Data analytics in retail is the process of analysing retail data, such as sales, inventory, foot traffic, and pricing, to get information about product trends, customer purchase patterns, sales patterns and sales history, footfall patterns, inventory mapping, and pricing impacts. Such analysis can help retailers predict outcomes, develop buy plans, identify present and future trends, manage inventory levels, discounts and sales, OTB (open-to-buy), and make profitable business decisions.

Benefits of Data Analytics in The Fashion Industry

•  It can help to identify vendors and suppliers who meet the specific requirements of a retailer, like a supplier who adheres to compliance standards of sustainable production.

•  It can help designers plan multiple lines and develop an optimum product mix to achieve maximum sales and reduce inventory waste.

•  It can analyse consumer buy patterns and predict their future purchases.

• Data analytics can generate reports through data visualisation. These visualisations can help to identify and rectify bottlenecks such as low productivity, production targets, equipment downtime, and occurrences that affect quality on a production floor.

• Prescriptive analytics and artificial intelligence can help brands ensure high-quality production and streamline production processes for optimisation.

•  Data analytics provide insights into the performance of products, distribution channels, and customer preferences, which can help to manage inventory levels and avoid stockouts and overstocking.

Sources of Data

Retail data can be obtained from various sources, including:

•   Point of Sale (POS) Data: The POS gives transactional data about each sale, including items purchased, quantities, prices, and returns. The data provides insights into average basket size (value and volume), customer purchase patterns, top-selling items, and customer preferences. It helps retailers track inventory levels in real time, minimising out-of-stock situations and excess inventory. Historical sales data helps forecast demand, identifying popular trends and slow-selling items. It gives essential information about individual customer purchase patterns or purchase behaviour. This information can help design marketing strategies for individual customers, such as providing special offers on birthdays and anniversaries. This will improve customer experience and engagement with the retailer. 

   The POS data helps retailers evaluate the effectiveness of pricing strategies by monitoring the impact of price changes on sales volumes and revenue. Retailers can identify the optimal price points for products to maximise profitability. This data can be used for sell-through analysis and inventory turnover, thereby helping to evaluate the store’s performance. The data can also indicate the effectiveness of specific marketing strategies that drive sales. As the data monitor, SKU movement can detect missing items in the store and pilferages. POS data can be shared with suppliers to smoother the inventory replenishment process. Therefore, this data is an important KPI for the retailer that can be leveraged to enhance retail efficiency, profitability, and customer satisfaction.

•   Web and Mobile Analytics Data: Web and mobile analytics tools, such as Google Analytics, Mixpanel, and Adobe Analytics, are integrated into websites and mobile apps to collect relevant data. The data collected may include page views, clicks, navigation paths, session duration, device information, location etc. This data helps track conversions, including completed purchases, sign-ups, form submissions, cart abandonment, landing pages, and time spent on each page. Conversion tracking can measure the effectiveness of digital marketing campaigns and website/ app performance. The data can be used to identify customer cohorts and help in customer segmentation, targeting, and product or brand positioning. This data can leverage business strategies by knowing how customers navigate various pages and what they see more. This can be done using heat maps and click maps that visually represent user interactions on websites and apps. Heat maps show areas of the page that receive the most attention, while click maps reveal where users click. This information can help to identify popular content and optimise layouts for better user engagement.

Fashion retailers can use this data to provide personalised experiences to their customers by giving suggestions based on their preferences, behaviour, and previous interactions. For mobile apps, analytics can provide insights into app crashes, loading times, and user engagement, helping businesses optimise their apps for a better user experience. The data can be used to do funnel analysis describing customers’ steps before deciding what to purchase. The term ‘funnel’ is used because the number of potential customers typically decreases as users progress through the process, similar to a funnel’s narrowing shape. An e-commerce funnel might include the steps of ‘Visited homepage’, ‘Added to cart’, ‘Initiated checkout’, and ‘Completed purchase’. Each step in the funnel represents a specific user action. If the customer does not purchase, one can identify the drop-off points that help pinpoint areas where users face issues or lose interest.

