Measure the performance of your Virtual assistants with AI-powered surveys

4.70 billion people use social media platforms globally, and most of these users frequently interact through chatbots and virtual assistants. The user interactions are dynamic in nature, with substantially diverse customer journeys and built-in human handoff; this necessitates a different method of gathering and analysing customer feedback via quick surveys.

Traditional surveys have a very low response rate since they are time-consuming to complete and the majority of users receive multiple requests for them every day. These requests are all monotonous and contain static questions.

A new generation of AI-powered automated conversational surveys is assisting industries in gaining deeper insights into the customer experience without the use of traditional surveys. AI-powered conversational surveys embedded with deep machine learning techniques and text analytics provides Voice of Customer (VoC) metrics such as Net Promoter Score (NPS), customer satisfaction score (CSAT) , higher response rates, and more verbatim feedback.

A combination of quantitative and qualitative survey methods is used to obtain comprehensive and in-depth feedback responses. Quantitative data presents the numbers, while qualitative data gives detailed information to completely comprehend the feedback results.

Types of surveys

Quantitative Surveys

The term "quantitative feedback" refers to customer feedback that can be statistically analysed. There are various methods used to collect quantitative responses such as websites or mobile apps. Due to their objectivity and quantitative nature , they are simple to understand and assess: in the form of CSAT, NPS and CES scores.

Quantitative Surveys can be further categorised into the following:

1. CSAT Score: Customer Satisfaction Score, often known as CSAT Score, is a statistical tool that businesses use to determine how satisfied customers are with a particular interaction or with their overall experience. The scores are evaluated on the scale of 1-3, 1-5, or 1-10 on a scale. It is also available in the form of a star or an emoticons scale format.

Example: On a scale from 1 to 5, how satisfied were you with the product?

1 2 3 4 5

Dissatisfied —-------------------------------------Satisfied

2. CES Score : Customer Effort Score (CES) is a customer experience survey method that enables organisations to assess the ease of customer interaction and resolution queries. CES scores help the business in improving their customer experience. It ranks a customer journey from "extremely difficult" to "very easy" to complete.

Example: How easy was your issue resolved on a scale of 1------5?

1 2 3 4 5

Strongly disagree-----------------------------Strongly Agree.

3. NPS Score: The Net Promoter Score, or NPS, is a metric used to gauge customer satisfaction with the company's goods and services on a scale from 0 to 10. Positive and higher ratings show that the product or service has a positive impact, whilst negative and lower scores show that it has a negative impact. It is computed by asking customers how likely they are to recommend your product or service to others .

How likely are you to tell a friend about a product?

1 2 3 4 5 6 7 8 9

No chance ------------------------------ Likely

Benefits of Quantitative Surveys

1. It is an easier and convenient way to collect, collate, and scale data that does not require much interpretation.

2. It is easier to track quantitative feedback over time and visualise it on dashboards to spot any unexpected changes.

Qualitative Feedback

Customers can give honest feedback by openly expressing their opinions and suggestions about a product and its features. Open-ended questions, also known as qualitative responses, allow respondents to respond in any way they want without being restricted to a limited set of questions.

Quantitative Surveys can be further categorised into the following:

Sentiment Analysis : It is the most effective method for determining customer attitudes toward a product or service. Customers can share their feelings and rate them as positive, neutral, or negative based on the words and emotions associated with it.

Open Ended surveys: Open-ended surveys allow customers to freely express their ideas and opinions about a product and its features.


What about this product appeals to you the most?

What is your least favourite thing about this product?

How does our product match up against the competition?

Exit Surveys: Exit surveys, or surveys conducted after people opt out of their subscriptions, are excellent sources of both qualitative and quantitative data. Exit surveys are also referred to as churn surveys.


We'd like to hear your thoughts:

The processing fee is exorbitant.

I had problems with the product.

I had difficulty obtaining assistance.

Others include:.................................

Benefits of Qualitative Surveys:

1. The open-ended characteristics of qualitative feedback allows for an extensive discussion.

2. It is more advantageous when a company tries to understand customer preferences and focuses on customer feedback.

Top Use Cases of AI-powered Surveys

The conversational and dynamic nature of interaction bodes well for the collection of precise qualitative and quantitative feedback. Furthermore, surveys based on specific customer journey stages can provide deep insights into specific intent and help improve the customer experience by leaps and bounds. Some examples of conversational AI survey use cases are:

Agent Performance: Customers find live chat to be an easy way to engage and receive quick responses. And, as quickly as this tool, also referred to as agent handoff connects and interacts with customers, it also collects feedback from the customer once a chat ends. It collects customer feedback on agent interactions using CSAT, NPS, and CES surveys.

Channel Performance: Customers can rate their overall experiences on social media channels

For example, how likely are you to recommend a channel to a friend, relative, or colleague based on their experience?

1 2 3 4 5 6 7 8 9 10

NLP for Interpreting Qualitative Feedback

Machine Learning (ML) and Natural Language Processing (NLP) are two of the most important AI subsets that help with feedback analysis. NLP-powered technology examines each customer feedback response that is processed in order to provide each customer with a unique and personalised response.

Natural language understanding (NLU) and sentiment analysis have allowed chatbots to predict the user's mood, which is useful in determining whether or not the conversation is on track. As a result, whenever the chatbot detects that the user is about to get frustrated, it simply redirects the conversation to the human agent via the "Chat with a human agent" menu option. If the end-user believes the chatbot is unable to solve the problem, they can choose this option.

Feedback from chatbot surveys increases response rates. Customers are more likely to respond to surveys and provide feedback if they receive personalised responses. Intuitively, a chatbot-powered conversational survey retains the benefits of online surveys while also providing several additional benefits.

1. A chatbot can frame survey questions in a more personalised, conversational format, increasing customer engagement.

2. Insurance service providers can use surveys as a powerful tool to improve their products and services.

3. The surveys can help improve the AI algorithm and customer journeys.

Insurebuddy cognitive insurance solutions assist insurers in automating the analysis of survey responses and focusing on maximising customer feedback.