IBM’s findings show that only 14% of customers are satisfied with their eCommerce experience. Even fewer–a mere 9%–can say the same about in-store shopping. But what if there’s a way to secure a position among the best experience-centric retailers while reducing operational costs and preventing issues on all levels?
It is possible if you know your customers and your potential. In other words, if you have the right information and tools to process it. That’s the power of data analytics in retail, and we’ll delve into its particularities in this article, exploring how it works and how it can benefit you specifically.
The Rising Importance of Analytics for Retail
63% of professionals across industries admit that data analytics increased their productivity. Data intelligence tools helped 90% of revenue operations professionals expand their markets. 70% of marketers reduced their spending thanks to better visibility and precise targeting.
92% of global business leaders report that investment in data analytics creates measurable results, yet only one in five has a data culture established in the company. Retail is no exception in driving benefits from data. For example, advanced analytics is the largest emerging source of value creation for grocers.
Retail data analytics is about knowing more and getting more accurate insights into your industry, customers, logistics, and other factors of great importance for your decision-making. Backed up with the right team and tools, it drives several benefits.
The Benefits of Retail Data Analytics
The analytics in the retail sector empowers businesses to accelerate and enhance decision-making across the value chain. Below are the most outstanding benefits of this technology for retail and eCommerce:
- Enhanced customer personalization: Data analytics helps retailers make immediate decisions on what to offer individual customers.
- Optimized inventory management: Analytics-driven demand forecasting reduces operational costs by managing item availability and predicting demand.
- Improved supply chain efficiency: Understanding what, when, and where exactly you need eliminates all the inefficiencies of guessing and inaccurate predictions.
- Better customer retention: Analytics in retail highlights what customers or sectors require increased attention and allows for adjusting the offers appropriately.
- More effective marketing campaigns: Retail customer analytics offer insights into your audience’s preferences, demographics, buying behaviors, and more, aiding in more precise targeting.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“Retail is all about understanding your customers and anticipating their needs. It always was, but businesses are better equipped with data now. We’re not just talking about numbers; we’re talking about turning those numbers into an actionable checklist.”
Types of Data Analytics in Retail
The types of retail data available for analysis are as diverse as methods for interpreting them. We’ve broken down the most common types of retail analytics using several classification approaches to help you better understand what information retailers typically gather and how it can be useful.
By Device Data Analytics
It’s essential to understand the particularities of interactions across different platforms to optimize the overall shopping experience and extend your reach. The main retail analytics examples here are:
- Mobile analytics includes metrics like session length, bounce rates, and app-specific conversion rates. Retailers can use this data to enhance mobile experiences, ensuring fast, seamless, and user-friendly interfaces.
- Retail web analytics shows customer browsing patterns, clickstream data, product views, conversion rates, etc., on PCs and laptops. The insights from retail web analytics help improve website design and optimize the user journey.
By Temporal Focus
You can look into analytics grouped by their time orientation, focusing on past performance, current conditions, or future predictions. These data allow you to see the dynamics in performance and serve a specific purpose in shaping a strategy.
- Descriptive analytics focuses on historical data to understand trends, behavior, and performance. Retailers use these insights to set realistic benchmarks for performance and identify patterns.
- Diagnostic analytics delves into why things happened (instead of what), helping identify the root causes of retail analytics trends or anomalies.
- Predictive analytics: It relies on historical data combined with current performance to forecast future outcomes, trends, and behaviors to predict future outcomes.
- Prescriptive analytics: Based on predictive analytics, it suggests specific actions to take to optimize decision-making on all levels.
- Real-time analytics: This feedback enables immediate insights into various aspects of your process and business performance and corresponding responses.
By Channel Analytics
Businesses need to track how each channel contributes to the overall customer experience. This group of data analytics for retail helps understand and optimize performance and engagement across all sales and interaction touchpoints.
- Omnichannel analytics: It examines customer interactions across all channels: in-store, online, mobile, and social media.
- Location analytics: It focuses on geographic data to optimize store locations, foot traffic, and marketing efforts in specific areas.
