“Machine intelligence is the last invention that humanity will ever need to make.” – Nick Bostrom
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 analysing, processing and performing regression testing on the data. It gives accurate results and helps organisations build statistical models based on real-time data.
Forms of Data Analysis
Organisations collect a large set of data for fulfilling their business goals. According to the Data Dilemma Report, 12.5% of staff time is lost in data collection. That’s five hours a week in a 40-hour workweek.
There are different ways of how data could be analysed, used and implemented to form effective business models that would help in scaling the organisation.
There are broadly three different ways of how data can be analysed and used by an enterprise:
Descriptive analytics – Insight into the past
It is the most primary stage of data processing that creates a summary of previous data to yield useful information that might be useful. It informs organisations about “What has happened” and how they can learn from their past actions to make better decisions in the future.
Predictive analytics – Understanding the future
Predictive analytics is a trending strategy that helps companies scale, increase lead generation, enhance sales, boost engagement and drive higher ROI. It uses various statistical modelling and machine learning algorithms to analyse past data and predict future outcomes.
Prescriptive analytics – Solutions on Possible Outcomes
Prescriptive analytics is a relatively new form of data analysis which uses a combination of machine learning, computational modelling and business rules to recommend the best course of action for any possible pre-defined outcome. It uses optimisation and simulation algorithms to guide organisations towards a safer path by suggesting useful solutions.
Companies Transforming How Machine Learning is Used
Analytics has been changing the way business models work; it is transforming marketing strategies, sales plans, customer acquisition methods and revenue models as well. With companies delving in deeper in large data sets to increase efficiency, gain competitive advantage and boost sales, they are exploring more fragments of data analysis and modifying it to their benefit.
That’s why companies are focusing on Machine learning algorithms via which they can develop a comprehensive analytics strategy to achieve business goals. It is essential to learn how ML can affect your business and can improve it in ways conventional methods could not.
According to Forbes, Amazon uses machine learning for its same-day shipping processes, and their current ML algorithm has reduced the ‘click-to-ship’ time by 225%.
Here are some examples of smart implementation, insights from experts, and business use cases to give you a fair idea of how you can use machine learning to benefit your organisation:
- In its inner content management system, HubSpot utilises Kemvi’s DeepGraph machine learning and natural language processing technology to define trigger events, pitch prospective clients better and serve current customers.
- Pinterest acquired a machine learning company, Kosei, which specialises in commercial applications of machine learning. It now uses the technology in almost all its business operations, including content delivery, advertising monetisation, churn reduction and spam moderation.
- Twitter utilises machine learning technology and AI to assess and rank tweets in real-time using different metrics to show tweets that have the potential to drive the most engagement.
- Machine learning with software such as IBM Streams and DataTorrent enables companies to uncover anomalies so that they can take immediate action to analyse fraud or obtain greater understanding into online behaviour.
- According to Business Insider, Google’s AI, AutoML, which helps the company build other AIs for new projects learnt to replicate itself in October 2017.
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:
Reducing Customer Churn
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.
Detects Fraudulent Transactions
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.
Implementing Machine Learning in your Organisation
Whether you are a small scale startup or a full-fledged company, you can easily incorporate Machine learning to get better data analysis. There is no need to build machine learning tools from scratch. You can avail affordable, specialised systems that will provide support to your business models and help you simplify your datasets.
Machine learning systems will optimise the next best option for your customers and slim down the abundant analytics. Adopting machine learning will bring fundamental changes in the way your company collects data, processes it and gives the output. Make sure that your team is well equipped to handle and manage machine learning systems to get the desired results.