AI for Retail: Personalization at Scale
Deliver hyper-personalized experiences, optimize inventory, and drive revenue growth with AI that understands your customers and operations.
Customer Expectations
Demand for Amazon-level personalization and service
Inventory Optimization
Balancing stock levels with volatile demand patterns
Conversion Rates
Generic experiences failing to drive purchase decisions
Operational Costs
Rising labor and fulfillment costs squeezing margins
Your Best Customers Are Becoming Interchangeable
You know who bought from you last month. You know what they've clicked on. You know their size, their color preferences, when they usually shop. But here's the problem: someone else knows it too. And they're using it to send that customer a better offer than you will. So your customer shops there instead. Next week they see a personalized email from a competitor and come back to them. You're losing the customer you actually have because you're stuck between generic at-scale and actually-personal-at-scale.
Meanwhile, you've got inventory that doesn't match demand—you're drowning in winter coats in March or running out of the one style people actually want. Your team is manually optimizing prices. Your margins are being compressed by customer acquisition costs. And the painful irony is you have all the data needed to fix this. It just isn't connected.
2–3x
higher AOV when recommendations actually fit the customer
25% less
inventory waste with demand that's actually predicted
3–5% better
margins from pricing that responds to actual conditions
The retailers actually winning aren't trying to out-Amazon Amazon. They're using AI to understand their specific customers better, predict what they'll want before they know it, and optimize operations so margins survive. It's boring. It's table stakes. And it works.
Our Three-Layer Approach for Retail
Retail AI requires real-time personalization, integration across channels, and continuous optimization.
Layer 1: Advisory & Governance
Before you build anything, we map out what you actually have—customer data that's siloed, operations that aren't connected, manual processes that should be automated. We show you which investments will actually move the needle on margins and customer retention, and which ones are just noise.
- • Assessment of your customer data and operational systems
- • Honest prioritization of high-impact AI use cases
- • Customer data strategy that doesn't creep people out
- • Integration roadmap with your existing commerce stack
- • Clear ROI modeling so you know what you're actually going to make
Layer 2: Build & Integrate
This is where your data becomes useful. We connect your customer insights, inventory system, and pricing logic so they actually talk to each other. That customer who usually buys blazers gets shown blazers. Your inventory tells the system what's actually in stock. Your prices adjust for demand instead of waiting for a spreadsheet update.
- • Recommendation engines that actually understand your customers
- • Inventory integration so customers see real stock
- • Dynamic pricing that works across channels
- • AI-powered search and visual discovery
- • Chatbots that help instead of frustrate
- • Integration with Shopify, Magento, Commerce Cloud, your CDP
Layer 3: Operate & Scale
Once AI is running, you measure what matters: Is conversion actually up? Are we making better margins? Is inventory moving faster? We run constant A/B tests to find even better recommendations. We monitor prices and adjust when market conditions change. We evolve customer segments as tastes shift. This is where you compound advantages.
- • A/B testing to find what actually increases revenue
- • Real-time monitoring of conversion and margin impact
- • Demand sensing that responds to real conditions
- • Automatic customer segmentation that evolves
- • Attribution so you know which channels actually drive value
- • Price monitoring and dynamic adjustments across channels
Functional Use Cases
AI applications across customer experience, merchandising, and operations.
Customer Experience & Personalization
Product Recommendations
AI-powered recommendations based on behavior, preferences, and lookalike customers
Visual Search & Discovery
Image-based product search and style matching for fashion and home goods
Virtual Shopping Assistants
Conversational AI for product discovery, sizing, and purchase assistance
Size & Fit Advisors
AI-powered size recommendations reducing returns for apparel and footwear
Merchandising & Operations
Demand Forecasting
ML models predicting demand at SKU-store level for optimized inventory
Dynamic Pricing
Real-time price optimization based on demand, competition, and inventory
Assortment Optimization
AI-driven product mix decisions for stores and online catalogs
Markdown Optimization
Intelligent clearance pricing to maximize margin while clearing inventory
Marketing & Engagement
Personalized Campaigns
AI-generated segments and personalized email/SMS content at scale
Churn Prediction
Identify at-risk customers and trigger retention campaigns proactively
Content Generation
AI-powered product descriptions, ad copy, and social media content
Customer Lifetime Value
ML models predicting CLV to optimize acquisition and retention spend
The Numbers That Matter
Revenue Growth
- 35% increase in conversion rates with personalization
- 20-25% higher average order value with recommendations
- 15% boost in customer lifetime value
Operational Efficiency
- 25% reduction in inventory carrying costs
- 30% fewer stockouts with demand forecasting
- 50% decrease in customer service costs with AI assistants
Margin Improvement
- 5-10% margin gain from dynamic pricing optimization
- 20% reduction in markdown costs with AI optimization
- 15-20% decrease in product returns with AI size advisors
Customer Retention
- 40% improvement in customer satisfaction scores
- 25% increase in repeat purchase rate
- 30% reduction in customer churn with predictive interventions
Technology Integration
Seamless integration with your retail technology ecosystem.
E-commerce Platforms
Native integration with leading commerce platforms:
- • Shopify / Shopify Plus
- • Salesforce Commerce Cloud
- • Adobe Commerce (Magento)
- • BigCommerce, WooCommerce
Customer Data & Marketing
Integration with customer data and engagement platforms:
- • CDPs (Segment, mParticle, Tealium)
- • Marketing automation (Klaviyo, Braze)
- • Analytics (Google Analytics, Mixpanel)
- • CRM (Salesforce, HubSpot)
How We Work With You
Typical Engagement Timeline
Personalization Assessment (2-3 weeks)
Data audit, use case identification, and quick-win opportunities
MVP Deployment (6-8 weeks)
Launch initial personalization feature with A/B testing
Scale & Optimize (12-16 weeks)
Expand across channels and add additional use cases
Continuous Improvement (Ongoing)
Ongoing testing, optimization, and new feature development
Common Starting Points
Quick Wins
Product recommendations, search optimization, chatbots
High Impact
Personalization engine, dynamic pricing, demand forecasting
Strategic
Omnichannel personalization, predictive CLV, visual AI
Stop losing customers to competitors who understand them better.
Let's talk about what's actually broken: inventory predictions, pricing decisions, how you use customer data. We'll show you the specific moves that protect margins while building real customer loyalty.
Let's Talk