The business world is made of little experiences that shape customer centricity — a quick reply to a complaint, a tailored product suggestion, or a smooth omnichannel experience. These seemingly small stories are the building blocks of lasting customer relationships.
In a world where competitors are a click away, these stories bind customers to your brand.
But how do we capture these experiences? Well, the approach is ever-evolving, with AI now enhancing voice of the customer (VoC) analytics and customer experience (CX) as a whole.
Let’s explore how AI is revolutionizing VoC analytics and why it matters for your business.
VoC analytics links companies with customers by analyzing customer feedback to uncover their feelings, needs, and expectations. It helps you deliver a product or service that users want and can connect with.
Without VoC analytics, it is difficult to gauge if investments are being spent in the right place. Too often, companies create products or services that fail to connect with customers. The result? Resources funneled into initiatives that don’t deliver.
Since its inception in 1993, when Abbie Griffin and John R. Hauser coined the term in an MIT paper, VoC has evolved in several ways. Originally relying on surveys and verbatims, VoC programs now allow you to capture feedback on a larger scale in less time.
Thanks to AI, you can gain insights into customer sentiments without the laborious task of manually reading through each comment.
AI excels at processing vast, unstructured datasets, such as from customer surveys, with precision. Here’s how it helps businesses shift from reactive to proactive:
Many companies struggle with data overload when managing CX and capturing VoC. Advancements in AI now enable CX management tools to handle vast amounts of data, which can give companies a real-time view of customer sentiment and preferences.
AI-driven sentiment analysis can address problems before they escalate by working with analysts to spot shifts in customer emotions or issues. This real-time smoke alarm is crucial in maintaining a positive CX.
One of AI's key advantages in VoC analytics is its capacity to drive personalization at scale. AI algorithms analyze individual customer data to offer tailored recommendations that strengthen customer relationships and create brand advocates.
AI can analyze past data to predict consumers' needs and behavior. As a result, companies can foresee risks and improve customer experience.
For example, if AI predicts a customer might cancel their subscription, the system can prompt retention actions, such as offering a discount or tailored support, based on effective past strategies.
AI helps businesses gain deeper, more actionable insights from VoC data. These tools also make it easier for companies to refine their customer experience strategies by automating processes like data collection, analysis, and sentiment interpretation.
Natural language processing (NLP) is the core of AI and modern VoC tools. It enables machines to understand human language by decoding its structure and deriving useful information from it.
While customer feedback is a great source of knowledge, it often hides in silences or nuances. VoC tools use NLP to help businesses understand customers' sentiments on a large scale by going beyond the surface.
NLP can detect sentiment, sarcasm, irony, and even dialect variations to help businesses grasp the true meaning behind customer comments. By analyzing unstructured text, NLP breaks down content into themes, topics, and sentiments and provides actionable insights.
Knowing when a customer is dissatisfied is one thing, but understanding what makes them unhappy makes all the difference. With this in mind, modern VoC programs also use text and sentiment analysis.
Text analytics is the result of NLP and machine learning (ML) working together to understand the whole picture. They turn raw, unstructured text into clear insights. Sentiment analysis, in particular, enables businesses to efficiently process vast amounts of customer feedback, quickly identifying what’s working and what needs improvement.
Text analytics and sentiment analysis scale through unstructured data to find the key issues that affect client satisfaction and loyalty.
VoC platforms use AI-powered facial analytics to analyze and interpret nonverbal cues of visiting clients or customers, such as a smile, frown, or frustration. This real-time analysis offers a more profound understanding of customers’ satisfaction, enthusiasm, and sentiment toward a particular product, service, or experience. Brands can use this understanding to tune their approach more finely.
Sentiment can also be linked to a specific demographic if needed. For instance, in a retail business, facial analytics can detect frustration among customers at the point of purchase and immediately improve their experience. This enables businesses to gain deeper insights into how different customer segments experience and respond to their products or services.
Real-time feedback processing is just as its name suggests — monitoring customer feedback as it happens. Imagine learning, in the shortest time, about a customer's experience with the brand. AI solutions let you do this 24/7 across multiple channels.
AI in VoC analytics not only provides a constant stream of up-to-date information to guide you but also alerts you to big swings in customer sentiment, enabling you to take corrective action swiftly. You can quickly spot a problem before it grows. As the saying goes, “Prevention is better than cure.”
Personalization is like having a personal shopper. AI can read customers’ tastes and preferences and tailor recommendations accordingly. It's easier to hold customers' attention when they are recognized. Tailored suggestions make customers feel important, and AI does exactly that.
For example, in e-commerce, a recommended system can boost sales. AI is used to analyze users' browsing and purchase history to give them a unique experience.
A VoC analytics feedback loop is a systematic process businesses use to collect, gather, and analyze feedback. It uses VoC data to inform decision making and make improvements to satisfy customer needs.
In customer feedback management, you have two major players: the inner and outer loop. Think of the inner loop as your first responder. It handles real-time customer feedback and makes immediate fixes. In contrast, the outer loop is about long-term improvements based on patterns/trends. AI has had an impact on these feedback loops as well. Let’s see how.
