In this day and age, big data has not only come to dominate board rooms by fueling business decisions, but now, decision-makers are leveraging predictive analytics based on big data to power business-critical marketing decisions.
This is being done by analyzing customer information, be it existing customers or target audience, at a granular level. The sheer volume of data generated when people interact with websites, smartphones, and app, allows businesses to analyze a customer’s purchasing history, buying pattern, income, and interests. This, in turn, helps businesses create custom-made digital marketing campaigns for particular demography, resulting in a more targeted approach that ensures maximum impact.
Big data analytics allows marketers to analyze past data of their customers and predict their future behavior online. Although this raises many questions around customer privacy, the data collected and analyzed is anonymous in most cases. This type of big data analytics helps brands engage and interact with their customers in a targeted manner.
According to recent studies, there are three main benefits of using big data for driving digital marketing campaigns – a detailed understanding of customer insights, enhancing supply chain, and fuelling business promotion and campaigns.
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How Big Data is being used for targeting customers online
Brands are leveraging data-as-a-service providers to collate and analyze data about customers. The data is collected from multiple resources and includes basic information like name, social media handles, email addresses, and the likes. However, the heart of digital marketing comes in the form of data collected about users’ online activities, which brand they follow on social media, their online activity, how often they purchase particular products, and so on.
In this day and age, the number of devices with which people are interacting is increasing. Gone are the days of simple smartphones, laptops, and tablets. allows consumers to interact with a range of devices like AI-powered home assistants, connected microwaves, refrigerators, and temperature control systems. Carmakers are pushing to make people’s interaction with their cars completely digital. As the interaction points increase, so does data collected by these interactions.
Some data sources being used to collect customer data are:
- Web Mining – This involves collected unstructured data from websites, including activity logs.
- Social Media – Social Media data such as ‘likes,’ shares, and check-in provide valuable insights into a consumer’s behavior.
- Search Data – Search data helps marketers chart out their target audience’s web journeys and help plan digital campaigns.
- User Generated – This category includes data from surveys, online communities and forums, and the web in general.
- Transactional data – This type of data is generated when businesses interact with each other and their customers. This includes financial information, buying patterns, purchase history of the targeted audience. When analyzed, this data gives the buying propensity of the TG and helps conceptualize targeted digital marketing campaigns.