When your customer browses online, adds items to their cart, and then picks up their order in-store, does your analytics solution show you one journey or three separate transactions?
If you’re still measuring success channel by channel, you’re missing the bigger picture of how retail omnichannel analytics can reveal the true path to purchase and operational efficiency.
Here's how to connect your data across every touchpoint, measure what actually drives growth, and turn your omnichannel strategy into measurable bottom-line results.
What is retail omnichannel analytics?
Retail omnichannel analytics is a data strategy that creates a unified view of the entire customer journey by pulling in customer, product, and operational data across all physical and digital touchpoints. This means tracking how channels work together rather than measuring them in isolation. You see the complete picture of how online and in-store experiences drive your business forward.
Journey measurement plus operational optimization
True omnichannel analytics follows your customer's complete path, no matter where it starts or ends. It connects the dots between a customer browsing on your app, adding items to their cart on their laptop, and picking up their order in a physical store. It’s especially important for tracking purchase paths such as:
Buy online, pickup in-store (BOPIS)
Buy online, return in-store (BORIS)
Ship-to-store
Ship-from store
By seeing the full picture across multiple channels, you can spot friction points and fix the operational issues that cause them. This approach helps you make smarter decisions about inventory allocation, marketing spend, and the overall customer experience.
It's not "more dashboards"
Omnichannel analytics isn't about building more static dashboards that show siloed channel metrics. When each channel has its own dashboard with different definitions and refresh schedules, you get conflicting numbers that don't tell a coherent story. Teams waste time reconciling spreadsheets because one ecommerce dashboard shows one conversion rate while your in-store system reports different customer counts.
Instead, omnichannel analytics is about moving to a model where you can ask questions about cross-channel performance and get immediate answers. This is a big shift from older BI tools, where a simple follow-up question like "how did your in-store pickup promotion affect online cart abandonment?" would send you back to the analyst queue for days.
Verivox, a leading European comparison platform, was hitting this wall hard. Teams were waiting days for the data they needed to understand key purchase paths, like how a customer's mobile search connected to their desktop conversion. After embedding ThoughtSpot Analytics, they turned scattered channel data into a single source of truth that delivered instant insights about cross-channel journeys. With ThoughtSpot, they saw 70% employee adoption across all Verivox divisions.
Key data connections for omnichannel analytics
To get a true omnichannel view, your data foundation has to help different systems talk to each other. This requires connecting, or joining, key datasets to build a complete "journey ledger" for every customer interaction.
Without this foundation, your analytics will remain fragmented and incomplete. Here are the five connections that tend to make the biggest difference:
So where does all this data live? Depending on how mature your analytics are, it might be totally siloed in different systems, or it might already be stored together in a cloud data warehouse. The good news is that modern analytics platforms can work with your data wherever it lives today, so you can start getting value without a complete infrastructure overhaul.
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KPIs that matter for omnichannel retail success
Once your data is connected, you’re on the way to tracking metrics that reflect the true, cross-channel customer journey.
Journey conversion rates
Journey conversion rates reveal the true effectiveness of your omnichannel strategy by tracking complete customer paths from initial interest to final purchase, regardless of where those touchpoints occur. These are some common examples:
BOPIS conversion rate measures the percentage of customers who add buy-online-pickup-in-store items to their cart and complete the purchase. When this metric drops, it can indicate issues with inventory accuracy or pickup wait times.
Research-to-purchase time by channel mix shows which channel combinations accelerate decisions versus creating friction. This helps you understand which paths move customers toward purchase most efficiently.
Cross-channel funnel completion tracks how many customers who browse online purchase in-store within seven days, revealing whether your digital presence drives foot traffic and converts digital engagement into physical store visits.
Fulfillment and pickup experience
Your fulfillment operations directly impact whether customers complete their omnichannel journeys or abandon them. Every delay at pickup or split shipment creates friction that erodes trust and increases costs. These metrics reveal where your operations enable seamless experiences and where they create obstacles:
Pickup readiness time measures how quickly stores prepare BOPIS orders from placement to customer notification. Longer wait times often signal staffing or process issues that frustrate customers.