Traditionally, managers in the inner loop would sift through feedback, make sense of it, and then scramble to put out fires. But AI has transformed this process into something far more powerful and efficient. Here are some ways how:
AI goes through vast data and reviews from emails, chatbots, social media, and surveys to pull out valuable insights and ensure you never miss a beat when understanding your customers.
Using AI, feedback requests are tailored to individual customers, making them feel less like a mass survey and more like a genuine conversation. It's the difference between a mass-produced holiday card and a handwritten note. One is forgettable, while the other makes you feel valued.
In feedback loops, timing is everything. AI takes the guesswork out of timing and uses data to determine the optimal moment to reach for feedback. It makes sure you reach out at the right time and on the right platform, whether it's a quick text after a purchase or an email after a customer service chat.
With AI, you can automate feedback collection across all the platforms your customers love — email, SMS, phone calls, and more. This consistency makes sure you’re gathering feedback efficiently where your customers are most likely to engage.
AI creates personalized follow-ups that resonate with your customers. These could be thank-you notes, requests for more details, follow-up emails, or proactive solutions to a problem they didn't know you knew about. AI, thus, boosts response rates and engagement by optimizing the timing and content of follow-ups.
AI dramatically accelerates the process of identifying and correcting systemic problems — something the traditional outer loop falls short of. Its ability to continuously learn from data also ensures that the outer loop remains responsive to evolving customer needs, driving long-term improvements.
AI can work non-stop in the background, reviewing customer feedback, social media posts, and support call records. Rather than merely relaying what the customer said, it quickly predicts what they may say in the future. Moreover, AI can show you emerging trends to adopt before your competitors do.
By analyzing feedback data, AI highlights areas where your product or service may need improvement. For example, if it detects an increase in complaints about a certain aspect, your product or service team can prioritize fixing the area.
Drawing on AI for VoC analysis does not come without its challenges. Here are three big challenges that may be blocking you from unlocking the full potential of AI in your VoC strategy:
Despite the hurdles an AI-driven VoC might pose, every customer-centric company can benefit from it if done right. That said, here’s how to adopt AI-driven VoC analytics across the entire organization:
Senior management’s support for AI-driven VoC analytics is key to achieving business goals.
For example, a retail CMO advocating for AI-based VoC could present the benefits to a board that might otherwise be unfamiliar with VoC research. They could also emphasize how real-time customer feedback can build brand loyalty.
For both VPs and managers, the main challenge is using AI-based VoC analytics in their departments' work. This means choosing suitable AI tools that teams can actually use. Managers must also plan procedures to create workable strategies using VoC analytics.
A company's VP of marketing may use AI to analyze customer sentiment about a new product or VoC data in the marketing plan to improve ROI. It allows for real-time changes to messages or campaigns.
It is common for frontline staff to first contact clients. As such, organizations may find it beneficial to invest in AI-powered VoC analytics. These solutions and the level of automation AI can help the frontline to attend to a customer's need or concern as it develops.
Real-world examples show the true potential of AI in VoC analytics. Let’s review four strong case studies that show how potent AI can be when applied to VoC analytics and get your team aligned:
Source: Amazon
Amazon is the perfect example of AI-native VoC analytics as it utilizes collaborative filtering and deep learning. After analyzing the customer's browsing and purchase history, it recommends products of interest. This self-service feature increases customer satisfaction and leads to more purchases, boosting annual revenue. AI also helps manage stock and predicts which products will be in high demand and which won't. This reduces overstock and stockouts, improving overall CX.
Source: H&M
H&M has recently integrated AI customer care through chatbots. The system interfaces with H&M’s customer relations management (CRM) to support relationships, cut response times, and boost customer satisfaction. Another positive outcome is lower operations costs for customer service.
Source: Cleveland Clinic
VoC analytics through AI has been incorporated into Cleveland Clinic patient experience programs. The clinic used the analytics to get survey results, social media comments, and reviews. The VoC program helped them analyze and fix any problems with patient treatment. With an AI system that categorizes feedback by wait time, staff attitude, and treatment, the hospital swiftly acted on the results.
Source: Cisco
In a B2B market, loyalty is key to building strategic business relationships. Cisco’s AI platform extracts data from customers' surveys, support calls, and social media. It aims to find their main concerns and where companies are failing. Cisco's VoC program also aims to understand its large enterprise clients and how to serve them.
This has resulted in more effective customer relations and proactive customer service provision. By preempting the causes of crises, Cisco has boosted customer satisfaction. This, in turn, has increased loyalty.
AI is central to today’s VoC initiatives. The prospect of tomorrow’s customer experiences is now settled: it will be AI-driven.
AI is changing how companies analyze VoC data. The process of analyzing is becoming quicker, more precise, and universal. When businesses use AI-native VoC analytics, they can create more value propositions aligned with customer expectations, leading to improved customer experiences, business success, and brand advocacy.
Learn how to transform feedback into strategy with the voice of customer methodology!
Edited by Monishka Agrawal
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