- In-store analytics: It shows how long shoppers stay in a particular store, what products they interact with, and which displays attract the most attention.
- eCommerce analytics: Data analytics in eCommerce refers to insights into online retail analytics, such as website performance, shopping behavior, and online conversion rates.
Retail Analytics Use Cases
Data analytics has revolutionized how retailers operate. The diversity and depth of the insights you can get now help fine-tune everything from customer interactions to back-end operations. There’s a common misconception that only large businesses can afford to take advantage of advanced analytics in retail. However, this is not true.
Data analytics is affordable and can be game-changing for retailers of all sizes and specializations. Medium and small businesses can start with smaller datasets and expand their capabilities over time. The versatility of use cases proves it.
Customer Segmentation and Personalization
Customer segmentation is one of the most powerful use cases for data analytics in the retail industry. You can extract detailed information about your audience and categorize them according to behavior, preferences, demographics, etc. Based on this, you can create highly personalized marketing strategies that target narrow groups and individual preferences.
You can show tailored product recommendations, offer discounts based on a customer’s past purchases, or launch personalized email campaigns. This custom approach boosts engagement and increases customer loyalty, known drivers of repeated purchases.
Demand Forecasting
Predicting demand allows for avoiding stockouts, preventing overstocking, and cutting operational costs since you only invest in the necessary items and have them in the right place at the right time. It also prevents losing customers who could buy the missing items from a competitor.
Demand forecasting in retail with machine learning relies on historical sales trends and seasonal fluctuations. It can also consider store analytics and external factors like economic conditions or weather patterns. You can include parameters at all supply chain chains to provide a better overview and ensure product availability.
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Dynamic Pricing Strategies Development
One of the latest applications of machine learning in retail, dynamic pricing, uses data analytics to adjust the cost of items in real time based on demand, competitor pricing, inventory levels, and other factors. With dynamic pricing, you can maximize profit margins while remaining competitive.
Dynamic prices don’t only focus on discounts. You can set lower prices for items that are not moving quickly, but just like that. You can increase the prices for high-demand products when the stock is low.
In-Store Layout and Product Placement Design
Data analytics for the retail industry assists with managing offline shops, too. Retailers can use in-store analytics to monitor foot traffic and identify high-traffic areas. It can also reveal the patterns of customers interacting with displays and products. This data helps in designing the optimal product layouts.
For example, based on retail merchandising analytics, stores can optimize product placement to put the high-margin items in the visible spot and guide customers through a layout that encourages more purchases. This approach increases sales and generally enhances the shopping experience.
Predictive Maintenance for Retail Equipment
From POS systems to refrigerators and inventory management tools, retailers need to keep everything functioning. Retailers using analytics to monitor the condition of this equipment can identify issues early and prevent them instead of fixing them afterward.
Predictive maintenance keeps the process uninterrupted, preventing breakdowns or downtime. You can track usage patterns, machine performance, environmental factors, and other equipment-related data to know what is likely to fail and schedule maintenance accordingly.
Customer Lifetime Value (CLV) Prediction
CLV helps understand the total revenue a customer is expected to generate throughout the relationship with the business, so it is one of the core metrics. Retail business analytics help calculate this number quite accurately. You can look into each customer’s purchase frequency, average order value, and overall behavior trends to understand the following interactions.
Knowing the potential customer lifetime value allows retailers to increase it. You can tailor marketing campaigns to target prospective spenders and allocate the efforts accordingly. For example, you’ll be able to focus on retaining high-value customers and nurturing lower-value ones to increase their spending.
Fraud Detection and Prevention
Retail analytics can play a crucial role in identifying and preventing various types of fraud–payment scams, account takeovers, etc. With data analytics, you can set the rules that define safe behavior and analyze the patterns that deviate from the norm to respond immediately.
For example, unusual purchasing behaviors or high-volume return requests will trigger alerts for further investigation that involve a customer support or security specialist. Machine learning models for fraud detection can react to such events more effectively than humans, flagging suspicious activities in real time.
