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.
Big data for big marketing
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:
- Poor data quality
- Data complexity
- Rapid changes in data and trends
- Lack of proper tools
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 drives rapid, robust big data marketing analytics
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.
It improves data quality
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. Models can be trained to prioritize data based on quality issues and to recognize when data potentially breaches data privacy regulations. Better input data, in turn, leads to more actionable output information for use in developing marketing strategies.
It helps make sense of a wide variety of data sources
Because marketing data comes from a host of sources and takes various forms, it is not always simple to obtain a coherent analysis using traditional methods. AI and machine learning can coordinate diverse data sets, which, in turn, supports data monetization.
Data complexity will not get easier. Instead, the number of devices, apps, and connections is expected to continue growing rapidly in the coming years. Population increases result in device increases and a usage increase. In fact, it is predicted that by 2030, the average individual will have fifteen different devices on average for accessing the internet.
It allows users to interact with data in intuitive ways
Among many other benefits, AI platforms allow users to query data in intuitive ways, often in ways that are even more readily accessible to the average end user than methods such as SQL queries. With properly implemented AI, users don’t need to be programmers to obtain useful information. Instead, they can essentially converse with the analytical models, using natural language queries and natural language report generation.
It ramps up analytics speed
The speed of processing data is crucial. Trends change rapidly, and marketers must keep on top of these trends and take advantage of them as they happen.
This has been demonstrated during the pandemic when consumption patterns and distribution channels took a U-turn because of lockdown restrictions and social distancing recommendations. Without AI tools to process data quickly, marketers risked missing opportunities because of failure to identify relevant trends.
Data analysis using traditional SQL queries and manual post-query review is now insufficient. Automation of data analysis using AI can help marketers respond more rapidly to market changes.
It can improve targeted marketing campaigns
Targeted advertising is an area where AI has been very effectively used. Everyone is familiar with going onto Facebook only to see a variety of ads displayed on their feed that is specifically related to their interests. This is because Facebook and Google use data such as buying behaviors and activity to serve such advertisements.
Why artificial intelligence is not a perfect solution
While AI is a useful data analytics tool, marketers must be aware of its limitations as well.
Big data marketing analytics can suffer from bias in several ways, including the inadvertent insertion of bias during data creation (i.e., poorly crafted survey questions) or bias in analytical models’ programming. AI and machine learning algorithms themselves are also a source of potential bias.
When properly implemented, artificial intelligence can remove bias from marketing data analysis. Market researchers can also utilize AI to develop neutrally-phrased survey questions, improve their content and reliability of existing surveys, screen data more effectively for potential bias issues, and remove observer bias from data analysis.
Human interaction is still essential to effective marketing. Until AI can evolve to include human emotional characteristics such as empathy, excitement, and joy, marketing professionals must continue to fulfill these roles.
When handling our data, the potential that AI holds is limitless. By analyzing our digital behavior and habits, it can potentially influence human decision-making. Fortunately, as the amount of data being created increases, so does the number and sophistication of available analytical tools to support marketers in making sense of it.
In conclusion, artificial intelligence is one of the best available tools for improving the quality of marketing data analytics, despite its limitations, and one which marketing professionals would do well to embrace sooner rather than later.