US ecommerce sales topped roughly $1.23 trillion in 2025, accounting for 16.4% of all retail sales. Meanwhile, new sales channels are growing. TikTok Shop, barely two years old in the US, pulled $4.9 billion in Q1 2026, nearly double the year before.
Take Fenty Beauty. The brand sells through their own site, TikTok Shop, the Shop app, Roblox, Sephora wholesale, and physical pop-ups. Each channel generates its own data in its own format.
“The ability to jump in and be on top of the trends is really the difference between being successful and being average,” says Sapna Parikh, chief digital officer of Kendo brands, which operates a portfolio that includes Fenty.
There’s an ocean of money moving across the internet, but the data tracking it is fractured. A retail business intelligence tool acts as the single source of truth, pulling disconnected streams into a single dashboard for you to make better-informed decisions.
What is retail business intelligence software?
A retail business intelligence (BI) software solution aggregates data from across a retailer's operations, including sales, inventory, marketing, customer behavior, and financials, and turns it into reports, dashboards, and analytics that can inform business decisions.
A digital dashboard can track data and generate reports, but the data it draws from is usually limited to the individual digital tool or platform the dashboard is a part of. A retail business intelligence tool can draw and compile data from multiple sources, such as a retailer’s ecommerce platform, point of sale (POS), inventory management system (IMS), customer relationship management (CRM) and more.
There’s more overlap among the terms business intelligence, analytics, and reporting, but there are distinctions there as well.
While reporting tells you what happened, retail analytics tells you why. Retailers run reports to give a snapshot of data within a given timeframe; and analysis can look at that data with a bit more insight. Business intelligence is the layer that lets teams monitor, compare, forecast, and act on that data continuously, across multiple channels and departments.
Why is retail business intelligence more important than ever?
Digital commerce is bigger than ever. Data from the Census shows that US ecommerce sales reached $1.2337 trillion in 2025, up 5.4% from 2024, and ecommerce represented 16.4% of total US retail sales.
The retail industry has always run on data, but what's changed is how fast that data needs to move. There are three windows of compression driving the urgency for retail intelligence.
The first is data fragmentation at scale. For example, 63% of non-grocery retailers say siloed data prevents them from responding to real-time demand changes, according to Pymnts Intelligence's February 2025 retail data study. Every new channel a retailer adds compounds the problem. And with more and more retailers selling direct-to-consumer (DTC), wholesale, in marketplaces, and through social selling, that’s a lot of channels.
You have different platforms with different data structures, and no shared language between them.
The second major factor is AI readiness. Since January 2025, AI-driven traffic to Shopify stores has grown eight times year over year, with orders from AI-powered searches increased by a factor of 15. Agentic commerce—in which AI agents help shoppers discover, compare, and purchase—is moving faster than most retailers' data infrastructure can handle.
For example, when Walmart tested AI-assisted checkout through ChatGPT, purchases inside the chat converted at one-third the rate of Walmart’s own website, because the agent had no access to the customer's cart history, loyalty status, or saved preferences.
The third factor is decision speed. Sixty nine percent of consumers say they're more likely to buy when retailers adjust offers in real time as they browse, yet 79% say retailers regularly get personalization wrong through irrelevant or mistimed outreach, according to Amperity's 2026 “State of Personalization in Retail” report.
Take outdoor gear brand Decathlon. After adding modern BI capabilities with ShopifyQL Notebooks, Decathlon CTO Tony Leon said: "Without using Notebooks, I would have done an extract in Google Sheets or Excel and delivered it to leadership. The problem with that is it's just one shot. It's out of date."
The move to live data gave the team 50% more powerful analytics and, more practically, the ability to track peaks and drops in sales and compare time periods.
The core features to look for in a retail business intelligence solution
Gartner's “Critical Capabilities for Analytics and Business Intelligence Platforms” identifies four criteria that any serious BI platform needs to support:
These criteria can also provide a useful framework for understanding which business intelligence functions are covered natively on Shopify and which are not, so you can make the call on what, if anything, to add on top.
Metrics creation
The BI platform needs to connect to your data sources, draw and standardize the required data, and surface standardized metrics your team can use. For retail, this means consistent definitions and formats for metrics like revenue, margin, return rate, and customer lifetime value (CLV).
