CX Insights: Predicting the Trends of Tomorrow
Predictive analytics utilizes data-analysis technologies and statistical techniques to give business managers the thing they want most: the ability to predict future demand, trends and problems. Over the past few years there has been a growing consensus is that predictive analytics is a must-have for growing businesses. This week’s CX Insights assesses the value of predictive analytics today and discusses how to maximize its potential to improve CX.
The mistaken predictions for the outcome of American election and the British Brexit vote have left many questioning whether predictive analytics is truly as great as it sounds. However, Eric Berridge, CEO of the Bluewolf Group, argues that it is not predictive analytics that it is flawed; it is how we use it. Berridge argues that the faulty predictions made in the American election and Brexit vote were caused by a lack of data. To be accurate, pollsters and businesses have to grab data from every data source, evaluate that data and use it to make predictions. Otherwise, Berridge argues, predictive analytics will never truly be accurate.
The ability for a company to address a customer’s issue before they’re even aware of a problem will not only be a game changer in CX in years to come, but will also be an expectation. Over half of consumers expect that by 2020 companies will anticipate their needs and make relevant suggestions before they initiate contact. This is and will continue to be an imperative in the Age of the Customer, as customer service is key predictor of customer loyalty. Consequently, Vala Asfar
Communication service providers (CSPs) should be at the forefront of innovation in Big Data because they, more than any other industry, have access to the most data. However, due to their size, CSPs are struggling under the weight of their infrastructure and smaller and more versatile firms are leading the big data movement. Arnab Chakraborty [computerweekly.com] argues that in order to deliver on a truly differentiated customer experience, CSPs need to leverage the data they have to use predictive analytics to its full potential.
As part of their research on predictive analytics, the good folks at econsultancy joined with IBM to identify how automation and artificial intelligence have changed the use of analytics. Arliss Coates [econsultany.com] provides an in-depth list of nine best practices that telecom marketers should be aware of when using predictive analytics.
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