With all of the market data being piped in from different sources, it’s a constant challenge for marketers to connect the dots.
The use of artificial intelligence (AI) and machine learning (ML) by marketers to execute intelligent strategies and campaigns at scale is undergoing a revolution. Market data management is a crucial area where AI and ML can be applied effectively.
This essentially transforms AI and ML into helpful marketing tools. Thus, businesses can better comprehend all the incoming data pertaining to market activity, including who is purchasing products and other significant buying trends.
AI and ML can assist marketers in sorting, organizing, and presenting this information in a manner that is more digestible within the marketing program.
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Here are the three primary steps for implementing AI and ML in market data management:
Connecting data across teams
Data growth is exponential. It does not sit idle in any company’s databases and data management platforms but introduced in streams.
Often, marketing is just as dependent on this data as the product divisions that utilize it. Consequently, AI and ML can assist teams in determining which data should be used for marketing, which data should be used for product design, which data should be used for finance, etc.
Therefore, AI and ML can aid in the creation of data placement rules. And it helps if this data is displayed on a dynamic dashboard as opposed to cumbersome spreadsheets.
In order to begin making all of this market data more manageable, however, marketers who own the data must connect with other departments that will benefit from it. Additionally, marketers must maintain close contact with data engineers.
Data engineers comprehend the origin of the data and how it may be transformed from one system to another, as well as whether data is being archived or not. Since they are familiar with all data sources, data engineers are the first individuals to consult regarding data quality issues.
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Evaluate where AI and ML can solve problems
Connecting the dots is a constant challenge for marketers due to the influx of market data from a variety of sources. Frequently, data engineers are responsible for manually entering and ensuring the comparability of crucial financial and product data.
Consequently, these labor-intensive functions can be identified as areas where AI and ML tools can improve the efficiency of market data management. AI and ML can, so to speak, detect and correct these flaw patterns for marketing teams.
Implement essential programs supported by progress reports
Once these areas have been identified, implement a program that uses AI and ML so that data professionals do not have to manually inspect every data point.
A simple illustration would be the storage of service information in multiple locations within an organization. In some locations, the data may be classified as service data, whereas in others, it may be classified as product data. Using an algorithm to identify and combine these ostensibly distinct data sets is a significant business problem that AI can solve.
For this case or any other AI-based market data management program, ensure the issue is documented in a report. In this manner, leadership will be able to comprehend the problem and how AI and ML are being utilized to resolve it.
CMOs need reports to ensure that they have identified the most important issue for the business so that the business recognizes that this information is extremely valuable to them.
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