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How Does AI Enrich Predictive Analytics? You Need To Know!

How Does AI Enrich Predictive Analytics? You Need To Know!

Predictive customer analytics is increasing the scope for identifying these emotions

AI allows a brand to innovate and then communicate their product in a much more effective way

AI will continuously enrich predictive analytics further

With emerging technologies like artificial intelligence (AI), Machine Learning and predictive analytics, the infinite potentials hidden in customer data, is being unearthed and leveraged to maximum benefit. Today, AI in predictive analytics is being used in various scopes of business to modify operations and generate higher revenues. Let us explore how AI is playing a prime role in improving predictive analytics.

When the predictive analytics and computational power are paired together, the combination enables businesses to determine their potential clients and expected customer behaviour, based on the data gathered over a long time.

Behavioural Patterns Are Offering Insights

Since humans don’t always make purely logical decisions, emotions like trust and intuitions play a vital role in it. So, predictive customer analytics is increasing the scope for identifying these emotions.

Companies are now trying to make the most of customer behavioural patterns leveraging AI. And thanks to the power rendered by predictive analytics, it is paving a brand new way towards customer intelligence. Predictive analytics enriched with AI has offered businesses the ability to fathom beyond what the historical data sets say. It can now suggest possible solutions to avoid a certain undesirable situation.

One of the Predictive Analytics examples related to anomaly detection can be cited about the PayPal-Rapidminer collaboration, which was mainly to detect the intentions of their top customers and their grievances. This ultimately helped them in shaping better customer experiences.

Social Media Analysis And Detection Of Anomaly

Anomalies can be detected when, after data analysis, the system pinpoints something that is a deviation from usual operations i.e. something that looks unusual. For example, predictive analytics and AI in ecommerce can alert businesses whether by releasing a video, they can gain a satisfactory response from customers or not.

From anomaly detection, they can get a fair idea about what works in their favour and what must be changed. It can largely help organizations in identifying features that can convert a potential customer into a loyal one, or say, pinpoint a set of features that might lead to a drop in the number of customers.

Thanks to digital transformation the way how information is generated and processed today has totally changed. Now companies can easily understand their customer’s perspective towards their brand by tracking user comments on social media.

Also, it allows a brand to innovate and then communicate their product in a much more effective way. Customer satisfaction is the key element known to increase sales and thus, if a happy customer writes good things about your brand on your social website, you will automatically appear credible to most users.

Healthcare

AI has improved predictive analytics for the healthcare industry as well. Google was the pioneer which stepped into the healthcare domain using predictive analysis. Today, global healthcare organizations have started to leverage the power of predictive analysis and online pharmacies are delving into customer’s medical history, prescription and dosage of medicines, to estimate future purchases, detailed market trends of medicine buyers.

As more and more data is generated by systems today, AI will continuously enrich predictive analytics further, which will help businesses achieve greater accuracy and better customer relationships.

Note: The views and opinions expressed are solely those of the author and does not necessarily reflect the views held by Inc42, its creators or employees. Inc42 is not responsible for the accuracy of any of the information supplied by guest bloggers.