Deep Diving into Retail Data Analytics: How Can Businesses Benefit from It?
Before the advent of retail data analytics, retailers depended on traditional business methods. They determined the demand for products based on hunches and intuition instead of using calculative methods and rationality of data and statistics. Critical business decisions like staffing, opening a new retail store in a specific location, pricing, etc., were based on intuition instead of a 360-degree view of the market and demographic study of retail store locations.
Of course, this was not the ideal approach since the retailers might end up stocking more or fewer products without a comprehensive understanding of the trends.
With retail data analytics, entrepreneurs and retailers can study customer behavior data, marketing forecasts, and product demands. With the insights generated, they can determine competitive product pricing strategies, target specific customer needs, and create personalized marketing campaigns to increase the overall efficiency and boost sales.
Retail analytics in a nutshell
Retail data analytics involves collecting and studying data on supply chain movement, sales, inventory levels, pricing, consumer demand, and competitor strategies. One of the most crucial aspects of retail analytics is the ability to foresee the competitor's moves by having access to their present data. This proves to be a game-changer for retailers.
The insights on demand and supply data too can also help retailers make crucial procurement and marketing decisions, if executed correctly. It allows retailers to gain meaningful insight into the performance of their stores and products, customer behavior, and vendors, among others. Retail analytics gives retailers precise customer insights along with a detailed breakdown of their business processes and identifies core areas for improvement.
The key dimensions that help retailers stay ahead of the competition
Digital transformation demands increased flexibility and a competitive mindset that will set retailers apart from their competitors. Gartner, Inc. predicts that 75% of companies will shift from piloting Artificial Intelligence (AI) to operationalizing it by the end of 2024. This will drive a five-fold increase in analytics infrastructure and streaming data. To achieve this, retailers must combine state-of-the-art technology and business patterns with market-disrupting innovation.
The core aspects that can help retailers maintain their competitive advantage include-
Personalized recommendations
Traditional marketing methods are getting obsolete, with personalization increasingly playing a critical role in the modern paradigm of the digital market. The reasoning behind it is simple - everyone has unique preferences and pain points. Hence every customer deserves to have a customized experience while shopping best suited to their individual needs.
Retail data analytics facilitate the study and analysis of the behavior of every customer to suggest personalized products and promotions and facilitate natural customer-brand interactions. Retail data analytics allow retailers to take advantage of the history of customer interactions with various products to construct a picture of their interests and tastes. Thus, they can make highly customized recommendations to cater to individual needs.
Demand forecasting and inventory planning
Retail data analytics use demand forecasting to project future trends in sales and predict product demand based on qualitative and quantitative customer data. It helps retailers estimate their stores' sales and revenue for a specific time frame in the future. With an approximate figure in mind, they can better determine inventory, marketing, product offering, and staffing decisions.
Historic sales data plays a crucial role in demand forecasting. It considers other aspects like economic trends, expert insights, predictions and opinions of the sales team, and customer feedback. Demand forecasting enables retailers to make data-driven decisions regarding stocking the right products in the inventory, assess the risk of launching new products, and run more targeted marketing campaigns.
Price optimization and competitive pricing
Harvard Business Review reveals that even a 1% increase in the pricing strategy can yield as much as an 11% increase in profit on average. An American-based retail shop, Stage Store, experimented on their products' pricing to confirm this. Their experiment revealed that the demand for a product increases again when the pricing gradually reduces from the period when the demand declines.
It contradicts the traditional retail practice of cutting down the price with the end of the buying season and the subsiding demand for the product. Predictive analytics plays a vital role in determining the rise and fall of product demand.
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The importance of data analytics for businesses
A diverse range of business operations and technology applications yields colossal data mounds. When retail data analytics solutions are re-defined to a more intelligent mechanism, businesses can leverage this data to gain a competitive edge.
Retailers can harness the incredible power of retail data analytics to explore new business methods and retail software solutions with the combination of forecasting, advanced analytics, a high-performance blend of insights, and visualization.
The most notable benefits of using retail data analytics are-
Predicting trends
With deep insight and analysis of the data collected regarding the pricing and selling of a product and customer interaction with it, retailers would end up stocking the right inventory. Retail data analysis predicts trends and helps retailers understand which product they should stock and in what quantities.
Forecasting demand
Likewise, analyzing the collected data helps retailers understand the demand fluctuations in products. Retail data analytics predict market demand and aid retailers in making crucial business decisions.
Understanding customer behavior
Data analytics aid businesses by enhancing their prevalent benchmark of customer activities. It offers in-depth insights into the customers’ needs and their individual preferences. Also, by accumulating data in a central location, data analytics makes it accessible to the whole customer service team and ensures a more acceptable consistency of service quality.
Optimizing pricing
Retail data analytics deals with price sensitivity, a factor that plays a critical role in determining the maximum retail price a customer will pay for a product. Unless retailers have actual data, it becomes impossible to adjust the prices of products in the retail stores and retain a competitive edge.
Retail analytics for smart retail solutions
If you are still not convinced of the importance of retail data analytics and its impact on businesses, you can look at how some of the biggest global companies have integrated it within their business models.
Enterprises like Amazon and Google rely entirely on business analytics models and use big data to provide enhanced customer services. Data analytics are widely used in hospitality, health, e-commerce, travel, and big retail chains. Using data analytics, companies offer personalized customer services and target marketing campaigns based on customer demography. It also helps them in enhancing customer experience and addressing potential service issues.
Statistics show that by the end of 2022, the global retail analytics market is expected to reach USD 8.6 billion. The figure will continue to rise over time. In such a competitive scenario, if retailers aren’t using retail analytics, they miss out on an ocean of opportunities. Hence, adopting retail analytics is the way to go for modern retail businesses.