As businesses collect more and more information about their customers’ online behavior and habits, the need for a retail data strategy becomes more pressing. Why? Because collecting data is the (relatively) easy part. Knowing what to do with it? That’s much more difficult.
The potential of data and strategy in retail is evident: The global retail analytics industry is set to grow from $10.6B in 2025 to $39.6B over the next seven years.
Global retail analytics market
Source: Market.us
Many retailers are struggling with siloed systems — eCommerce platforms, in-store point of sale (POS) systems, inventory management systems, and CRM tools all speaking different languages. This fragmentation makes it tough to build a unified customer view or act on data in real time. The result? Sluggish decision-making, security vulnerabilities, and missed opportunities.
What’s often blamed on legacy systems is actually the outcome of patchwork systems that weren’t designed with today’s omnichannel reality in mind. Instead of enabling data-driven strategies that center on the customer, it holds businesses back.
In this article, you’ll learn how to do things differently. You’ll see how strong retail data strategies can lead to real-time decision-making, improve customer experiences, and increase your profits. We’ll also explore how to create a retail data strategy that works for your business in 2025.
Key takeaways
In this article, you’ll learn:
- The importance of a real-time data and analytics strategy in retail and its business impact
- The security risks of customer data and how to combat them
- The role of good data governance
- Why a Customer Data Platform (CDP) is so important
- How you can engage customers through real-time activation
- How the cloud lets data systems flex and scale
- How to measure success once your strategy is in place
- What successful tech solutions look like
The importance of real-time data processing and edge computing
For any business developing a data analytics strategy for retail, time is (literally) money. Real-time data processing helps retailers stay ahead, whether helping them update stock levels or personalize offers for customers. Minus real-time access to big data, businesses risk missed sales, outdated inventory, and poor customer experiences.
What are the current challenges?
Legacy batch processing limitations: Batch data processing (collecting and analyzing data in batches over time) is still common. But this method can cause delays due to data analytics not being able to keep up with fast-moving retail environments.
Data latency issues: Slow data transfer between stores, warehouses, and cloud systems can lead to problems including delayed stock updates and delayed responses to customer activity.
Missed sales opportunities: When inventory data isn’t updated rapidly, stores might show stock as available when it’s actually sold out, frustrating customers (and resulting in lost sales).
Customer experience (CX) gaps: CX also suffers without real-time data. Shoppers expect personalized recommendations and dynamic pricing, but retailers struggle to deliver this with old data.
What are the key technologies needed for real-time retail data processing?
- Edge computing infrastructureRather than sending data to a distant cloud server, edge computing works by processing data locally, closer to its source (like inside a store).
- With in-store processing capabilities, devices can handle data instantly on-site, minimizing lag.
- Real-time inventory updates mean that stock levels are updated automatically as items are scanned or sold.
- Stores can make instant decisions with no need to wait for cloud servers if they use local data processing.
- Reduced cloud dependency can reduce costs and increase speed if less data is travelling back and forth.
- Stream processing architectureStream processing lets businesses analyze real-time data as it comes in.
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- Implementing an open-source platform like Apache Kafka allows you to handle multiple data streams.
- Integrating a cloud-based tool like AWS Kinesis enables rapid processing of large streams of data.
- A real-time analytics pipeline lets retailers spot trends and issues as they happen.
- An event-driven architecture sees systems reacting immediately to what’s happening in stores, like price reductions or sales.
What’s the business impact of these technologies?
Combined, real-time data processing and edge computing can deliver immediate benefits. Instant inventory updates prevent overselling or missed sales because stock levels are always accurate. Real-time personalization is great for shoppers, who receive tailored offers based on their behaviors and preferences.
Dynamic pricing means that prices are adjusted in real time based on customer demand, stock levels, and market trends. Meanwhile, a better customer experience (thanks to quicker service and more accurate personalization) means happier customers and more sales.
Traditional batch processing vs real-time processing
|
Feature |
Traditional batch processing |
Real-time processing |
|---|---|---|
|
Data processing |
Data is processed in batches and at set times |
Data is processed as it arrives |
|
Data latency |
High: minutes/hours |
Low: milliseconds/seconds |
|
Inventory updates |
Delays due to unsynced inventory |
Real-time inventory updates |
|
Personalization |
Limited by occasional recommendations |
Dynamic due to real-time recommendations |
|
Scalability |
Large amounts of data cause issues = less agile |
Can handle large data streams = flexible |
|
Customer experience |
Lack of real-time data causes inconsistencies |
Highly responsive |
|
Sales opportunities |
Sales can be missed due to lack of up-to-date data |
Constant stream of sales opportunities |
|
Infrastructure dependency |
Relies heavily on centralized cloud |
Less reliance on cloud computing by leveraging edge computing |
Why data security and a compliance framework are key
As customers continue to share more of their data while shopping online, safekeeping their personal information becomes increasingly crucial. As retailers gather more and more personal data — whether payment details or shopping preferences — the risk of cyber attacks and data breaches grows. Retailers have to ensure strong data security across their stores, websites, apps, and beyond.
