How often do you check the weather for next weekend or the next few days? Meteorologists use predictive analytics to predict upcoming weather. Predictive analytics can be used to forecast upcoming trends or user behavior. There are pros and cons when it comes to predictive analytics. Corporations must look closely at their analytics and make sure they are acting in their users’ best interests.
Using Predictive Analytics
Predictive analytics can be used in digital marketing to predict the future behavior of users or upcoming trends and events. These analytics can be completed manually or with AI. Typically, AI analytics are more credible than manually creating predictions. One predictive analytic tool is regression analysis. This tool tries to determine the relationship between two variables or multiple variables (multiple regression). You can compare the purchasing habits of people located in different zip codes within a state, county, or region. Perhaps people living in eastern zip codes purchase more granola bars compared to cookies. While western zip codes purchase more cookies compared to granola bars. You would target eastern zip codes with granola bar ads and not ads about diaper cream. You can use predictive analytics to determine how likely people will move in the future and where they move to. Perhaps more users will move to the western zip codes in the next decade. You will have to constantly tweak your strategies to make sure you’re maximizing your brand’s presence.
The benefits of predictive analytics are that you can have more confidence believing in future events or trends. If you have correct data that supports event A occurring, you can be more confident that event A will happen instead of event B. The downside of predictive analytics is that data can discriminate along racial, sexual, and socioeconomic lines. If your data predicts that marketing strategy A will negatively impact a certain socioeconomic class, then you shouldn’t proceed with marketing strategy A. Some corporations will pursue a negative marketing strategy despite the consequences.
Predictive Analytics Examples
- Fraud detection: Examine actions on a company’s network and identify abnormal actions on the network.
- Purchase Predications: Retarget ads to users that are most likely to purchase again.
- Customer Segmentation: Break a customer base into specific groups based on certain attributes.
- Operational Improvement: Forecast inventory, resources, and use them more efficiently.
Predictive analytics are used to forecast future events or trends in your respective market. Understanding the future of your business can influence upcoming brand decisions. With the increasing use of AI technology and personal data collection, predictive analytics is more important than ever before. Stay ahead of the competition by implementing predictive analytics in your digital marketing strategies.
