Technology has the power to disrupt and progress business operations, especially as its capabilities advance. In this blog, we will discuss how AI is rapidly transforming contact centres and what this means for the industry.
The Role of Agent in an Evolving Contact Centre
The implementation of AI in the new-age contact centre model has been one of the most significant disruptions for the industry. In a conference in 2017 by the World Economic Forum, conversations revolved around job security in light of technological advances.
Disruptive technologies have reshaped the way we work in many industries. Customer support isn’t any different, with contact centres seeing a massive shake-up over the last decade. AI has allowed employees to be more efficient and productive as AI-powered bots take care of routine enquiries. Concerns, however, have arisen around job security, with many believing that technology could become advanced enough to take on all the responsibilities of an agent’s job. The reality is that the role of the customer support agent has changed but not become extinct. Agents are taking on more complex and relationship-dependent problems that AI is not equipped for, such as customers needing in-depth and unpredictable technological support. While this does mean the role is more challenging for employees, the time saved by virtual assistants has allowed staff to take in-depth training to improve their skills and resolve these customer issues.
A combination of AI and human agents is more important than ever to deliver the quality customer service expected. Customer centres will need to begin to phase out the outdated methods and solutions that are no longer optimised for a modern world.
Improving Agent Performance
Agent performance is typically measured by quality assurance metrics such as average handle times, for example. Before the implementation of AI in contact centres, managers could only assess and make improvements on agent performance by the analysis of random samples of agent and customer interactions. The insights derived from this sampling was then used to shape future training programs. Understandably, in a study by Contact Babel in 2017, 80 percent of contact centre managers reported the biggest hurdle to performance improvements was limited time to analyse data and put its insights into meaningful action.
AI has overcome this weakness by facilitating managers to record calls to monitor agent scripts with speech analytics. Through machine learning algorithms, faults and successes are highlighted efficiently and effortlessly to monitor performance against set benchmarks. Insights from this process can go on to mould better training programs.
Improving Customer Experiences
Machine learning has also paved the way to improve customer experiences with sentiment analysis. This type of analysis can pinpoint customer feelings by monitoring customer commentary in both text and speech. Ultimately it unearths the perceptions of the contact centre and the business it represents to develop better experiences. For example, sentiment analysis can determine why customers contacted the company, the processes that caused frustration or happiness, and whether the agent is meeting the expected level of service.
AI technology is dynamic. It can learn from multiple touchpoints, which is beneficial when customers use a variety of channels such as live chat, messaging and self-service channels to contact support.
The most important takeaway point for contact centres is that the insights derived from AI are both actionable and usable by anyone, not just expert data analysts. AI can help agents deliver direct improvements to their customer support.
Expert Up-sellers
One objective for agents may be to upsell whenever possible, especially when customers are deliberating purchase decisions. For example, when customers are talking to a virtual assistant on a live chat function on a website. AI is now intelligent enough to be an expert up-seller, offering real-time guidance, such as product information and recommendations based upon the context given by the customer.
Speech analytics utilised in phone-calls can detect phrases and moments where rapport is established in upsell efforts. Analysis of these moments determine methods that work which can then be funnelled into how virtual agents work. It can even distinguish strategies to use depending on the type of customer!
Reducing Overhead Costs
Customer support can be a costly business. IBM reported that globally, an average of 265 million customer support requests are made each year – these cost businesses around $1.3 trillion. Repeatedly, AI-powered bots, live chat solutions, dynamic IVR technology and other technologies have been shown to reduce costs. Chatbots Magazine suggests costs can be reduced by nearly 30 per cent! Juniper Research also found that interaction costs could be lowered by as much as 70 per cent by 2022 for businesses who use chatbot automation. This is because bots can be used in around 40-80 per cent of customer service requests. Cost savings from AI-powered chatbots are estimated to save global companies around $8 billion per year. What’s more, these technologies will transform contact centres into more efficient business models, which offers more time as an additional resource.
The most significant cost reduction comes from staffing, which equates to around 75 per cent of overhead costs. AI-powered solutions allow for easier and cheaper scalable growth, overcoming the obstacles that come with increasing demand (such as 24/7 service and multiple language functions). Staff costs also encompass sick pay, holidays or expenses associated with staff retention, which comes as no concern when integrating AI solutions.
Managing Increase in Enquiry Volumes
Enquiry volumes are rising as shopper habits change. Increasing figures show that 51 per cent of purchases made by a shopper is made online, up from 48 per cent in 2015 and 47 per cent in 2014, according to Fortune Magazine. Couple this with customers wanting to know more about products and services before purchase commitment through multiple channels, it is no surprise that enquiry volumes are on the rise. Customers want to ask complex and multifaceted questions that can’t be easily answered by the FAQ page. They, therefore, need an agent who can understand and answer them before they are willing to buy, which sees them turning to contact centres more and more.
IBM noted a virtual agent could answer and resolve around 30,000 customer support queries a month. They can also recognise multiple use cases, which makes it possible for them to resolve simple to moderate requests. Advances in AI will only increase their capabilities.
With contact centres evolving to fit this modern consumer world, it’s no wonder that the adoption of new technology within contact centre models is increasing year-on-year. According to a Deloitte study, 33 per cent of contact centres plan to invest in AI bots for their customer service in the near future. These companies noted the main reason for chatbot integration was to improve customer experience across all their channels. If you would like to discuss how AI-powered solutions could revolutionise your call centre, get in touch with us today.
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