Predictive Analysis: A Must Have feature for Retailers

Converting one-time customers into regular brand consumers is one of the most daunting tasks for retailers in a competitive market. Indeed, the quantity of data generated by a single transaction today may lead to the production of important insights that may be utilized to convert consumers to customers. To properly comprehend and engage this new wave of customers, businesses must continually monitor and analyze all data points and trends across all company processes. Using a retail ERP system, small and medium-sized enterprises may reduce human errors in their supply chain and improve the efficacy of their services. However, more crucially, they employ predictive analytics to provide better customer service.



Every year, companies collect a outrageous amount of data, and this figure is increasing at an exponential rate. According to IDC, the global datasphere will contain 175 Zettabytes (ZB) of data by 2025. In comparison, in 2018, that amount was 33 ZB.


Retail outlets platforms collect data quickly, which helps to generate data-driven insights and make critical strategic choices. By merging retail data with predictive algorithms, you can anticipate consumer demands and create a tailored experience for each individual customer.


But what exactly is Predictive Analytics?


Predictive analytics is a type of advanced analytics that uses data to predict future trends, consumer behavior, and actions by obtaining important insights and generate predictions about how things will behave in the future. This is done by using variety of computing techniques such as predictive modelling, data mining, AI tools and algorithms. With such information, retail businesses can produce better items, target appropriate customers, increase conversion rates, and remain ahead of the competition. It also enables demand forecasting, inventory management and movement, and assortment planning by evaluating behavioral patterns.


Predictive analysis is one of many ways used by retail sectors to expand the market and include services and commodities. As an enlarged component of business intelligence services, it has shown to be highly beneficial in estimating time-frame demand for products and services required in specific places.


Some of the advantages of Predictive Analytics in Retail Business are:

  • Improved Price Optimization:

Analyzing a customer's behavior may help assess demand for items and services, which in turn determines the proper cost for a product. Price determination can be achieved by comparing two or more commodities in the same category, which takes time and makes real-time price modifications difficult.

Predictive analytics will rely on large data sets. Pricing, purchasing history, planned profit margins, available goods, browsing history, and other variables are all considered which in turn aids in the real-time tracking of all the aspects while comparing the results to data from the current and prior year.

  • Inventory and Supply Chain Management made simple:

Retail ERPs and their Predictive Analytics improves inventory management by identifying popular and unpopular items as well as product categories. It assists retailers in forecasting sales, improving sourcing, order fulfilment, delivery, and customer returns. In addition, merchants may prevent "out of stock" products and improve inventory management in their online store. Additionally, the retailer might benefit from improved warehouse space use and cash flow management.

  • Increase engagement and personalization:

Perhaps one of the most difficult test retailers face in a commoditized area is changing over single one-time purchasers into regular consumers. As a matter of fact, the amount of information produced by a solitary deal today might help give significant bits of knowledge that can be used to change purchasers over completely to clients. Large organizations are now utilizing this to follow shoppers' ways of behaving, search chronicles, purchasing decisions, and the sky is the limit from there. By consolidating retail information into predictive modeling, a retailer can predict customers’ needs and foster a personalized experience for each encounter for each customer.


Customer expectation and personalization define retailing in today's world. Retailers who develop and use ERP have a competitive advantage over their competitors. Arure has worked with a variety of retail clients throughout the years. So, get your free Arure Solutions demo now.


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