Shopify’s Analytics natively covers core retail metrics like net sales, average order value (AOV), returning customer rate, and returns data; —and the metrics are easily accessed through more than 60 prebuilt reports, with a customizable dashboard that refreshes in near-real time. As of April 2026, the dashboard also surfaces data-driven insights automatically: analyzing sales, sessions, and fulfillment data daily across products, regions, channels, and customer types, and flagging the top five findings by business impact.
But native reporting does have its limits; for example, marketing teams looking to implement multichannel attribution may choose to combine Shopify data with ad platform data inside a business intelligence tool.
Data storytelling
Gartner defines this use case as pairing interactive visualizations with narrative tools so nontechnical users can read and act on the data without needing an analyst to translate.
With Shopify Sidekick you can query your store data in plain language; from there, the tool builds the report, translates it into ShopifyQL, and visualizes the output in bar, line, or donut charts, exportable to CSV. As of the April 2026 dashboard update, Sidekick now launches automatically alongside data-driven insights, preloaded with useful findings, so you can ask follow-up questions without even building a report first.
German apparel brand Snocks implemented Sidekick across four departments, cutting report-building time by up to 98%, from 30 minutes to mere seconds per query.
“Everyone has worked with AI by now. But having it sit right inside Shopify, connected to your own data, makes it so much more usable,” says Kevin Foitzik, group head of onlineshop and IT at Snocks.
But Sidekick operates on Shopify data only. For cross-channel storytelling, you can use a retail business intelligence implementation layer that can hold data from outside the platform.
Composable analytics
Gartner's criterion here includes embedded analytics, API and SDK support, and automated workflows.
Shopify Flow is a no-code automation platform built on a trigger-condition-action model: a data event fires, a condition is evaluated, and an action executes. For example, if a customer makes their second purchase, they're tagged as VIP and enrolled in a loyalty workflow.
A drag-and-drop interface makes Flow easy to use for nontechnical teams. On Advanced plans and above, Flow also supports custom HTTP requests, opening connections to systems outside Shopify.
But where it reaches its limit is when the data triggering a workflow lives outside Shopify, like ad-spend thresholds or wholesale portal activity.
Governance
You need controls over who can access what data, and guardrails to make sure the numbers different teams are looking at are the same ones.
Shopify's role-based permissions system lets you create custom staff roles with granular access controls, like giving a merchandiser access to products and collections without exposing financial data. And organization-level permissions let multi-store operators control which users can view aggregate data across all stores versus detailed reports within individual ones.
But analytics pages display unfiltered sales data across all company locations, even when location-level restrictions are applied to a role.
What are the top retail business intelligence use cases?
The retail business intelligence software market was valued at $4.18 billion in 2025 and is expected to reach $10.38 billion by 2034, driven by the ecommerce boom, omnichannel complexity, and the competitive pressure to make faster, better-informed decisions.
Further, Salesforce's sixth edition “State of the Connected Customer” report, based on a survey of 14,300 consumers globally, found that 73% of customers expect better personalization as technology advances. And the retailers best positioned to deliver it are the ones who can see their customer data across channels in one place.
Omnichannel performance analysis
Deloitte's Q3 2025 “Retail & Consumer Trends” report calls Gen Z "the most authentically omni-shopping generation": 64% use social media to research products before buying, and 35% use it to discover products in the first place, nearly twice the rate of older generations.
You have to know which channel gets the credit, because attribution decides where the budget goes next quarter. And to answer that question, you need sales and conversions by channel, in-store transaction data, and order-origin and fulfillment information across touchpoints.
For Shopify brands, Shopify POS and unified customer and order data handle the foundation; every in-store and online transaction feeds the same back end, the same customer record, and the same inventory count.
Trévi, for example, a Québec-based pool and spa retailer with nine physical locations, was running Magento alongside decades of other legacy systems. They had fragmented data, slow promotion launches, and buy online, pick up in-store (BOPIS) orders failing because stock visibility wasn't syncing in real time.
Moving to Shopify's back end and POS Pro consolidated everything. Trévi saw 162% revenue growth, a 47% conversion rate improvement, and a 12% increase in AOV.
According to an independent EY study, retailers on Shopify POS see an average 8.9% uplift in annual sales, 22% lower total cost of ownership (TCO), and 20% faster implementation time compared to other solutions.