Ranged against them are different security challenges they must master, including:
- Payment fraud: Hackers target payment systems to steal credit card details.
- Loyalty program data breaches: Loyalty accounts are easy targets and often hold valuable personal information.
- POS malware attacks: If point of sale (POS) systems become infected with malware, customer data can easily be stolen from them.
- Insider threats: Any employees with access to sensitive data could misuse it.
- API security breaches: If they’re not secure, APIs that connect retail systems can be exploited.
The role of a zero-trust architecture
A zero-trust architecture is one way that retailers can get the security they’re looking for. This approach assumes a position of zero trust, which means that no one, whether inside or outside the network, is trusted. Instead, every single user and system must be verified before gaining access. Let’s look at some of the benefits that a zero-trust architecture offers:
- Identity-based access control: Only verified users can access sensitive data.
- Micro-segmentation: Networks are divided into smaller sections, which limits how far an attacker can move once they have broken in.
- Continuous verification: Rather than checking only at login, systems constantly check user identities and device security.
- Least privilege access: Users only get access to the precise data they need to do their jobs.
Compliance requirements for retail data
Aside from their security responsibilities, retailers have to follow strict data privacy requirements. Failing to do so can lead to heavy fines and reputational damage.
Based on where they operate and who their customers are, retailers may need to comply with multiple laws and regulations, from the GDPR (General Data Protection Regulation) in the EU to China’s PIPL (Personal Information Protection Law), which sets strict rules on how companies handle personal data.
How data governance works in retail
Companies need to invest in good data governance to ensure their customer data is used responsibly, securely, and in compliance with laws. Done well, data governance can help them proactively build trust with customers rather than simply avoid fines.
Data should be classified and organized based on its sensitivity, with the most critical information receiving the highest protection. There also need to be retention policies in place: strict rules about how long data should be stored and when it should be deleted.
Detailed logs that audit and track who accesses data and when can help identify suspicious activity. To make sure these logs are accurate and traceable, data should be mapped to show its lineage and origins.
Unifying the experience with a Customer Data Platform (CDP)
It can be a challenge for retailers to create a smooth and personalized shopping experience given that customer data is often scattered across different channels. That’s where a Customer Data Platform (CDP) comes into play.
A CDP brings together customer data from multiple locations to build a unified view of each shopper, making it easier to personalize marketing, improve customer service, and — ultimately — boost sales.
Integrating a CDP in retail
When customer data is fragmented across different systems (such as a POS system, online shopping platform, and social media), it can be a recipe for crude personalization, wasted marketing efforts, and poor sales. A CDP offers to solve this by pulling data from all these sources into a single platform, providing businesses with a 360-degree view of their retail customers.
Source: Lytics
Data sources integration
A strong CDP takes data from various retail touchpoints in the customer journey and integrates them:
- POS systems: In-store purchases and customer interactions.
- eCommerce platforms: Online shopping behavior, including browsing and purchase history.
- Mobile applications: App usage, push notifications, and mobile shopping data.
- Social media: What customers have liked, commented on, and shared on platforms like Instagram and Facebook.
- Customer service: Customers’ experiences as recorded by support tickets, advisor chat history, and customer reviews.
By integrating data from these different channels, a CDP can create detailed customer profiles for retailers, reflecting shopping intention, behaviors, and more.
Customer profile management through a CDP
Having created integrated customer profiles, the CDP helps retailers personalize those profiles further:
- Identity resolution unifies data from multiple sources for a single customer (even if they use different devices or accounts).
- Identity graphs help to visualize the connections between customer data points, following customer journeys across different channels.
- AI-driven segmentation uses artificial intelligence (AI) to profile groups of customers based on their behavior, preferences, and demographics.
- Profile enrichment gives a fuller picture of a customer by adding information like social media data or purchase history to their profile.
- Preference management records customers’ communication preferences.
- Consent tracking makes sure customer data is handled in line with all relevant privacy requirements.
Real-time activation for better customer engagement
A CDP collects and manages data, but what is its real purpose? The answer is real-time activation. If they can understand what customers are thinking and feeling while shopping, retailers can engage customers at the right time, in the right place, with the right offer.
That might take the form of personalization engines constantly creating bespoke shopping recommendations and content in real time, or marketing automation in the form of trigger emails, push notifications, and online/social ads.
Integration with customer service tools can give support teams immediate access to customer profiles, improving service and response times. Loyalty programs, where loyalty points are earned by customers and logged and tracked by the program in real time, can also be used for this purpose.
Scale and speed: Cloud-native data architecture
What happens when retail businesses, as they grow, begin to handle ever-larger volumes of customer data? A flexible and scalable system running on cloud-native data architecture could be the answer.
