What is Retail Data Analytics, and How Can It Transform Your Bottomline

What is Retail Data Analytics?

Inflation in Western economies has impacted the retail sector significantly. The rise in costs due to global supply issues and energy price increases has led consumers to change their spending habits. People are reducing non-essential purchases and showing a preference for ‘specialist’ food and drink brands over regular groceries. This is the reflection of a new trend towards choosing quality products even in a constrained spending environment. And how was all this analysis possible? The use of data analytics to optimize retail operations has added a new dimension to the industry.

Retail data analytics turns the art of running a business into science by accumulating sales data, identifying trends and delivering them in an easy-to-read and understandable format for intelligent decision-making.

For retailers, data is the most influential asset and the biggest challenge. It’s the key to understanding what consumers want and unlocking hidden insights that bring huge opportunities to transform their bottom line. However, many retail businesses cannot leverage their existing data to its full potential. According to a report published by Forrester, 74% of firms want to be data-driven and only 29% successfully connect analytics to action.

Retail data analytics is crucial in today’s competitive retail landscape, guiding retailers toward customer satisfaction, efficient inventory management and profit maximization. This field encompasses descriptive, predictive and prescriptive analytics, using machine learning and artificial intelligence to uncover insights from various data sources, including sales transactions and online activities.

Evolution of Retail Analytics

Retail Analytics has come a long way since the concept first emerged in the 1990s. First came the reign of spreadsheets. Spreadsheets were primarily developed as financial accounting tools with investigating capabilities, including reporting and analyzing data at summary and detail levels. However, spreadsheets lack the ability to scale, preventing retailers from performing in-depth analyses of their data needed for omnichannel retailing.

Early 2000 Was the Era of Predictive Analytics

Predictive analytics uses historical data and machine learning to predict future outcomes that can help make better business decisions. The techniques used then were statistical modeling and recurrent neural networks to gain meaningful insights and become a master of their business domain. However, the retail space today has become much more complex and competitive.

Dashboards Are the Present of Retail Data Analytics

At present, the retail industry is capturing billions of data from marketplaces across the globe and retailers are struggling to effectively monitor their data to churn insights. Techniques like statistical modeling and recurrent neural networks are not enough for today’s scale of data.

Dashboards are the present and future of the retail industry. It gives a quick overview of summarized metrics with complementary visuals making it easier to understand the meaning of your data. Dashboards give you the flexibility to dig deeper into data and find specific metrics to identify red flags and explore new opportunities to pursue.

Challenges in the Retail Sector

Retailers are expanding their marketing strategies to include digital advertising, email marketing, social media and touchpoints with customers on multiple channels. As a result, internal and external data volumes have increased exponentially. They need in-depth insights to predict performance and determine prescribed actions for their targeted future. Let’s look at some of the challenges retailers face today.

Surplus Stock and Wastage

Stocking the right amount of inventory can be challenging for retailers. They are often stuck on the waste/availability seesaw. They either have costly excess inventory, which leads to wastage or understock, which lets them miss out on sales. According to the Guardian, approximately 45% of all fruits and vegetables, 35% of fish and seafood, 30% of cereals and 20% of meat and dairy products are wasted by suppliers, retailers and consumers every year. Retailers usually have to sell the overstock products at a significant discount, resulting in revenue loss. Many retailers in grocery, apparel, electronics supply and household space lose billions of dollars on surplus stock every year.

Retail Fraud

In 2022, the U.S. retail sector experienced a significant escalation in shrinkage, with losses totaling $112.1 billion, marking a 19.4% increase from the $93.9 billion reported in 2021. This rise in shrinkage is attributed to multiple factors, including shoplifting, with an average loss per incident of $514.71. Organized retail crime (ORC) contributed substantially to these figures, accounting for $41.5 billion in losses. The surge in e-commerce has correspondingly increased the exposure to online fraud. This digital dimension of retail crime poses significant challenges to existing security frameworks. Smaller retail entities have been notably affected, leading to direct economic consequences such as increased product prices.

Omnichannel Integration

Retailers are tapping into diverse buyer touchpoints to sell their products. According to a recent report, consumers utilize most of these channels and expect similar capabilities everywhere. Retailers now need to account for the ease of shopping experiences across various channels. Effective omnichannel capabilities require robust back-end systems. Companies with successful omnichannel customer engagement have reported a significant increase in annual revenue. Early movers have stolen the advantage and the rest have since been playing catch-up. It is a formidable challenge for retailers to build a strategy to utilize customer segmentation, journey mapping and technology integration​.