•   Retail In-store Foot Traffic and Heat Maps: Retailers can use sensors or cameras, Wi-fi, or Bluetooth to track foot traffic within their physical stores. Heat maps and customer flow analysis help optimise store layouts and product placements. Foot traffic analysis involves tracking the number of people entering and exiting the store, identifying the high traffic periods in a day and days in a week, traffic during a marketing campaign to know its effectiveness, and traffic during regular days vs. sales periods. The data can include entry and exit timestamps, the number of visitors at specific locations, and individual paths. Visual representations of the data are known as heat maps. Heat maps use colours to represent the density of foot traffic in different areas such as high-traffic regions are depicted with warmer colours (e.g., red or orange), while low-traffic regions are shown with cooler colours (e.g., blue or green). This helps to identify high-traffic areas within the store so that businesses can place popular products or promotions in these areas to increase sales. This data can help with crowd management and also for security purposes during special occasions. A retailer can identify potential bottlenecks or congestion areas and take measures to ensure the safety and comfort of visitors. It can show which areas attract more interest, which displays lead to higher engagement, and how customers navigate the space.

•   RFID and Barcode Scanning Data: Radio-frequency identification (RFID) tags and barcode scanning can help retailers track inventory movements accurately and efficiently. These technologies provide real-time data on stock levels, sales, and product popularity. They can streamline various retail processes, such as checkout, pricing, restocking, and receiving shipments, enhancing operational efficiency. It can help in faster customer checkout leading to enhanced customer experience. It helps prevent theft by monitoring and tracking high-value items more effectively. 

   RFID technology is better than barcode scanning and contains electronically stored information and can be embedded in product labels or tags. This can enable better visibility into the supply chain, providing real-time tracking of products from manufacturing to the retail store. It can be used to develop ‘smar’ shelves and displays, automatically updating inventory levels and triggering reorder notifications when items run low. Barcode scanning is a widely used method in retail to identify and track products and their information. Each product has a unique barcode printed on its packaging or label, containing product details, such as the item’s SKU, price, country of origin, and other information. When a product is scanned at the point of sale (POS) or during inventory management, the information is captured and processed electronically, speeding up the transaction process, reducing human error, and ensuring accurate pricing and inventory data.

•   Customer Surveys and Feedback Forms: Customer surveys can be conducted in-store, online, or through mobile apps. Retailers can ask about shopping experiences, product satisfaction, concerns, and suggestions for improvement.

•  Loyalty Programmes: Retail loyalty programmes are marketing strategies retailers implement to incentivise and reward loyal customers for continued patronage. It is a cost-effective method to retain customers. These programmes collect customers’ data and use it to be in touch with them. These programmes aim to foster a solid and lasting relationship between the retailer and its customers, encouraging repeat business and enhancing customer loyalty. Customers are given special discounts throughout the year; they can earn redeemable reward points giving added value during repeat purchases. There can also be points-based programmes where customers earn loyalty points for every purchase, which can be redeemed afterward for rewards or discounts. In addition, there are some other popular loyalty programmes that most retailers implement, which include tiered loyalty programmes, cashback programmes, membership programmes, and referral programmes.

•   Social Media Data: Data obtained by monitoring social media platforms can provide insights into emerging trends, the latest conversations about brands and products, social media influencers, customer sentiments, feedback, and reactions to products or marketing campaigns. Such data is enormous and can be collected from several platforms. It can be used for sentiment, cluster, network, and GIS (Geographic Information System) analysis. It can provide businesses insights about their audience, manage their online presence, and make data-driven decisions on marketing, customer service, reputation management, and overall brand strategy in an ever-connected and dynamic digital landscape. 

