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Predictive analytics is the technique of finding patterns in data and forecasting future trends. Statistical methods, Artificial intelligence, and data-mining models are employed for analyzing the customer’s past purchases, buying patterns, wish lists, products they might need in the future, etc. This helps gain varied customer insights and strengthen customer relationships. As per research, the global market for predictive analytics will grow at a rate of 21% and reach approximately USD 10.95 billion by 2022. The following article talks about the role of AI in predictive analytics and how it’s revolutionizing the business-customer relationships.
AI reduces the complexity of data
There is no doubt that humans are intellectual beings and possess the ability to take tough decisions in times of crisis. But when we aren’t surrounded by imminent danger, we are overtaken by emotions. A recent study by Harvard Professor points out that 95% of our decisions are made subconsciously. This proves that if customers are disgruntled by your services or have a negative opinion about your products, you may soon go out of business.
AI helps to gauge customer emotions and predicts their behavior. This helps find potential buyers from a long list of prospects – a matrix that maps product codes with customer names – that would have otherwise been a complex and tiring job.
Consider the following example that explains how AI reduces complexity. You are an FMCG giant that employs AI algorithms to know which of your customers buy razors and shaving creams along with aftershaves. If the data lists 80% of your customers who have a similar buying behavior then you can easily analyze the remaining 20% of the customers to whom you can recommend the aftershave.
AI conducts anomaly detection
One of the most important roles of AI in predictive analytics is in the field of anomaly detection. It analyzes signals that indicate out-of-the-usual operations or expectations. It examines which videos would likely go viral and make a buzz in the market. It also inspects the causes that convert prospects into customers or make them lose forever.
For example, Paypal collaborated with Rapidminer to build a sentiment analysis software that helped them analyze more than 1,50,000 digital posts. This helped Paypal understand that their users were facing login issues. After the software was integrated successfully, the password recovery rate became 50% more than pre-integration times.
Predictive analytics is an important tool for building strong customer relationships. When coupled with artificial intelligence, its productivity increases exponentially and helps businesses gain actionable insights in a faster and efficient way.
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Disclaimer: All the information, views and opinions expressed in this blog are those of the authors and their respective web sources and in no way reflect the principles, views or objectives of Sage Software Solutions (P) Ltd.
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