How Can Data Improve Customer Targeting

Brands today are starting to realize the important roles customers play in every big decision. Every new change has to start and end with the customers in mind.  Due to this, it is important for brands to understand the behaviours of their customers and the best way to analyze their audience is through understanding every interaction a customer has with the brand. Though it may be a tedious task due to its high volume, this data is invaluable to a brand. Not only will they be able to understand what their customers’ needs, wants and pains are, they will also be able to measure how to approach future and potential customers by analyzing data.

There are a few methods of doing this, one being through marketing research such as online and offline surveys, sale reports and demand trends. By analyzing this data a brand would be far better apt at predicting and catering to the needs of their customers, thus proportionately better at retaining their customers as well.

The type of data accumulated through this type of market research can be heavy and difficult to analyze due to it being elaborate in nature. This data can be referred to as Big Data. By using data analytics, we can derive information such as the socio-economic behaviours of customers and their spending habits along with demographic insights. This can then be used by brands in targeting specific products or services in certain areas. Targeting customers can only be done through data and different data can be utilized to gather different information. All of this information can then be used to give customers the best possible experience.

At the end of the day customer satisfaction will depend on how proactive brands are in taking advantage of the information obtained from the analysis of Big Data and applying this information in ways that benefit their customers. In this day and age, the success of a brand will depend on how it chooses to differentiate itself from its competition by truly understanding its target audiences.

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Data Science and Business Intelligence

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