Inventory forecasting
Nvidia’s 2025 “State of AI in Retail and CPG” survey found that inventory management is the top AI use case in physical stores, with 59% of respondents saying their supply chain challenges have grown in the last year and 82% planning to increase AI investment in supply chain in the next fiscal year.
Shopify's native inventory tracking gives you access to real-time stock levels across multiple locations, with POS Pro syncing in-store and online inventory from the same back end.
For merchants whose inventory data lives beyond Shopify, Shopify's Global ERP Program offers certified integrations with Oracle NetSuite, Microsoft Dynamics 365, Acumatica, Infor, and Brightpearl, syncing orders, inventory, and financials in near-real time.
Take Death Wish Coffee. They used that ERP connection to track inventory across multiple warehouses before running a Super Bowl ad. This allowed them to process more than $250,000 in orders in two hours while handling 150,000 simultaneous visitors and sustaining 200% year-over-year growth.
Customer segmentation and personalization
A recent Forbes piece posits that merchandising assumptions built on age-based segmentation no longer hold. When adults are spending more on toys than children and tweens are buying serums, the segments that move decisions are behavioral.
For example, you need to segment VIP customers by spend and frequency, lapsed customers by recency, first-time buyers by acquisition channel, and high-return-rate cohorts by product category.
Each is a different data question and a different margin outcome.
Shopify's native customer segmentation lets you build segments directly from customer profiles, with prebuilt templates for high-value customers, lapsed buyers, and abandoned checkouts.
These segments then feed into Shopify Messaging for email and SMS campaigns, into Shopify Flow for automated workflows, and into Shopify Audiences, which converts first-party customer data into prospecting audiences for Meta, Google, Pinterest, and TikTok.
“Shopify has enabled us to leverage insights from millions of direct connections that merchants have with their customers, so we can reach high-intent buyers. The reporting Shopify offers is a game-changer for DTC merchants,” says Josh Bultz, chief revenue officer at Nathan James.
Fraud, risk, and operational automation
Riskified's Q1 2026 "Agentic Commerce Pulse" found that 53.9% of consumers believe AI could increase the risk of online fraud, with 73.9% expecting strong authentication safeguards on every transaction.
As AI-driven commerce accelerates, the fraud surface expands with it.
Grüns, the nutrition brand that hit a $50 million run rate in their first year, built their entire ecommerce operation on Shopify before launch. As the business scaled, Shopify Flow became foundational, particularly for fraud prevention.
"One of the biggest things I got out of Flow is when we would get attacked by Amazon resellers or fraudulent stuff," says founder Chad Janis.
Shopify's fraud analysis reviews every online credit card order using machine learning (ML) trained across the entire Shopify network, and Flow acts on that signal automatically. Shopify Payments adds dynamic 3D Secure checkout, proxy detection, card testing protection, and a dispute management dashboard covering chargebacks, authorization rates, and order risk trends.
A four-step framework for choosing your retail business intelligence solution
Michael Relich, former CEO and COO of PacSun and Guess, said in an interview with Strategy that most analysts who work in retail spend Sundays anticipating every possible question from executives in Monday business reviews, only to find they've missed something—"a cycle of reactive work throughout the week."
The right platform can help you break that cycle. Here's how to find it.
1. Start with your business questions
Write down the three decisions you need to make this quarter that you currently can't make confidently because you don’t have access to analysis that you can use, either because the data isn't there, isn't current, or lives in too many places to pull together.
Strategy's 2025 "Retail and CPG in Focus Report" found that 50% of retailers cited unrealistic expectations around speed-to-value as a top barrier to scaling business intelligence in retail.
A company may buy a retail BI solution thinking insights will come to light automatically, without defining what decisions the tool needs to support. The more productive approach is to identify the decisions you're currently making on incomplete, unreliable, or old data, then work backward to what high-quality data those decisions require.
2. Audit your data sources and integrations
Map every system that generates data relevant to your business: Shopify, ad platforms, your third-party logistics provider (3PL), ERP, wholesale portals, POS, email, and SMS tools. Identify the data each system produces, how current it is, and whether it can be exported or connected to an external tool.
The Strategy report found that 35% of retailers experience inconsistent answers to the same data questions, and 31% lack a formal data strategy. That means too many retailers are conducting business without knowing what data they have or where it lives.
