Execs In The Know- A Global Network of Customer Experience Professionals

2018 Predictions for the Service Leader: Part 4 – Technology

We surveyed a number of customer service/experience leaders, from many of today’s leading brands, in our community to get their predictions for 2018. Over the coming weeks we will be releasing their thoughts on specific CX topics including customer expectations, channels, operations, technology, use case studies/data, and security/risk.

Click here to catch up on Part 1 – Customer ExpectationsPart 2 – Channels, and Part 3 – Operations.

The fourth installment of this series focuses on thoughts on technology.

• AI will begin to automate tasks for the agent and the customer – however AI will take longer to mature than previously thought, due to data infrastructure not mature enough to fully leverage.
• Companies will begin to shift from handling transaction to personalizing service.
• Digital will continue to experience strong growth, powered by mobile usage.
• Messaging channels will continue to grow social volumes.
• More conversations around the “social ethics” of AI deployment.
• Continued push on technology – moving infrastructure to the cloud.
• Deep/machine learning will play a role in 2018 for brands to meet customers where they are. I think brands are starting to get on board with the concept of machine learning and using data to predict behavior. But deep learning can extend beyond things like chatbots. If a brand can predict what a customer might be looking for, it can tailor content to the customer that allows the customer to self-service, send push notifications, etc.
• Thinking about AI secondarily to the types of use cases and outcomes desired that require it.
• Companies will have to address AI and ML in their strategies for customer acquisition and engagement. 2017 may be seen as a year of hype as lots of new vendors emerge on the scene. Expect consolidation and actual AI/ML deployments to increase in 2018.
• Interest in – and deployment of – customer journey analytics tools will increase, as companies try to identify and solve problems upstream before they occur.
• Strategic enterprise automation (front to back office) will help businesses begin to improve the customer experience, while optimizing back office operations, leading to stronger sales and a better bottom line. Examples include automated machine learning/AI, self help, chatbots/avatars, to RPA and beyond.
• People continue to overestimate innovation in the short term and underestimate it in the long term.
• We will see the first practical examples of AI in the areas of contact center routing, compliance and quality, with practical use cases.
• Some brands are testing AI for knowledge management (ie. agent support tool powered by AI vs. a traditional knowledge management tool). A lower risk approach to testing AI vs. directly with clients.
• Over the next 3 to 5 years, I believe that the majority of inquiries that come into the contact center will no longer be voice or email, but some form of messaging. SMS is only one form of messaging: it encompasses a broad swath of methods including iMessage, Facebook Messenger, Kik, WhatsApp, and more. Many people are following WeChat, which is now one of the largest standalone messaging apps with over 963 million monthly active users. Part of the reason it has been so successful is that you can do more than just send messages. Apple is moving down the path with the announcement of its Business Chat messaging solution which is an extension of iMessage and will include some ApplePay capabilities. The convergence of these technologies will be what ultimately tips the scale to shift consumers and businesses away from phone and email to messaging.
• Maturity issue with emerging virtual assistance technologies (chatbots, AI capabilities, etc.), continues to lag – requirements for specific user case studies at an all-time high.
• Buzz and hype are high, but adoption is low. Brands realize they need more data in order to leverage these technologies to be accurate.
• I’m not convinced that brands have the capacity/talent/full understanding of what precisely goes into machine learning and true AI (beyond chatbots). I think we’re going to see a wave of failed adoption on that front.
• Technology – Proliferate of technology out there – from measuring CX, to ‘omnichannel’, to speech analytics.

Stay tuned next week for the final installment in the series Part 5 – Use Case Studies/Data and Security/Risk. 


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