SAG_Twitter_MEME_We_Know_Feb17.jpgImagine a credit card customer call center representative knowing what your problem is before you tell her.

Or picture a store that has an assistant waiting for you with suggestions for clothes in your size and color when you walk in. 

This is known as “anticipatory customer experience” and it will allow retailers to manage customer expectations early in the sales process. This in turn can help retailers to solidify an emotional attachment to their brand, offering customers what they want before they know they want it.  Doing this is incredibly complex, and becomes more difficult with the increasingly sophisticated customer journey.

“You should always try to be at least one or two moves ahead of your customers, anticipating what they might want or need,” said Shep Hyken, a customer service expert and author.

Winning retailers will be the ones who create an emotional connection with customers –via consistent, reliable customer experience. Anticipation is a part of that; being able to predict what the customer will do next during their interaction with the company gives retailers an edge.

For example, if you can predict how likely a customer will take a specific special offer or not, you are less likely to waste time and resources on targeting the wrong customers. Or maybe you can predict how likely a customer is to leave his mobile phone or Internet provider based on his actions so far; i.e. how many times has he called to complain about something? This would give your customer service department the opportunity to reach out to him with a special offer before he cancels.

Based on the discovered likelihood, the retailer or service provider can modify things to get the result they want. It all hinges on data collection and management, with insights used to provide predictions as to what your customer might do next. Being able to do this consistently across any channel in real time is very difficult.

This is where real-time predictive analytics and artificial intelligence come in; monitoring past actions, behaviors and combining them with real-time actions in order to inform a decision and automate a response/action.

The Internet of Things plays a big part here, too. Unique business insights can be provided by combining predictive analytics, machine learning and streaming analytics to the data gleaned from IoT devices.

By orchestrating data, streaming analytics and AI across all channels we really can know what our customers want — even before they know themselves.



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