Shopify retailers have a big headstart on data integration. Shopify's unified API reduces what would otherwise be 28 sprawling integration points to a single hub, with prebuilt connectors for major ERPs including NetSuite, SAP, and Microsoft Dynamics, and an app marketplace of over 8,000 preintegrated apps for data analysis and other functions.
3. Evaluate usability and self-service adoption
"If you can ask natural language questions and get immediate answers, that's extremely powerful," says Michael. On the other hand, If using the platform requires SQL knowledge, a data team, or a training program, adoption will stall.
Can your store manager, your buyer, and your founder each answer their own questions in the BI platform without help? Ask the vendor to demonstrate that specifically. If you’re on Shopify, Sidekick sets the benchmark for usability: ask in plain-language questions, get instant reports, all without the SQL overhead.
With the help of Shopify Sidekick, fashion brand Maggy London is able to run six labels with just a four-person ecommerce team. The tool helps them uncover optimization opportunities that would normally require an analytics team or expensive third-party tools.
Their reporting time improved by roughly 10 times; the team freed up at least a full day of work per week, and they avoided having to pay for additional data and analysis.
“Sidekick turned our ecom team into a strategic intelligence hub for the whole company. The insights we pull don't just improve our website—they inform design and product development,” says Sara Bako, Maggy London’s president.
4. Calculate the total cost of ownership
The Strategy report also found that 53% of retailers cite cost as the top barrier to scaling retail business intelligence, and that the cost problem typically starts after the license is signed.
Sustainable outdoor furniture brand Polywood's chief digital officer Benjamin Spiegel described the hidden cost of implementing the wrong platform for his business: “We spent hundreds of hours of development resources to build integrations with third parties when other platforms already had it out of the box.”
After migrating to Shopify, Polywood achieved a 22% increase in conversion rate, 12% higher AOV, redirected 100% of development resources from maintenance to innovation, and reduced total cost of ownership by six figures.
“[With] the other competitors, customizing it becomes a million-dollar project. Here, customization is easy,” says Benjamin.
In your evaluation, the full cost picture should include license or subscription fees, implementation and integration work, data preparation and cleaning before the tool can run accurately, user training, and ongoing maintenance as your stack evolves.
For Shopify retailers, the starting point is more favorable than most. Shopify's independent consulting firm TCO study found its total cost of ownership is on average 33% better than competitors, with 23% better platform costs and 19% better operation and maintenance costs.
Retail business intelligence software FAQ
What is the best software for retail business?
There's no single answer; the right platform depends on where your data lives, who needs to use it, and what decisions it needs to support.
That said, the most widely used BI in the retail industry are Microsoft Power BI (23% market share), Tableau (17.8%), and Qlik (10%), according to 6sense's May 2026 adoption data.
For Shopify brands specifically, the starting point is Shopify Analytics and Sidekick. Dedicated BI software only becomes relevant when sales data from outside Shopify needs to be pulled into the same view.
How can AI be used in retail business?
Per Nvidia’s third annual “State of AI in Retail and CPG” survey, the highest-impact use cases in the retail sector are:
- Customer demand forecasting and supply chain management; 64% of respondents reported increased supply chain challenges year over year, with AI the primary response.
- Consumer behavior analysis and personalization for marketing.
- AI agents that improve customer service by handling queries and surfacing real-time insights for front-line staff.
Predictive retail analytics, using historical sales and behavioral data to anticipate what customers will buy, when, and where, is the second-largest line item in retail AI budgets after personalization, with retailers reporting 30% to 50% cuts in forecast errors.
The constraint across all of these is data quality. AI outputs are only as reliable as the data fed into them.
Read more: AI in Retail: 10 Use Cases and an Implementation Guide (2026)
What is the latest trend in business intelligence?
The defining shift in 2026 is agentic AI. These are BI tools that don't wait to be asked a question, but monitor data continuously, surface anomalies, and in some cases act on them autonomously.
BARC's 2026 BI Trend Monitor identifies data quality management as the top-ranked priority for the year, ahead of AI itself.
Predictive retail analytics sits alongside agentic AI as the second major trend. The shift is from BI as a reporting tool to BI as a decision engine that anticipates customer demand, flags supply chain management risks, and models the customer loyalty and customer satisfaction outcomes of decisions before they're made.
