In 2017, Aeromexico launched a customized AI system, a neural network that was built on the foundation of machine learning.
At the Facebook’s F8 Global Conference, the company announced that it went from zero to 96% automation of its customer service function and that its AI-powered platform, which learns as it goes by scanning and analyzing previous customer service transcripts, reduced the average customer service resolution time from an average of 16 minutes to just 2 minutes.
I cited the above example to highlight just how important AI has become for customer engagement. The sheer volume of fragmented consumer data that is generated these days often erodes the customer experience. The data that is available is disorganized and generating insights out of them becomes a challenge. Success is also limited when data is unorganized.
Insights don’t work in a vacuum. Data becomes valuable and actionable only if it can be matched back to the customer and deeply analyzed across all channels. Unlike traditional systems that rely on loyalty programs and the like, AI looks at end-to-end customer engagement through the customer’s whole lifecycle.
Creating a sustainable engagement cycle where consumers both enjoy and are rewarded for proactively improving their favorite products is the next step in this evolution.
Companies can leverage AI in a variety of different ways:
Winning customers is only half the battle. While a 5% churn seems mostly harmless on the surface, if compounded annually, you could be losing a lot of customers and burning that much-needed capital.
AI can help you solve this problem proactively by leveraging its predictive models, which can be used to forecast churn among disengaged customers. By deploying machine learning algorithms, you will also be able to learn the exact reasons that are causing the churn.
More than personal engagement, customers also demand engagement at the right time, on the right device and with the right message. They expect a true, integrated cross-channel experience. Extreme personalization is the norm today and it requires engagement for context, content and behavioral data.
Through AI, one can create hyper-personalized messages based on individual consumer behavior, historical data, transactional history, etc. and relay those messages to the appropriate channel at the right time
Provide real-time recommendations
AI is becoming the best weapon of companies to reach customers in real time and increase their levels of engagement. Analytically generated recommendations, next best actions and contextually correct product offers delivered in real time to all customer touch-points are utilizing powerful AI and machine learning applications.
Some real-world examples of AI providing real-time recommendations include Netflix, Gmail, Google Maps and Facebook.
Automated customer service through chatbots and virtual assistants make them quick and hassle-free. Front-end chatbots can handle first-level queries by providing answers to simple questions and frequently asked questions.
AI can be used to route inquiries to interpret incoming messages and develop initial responses that can be edited by the service representative, or find relevant knowledge-based content and deliver the same to the rep.
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AI enables employees to work faster and more efficiently, thus improving customer loyalty and satisfaction. AI tools make it possible to anticipate customer needs and to react proactively to them.
It can also be powered to enable marketing strategies that are already in place and to ramp up the potential of customer analytics by helping companies understand exactly what kind of data they have, what it means and where the untapped possibilities lie.
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