Did you know that by 2023 the worth of the AI market is expected to hit the $500 billion mark?

The world of business is ever-evolving, always finding better ways to outsmart their competitors and to survive better in the market. Therefore, it is an instinct of businesses to implement AI, big data, and business intelligence tools to fare better during these times of tough competition. 35% of companies have already begun incorporating AI into their business according to a report from IBM.

In this blog, we will take you through three use cases which establish the fact that the implementation of BI & ML together can be a game-changer for your business.

What is Business Intelligence & Machine Learning?

Before getting to the details of the use cases, here is a brief explanation regarding BI and ML in simple words.

According to IBM, business intelligence refers to the ability of a tool to consume any type of data so that it can transform it into an insightful form such as charts, graphs, reports and dashboards.

A typical example of BI would be how the Coca-Cola Bottling Company improved its access to real-time sales data by employing business intelligence tools. With the help of their BI platform and team, they automated their entire manual reporting process. Thus, they reduced the sales data reporting time by 260 hours a year, thereby gaining earlier insight into their sales and operations.

Machine learning, on the other hand, is a subset of Artificial Intelligence that utilizes data and algorithms to mimic the process of human learning. The term “Machine Learning” was coined by IBM’s Arthur Samuel. Machine learning plays a key role in providing useful insights after the data is mined.

A real-world example of machine learning that we experience in our daily lives are speech recognition software and devices like Amazon’s Alexa, Google Home, and Xiaomi Miio.

But if you wish to know how to differentiate between machine learning vs AI, then AI is described as the concept used to make machines intelligent whereas ML is a branch of AI. Machine learning comes to the machine’s aid to learn something and gradually improve its acumen regarding the same.

How business intelligence tools and machine learning can benefit your business?

We have used three use cases to explain how BI and ML can improve your business.

1.    Improving chatbot performance

According to Thomas Griffin of OptinMonster, chatbots can be improved with the help of business intelligence tools to understand more about the target audience. Further, the data collected by chatbots can be interpreted with the help of machine learning. This information can in turn help you connect with potential leads and generate sales and conversions.

2.    Forecasting in e-commerce

Machine learning and business intelligence can together help make effective forecasting in e-commerce such as:

• 1. Predicting the demand for a particular product would help in meeting the demand in real time. In turn, it would help retain clients.
• 2. Combining ML and BI tools can help forecast and make personalized recommendations for customers.
• 3. Consumer lifetime value prediction (CLTV) can make it easier for you to allocate marketing expenses better.
• 4. Forecasting if a customer will purchase something in real-time can help you decide better on the inventory.

3. Lead generation automation

If you wish to close more deals with your customers, performing a manual search for potential customers would be a time waste. Instead, implementing business intelligence tools at this stage could help you automate the lead generation process. Further, incorporating ML would help to enhance the process gradually. Creating an inbound funnel would enable you to find the right customers instantly. Analytics reports and dashboards coupled with AI tools will ultimately boost growth.

How does ML better harness the power of BI?

First and foremost hiring data scientists to develop data visualization models for gaining initial insights about the data acquired is a key step. The data scientist can help overcome some common business challenges by performing fraud analysis, market basket analysis, and risk analysis.

Next, machine learning can step into the same space to take relevant action for fraud analysis or acquire better insight regarding the customer’s online purchase habits. ML can also improve bots to collect useful data regarding potential customers. Forecasting is also a key area that benefits due to machine learning.

Ending Note

Big data and data visualization with the help of tableau reports and dashboards are the need of the hour nowadays for all data analytics companies. Organizations are relying on business intelligence tools heavily to get the best out of the collected data. If you are looking to perform tasks and manage your business more efficiently, reading about the three use cases must help you etch a roadmap for employing AI and BI in your business.