What can artificial intelligence (AI) and machine learning (ML) do to improve the customer experience? AI and ML have already been intimately involved in online shopping since, well, the beginning of online shopping. You can’t use Amazon or any other shopping service without getting recommendations, which are often customized based on the vendor’s understanding of your traits – your purchase history, your browsing history, and possibly much more. Amazon and other online businesses would love to invent a digital version of the (possibly mythical) seller who knows you and your tastes, and can infallibly guide you to the products you’ll enjoy.
To help machines understand human language, an interdisciplinary solution combining linguistics and computer science was developed: natural language processing (NLP). NLP has been around for a while, but lately, it has benefited from recent developments in machine learning and deep learning techniques. Machine learning (ML) is a subfield within artificial intelligence that creates algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed.
Intricate understanding of the customer
Companies are required to collect customer data for relationship management. However, to improve customer relationships, companies must implement machine learning systems that can process Big Data that encompasses huge amounts of data from previous customers to perform highly accurate analysis. This will shed light on the customer touch points and the entire buyer journey. Using this historical data, the machine learning system should be able to predict customer behavior and match it to actual customer action to further improve the predictive engine.
An important factor in managing relationships and increasing revenue for existing clients arises from churn management. Companies that can implement ML capabilities effectively could manage customer expectations, reveal the root causes of account cancellations, and spot the early signs of risk pushing customers to quit. This will help the company take corrective action where appropriate to improve customer retention.
It all starts with better data
To make that vision a reality, we have to start with some heavy lifting at the back. Who are your clients? Do you really know who they are? All clients leave a data trail, but that data trail is a series of chunks and it’s difficult to relate those chunks to each other. If a customer has multiple accounts, can you tell? If a customer has separate accounts for business and personal use, can they link them? And if an organization uses a lot of different names (we recall a presentation where someone talked about the literally hundreds of names that were resolved with IBM), can you discover the one organization responsible for them? The customer experience starts with knowing exactly who your customers are and how they relate. Cleaning your customer lists to remove duplicates is called entity resolution; It used to be the domain of large companies that could afford large data teams. We are now seeing the democratization of entity resolution – there are now startups providing entity resolution software and services that are appropriate for small and medium-sized organizations.
Better customer support
Customer retention suffers when companies take more time than necessary to resolve inquiries and problems. However, there are limited resources in terms of personnel; Therefore, Natural Language Processing (NLP), a subfield of ML that enables computer systems to understand written and spoken human language, can provide a better experience by allowing customers to explain their problem using their own thoughts. AA algorithms could predict the root cause behind the initiation of the customer support inquiry and transform the content into an actionable message for the customer support team.
Conclusion
We’ve only touched on the basics, and there are plenty of other machine learning algorithms and practical use cases to talk about. We recommend that you consult other resources on our website to further research the topic.
Our clients such as Uber, Transferwise, Monzo, and Spotify have identified critical business information from unstructured feedback using our platform, enabling organizations to create best-in-class customer experiences.
Machine learning can help businesses create a robust and personalized customer experience through an ML-based marketing strategy that will delight shoppers at every touch point and ultimately turn them into evangelists.