Such a system allows retailers to process large data streams from across their ecosystem of stores, eCommerce platforms, customer service systems, etc. It also lets them scale their data acquisition and use in the cloud and gives them the agility to quickly respond if they need more (or less) capacity.
Scalable retail data architecture: Core components
What are the key cloud-native components that create a fast, scalable, and reliable system?
- Data ingestion layer: Brings together data from different sources, such as POS systems, websites, mobile apps, and social media.
- Processing framework: Turns raw data into actionable insights, either in real time or through batch processing.
- Storage solutions: Secure, scalable storage for structured and unstructured data, often using cloud-based data lakes (like Amazon S3) or data warehouses (like Google BigQuery).
- Analytics tools: Tools like Tableau or Power BI help retailers make data-driven decisions, giving them dashboards, reports, and real-time analytics.
Making it all work: Integration patterns
In an API-first approach, every service connects through APIs, while an event-driven architecture ensures that real-time events (like customer purchases) drive the system. Large applications are swapped out in favor of smaller, independent microservices that are easier to update and maintain. Serverless computing means retailers can focus on building features, leaving cloud providers (such as AWS Lambda or Google Cloud Functions) to handle the infrastructure.
Scalability as standard (and automatic)
A cloud-native data system can scale automatically based on fluctuating demand.
- Auto-scaling capabilities: Traffic and usage informs the system’s capacity. During peak shopping seasons, the system can scale up to handle extra demand, then scale down again to save costs.
- Resource optimization: Only pay for what you use with efficient resource allocation.
- Cost management: Avoid surprise bills by using the cloud platform’s tools to track spending and manage costs.
- Performance monitoring: Keep track of system health, performance, and security using continuous monitoring tools like AWS CloudWatch or Google Stackdriver.
Source: Snowflake
Measuring the success of your retail analytics strategy: Key metrics and KPIs
How do you know if your strategy is working? Your business needs clear ways to measure its success. By tracking the right metrics, you can ensure that your data strategy is boosting revenues, improving your CX, and making your business more efficient.
We’re going to look at two different sets of metrics. The first are business metrics, which focus on outcomes like sales and customer satisfaction; the second are technical KPIs, which measure system performance.
Retail business analytics strategies: Business metrics that matter
These metrics show how your retail data strategy is impacting the overall business:
- Revenue impact: Have sales increased?
- Cost reduction: Are you looking at lower costs as a result of improving inventory management, streamlining operations, and reducing waste?
- Customer satisfaction: Your customer experience can be measured through your net promoter score (NPS) and customer satisfaction (CSAT) score.
- Market share: Understand your competitive position.
- Customer retention: Repeat business and loyalty program engagement are key indicators of success.
Technical KPIs for evaluating system performance
To ensure the retail data system is working smoothly and efficiently, it’s important to track the following:
- System performance: Tracks uptime and downtime.
- Data accuracy: How clean and reliable is your data?
- Processing speed: Tracks how quickly your system processes data and generates insights.
- Platform reliability: Can your system scale quickly without failure?
Intellias’s partner ecosystem: Implementing a data strategy for retail businesses
At Intellias, we work with leading technology partners to help retailers build bespoke data-driven solutions that are secure, scalable, and meet their business objectives. From data management to customer engagement and security, we offer end-to-end services so retailers can make better decisions to increase revenue and attract more customers.
Cloud providers
Intellias partners with leading cloud platforms like AWS, Azure, and GCP to build cost-effective retail data architectures that can scale securely and integrate real-time inventory updates and dynamic pricing. As a certified partner of major cloud providers, we offer our customers access to the latest cloud technologies.
Customer data platforms (CDPs)
By integrating Bloomreach, Intellias helps retailers unify customer data, enabling personalized shopping experiences and improved marketing strategies. Intellias offers custom Bloomreach integrations tailored to a retailer’s specific customer engagement needs.
Analytics tools
With Snowflake, Intellias provides scalable and flexible data analytics solutions, empowering retailers with deeper insights and better decision-making. Intellias specializes in creating custom analytics pipelines using Snowflake, ensuring data flows seamlessly from ingestion to insights.
Security solutions
Intellias partners with Riskified to strengthen eCommerce security with tailored fraud detection solutions and protect sensitive customer data — all while increasing the approval rate for legitimate transactions. Intellias also integrates Riskified with other security tools for enhanced protection.
Conclusion: Time to plan your retail data strategy
A strong retail analytics strategy can be a retailer’s secret weapon in 2025. Unified customer profiles can fuel deeper customer personalization, helping brands build stronger relationships with their shoppers.
Businesses that can process data in real time to personalize customer experiences — while guaranteeing the highest levels of security — will stand out in a crowded market. At Intellias, we help retailers use their data to push the limits of what’s possible. Does that sound like something you’re ready for?
