More than half of the entrepreneurs around the world believe “Growth” to be the key source of value from analytics, but just a few of them possess predictive analytics capabilities. So, what is it that stops businesses from being capable of predictive analytics?
The biggest and the major roadblock on the way towards predictive analysis is implementing the proper set of tools, which can drive powerful insights from the huge collection of data. The data analytics system needs to recognise and store the huge bulk of digital information and then through Machine Learning and Artificial Intelligence algorithms, businesses can bring about brand-new statistical patterns which turn out to be the backbone of predictive analytics.
Forms of Data Analysis & Their Importance in The Business Structure
There are 3 types of analysis being extensively used across various industries. They are all interconnected and built upon each other.
- Descriptive Analytics-
The most basic form of analytics which aggregates data and provides useful insights into the past.
- Predictive Analytics-
The next step in data reduction that puts to use different statistical modeling and machine learning methodologies to analyze past data and monitor future outcomes.
- Prescriptive Analytics-
This is a brand-new form of analytics which uses a compilation of business rules, machine learning, and computational modeling to decide the most exceptional course of action for any pre-specified result.
Beneficial Machine Learning-Based Use Cases for Predictive Analytics
With an evolution in the Machine Learning and Artificial Intelligence landscape, Predictive Analytics started finding its way into various business use cases. Paired up with Business Intelligence tools like Domo and Tableau, business executives can understand Data Analytics better.
Now here are some interesting potential use cases for ML-based predictive analytics in various industries
E-commerce- Making use of ML, businesses can gauge the customer churn rate and fraudulent transaction. Also predicting which product, the customer will prefer.
Marketing- There are too many examples of ML in B2B marketing. A common use case is recognizing and gaining prospects with traits similar to that of the existing consumers.
Customer Service- Customer Satisfaction Prediction platforms put to use a Machine-Learning algorithm in order to proceed with the results of satisfaction surveys, analyzing signals like the total time taken to solve a ticket issue, response delay problems and the particular wording of tickets cross-referenced with customer satisfaction ratings.
These days, technology companies are gaining an edge over the rest in the market by employing Machine Learning-based predictive analytics. Advancements in ML can help to find out hidden patterns in unstructured data sets and unravel new and beneficial information.