Predictive analytics is one of the most potent applications of data analytics. The best business intelligence tools like Power BI are equipped with numerous machine learning algorithms that help companies predict the Industry’s future.
In this post, we discover what predictive analysis is along with Microsoft Business intelligence tool Power BI and use cases and how it will shape the future of the world of business.
What is predictive analysis?
We need to have a proper understanding of the term before we get into details. Predictive analysis is the use of machine learning algorithms to identify patterns in data and predict the future.
Organizations prefer consulting and implementing Power BI office 365 for business intelligence and manage big data, enabling businesses even to identify Industry-wide trends. Traditionally companies have been making major business decisions based on the gut feelings of the management. This is what gives birth to the biggest problems in a company.
With the right approach and system, you can apply predictive analytics in every company’s division and make sure that every decision is based on data and not feelings.
Use cases and the future
To further get an idea about the future and why this will define the future of business, let’s discuss some examples.
- Companies in the logistics sector can identify upcoming workloads in specific months of the month
- Ecommerce brands can evaluate the orders they place to manufacturers based on the sales trend of a particular product
- Predictive analytics is a great tool used by companies to negotiate prices with their suppliers. Companies get alerts from Power BI at times when it is best suitable to negotiate as the suppliers are low on orders relative to specific months of the year
- Insurance companies can predict the value of the premium to a customer based on the crime rate of a particular locality and the likelihood of the customer asking for an insurance claim
- And many others
As you can see, predictive analytics can be used in every company to make decisions that will directly impact the bottom line.
TAM and ABM
The concept of ABM(account-based marketing) is defining the future of marketing in large corporations.
Let’s see how this is related to predictive analytics.
Traditionally companies have been targeting almost all of the addressable market in their niche. Sales strategies like cold calling were based on targeting every possible customer until one of them converts.
This leads to a lot of loss in terms of resources. Data has helped increase the efficiency of such systems. Many factors contribute to every sale.
For example, for a company that sells school management software, technically every school in the world is a prospective customer, but targeting every school would be the biggest mistake for such a company.
The marketing and sales division of such a company can significantly increase conversions by filtering the prospects with factors like:
- The annual revenue generated by the school to correlate it with the pricing of the software
- The current software used by the school(This can be used to target the schools which are using software that is known for particular issues in the niche)
- Demographics
- The subjects taught in the school
- Strength of the school
- Challenges faced by the school
All of these factors have a big impact on conversions and can be created into reports using Power BI report builder
Rules and factors like these can be fed into systems to create automated rules and identify the best target areas. Predictive analytics will then constantly filter the big data to increase conversions.
Predictions in customer service
Customer service generates a lot of data for most companies. This data consists of goldmines for businesses to identify trends and identify the customers for upselling and other types of selling.
Subscriptions based businesses identify the most probably customers for renewals based on data, which increases conversions.
The same data is used to reduce the number of raised tickets by predicting future issues and solving them in time. Data also helps companies to predict the best employees for certain tasks to increase productivity.
Social Media Engagement
Social Media marketing is one of the biggest data producers in the modern business world, and predictive analytics form a large part of it.
Social media algorithms are also based on predictive analysis, which is done with business intelligence tools. The types of posts you see depend on various factors, including area, age, the posts you have interacted with, and others.
This is the same case in predicting “Customers also bought” on amazon and the types of shows you are suggested on Netflix.
Conclusion
As you can see, that predictive analysis is scalable and based on logic. The crux of all the experiments and use cases is to use data to make decisions rather than guesswork for crucial business decisions.
With the right approach and knowledge, you can use predictive analysis in every division of your company and witness growth!