Capitalising on AI and contextual communications
In this blog, we run through the steps that businesses need to take to put them on the path of true communication transformation, enabling them to create far more engaging customer and business experiences.
Apply context: Artificial Intelligence becomes exponentially harder if you start with a completely blank canvas, so 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 behaviors, 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.
Feed the machine: 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.
Combine context and machine learning: 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.