Customer engagement and customer experience has never been more important. Advances in technology, the rise in social media, ever increasing expectations and more choice every day have meant that customers are more discerning than ever.
Not only is it necessary to maintain a competitive edge but strong engagement with customers can really help a company create loyalty and sales. Done badly though, and it can break a business and stop customer interactions in their tracks.
To be able to deliver a high-quality customer experience, at scale, many organisations are turning to artificial intelligence (AI) and automation. It may not always be obvious but many interactions that businesses have with their customers already involve these capabilities and that trend is projected to increase in the coming years. According to Microsoft “By 2025, as many as 95 percent of all customer interactions will be through channels supported by artificial intelligence (AI) technology”.
The use of AI to add intelligence to services is not a secret and as a result has added another injection of pace into customer’s ever-increasing expectations. Customers are being taught to expect highly personalised interactions that work across different channels.
Consequently, it is becoming essential that companies leverage AI and automation to enable proactive, intelligent, integrated and convenient interactions with customers.
That is easier said than done though and with so much hype around it can be hard to understand how AI and automation can be utilised. There are many options that vary in complexity and intelligence and therefore the potential benefits similarly vary.
Here is our guide to how some of the AI and automation capabilities that can be used to enhance customer engagement:
The rise of Robotic Process Automation (RPA) has introduced many to the potential of automation and using software to replicate simple activities normally completed by the workforce. From a customer engagement perspective this allows organisations to ensure that contact from customers is responded to promptly. Even if it is just to let the customer know that their request has been received and is being worked on, this is better than receiving no response.
Simple, repetitive processes, such as logging and fulfilling application forms can be completed using automation. This can speed up customer service response times and also frees up customer service agents to focus on more complex activities.
Obviously, automation should be implemented carefully and in the right places. A slow response to a request can be frustrating for a customer but possibly not as damaging as a response that is clearly wrong or confusing.
Chatbots have had quite a bit of focus over the past few years with many companies seeing them as a channel that needs to be added to their online presence as a matter of course. As a result, the chatbot market is now very crowded.
At the simplest level chatbots, or rather “live chat”, allow customers to engage with service agents in an organisation. This offers several benefits such as allowing a channel of communication for customers where the phone is either not possible or not preferred but it is also typically faster than an email.
The obvious drawback is that if a service agent is not available then the customer might be sat waiting on the other end of the chatbot getting frustrated. This is compounded by the likely scenario that a customer has tried to call the organisation, found that they could not get through to a human, so resorted to the online chatbot, only to find a similar result there.
To mitigate this risk – intelligence can be added to chatbots so that they are more capable of helping customers. Step one is give the customer options of what service they require and then ask questions to capture the right information and complete the request.
The benefit is that customer requests get processed quickly, efficiently and without having to engage a service agent. Plus, the chatbot can lead the customer through the process if they are unsure of what is required.
The risk is that if the customer is unsure of what service they need or if they use different terminology then they might not select the correct process, not see an option that is right for them or become intimidated by the myriad of options.
More advanced AI capabilities can be added to chatbots to enhance their intelligence. First off is the ability to ask the customer in plain English what service they require and then use Natural Language Processing capability to attempt to understand their requirement.
Once understood, machine learning algorithms can be trained to be able to identify the service or information it believes is the one most likely to meet the request.
The benefits are clear, providing a customer with the information they require will lead to a positive experience, but so are the risks, the identification of the request might be incorrect and the wrong information provided leading to more frustration.
If a solution cannot be identified, then a service agent could be brought into the conversation to attempt to help them. At that point, it is also possible to capture the request and resolution provided by the human to further train the machine learning algorithm, so it is more likely to identify the right solution in the future.
Beyond powering intelligent chatbots, AI is being used to enhance customer engagement – to provide a more intelligent and personalised service.
This is already being seen with many online retailers and media streaming services but it is increasingly spreading to other sectors. This is especially true when tech challengers arrive into a sector causing disruption and new competition to the traditional providers.
The future strategy of many companies will need to pivot towards offering personalised services that add value and make the lives of their customers easier. Using AI to transcend the simple transactional nature of businesses, moving towards a relationship with customers where the offerings grow and adapt with them.
This will be possible by capturing and learning from customer data. In much the same way that the best service agent consciously or unconsciously learns, from every interaction, how to provide the best quality service AI technology also needs that opportunity.
By taking data from each interaction and linking it with specific outcomes, Machine Learning algorithms can be trained to understand customers, when and how to interact, what they like and do not like, and what factors encourage deeper engagement.