HomeUncategorizedMeasuring and Personalizing Customer Experience with Conversational Analytics

Measuring and Personalizing Customer Experience with Conversational Analytics

Have you noticed how customer behavior is constantly changing? Users are becoming more digitally savvy and require online companies to meet their needs.

Supply follows demand, and numerous brands strive to please their prospects and clients. It raises customer expectations. When your competitors are better problem solvers, you lose too much.

With the need for faster and more flexible solutions, companies have started to employ conversational artificial intelligence (AI) and self-service channels, such as:

  • AI-chatbots (communicate with users via text)
  • Voice bots (understand a spoken question or request)
  • Virtual assistants (complete tasks from a user’s voice commands. They include Alexa, Siri, Google Assistant, etc.)

They let machines respond to users in a human-like manner, so it becomes hard to distinguish a robot from a person. These technologies need conversational data to learn, which you can apply to enhance the business and customer service. This article will cover three ways of using conversational data to measure and personalize the customer experience.

3 Ways to Utilize Conversational Data

What can influence a customer experience? Imagine visitors opening a website and waiting for a response. It won’t take long before they become annoyed and close the page if nothing appears on the screen. You may need to perform Magento 2 image optimization or similar strategies for your chosen platform.

Even if the loading speed is fine, there are numerous issues to prevent on the customer journey. For example, they include mobile-friendliness, UX/UI, and personalization.

According to numerous research, personalization plays a vital role for 80% of consumers in making a purchase decision. 72% of clients won’t respond to impersonal messages. Let’s discuss three ways to leverage conversational analytics for personalization.

1. Sentiment Analysis to Assess Customer Emotions

Sentiment analysis is a type of ML and NLP that extracts emotions, thoughts and opinions from audio or text. It analyzes keywords in a user’s utterances. These are words and phrases indicating a specific sentiment.

After that, the bot can tailor its response to deliver outstanding customer service. Machine learning technologies automate emotion recognition by the bot, which self-educates with words/phrases that correlate to different moods.

Suppose a customer says, “The goods took forever to arrive, were scratched and poorly packaged. I’m dissatisfied with your team.”

The bot may answer, “We are terribly sorry for the misunderstanding. We’ll contact you to discuss how to fix the problem by 4:40 p.m. today.

How does the bot leverage sentiment analysis? It applies emotional intelligence to recognize that the phrase “dissatisfied” expresses the customer’s fury and answers according to preset scenarios. As a result, the dialogue becomes more meaningful, engaging, and thoughtful.

How does the sentiment analysis procedure work?

The bot begins by determining the polarity of the conversation. It divides the sentiment into good, negative, or neutral. Possible emotions may include joy, anger, sadness, fear, disgust, curiosity, etc.

The AI and NLP technologies specify the degree or intensity of the emotion and put a numerical score on each sentiment.

This score influences what the bot will do next. For example, a high positive score means joy or happiness. If an utterance (voice or textual) gets such a mark, the bot may take a chance to upsell.

What is the possible scenario in a chat with a high negative score? For example, the bot may apologize or direct a customer to a live agent to resolve an issue.

2. Enhancing the Real-Time Agent Experience

How can real-time conversational analytics turn a poor interaction into a positive one? This section focuses on delivering intelligent, AI-driven advice to customer support agents. The AI system advises them on the best next steps to answer client queries.

Customer support specialists receive real-time targeted notifications on their performance. It helps them quickly correct mistakes and improves the conversation, reducing the chance of customer churn or complaints.

Let’s look at Cogito, AI-driven software. How does it help customer support employees improve their outcomes? It can recognize speech patterns, such as tone, tempo, pitch, empathy, pauses, word frequency and others.

While clients are on call, it provides coaching suggestions to the agents on how to reply appropriately. Once installed on the CRM, the software instructs agents to speak slowly, loudly, gently, amiably, pause, or start talking. The screenshot below shows how this assistant works. In this case, the bot advises the agent to speak less and allow a customer to respond.

3. Make Better Product Offerings

Like improving customer service, conversational analytics delivers significant customer data to innovate and design better products and personalize marketing campaigns.

Traditional web analytics may lack information on how to improve products or services. They only reflect customers’ reactions to what was shown.

What sets conversational user behavior analytics apart? They reveal which product aspects customers like and dislike and how products and services may better meet their needs.

It’s possible if you actively track or monitor calls. You may quickly identify keyword topics and share that information with other teams (such as product or engineering). In turn, specialists from different departments can use this data to fix what consumers dislike about specific products or features.

For example, you see an increase in customers calling for refunds. Employ tools like Dialpad to track every time the word “refund” appears in a custom moment. It will enable you to analyze conversational data with this trigger word.

You can also look through previous interactions to figure out why a client wants a refund. These findings are good indicators of common issues that lead to customer churn. So you’ll design strategies to tackle them.

As customers seek proactive and targeted engagement from brands even in B2B transactions, you have to prioritize their shifting needs and behaviors. That’s where conversational AI comes in handy.

Retrieve data from your chatbots and conversations with customer support agents. It will help you understand customer emotions, behavioral trends, and flaws in your service or products.

Conversational analytics is essential to measure and personalize the customer experience. It boosts revenue while improving client happiness and lowering customer churn.

Author

  • Art Malkovich

    Art Malkovich is co-founder and CEO Onilab, a full-service e-commerce agency with a focus on Magento. He keeps up to date with the latest trends in SEO, SaaS, B2B, and technology in general.

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Art Malkovich
Art Malkovichhttps://onilab.com/
Art Malkovich is co-founder and CEO Onilab, a full-service e-commerce agency with a focus on Magento. He keeps up to date with the latest trends in SEO, SaaS, B2B, and technology in general.

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