Customer Sentiment Analysis
You can also use Big Data analytics in retail to work on brand image, analyzing how customers feel about a brand or product based on their reviews, social media comments, survey responses, and other feedback.
These data can reveal how customers feel about their shopping experience in general, as well as about specific product offerings or services. You can amplify positive sentiments through marketing activities and address issues revealed via negative feedback. In both cases, artificial intelligence transforms customer service.
Are you already using artificial intelligence for customer behavior analysis?
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The Future of Retail Data Analysis with Emerging Technologies
Data analytics in the retail industry continues to evolve, and retailers are either to embrace the new technologies or eventually be left lagging behind. AI, machine learning, IoT, and augmented reality are the future of retail analytics that have already started reshaping the retail landscape. Below are some of the most prominent trends of retail analytics blended with other emerging technologies.
In-the-Moment Personalization with AI/ML
Approximately 80% of consumers are more ready to interact with a brand providing personalized experiences. Marketers confirm this, with 54% of professionals recognizing that personalization increases engagement. Almost half of experts report increased conversions. Another study shows that personalization marketing can reduce acquisition costs by 50%, lift revenue by up to 15%, and improve marketing ROI by up to 30%.
Effective personalization should rely on retail data analysis, which would be impossible without AI/ML technology. By analyzing customer data on the fly, it can tailor product recommendations, marketing messages, and promotions to each individual in real time. Retailers get a chance to deliver hyper-relevant experiences and improve them with time.
Hyper-Accurate Demand Forecasting
According to Gartner, demand forecasting is the most common use of ML in supply chain planning. 48% of retail organizations understand this and are already focused on redesigning their supply chain networks to avoid disruptions. In addition to increasing operational efficiency and reducing expenses, it improves sustainability. Since 73% of consumers are ready to pay more for sustainable products, this is not something to ignore.
One exciting way to apply demand forecasting in retail is by analyzing retail store analytics against user searches to adjust the item diversity and layout at physical shops. 65% of consumers report using a store or retailer app while shopping offline, checking for prices and availability. Using this data will help understand the demand better at specific locations.
The Evolution of IoT-Generated Data in Retail
Artificial intelligence and the Internet-of-Things (IoT) add a new layer to retail sales analytics. The diversity of IoT items increases along with the complexity of retail environments. The IoT retail market is predicted to grow 28.2% in a decade, from 2023 to 2032. Consumers seem to enjoy it: nearly 92% of people are excited or already tried an interactive in-store display.
In store analytics from sensors tracking customer movement or smart shelves monitoring inventory levels in real time enables you to gather data more comprehensively than ever before. The same goes for route tracking, equipment monitoring, and other supply chain elements and customer interaction.
Immersive Shopping and Virtual Try-On with AR/VR
According to Shopify, retail sales analysis shows that products with 3D and AR content demonstrate a 94% higher conversion rate than those with traditional static imagery. Such items see 40% lower customer returns. Moreover, three in four shoppers admit they are willing to pay more if a company uses AR, thanks to the transparency they get before buying.
Virtual try-ons and AR technology make the shopping experience more satisfying for both parties. Mixed reality in retail is still an evolving story, but it transforms the way businesses reach and interact with customers. It is expected that immersive shopping will account for over 7% of sales interactions in nonfood segments by 2027 and will see mainstream adoption in around ten years.
Advanced NLP and the Rise of Voice Commerce
72% of retailers already plan to use generative AI to change how their organizations operate. Although the transformative change of GenAI in some aspects is overrated, NLP and Big Data analytics in retail go hand in hand. For example, marketing and sales professionals can use data analytics to extract insights into various customer segments and GenAI to tailor exclusive offers and communication pieces.
Natural Language Processing (NLP) for retail business doesn’t end with text processing and generation. We’re also witnessing the progressing use of voice search. Amazon alone sold over 500 million Alexas between June 2015 and May 2023. 74% of consumers applied voice-based AI to complete at least some part of the buying process, while 58% used voice search to learn about local small businesses.
