Industries
Olakai for Retail & E-Commerce
Measure AI ROI, govern risk, and prove value across your retail organization and e-commerce operations.
AI Is Powering the Next Era of Retail. Now Tie It to Revenue.
Retailers are deploying AI everywhere — demand forecasting, personalized recommendations, dynamic pricing, conversational support, supply chain optimization. Early adopters report 10-20% improvements in conversion rates and significant reductions in inventory waste. The technology is delivering real results for those who deploy it well.
The problem is that most retailers can’t prove it. AI touches multiple channels simultaneously — e-commerce, physical stores, mobile apps, social commerce — each with its own data, its own tools, and its own metrics. A demand forecasting model might reduce stockouts online while the store team sees no benefit because systems aren’t connected. Seasonality compounds the challenge: models that work in Q2 can fail during holiday rushes. Meanwhile, PCI DSS compliance requires strict controls over payment data, and CCPA/GDPR govern how customer profiles are used for personalization. Without cross-channel measurement and unified governance, retailers can’t see whether AI is improving the business or just shifting problems between channels.
By the Numbers
Most retailers have piloted AI, but few connect deployments to measurable business outcomes.
The gap between AI experiment and AI advantage is a measurement problem — you can’t scale what you can’t prove across channels and seasons.
Example KPIs for Common Agentic AI Workflows
Retailers deploy AI across demand planning, marketing, customer retention, logistics, and support. Olakai gives you the KPIs to prove each workflow is working across every channel and season.
Example 1: Demand Forecasting · Operations
Inaccurate demand forecasting leads to excess inventory that ties up capital or stockouts that send customers to competitors. This workflow analyzes historical sales patterns, identifies trends and seasonality, incorporates external factors like weather and promotions, predicts future demand by product and location, and recommends optimal inventory levels. For retailers where a single percentage point improvement in forecast accuracy can mean millions in saved inventory costs, this is the highest-leverage AI deployment available.
Key KPIs to measure with Olakai
Forecast Accuracy Percentage deviation between predicted and actual demand by SKU and location.
Inventory Turnover Rate How quickly stock sells through relative to inventory held.
Carrying Cost Reduction Dollar savings from reduced excess inventory.
Example 2: Customer Segmentation · Marketing
Generic mass marketing yields diminishing returns as customers expect personalized experiences. This workflow continuously analyzes purchase history and engagement patterns, creates dynamic segments that evolve with customer behavior, recommends personalized offers for each segment, and optimizes targeting based on measured conversion rates. The result is marketing that feels personal at scale, driving engagement without the manual effort of traditional segmentation.
Key KPIs to measure with Olakai
Segment Conversion Rate Conversion rate per AI-defined customer segment.
Customer Lifetime Value Revenue per customer over the full relationship.
Campaign ROAS Return on ad spend by segment and channel.
Example 3: Churn Risk Detection · Customer Success
Loyalty program members who disengage don’t announce their departure — they simply stop buying. This workflow monitors purchase frequency, engagement with promotions, and browsing patterns to flag members showing early signs of churn, then triggers personalized retention offers before the customer is lost. For retailers where loyalty members spend 2-3x more than non-members, protecting this segment is critical to revenue growth.
Key KPIs to measure with Olakai
Loyalty Member Retention Rate Percentage of flagged at-risk members retained after intervention.
Revenue per Retained Member Average spend of members saved versus those lost.
Reactivation Rate Percentage of lapsed members who resume purchasing after outreach.
Example 4: Supply Chain Optimization · Operations
Supply chain inefficiencies eat into retail margins through delayed deliveries, suboptimal routing, and reactive supplier management. This workflow optimizes delivery routes considering traffic, weather, and capacity, evaluates suppliers by cost and reliability, identifies alternative suppliers proactively, and tracks sustainability metrics. For retailers managing complex omnichannel fulfillment, optimization across the supply chain compounds into significant margin improvement.
Key KPIs to measure with Olakai
Logistics Cost per Order Total fulfillment cost from warehouse to delivery.
On-Time Delivery Rate Percentage of orders delivered within the promised window.
Supplier Reliability Score Composite rating of supplier performance over time.
Example 5: Support Ticket Auto-Response · Customer Success
Retail support volumes spike dramatically during peak seasons, overwhelming human agents with repetitive questions about orders, returns, and shipping. This workflow parses incoming tickets, searches knowledge bases for solutions, provides step-by-step resolution guidance, and escalates to human agents only when automated resolution fails. During holiday periods when ticket volumes can triple, this keeps response times low without scaling headcount.
Key KPIs to measure with Olakai
First Response Time Minutes from ticket submitted to first meaningful response.
Auto-Resolution Rate Percentage of tickets resolved without human escalation.
Customer Satisfaction Score CSAT for AI-handled versus human-handled tickets.
Why Measurement Changes Everything
From Inventory Guessing to Forecast Confidence
Measure forecast accuracy, inventory turnover, and carrying cost savings across SKUs and seasons. When you can prove AI forecasting is reducing waste and stockouts, the investment case for scaling becomes obvious.
From Mass Marketing to Measurable Personalization
Track segment-level conversion rates, customer lifetime value, and campaign ROAS. Prove that AI segmentation is driving real revenue lift, not just sending more emails to more people.
From Loyalty Erosion to Revenue Protection
Measure retention rates, reactivation success, and revenue per retained member. When you can quantify the dollar value of every saved loyalty member, churn prevention becomes a provable revenue line.
From Supply Chain Chaos to Measured Efficiency
Track logistics cost per order, on-time delivery rates, and supplier performance. When you can connect AI optimization to margin improvement across your fulfillment network, operations earns its seat at the strategy table.
From Shadow AI Risk to Controlled Adoption
Replace “block everything” with “measure and govern.” See which unsanctioned AI tools access customer data, quantify the PCI and privacy risk, and bring high-value usage into the fold with proper oversight.
From Support Cost to Customer Delight
Measure auto-resolution rates, response times, and CSAT scores across peak and off-peak periods. Prove that AI support handles seasonal surges without sacrificing the customer experience.