Capitalising on AI and Communications

Posted 13 May 2021

A lot of businesses continue to rely on phone and email as their main types of communication. Work conversations like these have been normalised as a separate task or ‘special’ sort of interaction, which happen using a completely separate gadget or platform, away from the related task or process they are engaged in.  
The result is that these communications are devoid of context, and/or separated from the information pertinent to that exchange. Not only is this frequently frustrating and complicates the interaction, but it’s not how the digital generation want to communicate – or even, increasingly, how they are used to communicating.  

What is contextual communication?

Adding context to business communication is a simple step that can vastly improve its effectiveness. Contextual communications is the act of embedding rich media comms like voice, text and video capabilities inside an application, website or device – as an integrated part of the workflow and process of the application or site, rather than as an adjunct. It enables person to person, and person to application, interactions to become centered around, and informed by, the specific task.  This will result in improving efficiency and accuracy, as well as ensuring that users are informed to make the most intelligent decisions every time by presenting the most relevant transactional or trend-based data. It also facilitates a more natural flow between different communications types (for example, a chat session becoming a voice call) without losing that context or information.  It is enabled by the new-found prevalence of open web standards like WebRTC, which makes it straightforward to develop, and is pervasive across almost any connected device or web browser without needing to install plugins or download any special software – and tackles the problem of siloes.

How does it impact businesses and customer experience?

For many processes, it will mean workers and customers won’t even think about communication as a distinct, friction-bearing operation. Rather, it’ll simply be something that happens as they move in and out of the collaboration or communication phase of a task or workflow. 

For customer focused businesses, contextual communications allows service agents to much more quickly and easily predict or pre-empt why a customer is contacting them. An agent would be able to see instantly which web page a customer is on before they hit the ‘click to call’ button, how they got there, and what other services they use. Additional context such as that customer’s previous contact history can also set the stage for more meaningful interactions. 

For employees communicating with partner organisations outside of their firewall, contextual communications makes life easier by simplifying interactions, providing context and relevant information instantly and removing the need to download plugins. For example, a video conversation where participants simply click on a web link to get connected and that allows everyone to access the relevant current and historical documentation from within the same link – all without having to context switch to create a traditional conference phone call. 

Even entirely new services could be created. For example, forward-thinking housing associations, and other organisations that provide accommodation to vulnerable people, are already improving daily contact for thousands of people in real time. By analysing patterns of communication they can identify when the cognitive state of an individual is changing, and give a predictive assessment about the needs of every resident calling in to them before that call is even answered. 

Contextual communications is the open, pervasive and simple way to communicate that opens up new opportunities to add value to businesses. It makes it much easier for customers to interact with businesses using all, and any, available media. Key business functions like sales and support can make customer engagement quicker, more intelligent, human and ultimately more memorable – adding real value beyond cost savings and efficiency. 

Machine learning and AI in business communications

Where a business can record a conversation or interaction and its outcome, machine learning could be used to help determine whether it was an effective communication or not, and provide ways to make it more effective if needed. This machine-based future isn’t far off, and to prepare, businesses should start capturing, classifying and tagging their business communication data today. Building up a valuable database of different sorts of conversations, interactions and outcomes will dramatically improve the value of machine learning when it is introduced because there are more opportunities for the machine to identify patterns using actionable insights. These thousands of data points will become the basis of automated systems in the future of your own unique business and context.

How does machine learning add value? 

There are two ways that machine learning, or automated assistants, will function in the workplace: firstly programmed assistants or chatbots – already being used by some businesses, but increasingly prevalent – that are used for first line customer contact, dealing with the most common queries and making suggestions according to what’s in their database. 

Secondly, we have applications where machine learning can learn intelligently, based on real life data, to give usable and consumable outcomes. This is where machines can not only comprehend interactions and provide intelligent responses, but can also understand intonation and sentiment direct from voice recordings, learning even more about what’s going on at the customer end of the transactions. 

So, what are 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? 
1) 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 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 (see more about this in our previous blog post). 

2) 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. 

3) 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.