Delivering a positive customer experience is an important differentiator for many businesses, and AI and machine learning are now poised to transform business communications, introducing automation to make it much more effective. Many businesses are already automating some of their interactions, for example customer service centres are frequently using chatbots or automated assistants to help direct calls or answer the most basic enquiries.
But what’s needed for fully automated customer service is much more than ‘chatbots’. It’s a huge leap from the current theory and practice of AI, but, with the right foundations it will be achievable. There are three key steps businesses can start down the path to get there.
Artificial Intelligence becomes exponentially harder if you start with a completely blank canvas, but what if you already have a clue about what the interactions are about? By adding context to communications we can provide deep and insightful data about how customers interact with a businesses, including about their behaviours, attitudes, choices and so on, providing useful information upon which to base future systems, services and products. It’s a simple step, but an effective one and it’s achieved via ‘contextual communication’, which simply means being able communicate via any media within the context of a task or transaction.
An example of this could be voice and video communication right from within the shopping mechanism of a website, allowing a customer to find information and then interact with the business via that website – importantly, from the environment, or context, that they’re already operating within. This gives every party more effective engagement, as well as aligning with the way today’s consumers want to communicate: it’s simple, accessible and instant. By starting to add context to communications now, we are building up a database of learning from which to draw upon when we take the step to AI, while at the same time delivering business value today.
In its simplest form, machine learning is effectively pattern recognition, meaning that the more patterns it has to draw upon, the more intelligent it can be. It needs access to a database of conversations and business systems so it can learn and understand patterns and categorisations. Feeding machines with diverse real life data means they can comprehend interactions, provide intelligent responses, understand intonation and sentiment so they can be as effective as possible.
Logging why a communication was successful, as well as markers for the most productive conversations – as well as what a failing one looks like – all help to build up a valuable database. This gives more opportunities for the machine to identify patterns using actionable insights. By beginning to capture, classify and tag business communications, including call recordings and automatic transcriptions, as well as their outcomes and sentiments, businesses can get a head start in preparing for machine learning and AI automation.
Without context, machine learning and AI are severely limited in their ability to give good or accurate answers and follow the right process flow. Layering machine learning onto contextual communications reveals why a customer is there, what that customer’s journey was to reach that point, records the outcome and works out if the communication was effective or not.
Finally it provides ways to make it more effective if needed. By linking just the appropriate databases with CRM systems across the business – sales, marketing, contact centre – businesses can provide a really effective way to improve the workflows and processes that underpin customer engagements and experience, and feed that into machine learning databases. That contextual data about a customer, a transaction or big data trends allow better decisions to be made at the point of communication and enable a much more intelligent system that can deal with more requests.
Ultimately, businesses are keen to drive data-driven and automated personalised user experiences and the technology exists to deliver this. But context is the most important part in getting this right. Without it automation will fail, cause confusion and lead to frustration. The convergence of contextual comms and AI has the potential to be really exciting, freeing up human to human interaction time to the areas where greatest value can be added. This is where we’ll see fundamental transformations in how the real-time enterprise of the future will communicate – via human or machine, or a mixture of the two – with its employees and customers in context: at the right time, with the right information at their fingertips, and in the right application.