What AI Can Teach Us About VoC
Businesses have become customer-centric, and brands now know how important it is to listen to the voice of the customer (VoC), which is also known as the customer’s voice. It can capture, study, and reveal all your customers’ sentiments, expectations, and comments associated with your brand. Customer feedback enables you to know where, when, and how to begin to improve your Customer Experience.
While it has become necessary to get customers’ reviews for analysis, the amount of data involved will overwhelm human labor. This calls for the deployment of AI.
But, Is There Any Need For Integrating AI Into The Voice Of The Customer?
Revuze in the Laundry Detergents Liquids & Pods Report: H1 Trends And H2 Projections (2020) reveals that 82,343 reviews were collected and analyzed, but it necessitated the deployment of AI to extract 160,552 valuable quotes.
NTT in the 2020 Global Customer Experience Benchmarking Report  reveals that more than three-quarters of brands think that AI will have a positive impact on customer behaviors, as well as voice of the customer (VoC). According to the report, over 40% of brands have put in place structured voice of the customer programs to propel innovations and Customer Experience (CX) improvement.
From another dimension, the IDC’s Customer Experience Benchmark Study reveals that bad experiences will make 30% of customers abandon a brand and refuse to come back.
These are some ways you can deploy AI to better understand customer sentiments.
1. Churn Rate
If you can discover why some customers churn your product or brand faster than others, it will help to a greater degree to define new campaigns to keep them. This information can be obtained when you use AI to analyze the customer journey data.
The use of AI concepts of governed and ungoverned Machine Learning algorithms make it easy to discover why customers’ buying behavior affects their purchase intentions. The valuable insights you gain from this will enable you to reduce churn by building a solid standard of customer journey data.
The insights are valuable for keeping customer relationships fresh since you will now see the need to continuously test new marketing campaigns.
2. Mining Of Data
There must be a precise step-by-step plan that will enable you to always promptly conduct the text mining of both textual and unstructured data you gather. You should have a complete view of customers’ sentiments of your brand, and you can do this by building semantic models of unstructured text with the help of NLP.
Sentiment analysis affords you the opportunity of quantifying the emotions your brand evokes in the market. You can deploy sentiment analysis to have a total measure of customers’ emotions toward your brand.
If your marketing team makes it a practice to continually teach prediction models with customer data, it enables the team to better understand what will most and least captivate your customers and how to ensure they are not indifferent or angry. Having a good grasp of what makes customers change their purchasing intentions very fast is now possible.
3. Broadening The Span Of Speech Analytics
AI is making it possible to broaden the span of speech analytics, and you can now add contact center conversations, text-based customer feedback, and operational data from every customer interaction into your speech analytics. To ensure your customers remain loyal to your brand, you must provide consistent, unwavering, and dependable sales and service responses across every channel in real time, as well as understanding the need to keep every customer conversation in context.
The integration of AI and Machine Learning (ML) is ensuring that brands focus on customers and give them quality service as well as experience. This will go a long way in boosting your employees’ experiences. Your brand’s digital evolution will be enhanced, and VoC programs will become qualitative, ensuring a pristine Customer Experience.
4. Improvements In Customer Satisfaction
The insights you will gain while using AI will lead to the transformation of your call center from being first-line service providers to becoming critical differentiators that will enhance significant improvements in customer satisfaction and your overall financial performance. McKinsey reports how the use of advanced analytics by brands is responsible for the reduction in the average duration of one transaction by up to 40%, increase interactive voice response (IVR) containment rates by 5 to 20%, cut overhead costs by up to $5 million, and at the same time, ensuring employees’ output and quality service to customers.
5. Defining Customer Risk Threshold
AI makes it possible for brands to have valuable insights into customer risk thresholds. What enhances this is the ability to combine insights into customer behavioral and operational data you promptly obtain with AI and Net Promoter Score (NPS) data.
You can now apply the information to mitigate customer defection to your competitor. NPS is an important metric you can use to quantify the level of loyalty customers have for your brand.
Your ability to quickly find out which customer will likely churn is enabled by using deep learning neural networks to conduct a real-time analysis of NPS, customer behavioral and operational data. This is a process that would have taken weeks, which an AI-based analysis will provide the results in seconds.
6. Personalizing Service Recovery Responses
Customers have different attributes, and what may lead to a customer rejecting a particular product may even make another customer like the same product. Deploying AI to personalize service recovery strategies  for each customer brings about an improvement in retention rates, which ultimately lessens the high cost of customer churn.
With improved service recovery, you can enhance the re-acquisition of a customer after a service breakdown has happened. You need to have in place an effective service recovery strategy to ensure issues are settled beyond what the customer expects to receive as a response, and in real time too.
It’s only with AI-based techniques that you can be certain of tailoring or personalizing service recovery responses promptly. The techniques are proving very useful in ensuring that the customer relationship is fully restored after a service error.