In the past, humans were preferred over machines because there was a perception that humans were more accurate in their analysis of situations to make informed decisions. Fast-forward to 2018, we’re now seeing a complete focus on the customer in technology innovations.
Chatbots – or virtual assistants powered by advanced machine learning – is one of the technologies that will have the greatest impact on the customer experience. Most chatbots on the market today perform pretty simple, niche, and supervised tasks like answering questions or booking appointments accurately and quickly. But in the next five to 10 years, they will have a lot more context and data to play with.
By Combining them with advanced machine learning capabilities, they will become truly autonomous and able to perform complex tasks like delivering personalised experiences to both employees and customers. These automation and personalisation layers are what will make customer journeys and overall experiences seamless and smooth, across all types of channels and devices.
Personalised experience based on cognitive computing
To deliver this new level of experience that customers and employees expect today, organisations need to first implement cognitive capabilities into their internal and external business applications.
Cognitive computing is the simulation of human thoughts through processes in a computerised model. It involves self-learning systems that use data analytics, pattern recognition and natural language processing to mimic how the human brain thinks and works.
Organisations that want to truly empower their cognitive capabilities need to build dedicated platforms that allow the development of cognitive-ready applications across the business. Cloud-first and cognitive-first development platforms are paramount to building powerful user interfaces across any type of device. Only such platforms can harness big data in a meaningful way, to derive business insights and competitive advantage.
Business applications using machine learning algorithms can analyse immense volumes of data and make predictions. These predictions may be related to user preferences, client behaviour, reliability of manufacturing assets, or anything else that the application is designed to deal with. There are already great examples of that in the retail industry, with players such as Amazon leading the machine learning revolution. Tomorrow, more and more industries will walk in these technology giants’ footsteps.
Shaping the customer experience of the future
The next growth stage for machine learning is advancing software and machines autonomy and ability to learn like humans.
We’ll start to see chatbots evolve to become more intuitive in their decisions, and therefore more autonomous. This is possible through high-level declarative programming approaches, where developers describe the goals for chatbots, rather than giving them direct instructions. These advanced virtual assistants will enable organizations to staff their customer service operations 24/7. They will lead to a more level playing field for businesses of different life-cycles.
We’ll also see the rise of agent bots which can augment their abilities to execute complex tasks such as predicting when a customer is struggling and at risk of having a bad experience. This will give human agents the freedom to become more strategic and productive, seeing the creation of new roles dedicated to designing customer journeys.
Overcoming the data challenge
Data is now the core of any new-generation customer-focused strategy, whether you’re in the retail, finance or telco industry. But to harness it and turn it into a competitive advantage requires the right skills and technologies.
Dealing with multiple data sources, building the infrastructure that can process and analyse data, and automating communication between customer-facing and back-office platforms are the biggest hurdles for many organisations, especially SMBs. Many of them today pile up data mining and analytics platforms, combining them with app development add-ons, ultimately making the whole analytics engine overly complex and inefficient.
What they need is proper data science capabilities to automate the discovery of data that matters and automate the personalisation of customer journeys. Through data from multiple sources, they can adapt to each customer’s shopping habits, offering unique experiences based on past behaviour, and predict what customers will want tomorrow. In that way, organizations of any sizes can access the full potential of data analytics nor take advantage of the AI and automation revolution.
Finally, every organisation with a customer-centric approach needs to deploy connected mission-critical business applications that are quick to market, reliable, secure, and easy to evolve with changing customer needs.