Customer_CommunicationsNot just tools for streamlining customer engagement – AI and Machine Learning represent an opportunity for companies to create a better experience and more loyal customer by allowing them to rethink how they build context around each individual.

Tapping into these new technologies allows brands to cost-effectively enable relevant types of two-way communications with potential and existing customers. The essential communications of brands (bills, tax documents, statements, etc) traditionally lack personalisation and relevance, although informational, they do not engage the customer. With new technologies, brands can use digital channels to reimagine their essential communications.

Brand messages of high importance can now be personalised to bring additional value to the customer, it doesn’t have to look the same for everyone who receives it. Instead of just providing a customer with a service bill, providing them with tips on how they can cut down on costs or usage really gives out a sense of true engagement.

By tapping into AI and machine learning, plus some strategic planning and investment, businesses can transform their customer communications. The 4 steps below show this.

  1. Omni-channel experience – Understanding what touchpoints customers prefer is an excellent starting point of engagement. Consistent, seamless interactions are something that customers should experience whether they prefer to communicate via text, emails, social media or something else. This level of interaction creates a huge amount of data, which a lot of companies haven’t learnt to tap into to be able to gain insights about an individual customers preference and needs.
  2. Customers changing needs via AI – The detailed analysis of these massive volumes of structured and unstructured data received can be automated by taking a linguistics-based approach. The data analysed about certain customers can be mashed up, so that AI can determine what content to include in a specific communication, where it should be delivered, and how it should be presented.
  3. Getting specific with machine learning – In the past, there was not enough data to impress recipients, only basic information such as a customer’s name, location and career sector. With machine learning brands don’t need to focus on the limitations they face in segmenting customers. They are no longer constrained by their current number of customer profiles, with the information they receive from customers, they can now create an indefinite amount of profile segments. Brands are not only empowered by data but actual customer intelligence too, of which represents a changing landscape with customer preferences, actions and behaviours.
  4. Interactive digital communications – Digital communications, powered by AI can now be utilised to ask the consumer meaningful questions about their overall experience, for example – “Would you like reminders sent via text message, email or social platform”. Instead of classifying individuals into a broad segment, brands can now build up a unique profile for each customer. From this, focus can now be directed onto the information specifically selected for the individual, highlighting data and key messages that will drive engagement and desired actions.

Customers now expect every piece of communication they receive to be tailored to their individual preferences, and with these new technologies, brands are able to reach these continuously evolving expectations of the consumer.

The rate that AI and machine learning can sift through, analyse and respond to volumes is at a speed that humans just can’t match. As they get more advanced, they will be of great benefit to both the customers and companies that they serve. Using them to create a better experience and more loyal customer is not an opportunity companies should miss out on.