Significance of Data Analytics in Fashion Retail.
Fashion industry today is operating through various platforms thereby creating tremendous amount of data each second. This data can provide in-depth insights on customer behavior's and experiences, inventory optimization's, pricing strategies, merchandise assortment planning, and much more, making data analytics a necessity for retailers and businesses.
Consumer market preferences have become very dynamic and ever-changing and evolving. These preferences are influenced by various factors, including trends, societal changes, technological advancements, economic conditions, individual experiences, marketing campaigns, pricing strategies, impact of e-commerce, payment methods, accessibility and availability of products and services, experiential marketing, environmental and sustainability concerns, generational differences and other demographic factors. These factors shift over time, making the market very volatile and dynamic, affecting consumer preferences and choices. Such shifts can be visualised by analysing data available through different mediums like e-commerce, social media, physical stores, third-party reports etc. Therefore, data analysis becomes essential in retail management and determining its future.
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.
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 analyses 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.
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.
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