Knowing the customer’s journey and how they relate to your company’s products and services is essential to meet their expectations. Data mining can be used in businesses from various sectors of the economy: retail, telecom, financial institutions, companies in the health sector, among others.
See Some Applications Of Data Mining
Basket Analysis
This is a basic application of data mining. The basket analysis aims to identify the items purchased by customers and list them. This process looks for affinities in shopping carts. Whenever the consumer buys an item, he acquires a complementary element in the transaction.
In a pharmacy, for example, the customer who buys a pack of diapers also takes a pack of wet wipes. In an electronics store, whoever buys a cell phone gets a case.
Within a supermarket, the completion of the analysis can generate inputs to help reorganize shelves and group associated items. In this way, the establishment can achieve better sales results.
In addition, this information is essential to develop more efficient marketing strategies for companies. The methodology can also be useful for companies that provide services.
Predictive Analytics
Do you know when your audience will need your products or services? Predictive analytics serve to predict consumer actions. Data mining can find behavior patterns and identify trends by analyzing historical series and complex algorithms. You can predict the months when your business will see an increase in demand and manage inventory so that all customers are well served.
The strategy also anticipates negative scenarios. For example, customers who are likely to cancel services from a cable TV operator may exhibit similar behaviors. This information is valuable for the manager to take measures to retain the consumer.
Success Story: Target
In 2010, an executive at the North American retailer created a predictive model to anticipate customer needs and sell more. With mathematical models and analysis of consumption history, the company could predict customers’ pregnancies.
The data stored in the company’s bank is gigantic: purchases in different channels, such as physical and virtual stores, exchange of discount coupons, loyalty cards, etc. However, to predict pregnancies, a sampling of confirmed cases was necessary.
Initially, some marketing campaigns encourage mothers-to-be to inform about the pregnancy and the estimated date of birth of the babies. In this way, it was possible to obtain historical records of cases of positive pregnancies.
With this data, the company connected with the customers’ purchase records and identified indications that the buyer was having a baby on the way.
In the third month of pregnancy, the consumer buys many bottles of neutral body lotion. Two weeks later, they get vitamin supplements. This was the identified pattern!
Thus, the company could even predict the pregnancies of women who did not provide data in the previous marketing campaign. Predictive analysis served to anticipate customers’ needs and direct them towards purchasing baby products. Result: increased sales!
Database Marketing
Data: you’ve read this word several times in this text, haven’t you? And it could not be different. Any aspect of data mining has data analysis at its core! Database marketing, or database marketing, is a strategy for making industry decisions based on factual information.
Advertising campaigns, the launch of new products, the opening of new units and other activities in the sector are directed according to the data obtained and stored: demographic and psychographic information, purchase history, contacts at the SAC, surveys at the POS, interaction in social networks etc.
Gillette Case: Historic Gaffe
In 2017, Gillette launched a US campaign targeting 18-year-olds. The company sent kits with various brand products and instructions on shaving to young men who have recently reached the age of majority.
You must be wondering: but what is the problem with this initiative? The objective of the action is really interesting, if not for a “small” error. The company sent gifts to older men, teenage girls, and even middle-aged women.
Moral of the story: the company did not have a structured database! Database marketing could have avoided this blunder that reverberated around the world. Data mining in marketing minimizes the incidence of errors and results in more efficient campaigns.
Social Media Monitoring
In the virtual environment, users share information relevant to companies in a natural way. The content of individual posts, likes, comments, check-ins, searches and other elements generate a high volume of data. Therefore, social networks are gold mines for mining.
Monitoring social media is an opportunity to get to know customers better, offer personalized service, strengthen relationships, build loyalty and strengthen the brand.
However, to obtain conclusive data, it is essential to encourage the public’s interaction with the company to obtain the data that will be interpreted.
In addition to knowing your audience, following the customer’s steps on social networks guarantees your business’s competitive intelligence, as it is also possible to study the competition’s movements and identify consumption trends.
Nike Case: Engaging To Understand The Public
The sporting goods company created an app for race fans. The software was intended to provide the user with data on heart rate, distance covered, and speed, among others.
However, more than just using the app would be required. So Nike integrated the system with social media and encouraged amateur athletes to share the information on social media.
This action significantly increased the volume of data generated on the brand page. In this way, the company benefited from valuable data to better understand and serve the public.
Case Halls: Complaints On The Networks Make The Company Reconsider The Product
In 2015, the candy brand launched a new product: the Halls Mini. The release replaced another company item, the Halls XS. The packaging of both was similar. However, the launch had sugar in the formula, while the discontinued product did not have the ingredient.
Consumers did not like the news, and there was a rain of complaints on all social networks. The new formula melted more easily in the boxes, so many people bought the already unfit product for consumption.
Based on data from monitoring social networks, the company decided to end sales of the new product and resume manufacturing the bullet with the original formula.
The decision was published on Facebook and YouTube through an entertaining video with the core message: to err is human, to end error is Halls! The brand turned around and conquered the public with the initiative.
Also Read: Data Mining: What Is Data Mining, And How Does It Work
