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. This means communications are devoid of context, 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.
The beginning of a road toward contextual communication
Adding context to business communication is a simple step that vastly improves its effectiveness. ‘Contextual communication’ simply means being able to do the communication act within the context of a task. Businesses are starting to do this by using tools such as collaboration applications - think Slack, Microsoft Teams or HipChat. They allow a workgroup to discuss, share information and documents pertinent to a particular job function or goal they are working to achieve. Everything shared via that channel is to do with that task and allows the workgroup to operate efficiently. But still, this information flow is within a silo not directly related to the task at hand.
True contextual communication comes when you move your communications straight into a work context and a process ‘flow’. Technologies like WebRTC are already enabling ‘app-less’ voice and video communication from within websites, allowing customers to find information and then interact with the business via that website, and importantly, from the environment, or context, that they’re already operating within. By embedding communications in the natural places they should occur within a process flow, we can realise significant benefits both in terms of efficiency and user experience.
The next step: AI and machine learning
Machine learning and AI have garnered a lot of attention this year, and we’re starting to now understand how combining these technologies with contextual communication can help businesses unlock even more powerful ways of making communication more efficient and intelligent.
For example, 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. To do this, the machine needs access to a database of conversations and business systems so it can start to learn and understand patterns and categorisations and provide intelligent responses. For example, if I ask an advanced AI assistant, like Google, to “tell me size of Sharan”, it’s likely to reveal the size of a size of a VW car. However, if context is provided, for example, “here’s a space question: tell me the diameter of Charon?” it will understand it needs to provide the size of a moon in the solar system. Without that context AI is limited, and so its ability to give accurate answers is also limited.
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 can machine interactions support business communications
Fundamentally there are two different ways that machine learning, or automated assistants, will function in the workplace. Firstly we have programmed assistants, which are increasingly being used for first line customer contact. Assistants have the ability to listen to audio, transcribe it, analyse the text looking for key subjects and then direct the enquirer or customer to the correct outcome based on static analysis of the information they’ve asked for. This is a deterministic and fairly reliable system resulting in very few mistakes.
Secondly - and this is where it starts to become really interesting - is 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.
Customer service and ‘robo coaching’
A simple real world application of this intelligent machine learning is business insight into customer service enquiries. Where call recordings and outcomes have been classified and collected in a database, it becomes possible to create transcriptions and work out sentiments; to identify the best interactions and outcomes and who generates those and why; to find out what the markers are for a productive conversation, and how those conversations start; and understand what a failing conversation looks like.
This next step might then be ‘agent robo coaching’. If you know how a productive conversations starts, or what phrases can be used to re-direct a negative conversation to a productive one, it becomes possible to transcribe those in real time use machines to prompt agents to handle the interaction differently. For example, if an agent is talking about price, but the enquirer is really interested in quality, the nature of the conversation can be changed to deliver a positive outcome.
Beyond this we enter a world of fully autonomous customer service - the holy grail of AI in business comms - enabling businesses to save skilled customer service agents for tasks that require human logic, intuition, and empathy. In this scenario, machines need to know when to escalate an interaction because of value or lack of progress. For example, high net worth individuals contacting a travel company for round the world trips and safaris, or complaints about an accident on holiday would benefit human contact. Delivering automated customer service is only possible by attaching context to each conversation, achieved through collecting hundreds of examples of conversations within a context.
Machine learning and AI are already starting to show how they can streamline and support business communications, and there’s a clear development path to achieving fully automated customer service, or other parts of the business: for example, internal IT support functions. Ultimately, businesses are keen to drive data-driven, 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 and 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.
Rob Pickering, Founder and CEO at IPCortex
Image Credit: Tyler Olson / Shutterstock