Getting Started with Retail Data Analytics – Four First Steps
In a 2023 survey, the majority of businesses admitted that investment in data analytics was a top priority for their firms, but only 37% managed to use data efficiently. It’s already clear that retailers harnessing the power of data now will be in a better competitive position in a few years. If you are thinking about how to secure a competitive edge, consider getting started with the following steps. Based on our team’s experience, this works best as an entry point for innovating.
Step 1 – Clarify and Prioritize the Goals of Retail Analysis
Before diving into retail big data analysis, it’s crucial to define clear, measurable business goals. Think about what specific outcomes you intend to achieve. Avoid vague wordings like “improve sales” and focus on actionable and precise things like “increase customer retention by 10% over the next quarter.” It will help you align data analytics initiatives with tangible outcomes.
Step 2 – Take Stock of Existing Data and Evaluate Its Quality
Before implementing any analytics strategies, review what you already have. Make sure this data is complete, accurate, up-to-date, and relevant. Remove the duplicates, standardize the formats, and fill in the missing values to set a solid background for further data collection and processing.
Step 3 – Start with Basic Descriptive Store Analytics
Begin with gathering business intelligence and building the pool of descriptive analytics. It will help you understand user behavior and overall business performance, but it shouldn’t be only about generating reports and dashboards. Focus on identifying dependencies and patterns that provide actionable insights for decision-making and strategic improvements.
Step 4 – Experiment with Simple Predictive Retail Sales Analytics
Once you’re comfortable with descriptive analytics, you can start experimenting with predictive models to forecast future outcomes, such as sales trends or customer churn. It’s best to run small pilot tests on a subset of data first (for example, a particular retail store sales data analysis) and refine your strategies gradually, applying the evidence-based changes to a single product category or location.
Challenges in Implementing Data Analytics for Retail
By now, two things are crystal clear: data analytics can unlock tremendous value for retail businesses, but implementing these systems comes with some challenges. The role of our team is to spot the obstacles hindering the implementation of retail analytics data platforms or their use in the future and address them proactively. Based on our experience in data analytics consulting, there are four problems companies face most frequently.
Data Integration from Multiple Sources
It’s critical to understand not only the importance of data integration but also its particularities. Retailers deal with data from a vast array of sources, ranging from in store retail analytics to POS systems, mobile apps, and various third-party apps and feedback collection mechanisms. Hence, there’s a high risk of getting incompatible formats and siloed data and generating incomplete, fragmented, or inaccurate reports.
How to address it: Build scalable data pipelines and custom ETL processes (extract, transform, load) capable of integrating multiple data sources from the very start. You won’t face problems with accuracy and cohesiveness as the data pool is expanding and getting more diverse in formats.
Ensuring Data Quality and Accuracy
Retailers can collect large volumes of information, but it isn’t always clean, complete, or reliable. Inaccurate or incomplete data distorts analytics, resulting in poor decision-making and misguided strategies. You risk ending up with overstocked or understocked inventory, incorrect customer segmentation, and so on.
How to address it: Put robust data quality management frameworks in place, preferably with automated data cleansing processes. Having these validation rules helps standardize data formats, eliminate duplicates, highlight or fill in the missing information, and ensure you are dealing with reliable insights.
Responding to Dynamic Customer Behavior in Real Time
The factors triggering constant changes in consumer behavior can range from seasonal particularities to micro trends to competitor actions. What they have in common is an ability to undermine sales strategies and customer engagement, as a delayed response to any fluctuations often leads to lost revenue or dissatisfied customers.
How to address it: Real-time data analytics systems powered by cloud computing infrastructure and AI technologies can process vast amounts of data instantly. They enable retailers to react momentarily based on retail sales data analysis, optimizing their pricing strategies and personalizing offers.
Providing Personalization Without Privacy Intrusion
Customers expect advanced personalization. Yet, they are reluctant to share minimum personal information. Governments also support the latter, enforcing data protection regulations such as GDPR and CCPA businesses must comply with. As a result, you can get into a contradictory situation: it’s either insufficient data for proper personalization or regulatory penalties, mistrust, and reputational damage.