Lack of Knowledge for Decision Making

Though retailers possess huge amounts of data, they fail to extract insights from such massive datasets to make intelligent business decisions. Being a retailer, they are expected to be highly informed to make several decisions while releasing new products, such as:

• Which stores should have the supply of new products?
• Which flavor does a specific area love the most?
• Which store has the maximum demand?
• How to display the product?

Answers to these questions can determine the difference between profit and loss, but retailers struggle to have insights into granular level data. As data volumes are exploding, analyzing all of it to get actionable insights becomes increasingly difficult for retailers.

Ever-Evolving Customer Demand

To be at the top of the game, retailers must understand and adapt to customer preferences quickly. To maintain a competitive edge, retailers need to have a better understanding of their customers but analyzing years of historical data of loyal customers can take them hours and slows down their actions.

Types of Retail Data Analytics and How they can Help your Business

Retail data analytics encompasses diverse methodologies and tools to extract actionable insights, serving the multifaceted nature of this industry. Here are the most important types of retail data analytics:

Descriptive Analytics

Descriptive analytics helps retailers analyze “what” is happening in their business. It summarizes the performance of multiple business actions such as inventory changes, transactional history, promotional event success and so on. Retailers have been using descriptive analytics for retail for many years to make sure it answers the questions like:

• What are the response rates of the email campaign?
• What is the lead conversion rate?
• How many users visited the website page and the time they spent?
• But it doesn’t answer the “WHY.” To know the answer to “WHY,” retailers should combine descriptive analytics with other types of data analytics to recognize patterns and correlations.

Diagnostic Analytics

Diagnostic analytics uses statistical analysis, algorithms and sometimes, machine learning to perform an in-depth analysis of data and find correlations between multiple data points. Retailers use it to find anomalies and potential business problems as they occur, to understand what is going wrong and why.

Predictive Analytics

Predictive analytics helps retailers forecast the latest trends and shopper behavior by analyzing historical data and the relationships between variables. It tells the retailer – what’s next? This type of analytics uses determinations from both descriptive and diagnostic analytics to predict future outcomes. Predictions always come with a certain amount of uncertainty. Predictive analytics for retail is no exception. The result must be verified with analysts to understand whether the data is representative of their customers.

Prescriptive Analytics

This type of analytics helps retailers understand what actions will lead to the best possible future outcomes. Prescriptive analytics helps retailers address use cases such as –

• Adjustment of product pricing based on expected customer demands and other factors.
• Flagging nominated employees for further training based on incident reports in the field.
• Specific tools like customer analytics, market basket analysis, inventory analytics, price optimization, location analytics and sentiment analysis are applied according to their need. These analytical structures orchestrate customer insights, product associations, inventory management, pricing strategies, store placements and public sentiment analysis. Individually and collectively, they optimize customer experiences, ensure inventory efficiency and employ dynamic pricing for profitability.

Advantages of Retail Analytics

Increased Sales: Increase in sales happen by optimizing product offerings based on customer behavior and buying patterns.
Customer Experience: Personalization of marketing and sales strategies enables a tailored and satisfying shopping experience for customers.
Inventory Management: Predictive analytics for demand forecasting reduce the cost of overstocking and understocking.
Marketing Effectiveness: Customer data analysis for effective marketing campaigns targeted at specific customer segments result in higher conversion rates and better customer engagement.
Pricing Strategies: Effective pricing strategies based on granular analysis attract customers while maintaining optimal profit margins.
Operational Efficiency: Businesses can identify inefficiencies from supply chain to sales floor layouts to reduce operational costs and improve customer service.
Data-Driven Decision-Making: Real-time insights help retailers to respond effectively to market changes and consumer trends.

Retail Analytics Best Practices

With small retailers and big corporations in equal competition, the retail industry is a dynamic environment. Businesses need to optimize all aspects of their operations to succeed in this space. Ensuring the application of these best practices can help businesses leverage data analytics to its full potential:

High Data Quality: Prioritize accuracy, completeness and reliability in data collection to ensure valid analytics.
Linking Multiple Data Sources: Integrate diverse data sources such as sales, inventory and customer information for a comprehensive business view.
Source Data Expertise: Employ skilled professionals capable of effectively analyzing and interpreting data.
Real-Time Data: Utilize real-time data for timely and informed decision-making.
Comprehensive Analytics Platform: Implement an integrated, secure and end-to-end analytics platform with effective visualizations for in-depth insights across the business.

How Kyvos Transforms Data Analytics in the Retail Industry

With growing customer expectations, volatile markets and increased competition, retailers need to discover new ways to improve performance, increase analytics speed and reduce costs. They need a reliable retail analytics solution that can help them analyze massive volumes of data instantly.

