Nearly half of telecommunications companies are struggling to deliver intuitive and user-friendly interfaces for multiple touchpoints that customers use such as B2B online stores and mobile apps.
Meanwhile, another 40% of telco executives say their organizations find it difficult to provide customers with responsive and flexible customer service in order to respond quickly to customer inquiries, according to a 2013 e-commerce report from Intershop.
There are multiple factors that play into these challenges.
These include functional and channel silos that make it difficult for decision makers to get a comprehensive view of customer behaviors, interests, attitudes, preferences, and other feedback they can use to create the interfaces and support experiences that both B2B and B2C customers would like to receive.
Increasingly, telco business leaders are using predictive analytics to collect, analyze and act on customer behavior and feedback from across a variety of touchpoints to deliver the types of products, service offerings, and support experiences that customers crave.
There are a myriad use cases for big data and predictive analytics that telcos can apply for understanding and acting on customer expectations.
For instance, product managers for a wireless carrier can use customer sentiment data gathered through contact center interactions, customer surveys, and behavioral insights that customers share to gain a deeper understanding of the mobile apps that different customer segments (18-24 year old male gamers) are most interested in.
Product leaders for the wireless carrier can then drill down on this information to determine the types of mobile apps – along with the types of features and functionality wanted by the target group – it could develop itself or market from a third party .
Contact center leaders and other decision makers can use predictive analytics tools to prioritize customer support issues that customers would like to have addressed.
They can also discern the types of channels that different customer segments use for support – and when – to schedule agents for each channel more effectively to address the ebb and flow of customer demand.
Through the use of data visualization tools and techniques, product leaders can also view correlations between the types of channel support that can be offered to different customer segments at different times and the likely impact that such offerings will have on customer satisfaction, loyalty, and lifetime value.
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