There is a revolution in how marketers are using artificial intelligence (AI) and machine learning (ML) to help execute intelligent strategies and campaigns at scale. One important area where AI and ML can be put to good use is in market data management.
“This is basically turning AI and ML into a useful tool for marketing itself,” said Theresa Kushner, head of North American Innovation Center, NTT DATA Services, at The MarTech Conference.
In this way, businesses can better understand all the data streaming in that relates to what’s being done in markets, including who is buying products and other important buying trends.
“AI and ML can help you sort through, organize that information and present it to you in a way that makes it more digestible within your marketing program,” Kushner said.
Here are three main steps for how to get AI and ML to work in your market data management.
(Among the many ways of collecting market data, one is web scraping, discussed in depth here.)
Connecting data across teams
Data is growing exponentially. And it doesn’t just sit idly in your company’s databases and data management platforms. It gets piped in in streams, Kushner said.
“And oftentimes that data is just as important to marketing as it is to the product divisions that use it,” she added. “So using AI and ML can help you sort through where the data goes for marketing, where the data goes for product design, where the data is most important for finance, etc.”
Therefore, AI and ML can help with creating rules for which data goes where. And it helps if this constantly updated data is visible on a dynamic dashboard, as opposed to clunky spreadsheets.
But in order to get started with making all of this market data more manageable, marketers who own the data need to connect with the other departments that will benefit from it. Marketers also need to be in close contact with data engineers.
“[Data engineers] understand where the data is coming from and how it may be transformed from one system to another, where data is being archived or where it’s not being archived,” Kushner explained.
Because they know about all the sources of the data, data engineers are also the first people to check with about any data quality issues.
Dig deeper: Are you applying the right models for AI and ML?
Evaluate where AI and ML can solve problems
With all of this market data being piped in from different sources, it’s a constant challenge for marketers to connect the dots. Frequently, data engineers are the ones going in manually and making sure that important financial and product data are being compared on an even basis.
Therefore, these labor-intensive functions can be identified as areas where AI and ML tools can help make market data management more efficient.
“AI and ML can detect those patterns of defects, so to speak, and correct them for you,” said Kushner.
Dig deeper: Why we care about AI in marketing
Implement key programs supported by reports to show progress
Once these areas are identified, put a program in place where AI and ML can be used, so that data people don’t have to go inspect every data point themselves by hand.
A simple example would be where service information is stored in multiple places within the organization. In some places, the data could be tagged as services, but maybe elsewhere this data is kept as product data. Using an algorithm to identify and bring together these seemingly different data sets can be a very important business problem that AI can solve.
For this case, or for any other market data management program using AI, make sure that the issue is included in a report. This way, leadership will be able to understand, from the report, the problem that existed and how AI and ML are being used to solve it.
“You need reports to make sure that you’ve pinpointed the most important issue to the business…so that the business understands that this is very valuable to them,” Kushner said.
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