs, such as forecasts, recommendations or prices, are written back to the systems your teams already use. We avoid building parallel tools where existing platforms can consume the results.

How do you handle customer data privacy?

We design customer data platforms around consent, purpose limitation and data minimization in line with GDPR, CCPA and similar laws. Identity resolution uses hashed identifiers, access is role-based and retention rules are enforced automatically. For retail media and brand collaboration, we use clean rooms so partners can analyze overlapping audiences without exchanging raw personal data.

What is agentic commerce and how should we prepare?

Agentic commerce refers to AI assistants that research, compare and sometimes purchase products on behalf of shoppers. These agents rely on structured, accurate product data, clear availability and pricing and machine-readable policies. Preparing means enriching product attributes, maintaining consistent identifiers such as GTINs, improving product feeds and APIs and ensuring your own site search and assistants work well. These steps also improve SEO and marketplace performance today.

Do you work with CPG brands as well as retailers?

Yes. For CPG brands we focus on revenue growth management, trade promotion analytics, demand sensing from retailer and distributor data, retail media measurement and direct-to-consumer channels. We help brands combine syndicated, retailer and first-party data into a consistent view of category performance.

How quickly can we see results?

Many retail use cases can be tested within about three months using a controlled pilot, for example a group of stores for forecasting or a customer segment for personalization. Measuring results against control groups provides a clear business case for scaling. Our sprint demos and weekly written updates keep merchants and leadership informed throughout.