Data analytics help retailers to understand all the features of the chain business processes

Making decisions for a big chain store is a task that requires a quick response, data analysis, and deep knowledge of the retail chain and the influence of external factors. At any point in time, in order to make decisions, you need to see both the details and see the whole thing.

Datawiz BES provides data analysis to retail chains of any profile by Big Data methods and AI algorithms. End-to-end analytics, business intelligence, forecasting, identifying data, and other patterns make decisions faster and more accurate.

Datawiz solutions are often used by grocery chains. Since the range and number of checks are the largest there.

On the Datawiz BES platform, chain stores can analyze the data and calculate the probabilities of future purchases based on customer segmentation according to their preferences.

In the process of chain analysis, you can find out what brands customers prefer in different stores. In addition, you can analyze the quantitative ratio of male and female customers. You can analyze regional differences, location, and an assortment of the store. This data analysis provides insight into what influences the shopping cart.

On the other hand, data on individual stores make it possible to divide customers into clear segments (clusters) and to calculate the probability of future purchase of certain goods in every customer cluster.

Such data analytics allows you to discover patterns. Thanks to them, it is possible to predict the customers' behavior from each segment and make management decisions based on data analysis, whether it's promo campaigns or product delivery issues.

After all, it is much more convenient to make decisions when it's clear what priorities different groups of buyers have. Then the chain store can offer exactly what the customer needs - without additional surveys, or research, and without a long technology integration.

Datawiz BES allows you to work with data not only from retail outlets but also online stores. In this case, it is necessary to carry out another data analysis, namely the classification of accounts. Define multiple customer profiles, their favorite product categories, and average cart amount.

Client profiles could be divided as follows:

•Young mother - goods for children, clothes;

• Fashionista - women's clothing and accessories;

• Deviceman - appliances and accessories;

• Shoes - men's and women's shoes;

• Mainstream - inexpensive goods from the categories of clothing, footwear, and technology;

• Businessman - average order over $1,000;

• Others - sports, auto parts, or other non-standard product categories.

Such classification will allow retailers to recommend products from categories that customers are usually interested in and make special offers in each specific category.

Understanding the customer's lifestyle through analytics allows you to make more useful recommendations and win customer loyalty. The predicted result is an increasing of the average receipt.

With data and analysis, you can learn and understand the properties of any process in your business. The client's lifestyle and income level are just the beginning.

Deep analysis and forecasting will allow you to see the whole thing without missing the details. In a competitive environment, Big Data and AI algorithms can become an important advantage. This is exactly the case when knowledge is power.