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7 Ways Data Mining Trends can be Applied to Your Business

Data mining is not a recent trend. For years, it has been employed in various industry sectors such as banking & finance, logistics and retail as a means of analysing business intelligence. The process involves the use of complex algorithms, which facilitates the identification of patterns, classification and clustering that allow data segmentation. By analysing data, in the analog and digital formats, data mining helps to identify trends and variations as well as to forecast the probability of future events.

Data mining is classified into five main types, which depend on the nature of the data analysed. These include multimedia, ubiquitous, distributed, spatial and geographical, and time series and sequence data. While it is commonly considered a tool for financial or market research, its scope extends far beyond those applications.  

Let’s look at 7 ways in which data mining can be applied to business:

1. Customer Service Management

For monitoring customer satisfaction, a company may have access to audio data from the call centre set up to register complaints. Additionally, it might conduct focus group studies or monitor comments on its social media page to analyse customer feedback and opinions on its products and services. Instead of analysing each set of data separately, data mining combines all the available sets of data to provide quick and consolidated feedback.

For instance, after the company launches a new model of a vacuum cleaner, the focus group findings point to the high noise-level while operating the machine and the poor quality of the reusable dust collecting bag as the two main negative points. However, on adding the audio data from customer complaints to the mix, the result shows that the latter is the primary reason for the poor customer feedback. Based on this, the team might advise the recall of the product before it damages brand value.

Multimedia data mining can help to bring a fresh perspective to consumer market insights for evaluating parameters such as customer satisfaction or negative feedback, helping to devise strategies for more effective management of customer relationships.

2. Developing Brand Strategies

For a brand that retails online, studying the consumer’s online activity can give the consumer insights team a better idea about the platforms where the brand should be present. Data mining from mobile devices helps to analyse human-computer interaction and to understand how consumers behave online.

For example, based on the budget, the marketing team needs to advertise online to get visitors to their brand page. An analysis of ubiquitous data from the customers’ smart phones showsthat most of them spend more time on Instagram than Facebook. Accordingly, a higher spend can be allocated to Instagram to attract customers to the brand page and convert the leads into online sales.

Ubiquitous data extracted from the consumers’ smart phones or mobile devices can reveal the amount of time spent browsing through various portals as well as the products or topics that interest them. Based on this, the team can devise focused strategies such as increased advertising on platforms most visited by the customers, thereby boosting inbound marketing leads.

3. Measuring Campaign Performance

For marketing campaigns, analysing distributed data from across locations or organisations presents a broader perspective on the campaign performance.

For example, a post launch survey in a metropolis may indicate that the company’s new line of clothing is flying off the shelves. However, reviewing the data in conjunction with the distributed data mining of sales figures from smaller cities across the country might show that the line is not as popular, providing a more realistic measurement of the performance of the campaign. This insight calls for the team to identify the reasons for the poor response in some parts.

Instead of analysing data from a single source, with distributed data mining, a large amount of data from different locations or organisations is extracted and analysed to prepare reports and to provide more holistic market insights.

4. Planning Retail Presence

Data mining of spatial or geographical information from navigation applications and geographical information systems can identify clusters where most of the brand’s customers are located. It helps in making decisions regarding retail presence or investment.  

For a brand looking to set up a new store in the city, an analysis of geographical customer data can reveal the neighbourhoods where most of its loyal customers reside. If a store exists in the most populated location, then the team might look at the second location or the place where most of the customers go shopping as the best place for the new store.

Spatial and geographical data mining can contribute to smarter decisions on the locations that are best suited for retail investment so that the brand is more accessible to customers.

5. Product Planning and Development

Analysing cyclical or seasonal trends in the data can identify random events that occur outside the normal pattern. Time series and sequence data mining can be used by diverse sectors, including those affected by seasonal variances.

For example, based on data mining a cosmetic brand might notice a decline in sales of a sun protection cream during the winter months. Accordingly, suitable product strategies can be conceived, such as a new line of winter skincare products to tide over the seasonal decline in sales revenue.

Understanding cyclical trends can help the product management team to devise strategies to prevent loss of sales revenue during certain seasons.

6. Financial Data Analysis

Banks and financial institutions collect a vast amount of customer data, making it easy to employ data mining techniques to analyse or identify clusters and patterns, based on which they can customise product offerings.

For instance, extraction of data on customer spends can show patterns such as which cluster of customers are loyal and satisfied or those who are dissatisfied and likely to switch to another bank. Analysing the reasons for dissatisfaction can help to identify concerns such as a perception of high annual fee on credit cards, inflexible loan terms, among other reasons. Based on this the bank can devise programs to reach out to unhappy customers and offer them products to retain their business.

Similarly, financial data can be used to detect varied patterns such as areas prone to ATM thefts, customer clusters likely to default on loans, etc. based on which suitable measures can be implemented.

7. Targeted Marketing

Classification is a key factor in identifying customer behaviour, and mining multi-dimensional data sources can create more valid clusters for identifying customers for targeted marketing. For instance, when a bank acquires a new customer, identifying the cluster in which he falls, makes it easier to employ targeted marketing strategies.

Clusters can be based on a range of factors, including age and income, among others. For example, analysis of the extracted data of customers in the 18-25 age category might show that student loans or low interest credit cards are the most common financial products. When the bank acquires a new customer, who falls in the same cluster, they can offer a bundle of services that include these two products. Data mining helps to design cluster-specific strategies or communication that yield better results.

In the same manner, targeted marketing for new parents could include home loans, children’s education investments, etc.

Data Analysis for Effective Business Strategies

With the rise in the use of social media and access to newer sources of customer data, surveys, focus groups or consumer feedback through direct channels are no longer the primary resources for gathering market insights. Social data presents a wider perspective of the consumers’ needs, opinions and motivations, and the best part is that data mining facilitates access to this in real- time. While mining of social data comes with its own set of challenges, such as privacy issues, it is an invaluable source for analysing consumers’ online behaviours. When used along with traditional sources of customer and market data, it can help consumer insights professionals get a clearer understanding of the consumer’s world and thereby follow a more holistic approach to developing customer-centric strategies.

Artificial Intelligence will facilitate the use of all the different types of data mining simultaneously to present a complete picture of consumer behaviour, needs and opinions, giving the consumer insights team access to consolidated reports supported by realistic analysis of a varied set of data points.