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11, July 2017

Channeling Predictive Analytics for Customer Intent Prediction

Gone are the days when we used to bookmark or jot down information that is of value. We would proudly share this knowledge with interested parties. But nowadays, we take a different route. We communicate via social media channels to acknowledge one’s existence. In this digital world, this changes how customers talk about brands.

A plethora of tools and channels have been extensively used to understand customers and their needs. It was forecasted that analytics will bring out the revolution in the way organizations approach their customers. However, with social media becoming a part of the daily routines of customers, real-time predictive customer experience analytics is emerging as the differentiator in the success of a brand.

Predictive analytics have been put to extensive use to find out what customers like and what they don’t. Armed with this knowledge, organizations have aligned their campaigns to promote the curate products and solutions based on the insights.

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Marketers may have a preconceived notion of the customer demographic for their products or solutions. It is a combined result of individual assumption and a half-hearted market research. But with predictive analytics and proactive customer service, their marketing efforts can be streamlined to meet future demands of a focused audience.

A real-time analytical application will be able to determine who you are talking to, what they are trying to get done, and when they require help. Consumers often reveal their intent through their behavior across channels. For example, a retailer’s web page can reveal information about the products that users are interested in purchasing. It provides the groundwork for unravelling the so-called mysterious of customer intent. Blending the customer journey data of a user with his/her identity of and the context from their recent interaction histories across channels can substantially drive up the intent prediction accuracy. Factoring location data into this can further boost the prediction.

This application will be able to compare a customer’s behavior during a single interaction with the behavior of thousands of others using various statistical analysis and machine-learning techniques. It can also identify the exact points in the journey when the customer is most likely to take action or require an intervention. All this happens in real time during the regular course of the customer’s journey without requiring the customer to provide any explicit input.

The most critical step in application framework is the ability to apply the findings from the data to substantially improve subsequent interactions. Smart customer service applications can use the data that they generate to self-correct, and automatically learn from each interaction to enhance customer experience, prediction accuracy, and eventually – the outcomes.


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