How to address it: Anonymization techniques, secure data encryption, and transparent data collection practices allow you to secure customer data while still delivering personalized shopping experiences.
Custom vs Out-of-the-Box Retail Data Analytics Solutions
After concluding to move on with a data analytics strategy, your next big decision would be choosing the right solution for your business. The main options are a ready-made commercial tool and a custom-built system.
The Pros and Cons of Ready-Made Retail Data Analysis Apps
Ready-made data analytics apps are available almost instantly, but they also provide less flexibility. To be more illustrative, here are the pros and cons for you to consider.
Pros:
- Normally less expensive upfront with subscription-based pricing models.
- Designed for ease of use and doesn’t require deep technical expertise.
- Pre-configured and deployed quickly, allowing you to use key features immediately.
- Come with standard pre-built reports, dashboards, and data visualization tools.
- Updates, bug fixes, and customer support are handled by the provider.
Cons:
- Default features may not cover your needs, while customization can be limited or unavailable.
- Often packed with unnecessary features or, on the contrary, lack important built-in tools.
- May require third-party add-ons or additional development to integrate with your other tools.
- Limited control over scalability potential, as well as security and compliance.
- Relying on external providers can create limitations or disruptions.
We don’t claim ready-made solutions are unusable or flawed per se. There are popular and easy-to-implement data analytics retail solutions you can configure without extra help–for example, Omnisend, Salesforce, or HubSpot.
With Omnisend, you can set up retail marketing analytics to plan your campaigns and strategies better. Salesforce and HubSpot are more comprehensive and complex platforms that will require a little more time to figure out, but you can still do it without external help. You get more advanced analytics, especially if you integrate these platforms with your POS and CRM.
The Pros and Cons of Custom Retail Data Analytics Solutions
When discussing custom solutions, it may seem we can just reverse all the pros and cons since they are the opposite of commercial tools. Yet, it’s not entirely true, and there are some details to account for.
Pros:
- Tailored to specific data analytics retail needs of your business.
- Built with enhanced scalability in mind to accommodate large datasets in the future.
- Full ownership and control of the system without dependence on third-party vendors.
- Customizable reports and dashboards for more targeted retail analysis.
- Designed to fit perfectly with your current software stack.
- Custom security measures and full compliance with specific industry regulations
Cons:
- Higher initial investment but usually more cost-effective in a long-term perspective.
- Longer overall development time, although an incremental approach allows for gradual implementation and use of the needed features.
- Requires ongoing technical expertise to create and maintain.
- More prone to delays or failures if poorly planned or managed.
To determine which option works best, consider your needs, budget, long-term goals, and the features ready solutions offer. There is no one-size-fits-all answer, but weighing the pros and cons of each approach will help you set the search criteria and priorities.
Start Leveraging Retail Data Insights Like a Pro
Regardless of what solution you choose, one of the advantages of strategic technology consulting is the opportunity to facilitate the adoption of the retail analytics solution you choose, especially when your partner has extensive data and AI/ML expertise. We’ve mentioned that it’s possible to configure some off-the-shelf platforms without extra help. Yet, the setup can get time-consuming and come with some mistakes. Having a trusted partner solves this and several other difficulties.
Partner with a Reliable Retail Business Analytics Development Vendor
SPD Technology provides expert guidance at every step of data analytics planning and implementation. With almost 20 years in software development, we know how to navigate the complexities of data accuracy and insights generation. We develop complex applications that adapt to evolving business needs and help companies stay competitive. Below are the main reasons why numerous retail companies choose SPD Technology.
Strategic Partnerships with Leading eCommerce Businesses
Our team has built long-term relationships with world-renowned brands, including BlackHawk Network – a global leader in prepaid and gift card solutions. These collaborations helped us develop a deep understanding of the retail domain and solutions brands need to manage high-volume, complex, and multi-user environments.
Data and AI/ML Engineering Expertise Blend
Experts at SPD Technology have a profound knowledge of data engineering and AI/ML technologies. Whether you’re looking for in-the-moment personalization, predictive analytics, real-time decision-making, or something else, we have the skills and expertise to handle this request. The team will suggest a retail business analysis solution that can drive results.
