Role of data mining in retail industries

In the dynamic and fast-growing retail industry, the consumption of goods increases day by day which in turn increases the data collected and used. The retail industry includes the sales of goods to the customer through retailers. It covers from a local booth in the street to the big malls in cities. For eg: The grocery shop owner in a defined area would know about their customer details after-sales for few months. When he notes the need of his customer, it would be easy to enhance the sales. The same happens in the big retail industries. They collect customers’ responses to a product, the time zone, their location, shopping cart history, etc. Preference of brands and products help the company to create targeted ad to increase the sales and profit.

Knowing the customers:

What is the purpose of sales if the retailer doesn’t know who their customers are? It’s a definite need to understand about their customers. It starts by analyzing them with various factors. Finding the source by which the customer gets to know about that retailing platform would help in enhancing the advertisement of retailers to attract a completely new set of people. By finding the days they have frequently purchased can help in discount sales or special boost up on festival days. The time they spend buying per order can give us useful statistical data to enhance growth. The amount of money spent on the order can help the retailer in separating the customer crowd into groups of High paid orders, medium-paid orders, and low-paid orders. This will increase the targeted customers or help in introducing customized packages depending on price. By knowing the language and payment method preferences, retailers can provide required services to satisfy the customers. Managing a good business relationship with the customer can gain trust and loyalty that can bring a rapid profit for the retailer. The retention of customers in their company will help them to withstand the competition between similar other companies.

RFM Value:

RFM stands for Recency, Frequency, Monetary value. Recency is nothing but the nearest or recent time when the customer made a purchase. Frequency is how often the purchase had taken place and Monetary value is the amount spent by the customers on the purchase. RFM can surge monetization by holding on to the regular and potential customers by keeping them happy with satisfying results. It can also help in pulling back the trailing customers who tend to reduce the purchase. The more the RFM score, the more the growth of sales is. RFM also prevents from sending over requests to engaged customers and it helps to implement new marketing techniques to low ordering customers. RFM helps in identifying innovative solutions.

Market-based analysis:

The market-based analysis is a technique used to study and analyze the shopping sequence of a customer to increase revenue/sales. This is done by analyzing datasets of a particular customer by learning their shopping history, frequently bought items, items grouped like a combination to use.

A very good example is the loyalty card issued by the retailer to customers. From the customer’s point of view, the card is needed to keep track of discounts in the future, incentive criteria details, and the history of transactions. But, if we take this loyalty card from a retailer point of view, the applications of market-based analysis will be layered inside to collect the details about the transaction.  

This analysis can be achieved with data science techniques or various algorithms. This can even be achieved without technical skills. Microsoft Excel platform is used to analyze the customer purchases, frequently bought or frequently grouped items. The spreadsheets can be organized by using ID as specified for different transactions. This analysis helps in suggesting products for the customer which may pair well with their current purchase which leads to cross-selling and improved profits. It also helps to track the purchase rate per month or year. It manifests the correct time for the retailer to make the desired offers to attract the right customers for the targeted products.

Potent sales campaign:

Everything nowadays needs advertising. Because advertising the product helps people know about its existence, use, and features. It takes the product from the warehouse to the real world. If it has to attract the right customers, data must be analyzed. This is the right call to sales or market campaign performed by the retailers. The marketing campaigns must be initiated with the right plans else it may lead to loss of company by over-investing in untargeted Advertisements. The sales campaign depends on the time, location, and preference of the customer. The platform in which the campaign takes place also plays a major role in pulling the right customers in. It requires regular analysis of the sales and its associated data taking place in a particular platform at a certain time. The traffic in social or network platforms will give us the favoring of campaigned product or not. The retailer can make changes in the campaign with the previous statistics which rapidly increases the sales profit and prevents overspending. Learning about the customer profits and the company profits can enhance the usage of campaigns. The number of sales per one campaign can also guide the retailer on whether to invest in it or not. A trial-and-error method can be converted into a well-transformed method by the efficient handling of data. A multi-channel sale campaign also helps to analyze the purchases and surges the revenue, profit, and number of customers.

Data Mining plays a major role in segregating useful data from a heap of big data. By analyzing the patterns and peculiarities, it enables us to find the relationship between data sets. When the unprocessed raw data is processed into useful information, it can be applied to enhance the growth of many fields we depend on in our day-to-day life.

This article shows the data mining role in the retail and telecommunication industries.