Subodh Parulekar is the CEO and co-founder of AFour Technologies and is based out of Redmond, Washington. With a proven executive management track record, Subodh brings over 25 years of experience driving business strategy, sales growth, marketing, solutions architecture, and software engineering. After working extensively across multiple startups and MNCs across North America, he recognized the need to address certain gaps in software service offerings. This motivated him to join Disha Technologies, a pioneer in Software Testing and Quality Engineering, and his experience there was pivotal in enabling him to found AFourTech. Subodh holds an MBA from the University of Washington, M Eng. in Systems and Computer Engineering from Carleton University, and a BE from the College of Engineering, Pune. He is married and has two children, both of whom are pursuing their studies in the United States.
Every business wants to know how to do better – how to sell more products, reach more customers, and set up more stores. Much of the time, the answer to this lies in the past records about what worked and what didn’t. Enter predictive analytics, one of the core tools in every modern organization’s arsenal. Predictive analytics enables intelligent decision-making like never before with the benefits of speed and accuracy and multiple use cases.
Let’s dive a little deeper.
What is predictive analytics?
Predictive analytics is a technique that makes projections and forecasts business trends with the help of machine learning, algorithms, and big data. It involves analyzing business data to glean patterns, creating new models based on those patterns, and passing fresh data through those models to predict what could happen in the future. Most modern predictive analytics processes use machine learning, which constantly sifts through data and learns new patterns in real-time to reflect the latest needs and market trends.
Benefits of predictive analytics
Predictive analytics is a powerful tool when it comes to making business decisions. Assessing new initiatives against current market conditions can predict scenarios with high degrees of accuracy, allowing businesses to make an informed final choice rather than relying on guesswork. Some of the practical applications of predictive analytics include:
- Fraud detection/risk assessment: With cybersecurity threats on the rise and smarter hackers, predictive analytics can significantly identify potential vulnerabilities based on past risks while keeping a tab on real-time threats. It can also draw data from vulnerabilities that other companies in the same industry have faced and thus deduce whether the business in question might be heading in the same direction.
- Predictive maintenance: When used with intelligent sensors, predictive analytics can gather data from physical assets and predict when repairs and replacements may be required, thus reducing downtime and ensuring business continuity.
- Campaign enhancement: Based on data from past marketing campaigns – the ones that succeeded and the ones that didn’t – predictive analytics can help businesses choose the best channels, languages, and content categories to win over their audience.
- Credit scoring: Before extending credit to a customer, businesses can use predictive analytics to assess the customer’s credit risk. This ensures that the business doesn’t invest too much in customers who are unlikely to pay back.
- Behaviour pattern detection: Predictive analytics can give essential insights into how customers are likely to behave under different circumstances. For instance, it can predict which category of buyers is most likely to abandon a cart after adding items to it, or which demographic will appreciate a new product the most. Accordingly, the business can target those customers with tailored campaigns. Predictive analytics can also identify signs of discontent among customers so that the business can take steps to improve their satisfaction, thus reducing the churn rate.
Designing a forward-looking strategy
It is important to remember that implementing predictive analytics is not a goal. It needs to be tied to business objectives to be helpful so that the trends and behaviours that the model predicts can be used to drive better decisions. Having a reliable and sufficiently large dataset is vital, as predictive insights are only as good as the data they are based upon. It is equally important to keep revisiting the model and upgrading it as processes, products, or external circumstances change in ways that impact future decisions. Above all, have a talented team in place and ensure that everyone is on the same page about the role that predictive analytics will play. This way, human intelligence, and cutting-edge ML can come together to power the best decisions for your business.