Data Analytics Services Focused on ROI
We understand the business part of the work as well as the technical part. That’s why our data analytics services are always designed with ROI in mind. The team will create a solution you can use to turn the retail data insights gained at all stages and touchpoints into real business value.
Customized Data Solutions
Our team has been working with various domains and perfectly understands the value of customized retail data analytics solutions–scalable, high-performance, and delivering tangible results. Whatever your objectives and ultimate goal are, we can design or set up the functionality to support those ambitions.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“The reality is that data science in retail is no longer an option for those who want to stay competitive—it’s a necessity. But that doesn’t mean you need to dive into the deep end from day one. With the right steps, even if these are small right steps, any retailer can tap into this power and scale over time.”
SPD Technology’s Success Stories of Retailers Using Analytics
Nothing answers the question “what is retail analytics” more clearly than demonstrating the real-world examples of the solutions integrated with this technology and their impact.General insights, statistics, and advice can explain how everything is supposed to work theoretically. The following case studies will ensure that data analytics strategies and platforms drive real business value in practice by offering concrete evidence.
Case #1 – High-load Support AI Chatbot
Business challenge
Our client, a B2C eCommerce company operating in the fashion niche, aimed to implement the benefits of using chatbots and virtual assistants in their customer care flow. The intention was to automate support by integrating a chatbot that can handle high peak loads. It required creating a unified data format, ensuring response accuracy, and deploying a scalable solution.
Our approach
The development team chose the LangChain agent powered by Mistral LLM to serve as the foundation of the solution’s design. We implemented guardrails that check the trustworthiness of responses and data anonymization mechanisms to design a GDPR-compliant solution. The solution was packaged as an API with configurations.
Value delivered
The client received a chatbot that responds within less than 10 seconds for 99% of user queries and is uptime 100% during peak hours.
Case #2 – AI/ML for eCommerce Product Listings
Business challenge
A US-based online retail business runs a B2C web solution with access to a diverse selection of over 1,000,000 products. The company aimed to enhance the customer experience through performance, SEO, and UX/UI optimization. Later, they requested to expand the product range and improve product management.
Our approach
The development team started with retail store analysis and optimization of its page loading speed, navigation pathways, and UI elements, crafting a new SEO strategy in parallel. We integrated ChatGPT for fast and accurate product categorization, Tesseract OCR and a custom-programmed solution for image cleanup.
Value delivered
We efficiently scraped 300,000 products in three hours and helped the client expand their inventory to incorporate over 1,000,000 products. The enhancements in speed, SEO, and design resulted in three times higher performance and a 56% traffic increase.
These are just a few examples demonstrating how retail businesses can leverage retail industry data analysis with the right tools. Our team can help you achieve similar results.
Conclusion
Data analytics is rapidly transforming the retail industry, empowering businesses to stay competitive in tough competition and future-ready. From personalization to predictive maintenance, the business impact of Big Data is already vast, and it keeps expanding continuously as AI/ML, IoT, and other emerging technologies evolve.
Given the performance statistics and predictions, investing in a suitable data analytics strategy doesn’t just give businesses an advantage – it’s becoming critical. Whether you’re a small merchant just starting out or a large enterprise seeking to refine your strategies, business analytics in retail can refine decision-making, improve customer experiences, and drive measurable growth.
Though not the easiest task, implementing data analytics in retail industry isn’t as challenging as it may seem – especially with a reliable partner. At SPD Technology, we have the expertise and experience to develop tailored analytics solutions to turn your raw data into actionable plans. Get in touch with our team to explore how we can unlock the full potential of your retail business through data analytics.
FAQ
- How Can Data Analytics Be Used in Retail?
You can use data analytics to improve nearly every aspect of process management and decision-making. It helps better understand customer behavior, improve user experience across channels, forecast demand, optimize inventory management, and more. Retail Big Data analytics can enhance in-store operations and enhance fraud detection mechanisms. In short, it helps retailers handle challenges of any complexity and on any level.