A few years ago AI was merely the brainchild of sci-fi literature. Today, it is transforming virtually all industries – from international defense to global finance and even art with Japanese AI competing with humans in literary fiction contests and creating new styles of painting.
The AI revolution is changing the CX space, as well. According to Gartner, by 2020 eighty-five percent of client relationships will take place with no human interaction whatsoever. As we speak, Accenture claims, over 80% of chat sessions with customers can be resolved by a chatbot.
AI can help you improve your customer experience beyond streamlining client-facing interactions and minimising their associated cost, and the gains can be tremendous. Read more to discover several high ROI business opportunities that AI can help deliver.
Gather customer feedback with ease, speed and precision
AI allows you to harness the vast wealth of unsolicited and unstructured customer feedback, of “Big Data”. This information is now being used independently or alongside traditional research methods and primary data sources to provide a greater depth of consumer understanding.
For Martin Dimov, Director of Data Science at GemSeek, working with Big Data means working with all informational sources. For years his team has innovated in market and CX research by partnering with companies to aggregate and analyse all sources of knowledge about customers – including internal user data (CRM), as well as that obtained through social media, NLP, data scraping, and client surveys. The goal: to generate comprehensive and meaningful insights about every step of the customer journey.
Machine learning, the most basic application of AI, is an invaluable asset in gathering passive data about consumers’ online behaviour and utilising it to develop holistic profiles of customers. Why ask your client how often they use your app when you already likely track this? Primary data can then focus on asking clients why they are using the app to provide a deeper level of understanding.
Understanding your clients’ digital steps helps you improve your research efforts by identifying with customers through their actual actions – not what they claim to do – and also by narrowing down the questions that you actually need to ask them.
Manage your customers and keep them longer
AI predictive models allow companies to take control of their client engagement processes and dedicate effort and resources where it makes most sense. Whether you want to reduce your churn rate or increase the success of upsell initiatives, AI-powered predictive modelling can automate the delivery of smart, actionable insights.
Our client, one of the largest telecom providers in Western Europe saw a revenue boost of 5% within 6 months after we deployed a “superdetractor” project, providing management with fresh lists of customers diagnosed by our predictive model to be most “at risk” of leaving the business. With clarity who to get in touch with and knowledge about their previous usage and behaviour (including making complaints), the sales department was able to rescue a significant proportion of the customers who would have otherwise been lost.
Predict your weak spots and handle them ahead of time
Unexpected malfunctions in key technology can take a long time to fix and come at a great cost. Time to make repairs can hamper the availability of your service and lead to severe financial losses, as well as reputational hits. The beauty of AI is it can prevent operational crises by detecting what were previously unpredictable events and minimize their harm.
Wind turbines, for example, have sensors collecting an array of useful data which could be utilised by AI to predict that a particular part is likely to malfunction in a given time period. Informing you of this likelihood, AI makes it possible for preventative maintenance of the wind turbines to take place without losing time and money to fix a problem once it’s already happened.
Keep in mind
The recent Uber Self-Driving Car accident had many in shock upon the news of a pedestrian killed by an AI-powered vehicle in Arizona. This accident serves as a clear warning that we should keep in check our expectations of new technology and understand both its strengths and weaknesses. AI’s deep learning algorithms may outperform humans in some situations and make mistakes or lack common sense in other, rather trivial ones.
To that end, Martin Dimov emphasizes the importance of the human factor behind any AI project: with a deepening global shortage of data scientists, it is crucial to seek and keep talent or create long-term partnerships when lacking resources to build an internal team of experts.
The most precise academic models based on AI may turn out to be less helpful than less accurate models which nevertheless offer greater – and better – insight from a business perspective. Humans’ input along each stage of the process of setting up AI-driven projects, therefore, is key in order to bear fruit and not fall into the trap of the widespread illusions about AI’s all-encompassing superpowers.
With neuronetworks the latest trend in AI, Dimov warns against looking for a “panacea” when deciding how to tackle the challenges presented by digitalising customer experience, or any other aspect of the business process for that matter. He argues that flexibility and commitment to uncover relevant business insights should trump the desire to keep pace with tech fashion.