Predictive Analytics; what is it?
As the name suggests, this is a type of analysis related to predictions.
We will take a closer look at what predictive analytics is and how this form of predictive analysis can be used.
What is predictive analytics?
When discussing predictive analytics, we are referring to an analytical approach that relies on a large set of data sources.
The amount of data would be too overwhelming to analyze manually, so predictive analytics is used to identify specific patterns, allowing you to maintain an overview and make the right decisions in time.
If big data is part of your business model, predictive analytics can make a significant difference.
This is an AI-driven analysis that, based on machine learning, can be set up to predict specific patterns and provide insights into the future.
How can predictive analytics be used?
A company may have a hunch about certain customer behavior patterns. However, with multiple data sources, it can be difficult to determine whether these assumptions align with reality.
This is where predictive analytics comes into play. By analyzing real data, it helps predict what is likely to happen in the future.
As with many other machine learning models, it is essential to be specific in your queries.
Humans are good at picking up linguistic nuances and interpreting everything they see and hear.
An AI machine learning model, on the other hand, is programmed to respond to specific queries. Therefore, it is necessary to define precisely what information you seek.
Read more about which AI tools you can use in my post here.
An example could be customer segmentation.
For instance, which target group will respond best to a campaign? Or how can we identify valuable customers who are most likely to convert in the short term?
These questions may seem broad and general at first glance, but businesses have a clear understanding of their target groups, what “valuable” means to them, and what “short term” entails.
However, predictive analytics does not inherently have this knowledge, so it is necessary to specify which target groups are being analyzed, what defines a valuable customer, and how long “short term” is.
This approach ensures the best results, which can be used to make informed decisions.
Predictive analytics in email marketing
There are numerous examples of how predictive analytics can be applied, but one particularly interesting area is email marketing.
With predictive analytics, businesses can predict user preferences for their product catalog based on data about user behavior on their website.
With this information, email flows become more relevant and, most importantly, personalized for each user.
What are the best email platforms in the world? Find out here.
Another example is users who unsubscribe from a newsletter. Based on data about this type of user behavior, predictive analytics can help identify other users at risk of unsubscribing.
From this, emails with incentives, such as discount codes, can be created to retain users.
A final classic example is predicting the performance of an email campaign.
In other words, how many people will open the email and click through it?
Here, predictive analytics can once again draw on historical data to optimize newsletters and achieve the best performance.
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