AI-powered predictive analytics and how it can help your business
AI-powered predictive analytics is transforming the way companies execute business operations and make decisions. Learn how you can leverage it.

AI-powered predictive analytics and how it can help your business

Many businesses take advantage of big data analytics to stay relevant in today’s competitive and dynamic markets. They do this by enhancing their offerings via data insights. 

Companies are inching to adopt artificial intelligence (AI) to leverage data and gather insights that can prove helpful for them. However, before taking their product to market or going through with their AI strategy, leaders are using predictive analytics to understand customer behavior, their target market, growth, etc.

Using customer intelligence, AI, machine learning (ML), and other forms of data analytics, predictive analytics is transforming the way companies execute business operations and make decisions.

Predictive analytics is the process of using data, statistical algorithms, and ML techniques to identify the likelihood of future outcomes based on historical data. The end goal of predictive analytics is to go beyond knowing what has happened in order to provide the best assessment of what can happen in the future. 

Gartner elaborates defines predictive analytics as

“Predictive analytics describes any approach to data mining with four attributes:

  1. An emphasis on prediction (rather than description, classification or clustering)
  2. Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining)
  3. An emphasis on the business relevance of the resulting insights (no ivory tower analyses)
  4. (increasingly) An emphasis on ease of use, thus making the tools accessible to business users.”

It is applied to both structured data (such as financial transactions) and unstructured data (such as reviews, emails, and web forum entries). These analytics can be applied to almost any industry with a use case to predict anomalies in the future and be prepared to address it even before it occurs. For example, weather forecasting, detecting insurance fraud attempts, repairing machinery, and improving agronomic opportunities. usually, the guiding principle behind predictive analytics is gathering insights from past experiences that will help businesses to predict the future by following the same patterns.

When combined with AI, predictive analytics is able to make more accurate and detailed insights. AI accelerates the accuracy of predictive analytics. 

This is possible due to the machine learning application of AI which helps AI-based models to continuously learn and adapt. This in turn improves the accuracy of predictions over time. 

AI will further augment predictive abilities which can enable businesses to identify, engage, and secure suitable markets for their services and products, and increase the efficiency of and revenue from marketing campaigns. It will also help eliminate IT downtime which is usually an expensive affair. 

AI-based predictive analytics will also trigger intelligent alerts when anomalies occur or even before they occur.  In B2C and B2B businesses, it can help identify customers that are likely to churn or abandon the shopping cart, respectively. The AI system will alert the CRM or sales team, who can reach out to the customer in a timely manner and nurture the transaction back to a closed deal, using discounts or upgrades. A similar tactic can also be used to upsell or cross-sell a product or service to customers who are most likely to buy it. 

In banks and financial institutions, AI and predictive analytics can prevent fraudulent transactions by monitoring customer transactions and flagging transactions that seem to deviate from standard customer behavior. At call-centers and BPOs, predictive AI models can determine the staff required to handle call surges. This way the company can plan their staffing accordingly and reduce wait times for customers.

AI-powered predictive analytics can also helps businesses improve their teams’ productivity, reduce operational expenses, optimize resource management, and make the right asset management decisions. Running predictive analysis of customer data can provide businesses with insights that can help improve customer conversion rates, predict and prevent customer churn, reduce new customer acquisition costs, and personalize marketing campaigns to increase revenue. It also improves the speed to take the product to market. This way organizations more adaptable and agile to compete.

Forrester forecasts a 15% growth rate for the predictive analytics market through 2021. Meanwhile, Gartner has revealed that traditional software editors have begun to shift from classic descriptive and diagnostic analytics to predictive and prescriptive analytics. 

There is no doubt that most of the present industries are incorporating predicting analytics into their business framework. If you are looking to drive innovations, business decisions, and operations scalability via data and AI, let us know.

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