How AI-Driven Analytics Help Marketers Manage and Understand Mass Amounts of Data
To stay competitive in today’s world, organizations must have their fingers on their customers’ pulse at all times. Fortunately, increasing amounts of data about consumer preferences and behaviors allow marketing teams to identify the evolution of markets and customer demands.
Effectively using big data for marketing efforts is now far beyond the capabilities of manual data analysis. Marketers must consolidate enormous and varied data sources, segment the data into usable subsets, and extract actionable intelligence from the data. Due to the complexity and diversity of the data, traditional data analytics tools often fail to extract useful information successfully.
Artificial intelligence is a powerful tool for making the most of big data with speed, efficiency, and accuracy. And when properly applied, AI can significantly improve the quality of marketing data analytics as well.
Understanding the challenges requires understanding big data as a whole.
Big data, according to the definition provided by Gartner, is “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”
In other words, Big Data is a tool and process that helps companies utilize and manage huge sets of data. It can also help them analyze and figure out their customers’ motivations, which provides ideas for the creation of new offerings.
Businesses can collect data for marketing analytics from many sources, including targeted surveys, website traffic, online transactions, and help desks, among others. However, one of the largest data sources is sensitive information being sold by third-party providers. FinTech companies are particularly known for selling consumer information. However, over 60% of consumers choose to continue using their digital services despite the majority of users expressing concern over large firms exploiting or mishandling their data.
Even though the amount of data can complicate its analysis, there are several other issues that may be impacting data analysis success:
Big Data analytics demand tools that can help streamline the analysis process, such as reducing the amount of data collected, improving input data quality, and revising analytical algorithms.
AI is a tool that is designed to support marketers perform and analyze their data successfully.
AI can address concerns about big data analysis in every respect, from improving data quality, increasing the speed of generating analytics to simplifying the understanding of data trends.
More is more. Or is it less more? Or is it neither? In general, more data is not always better; instead, more high-quality data is needed. Lack of data quality has posed issues for the implementation of AI processes. Surveys indicate that 65% of global executives feel that investments in AI lacked value. This has been due to the poor data quality used within the AI systems in no small measure.
However, excessive amounts of irrelevant data is not the only data quality issue facing marketers. Having timely data is also crucial. Given the speed of the shift in consumer demands, using data that is even only a few months old to make important business decisions can result in disaster.
Data quality can also suffer if it was not collected in accordance with applicable laws and regulations. Marketing teams that use purchased data, in particular, should not only ensure that the data was legally collected but also that it is stored with high-quality protection.
Fortunately, AI and machine learning algorithms applied to data pre-processing can result in higher quality input data for analytical algorithms.
