Identifying customer sentiments has always been the priority for enterprises, as the majority of customers’ decisions are based on how they feel and think about a brand. Through this, brands can decide their business and market strategies and achieve a high customer satisfaction rate. When approached correctly, customer sentiment analysis provides ground-breaking results, making a remarkable difference in their customer’s experience.
What is customer sentiment?
Customer sentiment is a feeling or an opinion that a customer perceives about a brand. This can range from positive, negative, or neutral, making an impact on customer engagement. To identify customer sentiments, brands should go beyond what has been spoken and understand how it was spoken and what the feeling was and take an insightful view of the tone, context, and feelings to the core of every customer interaction.
When a customer connects with the brand, shares its reviews, come with questions, and asks for resolutions, they are portraying a state of emotion. The brands that grasp these emotions capitalize on the customers and become their favorite brands.
What is customer sentiment analysis?
Customer sentiment analysis is the process of accumulating, analyzing, and providing recommendations for acting on customer feedback. This analysis is based on customer interactions including both voice & text. Customer sentiment analysis is an automated process that detects emotions while customers are interacting with the brands. It gives an overview of customers’ perceptions of the brand, issue resolution, recording suggestions, as well as feedback.
Customer sentiment analysis allows brands to have a direct customer feedback on their offerings and fine-tune them as required to gain market share and resolve business challenges.
Using interaction analytics to measure customer sentiments
Interaction analytics starts with available raw data from every customer interaction. This data is then sorted and filtered to understand customer interactions accurately to get an in-depth overview of customer sentiments and dig out meaningful insights. With the help of interaction analytics software, this analysis can be further simplified and improved.
With proper utilization, interaction analytics can help brands leverage improved customer satisfaction, enhanced reputation, and increased profits.
What is interaction analytics and how does it work?
Contact centers record a huge volume of customer interactions on a daily basis. However, it isn’t practical and possible to listen to & analyzing each of these interactions manually.
By utilizing interaction analytics, brands can uncover hidden insights from customer conversations and understand customer expectations.
As the majority of customers like to talk directly with the brands on a call to resolve their concerns, it becomes easy for brands to understand sentiments through interaction analytics.
Interaction analytics uses artificial intelligence, algorithms, machine learning, and natural language process to scrutinize voice interactions. It identifies common customer concerns, trends, and expectations keeping customer sentiments at the center.
The most rewarding thing about using the latest AI technology is it lays a way for businesses to quickly understand customer expectations. It makes it easy for businesses to glance at customers’ requirements and act on them, instead of predicting or guessing their requirements. Based on the recommendations by the technology, brands can take well-informed data-driven decisions to improve customer engagement, product quality, and brand positioning.
Step 1: Gathering Interaction Audios
Interaction analytics processes the raw data in the source system. It then matches this data with metadata which includes important data related to the calls such as agent name, duration, and time.
Step 2: Speech Recognition
Now, the audio goes through the speech recognition process. The interaction analytics software turns the speech into text. As this happens, the tool also extracts acoustic signals, such as silence and agitation in the voice.
The gathered audio then goes under the speech recognition process where interaction analytics converts the audio into text. It further identifies different attributes of voice such as distress, excitement, silence, and more.
Interaction analytics can run a check through different formats of conversations enabling brands to have a multi-channel customer support module.
Step 3: Outcome
After speech recognition, the software then examines the interactions to identify language forms. The analytics tools then categorize these calls and their contents based on language and related characteristics. With the latest upgrades tools, it is also possible to identify KPIs and automatic scoring, keeping track of agent performance, resolution time, and customer satisfaction.
Step 4: Result
After completing these processes, organizations can further identify actionable insights for improving business performance. These insights enable companies to come up with solutions and strategies to bring a remarkable difference in customer satisfaction rates.
Benefits of using interaction analytics
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Improved Customer Satisfaction Rate
Customer interaction analytics eases the process of finding areas of improvement in the organization’s offerings, enabling brands to work on accurately improving efficiency.
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Better Understanding of Customer Sentiments
Understanding customer sentiments and acting upon them is the most reliable way for any brand to succeed and have a lasting impression on its customers. Interaction analytics lets brands understand customers’ requirements, pain points, and causes of frequently changing behavior.
A better understanding of customer sentiments not only allows brands to meet customer expectations but also allows them to take data-backed decisions to streamline their offerings, marketing & promotion strategy, and improve sales efficiency.
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Keep Track of Agent Performance
Interaction analytics not only analyses agent performance but goes beyond and allows you to focus on critical behaviors that influence customer experience. With the help of analytics technology, brands can identify coaching opportunities and recognize the best performers. It is a powerful way to understand agent behavior and how it can be improved.
Brands can use interaction analytics to determine agent behavior and understand their challenges, making targeted coaching easy and rewarding.
Based on the analysis, brands can streamline agent call scripts to further help agents improve their performance, overall productivity, and customer experience.
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Reduce Churn
As a brand, have you ever thought about why your customers are not returning? Well, the answer lies in the interactions. Interaction analytics helps you identify where customers are feeling dissatisfied and what is troubling them the most. With the identification of points of concern, you can work on effective solutions to reduce customer churn and increase retention.
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Reduce Expense
Cutting down on expenditure is important for many businesses. With the help of interaction analytics, brands identify clear customer expectations, eliminating the need for experiments and allowing the brands to focus on those expectations instead of doing experiments with their offerings.
Apart from that, interaction analytics also makes it easy for brands to predict customer behavior for specific situations based on previous patterns. This predictive analysis empowers brands to deal with turbulent or high-rush situations and meet customer expectations.
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Mitigate Compliance and Reduce Risk
Contact centers and brands need to adhere to several rules and regulations while interacting with customers. However, this can become tricky sometimes. Here, interaction analytics can help detect whether brand representatives are complying with the stated guidelines or not.
By tracking & analyzing the interactions in real-time, brands can identify & stay ahead of compliance violations at the moment. Empowering them to take timely actions to reduce risks and avoid situations leading to regulatory and compliance-related violations as well as fines.
Over the years, interaction analytics have empowered call centers and brands to maintain and improve customer experience. When combined with artificial intelligence and human intelligence, it is easier to uncover hidden insights from customer interactions. Through this, brands can leverage greater benefits and improve market and business presence by enlightening customer engagement.
Interaction analytics help measure customer sentiments and add to the overall customer experience. QEval is a pioneer when it comes to combining the power of Artificial Intelligence and the problem-solving capability of Human Intelligence to deliver outstanding performance. Take your organization to the next level by partnering with QEval. Contact us today!