  For example, if the trends show that consumers are becoming more interested in sustainable fashion, designers can opt for eco-friendly materials and processes in developing products. Brands like Zara and Shein analyse social media trends to understand consumer preferences and purchase patterns to serve better. Use of descriptive and predictive analysis can help to make decisions on designs, colours, styles, patterns, and quantity of inventory needed to optimise profitability.

Steps in Data Analytics

Data analysis is a systematic process of collecting data, eliminating errors, and identifying patterns. Customer data may be segregated into groups based on age, demographics, income, or gender. Sales data may be grouped based on seasons, festivals, marketing strategy, price, product features, product categories etc. Such data can be analysed using spreadsheets or software like Microsoft Power BI, Tableau, Google Analytics, IBM Cognos Analytics, SAS Retail Analytics, Adobe Analytics, and RetailNext. The data is cleaned to remove inconsistencies like redundant or duplicate data, errors, or incomplete information. Finally, the data is represented through charts for easy visualisation and comparison.

Types of Data Analytics

Data analytics can be categorised into different types based on the objectives, techniques, and processes used to analyse the data, as discussed below:

•  Descriptive Analytics: Analysing historical sales data provides insights into trends and patterns. It can help to determine sales targets for the current year, the impact of discounts, and seasonal fluctuations in sales.

•  Diagnostic Analytics: Diagnosing the reasons behind certain occurrences during the total sale period helps identify the root cause that impacts sales, like natural calamity, increased competition, change in fashion trend etc. It uses techniques like drill-down, data mining, and correlation analysis, to explain relationships between variables.

•  Predictive Analytics: This helps in trend forecasting based on past sales data and current sales analysis. It uses machine learning and statistical modelling techniques to predict customer behaviour, product demand, and potential risks.

•  Prescriptive Analytics: This suggests possible actions or solutions to achieve specific outcomes in the future. It uses optimisation algorithms to recommend the best action based on different scenarios and constraints.

Some Other Types of Data Analytics

•  Real-time Analytics: Real-time analytics focuses on processing and analysing data as it is generated, enabling immediate actions and responses. It is crucial in applications that require instantaneous decision-making, such as fraud detection, dynamic pricing, or real-time customer support.

•  Text Analytics (Natural Language Processing - NLP): Text analytics involves the analysis of unstructured textual data, such as customer reviews, social media posts, emails, and survey responses. Natural Language Processing (NLP) techniques extract insights and sentiment from text data.

•  Spatial Analytics: Spatial analytics deals with geographical data, helping to analyse patterns, relationships, and trends based on the location of events or objects. It is widely used in location-based marketing, logistics optimisation, and urban planning.

Data Analysis Techniques

There are several statistical tools and methods to analyse data. Some of the methods include:

•  Regression Analysis: This intends to study the effects of an independent variable on a dependent variable. It helps identify the strength and direction of the relationship and make predictions based on the model.

•  Factor Analysis: This helps to shrink the larger data sets into smaller, more pertinent factors that impact a particular phenomenon. It helps to identify specific trends in the data.

•  Cluster Analysis: This groups similar data points into clusters based on their attributes or characteristics. It helps identify and provide valuable insights into the characteristics of natural groupings or cohorts within the data.

•  Time Series Analysis: This is used to identify cyclical trends and product lifecycle trends and help in product and sales forecasts.

Conclusion

Incorporating data analytics in different segments of the fashion industry is no longer a luxury but a necessity for retailers and businesses seeking a competitive edge in the market. With the vast amount of data available, merchandise managers can leverage advanced analytics tools and techniques to unlock valuable insights, drive growth, and deliver exceptional customer experiences. Data analytics positions businesses to thrive in an ever-evolving retail landscape. It plays a pivotal role in production management, inventory optimisation, sales, marketing, pricing strategies, merchandise assortment planning, agile decision-making, markdown and clearance strategies, improved customer experience, vendor and supplier management, inventory turnover, and profitability.