96% of customers with a high-effort service interaction become more disloyal compared to just 9% who have a low-effort experience.
In recent times, customer-centricity has led to the emergence of a new spectrum in the customer experience (CX) space, “Customer Effort Score (CES).” Simply put, CES is the amount of effort that a customer puts in to reach a specific level of satisfaction after putting in a query. In other words, it is the ease a customer experiences while getting his queries resolved by a brand.
According to Gartner, “Effort is the strongest driver to customer loyalty.” As put by Andrew Schumacher, Senior Principal, Advisory, Gartner, “Exceeding customer expectations provides, at best, a marginal lift to customer loyalty. Our research finds that to win customer loyalty, customer service and support leaders must focus on consistently meeting customer expectations.”
A recently conducted CCW Digital Consumer Preferences Survey revealed easy and convenient experiences are the #1 way to attract customers. Indeed, by reducing customer effort, brands can deliver high-quality interactions at lower costs.
Post the COVID-19 pandemic, brands have adopted conversational AI to fulfill customer needs and deliver speedy, personalized, and proactive customer engagement.
This blog provides a detailed insight into how organizations can improve their customer effort scores with conversational AI.
Before delving deeper into the role conversational AI plays in improving customer effort scores, let’s first understand what conversational AI is.
Conversational AI is a group of advanced technologies like machine learning (ML) and natural language processing (NLP) that empowers computers to analyze, process, and respond to human utterances (text/voice inputs) naturally. Experienced through chatbots, voice bots, and virtual assistants, conversational AI offers a flexible, fast, and accurate medium for organizations to interact with customers every day.
In today’s highly digitalized era, customers are opting for self-service channels to research products, make purchases, and most importantly, get their issues resolved. Chatbots and virtual assistants provide instant, round-the-clock support while personalizing interactions to individual customer needs.
Customers desire to get their problems resolved with the least amount of effort. In fact, CCW Digital conducted a robust customer survey that found 62% of respondents are more likely to spend with brands that deliver easy experiences.
Let’s understand how conversational AI helps reduce customer effort and contributes to enhancing CES.
Omnichannel chatbots allow customers to easily access support in the channel of their choice – the platform that suits them or through which they have first communicated with the brand.
Moreover, a customer may wish to switch channels in the middle of a support case. For example, a customer communicating with a chatbot may ask for agent assistance or switch to another platform (Facebook Messenger to WhatsApp).
Omnichannel chatbots store customer information, such as purchase history, previous support tickets, and other demographics, by leveraging natural language processing, machine learning, and natural language understanding to deliver personalized engagement.
With a 360-degree view of each customer, such bots can retain the context of the original conversation, coordinate customer interactions across channels, and ensure customers can easily switch channels without the need to repeat information.
Considering the extensive customer base of enterprises, providing fast customer support can be challenging, and customers might often end up waiting for painstakingly long times.
In certain instances, customers end up looking for support when contact centers are closed for business.
That’s where conversational AI chatbots come into the picture. Bots operate 24×7 and provide timely responses to a host of customer problems, both complex and straightforward, in their native language. Thus, the caseload of contact center agents reduces, improving the Mean Time To Resolution (MTTR) and customer effort score in the process.
IVR continues to have a stronghold in contact center operations and remains the channel of choice for many customers.
However, an improved customer effort score means tackling the challenges related to traditional IVR, like navigating various menus and facing longer wait times before reaching the right agent.
In short, customers expect a speedy, hassle-free resolution of their queries, which involves a shift towards conversational IVR services.
Conversational IVR uses the text-to-speech capability of chatbots to provide interactive, accurate, and personalized human-like voice support.
For example, if a customer wants to book a flight, they can simply call up the support center and say, “Please book a flight to London.” Instead of sifting through the various IVR options, the bot will ask the customer for details, such as the preferred date and time of flight, book the next available flight, or directly route the customer to the concerned booking agent.
Interactive voice support and intelligent routing minimizes waiting time and improves MTTR and first-call resolution rates (FTC).
Instead of reacting to a problem, brands must take the proactive approach and anticipate customer needs to provide them assistance even before they ask for it.
Conversational analytics studies customer utterances, words, and phrases, along with customer sentiment, purchase history, and buying behavior, to understand customer preferences, intentions, and perspectives and deliver tailored interactions based on a customer’s unique disposition and demeanor.
Let’s take a simple example.
Suppose a customer buys a product by interacting with the brand’s chatbot. Now, the brand knows that the product will require installation post-purchase and will see the customer visiting the website or calling the contact center for details.
In such a scenario, the chatbot must proactively take the customer through a step-by-step installation process, saving the customer an extra step and reducing customer effort and calls per event in the process.
The Gartner Customer Effort Score (CES) is a customer experience survey metric that enables service organizations to account for the ease of customer interaction and resolution during a request.
CES is measured by asking the customer for feedback and scoring their response on a scale of 1 to 7, with 1 representing the highest level of disagreement.
Here’s looking at a few metrics studied by Gartner that outline the benefits of improving your customer effort score –
At Acuvate, we help clients deploy customer-facing chatbots with our enterprise bot-building platform called BotCore.
Our bots help improve customer effort scores with key capabilities, including –
To know more about BotCore, please feel free to schedule a personalized consultation with our experts.