Understanding Customer Experience Analysis and its Data Sources

According to a recent report by Walker, customer experience is there to overtake price and product as the top brand differentiator. Moreover, 86% of customers say they're willing to pay more for a better customer experience. 

A positive customer experience adds to the success of your business because a happy customer becomes your loyal customer and helps you boost your revenue. 

First things first, let's understand what customer experience is. 

Customer experience is the first impression of your brand that your customers have throughout the entire buyer's journey. Therefore, it impacts factors related to your brand positioning, including revenue.

What is Customer Experience Analytics?

Coming to customer experience analytics, it is the process of mapping the entire buyer's journey and the customer experience with the brand. 

The voice of the customer is derived from an AI-based machine learning platform to make data-backed decisions. Customer experience analysis evaluates both quantitative and qualitative data to monitor customer satisfaction.

Why Do Companies Need Customer Experience Analysis?

Customer experience analysis is required for a variety of reasons, which include:

• Boosting customer satisfaction
• Enhancing brand engagement and awareness
• Increasing sales conversions
• Better performance analytics
• Driving product improvements
• Improving public relations
• Augmented competitor awareness

What Are The Various Types Of Customer Experience Data Sources?

There are three different data sources to acquire customer experience data and analytics: direct, indirect, and inferred. So, without further ado, let's understand these data sources.

With customer experience data collection and analytics, you can use one of three data sources: direct, indirect, or inferred. Let's look at these data sources in more detail.

• Direct Sources

A data stream of customers who directly interact with your digital platform is registered into a direct source. For instance, you can consider these sources as customer interactions via phone, email, chats, or a response to surveys. 

Direct sources include all data sources for customer experience data collection, where you gather data as a result of direct interactions between the customer and yourself. These sources, for instance, include interactions on the phone, by email, through text chats, and by responding to surveys. We will examine these data sources in more detail later.

• Indirect Sources

Data streams generated through customer viewpoints and opinions on third-party platforms, mainly forums, social channels, news platforms, or review sites, are registered as indirect sources. However, unlike direct sources, these data streams add more value to your analysis as these might be more honest and transparent opinions of your customers. 

How Is Customer Experience Analysis Done?

First, to perform customer experience analysis, gather the relevant data, clean it up and process it through video content analysis, identify and extract entities, and remove critical aspects and features. 

Let's understand the customer experience analysis process in detail: 

Gathering the data

The data is gathered via different sources depending on your audience and business demographics. The various sources for data gathering can be social media comments or online product feedback forms, reviews, and others such as chatbot data, customer care emails, call center logs, and customer's purchase history. 

A machine learning platform fetches the data, and social media have live APIs to pull the data for analysis. You can even collect the data for customer experience analytics into a .csv file and upload that onto the sentiment analysis platform manually.

Processing the data

Now, the collected data is categorized and extracted via the relevant information provided for customer experience analysis. 

It uses natural language processing (NLP) and named entity recognition (NER) tasks.

The model processes the data, preparing it for sentiment analysis and text analytics by first separating any data that is not in text format and preparing it for analysis.

Analyzing the data

The sentiment analysis model is trained in a way that it can automatically analyze any big data for customer experience analytics. In this step, all the critical functions like aspect-emotion co-occurrence patterns, model’s multilingual capabilities, granular emotion analysis, etc., play their part in CX analysis.

What Insights Does a Customer Experience Dashboard Offer?

A CX dashboard gives considerable customer experience analysis insights such as topic clustering to show what aspects are typically associated with each other. 

It offers insights from multiple channels like videos, blogs, podcasts all in one place. It reads all formats such as text, video, or image and authorizes you to submit signs for keywords or mentions of your brands that pop up in social media listening, and more.