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Data Mining for Retail and Telecommunication Industries

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  • Last Updated : 26 Dec, 2022
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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.

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.

Role of data mining in telecommunication industries

In the highly evolving and competitive surroundings, the telecommunication industry plays a major in handling huge data sets of customers, network and call data. To thrive in such an environment, the Telecommunication Industry must find a way to handle data easily. Data Mining is preferred to enhance the business and to solve the problem in this industry. The major function includes fraud call identification and spotting the defects in a network to isolate the faults. Data mining can also enhance effective marketing techniques. Anyways, this industry confronts challenges in dealing with the logical and time aspect in data mining which calls the need to foresee rarity in telecommunication data to detect network faults or buyer frauds in real-time.

Call detail data:

Whenever a call starts in the telecommunication network, the details of the call are recorded. The date and instant of time in which it happens, the duration of call along with the time when it ends. Since all the data of a call is collected in real-time, it is ready to be processed with data mining techniques. But we should segregate data from the customer level not from isolated single phone call levels. Thus, by efficient extraction of data, one can find the customer calling pattern.  

Some of the data that help to find the pattern are

  • average time duration of calls
  • Time in which the call took place (Daytime/Night-time)
  • The average number of calls on weekdays
  • Calls generated with varied area code
  • Calls generated per day, etc.

By sensing the proper customer call details, one can progress the business growth. If a customer makes more calls during dayshift working hours, that makes them distinguished as a part of a business firm. If the night-time call rate is high, it may be used only for residential or domestic purposes. By the frequent variance in the area code, one can segregate the business calls because people calling for the residential purpose may call over limited area codes in a period. But the data collected in the evening time cannot give the exact detail of whether the customer belongs to a business or residential firm.

Data of customers:

When it comes to the telecommunication industry, there would be an enormous number of customers. This customer database is sustained for any further queries in the data mining process. For example, when a customer fraud case is encountered, these customer details would help in the identification of the person with the details in the customer database like name, address of the person. It would be easy to trace them and solve the issue. This dataset can also be extracted from external sources because mostly this information would be common. It also includes the plan chosen for subscription, proper payment history. By using this dataset, we can escalate the growth in telecommunication industries.

Network Data:

Due to the use of well-developed complex appliances used in telecommunication networks, there is a possibility that every part of the system may generate errors and messages. This leads to a large amount of network data being processed. This data must be separated, grouped, and stored in order if the system causes any network fault isolation. This ensures that the error or status message of any part of the network system would reach the technical specialist. So, they could rectify it. Since the database is enormous, when a large number of status or error messages get generated, it becomes difficult to solve the problems manually. So, some sets of errors and messages can be automatized to reduce the strain. A methodical approach of data mining can manage the network system efficiently which can enhance the functions.

Preparing and clustering data:

Even though raw data are processed in data mining, it must be in a well sensed and properly arranged format to be processed. And, in the telecommunication industry dealing with the giant database, it’s an important need. First, clashing and contrary data must be identified to avoid inconsistency. Making sure of the removal of undesired data fields heaping space. The data must be organized and mapped by finding the relationship between datasets to avoid redundancy.  

Clustering or grouping similar data can be done by algorithms in the data mining field. It can help in analyzing the patterns like calling patterns or customer behavior patterns. Group of frequencies is made by analyzing the similarities between them. By doing this, data can easily be understood which leads to easy manipulation and use.

Customer profiling:

The telecommunication industry deals with a large scale of customer details. It starts observing patterns of the customer from call data to profile the customers to predict future trends. By knowing the customer pattern, the company can decide the promotion methods offered to the customer. If the call ranges within an area code. The promotion made in that aspect would gain a group of customers. This can efficiently monetize the promotion techniques and stop the company from investing in a single subscriber but it can attract a group of people with the right plan. Privacy issues arise when the customer’s call history or details are monitored.

One of the significant problems that the telecommunication industry faces is that Customer churn. This can also be stated as customer turnover in which the company loses its client. In this case, the client leaves and switches to another telecommunication company. If the customer churn rate is high in a company, the respective company will experience severe loss of revenue and profit which will lead to its decline in growth. This issue can be fixed by data mining techniques to collect patterns of customers and profiling them. Incentive offers provided by companies attract the regular user of some other company. By profiling the data, the customer churn can be effectively forecasted by their behaviors like subscription history, the plan they choose, and so on. While collecting data from the paid customers, it’s also possible to collect data of the receiver or non-customer but with a set of restrictions.

Fraud detection:

Fraud is a critical problem for telecommunication industries which causes loss of revenue and also causes a deterioration in customer relations. Two major fraud activity involved is subscription theft and super-imposed frauds. The subscription fraud involves collecting the details of customers mostly from the KYC(Know Your Customer) documents like name, address, and ID proof details. These details are needed to sign up for telecom services with authenticating approval but without any type of intention to pay for using the service using the account. Some offender not only stops with the illegitimate use of services but perform bypass fraud by diverting voice traffic from local to international protocols which causes destructive loss to the telecommunication company. In super-imposed frauds, it starts with a legitimate account and a legal activity but with further lead to the overlapped or imposed activity by some other person illegally using the services rather than the account holder. But by collecting the behavioral pattern of the account holder, if a suspect is found on super-imposed fraudulent activities it will lead to immediate actions like blocking or deactivating the account user. This will prevent further damage to the company.

These fraudulent activities can be reduced by using data mining techniques to collect information of the customer and patterning their behavior like call details as said earlier can lead to the detection of frauds. When the data detection is performed in real-time, the frauds can easily be identified. This can also be done by comparing the account of suspected call behavior with the general fraud profiles. If the call pattern matches that of generic frauds, they can be detected. Instead of collecting data at the individual user level, collecting data from the customer level can enhance this fraud detection process. Sometimes the wrong classification of frauds may cause loss to the company. So, they must know the relative price of letting go of a false call and blocking a suspect for fraudulent activities with a legal account. The correct use of data mining would help in dealing with this issue with accuracy.

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