Split-ship rate tracks how often multi-item orders require separate shipments, indicating potential inventory positioning problems that increase costs and complicate delivery.
Order cancellation rate exposes the gap between what your systems promise and what your inventory can actually deliver. High cancellation rates directly damage customer retention.
Customer value metrics
Understanding which customers deliver the most value helps you allocate resources more effectively and design experiences that encourage profitable behaviors. These metrics show you the true economic impact of your omnichannel strategy and help identify which journey patterns correlate with higher customer value:
Repeat purchase rate by journey type compares how frequently customers return based on their preferred shopping patterns. Customers who use BOPIS may show different loyalty behaviors than online-only or in-store-only shoppers.
Customer lifetime value by channel engagement measures the total value customers generate based on how many touchpoints they use. This helps you understand whether multi-channel customers justify the operational complexity of serving them across platforms.
Availability and margin protection
Product availability directly impacts both revenue and profitability. When items are out of stock, you lose sales. When you substitute products or mark down excess inventory, you erode margins. These metrics help you balance availability with profitability across your entire network:
Stockout rate by channel measures how often customers encounter unavailable products in each channel. High online stockouts with available in-store inventory suggest poor visibility or allocation issues.
Substitution acceptance rate tracks how often customers accept alternative products when their first choice is unavailable. Low acceptance rates indicate you're offering poor substitutes or that customers have strong brand preferences.
Markdown exposure by fulfillment type reveals which inventory positioning strategies create excess stock that requires discounting. Products allocated for BOPIS but rarely purchased through that channel may need repositioning before they require markdowns.
With Liveboard Insights, you can monitor these journey-based KPIs in real time using AI-enabled dashboards. This gives you a consistent, up-to-date view of performance so everyone is working from the same set of facts, rather than using the outdated extracts that many traditional BI platforms rely on.
Top use cases for retail omnichannel analytics
These four use cases show how omnichannel analytics solves real business problems. Each includes a practical question your team can ask immediately to uncover insights hidden in siloed data.
As Manbir Paul from Sephora puts it, "the intimate details that data gives you, getting your clients so close to you, is a very different lens to look at data from"—and that lens becomes accessible to everyone when you remove technical barriers. An AI-powered analytics platform makes it easy to ask questions in natural language and get instant, actionable insights without SQL knowledge or complex navigation.
Turn omnichannel retail data into measurable growth
Moving to an omnichannel analytics approach is a significant shift, but it's how you can build more resilient operations and create better customer experiences. By connecting your data, measuring journeys instead of channels, and empowering your team to ask their own questions, you can turn your omnichannel retail strategy into real, measurable growth.
When you and your colleagues can get answers to your data questions instantly, you can act faster, align better, and spot opportunities previously hidden in siloed reports. See how you can make this a reality for your organization. Start your free trial.
Retail omnichannel analytics FAQs
Do I need a customer data platform to implement retail omnichannel analytics?
Not necessarily. While a customer data platform can help with identity stitching, the main requirement is a flexible analytics platform that can join data from multiple sources like your POS, ecommerce platform, and ERP system.
How should you handle consent and privacy when combining online and in-store customer data?
The key is building privacy into your data architecture from the start rather than bolting it on later, and implementing technical controls that make compliance automatic rather than manual. This means establishing consent frameworks that respect customers' data across all touchpoints, implementing role-based access controls that limit data exposure, and creating audit trails that document how customer information flows through your systems. When privacy is foundational, compliance can be a competitive advantage rather than a constraint.
What's a reasonable data refresh frequency for omnichannel retail reporting?
It depends on your specific use case. Operational decisions like inventory allocation and fulfillment usually need live data updates, while strategic analysis of customer behavior patterns might work fine with daily updates. A modern analytics platform should support both live and cached data to match your needs./