Kyvos helps retailers scale limitlessly and analyze enterprise-wide data to get instant insights into every aspect of their business. Kyvos creates an acceleration layer on top of existing data platforms, allowing retailers to use their existing BI tools for interactive access to massive data on the cloud and on-premise data lakes.

With Kyvos’ retail analytics platform, retailers can get a new level of interactivity. They can deep dive to the lowest level of granularity, something that is not possible with traditional BI technologies. They can create dashboards, time-series graphs and more on vast amounts of data and get summarized results of critical metrics across all business areas, including sales, marketing, supply chain and finance.

The analysis that took days and weeks can now be performed within minutes and seconds, helping retailers lower the time-to-insight and uncover those hidden retail data insights that can give a competitive advantage in the market.

Benefits of Using Kyvos:

YoY Analysis for Higher Accuracy

lot of resources and time. Kyvos eliminates this challenge by processing data models in advance. For instance, if you want to analyze two years of historical data, you get results instantly without any performance challenges. To learn more about how Kyvos helped a leading US-based grocery chain transform retail data analytics, click here.

Quicker Incremental Builds for Fact Adjustment

The retail sector often needs to adjust the facts data. For example, when a customer purchases items worth 1000 bucks and then, in a few days, for some reason, makes a return worth 300 bucks, it affects the specific transaction. Kyvos provides quicker incremental builds for fact adjustments. It helps retailers handle these types of adjustments in a cancel-correct or delta manner. Retailers can directly load adjustment data (specific transactional facts) in Kyvos without overriding old data. The time consumed to create an incremental build is reduced up to the order of a few minutes since no processing is done for the dimensions and existing facts data of the cube and the system quickly processes the adjustment data.

Deep-dive to Transaction-level Data

Kyvos enables retailers to map purchase data with other available metrics such as customer demographics and social portrait to provide an in-depth understanding of their behavior and purchase patterns. Retailers can deep-dive into any metric to analyze frequently bought items, store visits, frequency of buying, etc. Connecting the dots and identifying patterns helps predict future purchases and plan store inventory more effectively.

The Future of Retail Analytics

Economic Stratification Response: Retailers are adjusting to varying consumer economic statuses, affecting pricing and product assortment. They will increasingly leverage analytics to tailor pricing and products according to these differences, ensuring accessibility and profitability.

AI and ML in Pricing: Retail analytics, powered by AI and ML, will enable dynamic pricing models. It will provide insights into market trends, competitor strategies and customer demand, allowing retailers to adjust prices in real-time for optimal revenue management.

Offline Conversion Tracking and Digital Receipts: Analytics will enhance the understanding of offline consumer behavior by tracking digital receipts and loyalty program interactions. This data will provide insights into purchasing patterns, preferences and in-store behavior, aiding in the creation of more targeted marketing and sales strategies.

Omnichannel and Personalized Experiences: There is an increasing focus on integrating online and offline channels to provide personalized shopping experiences. By analyzing data from various customer touchpoints, retailers can create customized product recommendations and marketing messages, enhancing customer engagement across all channels.

Retail Automation: Automation technologies are being employed across various aspects of retail, including in-store operations and warehouse management, to enhance efficiency and productivity.

Augmented Reality (AR) Usage: AR is being used for interactive and immersive shopping experiences, such as virtual try-ons and in-store navigation enhancements. Adding data analytics to this mix will enable retailers to offer tailored recommendations based on purchase behavior.

Smart Store Technologies: Technologies like RFID and QR codes are being adopted for smarter store operations and enhanced customer experiences. Advanced retail analytics will be able to gather insights into product interactions and store layout effectiveness.

Conclusion

The growing complexity and volumes of retail data have warranted the quick and steady progression from basic tools like spreadsheets to sophisticated dashboards. Challenges like surplus stock, data analysis and changing consumer behaviors highlight the need for advanced analytical strategies. Retail data analytics is critical in transforming business decision-making from intuitive to data-driven.

Various types of analytics (descriptive to prescriptive) partake in providing a better understanding of business dynamics and forecasting sales trends. The future trends point towards AI and ML integration, omnichannel experiences, automation, augmented reality and smart technologies, underlining a shift towards more technologically integrated, customer-centric retail operations.

In light of all these developments, both suppliers and retailers need a self-service, easily accessible, interactive platform to follow the trail of their data, create better strategies and enable informed decision-making. With an innovative solution like Kyvos, you can bridge the gap between technology limitations and your business needs.

Kyvos has recently made major developments which can immensely benefit the retail industry. From a cloud-based analytical data warehouse for faster query responses to a universal semantic layer for streamlined data analysis, data mesh frameworks for improved data governance and advanced BI & reporting features, the platform helps improve retail businesses with data-driven analytics. To know more about Kyvos, click here.

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