What is Predictive Analytics and how it helps business?

Today, businesses leverage big data analytics to remain relevant in competitive and dynamic markets by enhancing their offerings via data insights. Companies are racing to adopt artificial intelligence practices to tap into data and extract information that can prove helpful for them. But before launching their product into the market or executing their AI strategy, leaders are employing predictive analytics to understand customer behavior, market and sales projections and many more. Together, with customer intelligence, AI, machine learning and other forms of data analytics, predictive analytics is slowly transforming the way we carry business activities and make market decisions.

Predictive analytics is basically the process of analyzing historical data, along with existing external data to find patterns and behaviors. Gartner elaborates the definition of predictive analytics as an approach to data mining with attributes emphasizing prediction (rather than description, classification or clustering) the business relevance of the resulting insights, ease of use, thus making the tools accessible to business users, while carrying rapid analysis that can be gauged in hours or days. It is applied to both structured data (transactions) and unstructured data (reviews, emails, and forum entries). These analytics can be applied to almost any business domain including weather forecasting, detecting insurance fraud attempts, repairing machinery and improving agronomic opportunities. Often times, the guiding principle behind predictive analytics is drawing insights from past experiences will help in predicting the future by following the same patterns.

When paired with artificial intelligence, predictive analytics is capable of making more accurate and detailed insights, even the existing dataset is white noise. This is possible since machine learning application of AI helps AI-based predicted models to continuously learn and adapt, thus improving and making more accurate predictions over time. AI will further augment predictive abilities which can empower brands to identify, engage, and secure suitable markets for their services and products, and boost efficiency and ROI of marketing campaigns. It will also help eliminate costly IT downtime. E.g. Appnomic CMO, Cuneyt Buyukbezci cited that his company leveraged predictive intelligence to handle 250,000 severe IT incidents for its clients with AI, which equals more than 850,000 man-hours of work.

AI-based predictive analytics will also enable sending intelligent alerts when anomalies occur.  In customer-oriented businesses, it can help identify customers that are likely to abandon a service or product. For instance, suppose a customer has not renewed his membership for an OTT platform or did not proceed with the purchase of an item that was added to cart on an e-commerce site. The AI system will alert the CRM or sales team, who can prompt an email or text message reminding him of the pending transactions along with lucrative offers or discounts. A similar tactic can also be employed to upsell a product or service to most likely to buy customers.

In banks and financial institutions, artificial intelligence and predictive analytics can prevent fraudulent transactions for banks by monitoring customer transactions and flagging transactions that deviate from standard customer behavior. At call-centers and BPOs, it can determine the staff required to handle sudden call surges. After the predictive system determines the number of personnel needed in the coming days or weeks, the call center can move towards staffing appropriately to keep wait times at an acceptable minimum.

Apart from that AI in predictive analytics can provide increased productivity, reduce operating costs, transform business and operating models and help with more efficient resource management and asset management decisions.  Moreover, by collating data-driven insights from customer data amassed from mobile, social media, stores, and e-commerce sites. Running predictive analysis of this data can facilitate the alleviation of customer conversion rates, predict and avoid customer churn, lower customer acquisition costs, and personalize marketing campaigns to increase revenue. It also improves speed to market, thus making organizations more adaptable and agile to compete.

Forrester forecasts a 15% compound annual growth rate for the predictive analytics market through 2021. Meanwhile, Gartner has revealed in its 2018 Magic Quadrant for Data Science and Machine LearningPlatforms, about traditional software editors shifting from classic descriptive and diagnostic analytics to predictive and prescriptive analytics among top of historic big players. This implies most of the current industries are slowly incorporating predicting analytics into their business framework. Doing so can make a huge difference for businesses, looking to drive innovations, business decision and operations scalability via data.