Data analysis can be challenging, given that it involves complex information and large data sets that are difficult to manage manually. This is where machine learning comes in. It is changing the way consumer-based companies are dealing with the vast data they generate

It is based on Artificial Intelligence (AI) on the idea that systems can automatically learn from data, identify patterns and make decisions with little human interference, and is being used to automate analytical model building.

Machine learning significantly reduces efforts, saves time and is a cost-effective tool which replaces multiple teams working on analyzing, processing and performing regression testing on the data. It gives accurate results and helps organisations build statistical models based on real-time data.

Benefits of Using Machine Learning for Data Analysis

With artificial intelligence and machine learning landscape evolving, data analytics is also growing at a rapid pace. More companies have started relying on the technology-aided by ML in data analytics to determine feasible solutions and a definite plan of action that could help them reduce customer churn, increase sales and generate higher revenue.

Some of the major benefits of using ML for data analysis are:

Customer churn is one of the biggest problems organisations face. The costs involved are often huge, which can lead to a significant reduction in the total revenue of a business. And companies that are either subscription-based or work in consumer space are highly impacted by customer churn rate. It tells them whether their customers are satisfied with their service, how well they are performing in the market and the specific areas where they are lacking. Businesses can use machine learning for predictive data analysis and reduce their customer churn.

Giants like Amazon, Google and Netflix, use machine learning-based predictive analytics to prevent customer churn and increase revenue. Netflix has heavily leveraged the power of machine learning, reported to save $1 billion every year on its customer churn. The company’s customer retention rate is exceptionally high, which means they can provide valuable information to customers and make them satisfied to ensure they don’t switch companies.

Machine learning is a highly sophisticated technology that closely analyses customer behavioural data. It tracks users’ actions and identifies if there is anything unusual. Machine learning enables companies to create algorithms that process large datasets with different variables and help them find hidden correlations between customer behavioural patterns and the likelihood of fraudulent activities.

Another important factor to consider machine learning for detecting fraudulent transactions is that it offers automatic detection with a faster data processing speed as compared to manual work. It helps in detecting identity theft where a scammer might breach a user’s account, alter personal data and information to get money or goods using a semi-fake profile. Smart algorithms detect suspicious activities and find inconsistencies with the previous set of personal data.

With more companies adopting online payment methods to make the customer experience smoother, the risk of exposing customers and their bank details to scammers have also increased. It is important to incorporate ML-based algorithms that would detect fraudulent activities based on descriptive statistics like averages, standard deviations, and high/low values that are crucial in analysing customer behaviour. Payments with large standard deviations are often picked up by these algorithms and stopped immediately.

Applying effective machine learning algorithms can help organisations increase customer acquisition and achieve better results. Customers are smarter than ever now; they expect a high level of personalisation from brands and refrain from getting in the “click-bait” marketing tactics. Unless you provide valuable information that is relevant for them, it is less likely that you will be able to convert them to your customers.

Machine learning helps maximise relevancy by creating personalised data. It smoothens the onboarding process by making it simple and quick. ML algorithms allow organisations to predict relevant products to offer as well as the best channels or messaging apps to capture the new customer’s attention. They help brands to create links between customer’s actions based on their previous data such as search history, previously bought products, interests with marketing initiatives that could lead up to purchase or sign up.

Companies use ML techniques to leverage customer experience, which is a key driver in boosting sales and generating higher leads. Brands that provide better customer support and an overall great customer experience tend to perform better than their competitors. ML algorithms process results of historical data including customer feedback, surveys, analysis from signals like the total amount of time spent to resolve an issue, response delay, and specific complaints that are registered along with satisfaction reviews and ratings.

Companies like Disney, Amazon, Burberry, American Express, Netflix, BMW, Yelp and HubSpot are some of the key players that collect personalised data sets for enhancing their customer experience. Once businesses have set up the infrastructure for customer data and analytics, they can use ML algorithms to focus on customer experience optimisation. They also help companies predict which customers are more likely to have an issue, and at what stage, using which brands can deploy pre-emptive measures and approach customers early on to offer the right solution.

Advantages of Machine Learning

Continuous Improvement. Machine Learning algorithms are capable of learning from the data we provide.

  • Automation for everything.
  • Trends and patterns identification.
  • Wide range of applications.
  • Data Acquisition.
  • Highly error-prone.
  • Algorithm Selection.
  • Time-consuming.

Machine Learning Blog