Why Is It Important To Understand Brand Experience?

Customers’ entire impression of their interaction with a brand is known as their brand experience. It is one of the main drivers of brand loyalty and consumer acquisition. 

 

Since brand experience is becoming an essential aspect for businesses these days. Let’s discuss the meaning of brand experience in modern marketing strategies and how machine learning (ML) techniques are used in brand experience analytics. 

What Is Brand Experience?

As the name implies, brand experience accomplishes all consumer feelings while interacting with a brand during the buying cycle. Because brand experience is a marketing function and is dependent on product experience, customer experience, and brand management, it is frequently used to forecast consumer behaviour. 

 

This is why conducting a voice of the consumer research can help you pinpoint significant chances for brand amplification.

 

The customer’s feelings toward a brand—such as joy, enthusiasm, or familiarity ultimately result in sales conversions and constitute customer brand experience on a general commercial level. 

 

Importance of Brand Experience

A developing and established firm can benefit significantly from a solid and compelling brand experience. 

1. Increased conversions in sales

Increased sales conversions are the result of a fantastic brand experience. Referrals from people who have used a product or service produce better outcomes since they inspire other customers to believe in the company and make purchases.

2. Enhanced brand recognition

Customers with a memorable brand experience are eager to share their good feedback on your social media channels and recommend your business to their friends and family.

 

Some people even highlight their experiences in comments and user-generated films on their social media pages, which promotes your business.

 

3. Promoting brand loyalty

A memorable brand experience promotes client loyalty. Customer experience research can help you understand how a consumer feels about a brand, which can help you formulate methods for creating a brand strategy that encourages brand loyalty.

4. Enhancement of brand equity

Customers utilize a brand’s strong brand experience with a product or service as a benchmark when interacting with other brands, whether they belong to the same category or not. 

 

Therefore, a positive consumer brand experience increases your brand’s perceived worth, resulting in higher brand equity, which is essential in assisting a business.

5. Brand differentiation

It is claimed that while many things are forgotten, feelings are never forgotten. How a brand interacts with a customer can create a persona in and of itself that sets your product apart from the competitors. Brands like Old Spice have profited from this by leveraging it.

 

How Can the Brand Experience Be Analyzed?

By recognizing and evaluating sentiment in customer experience data using machine learning algorithms, we examine the customer brand experience. This information is acquired from various sources, including social media monitoring, online review sites, blogs, and poll results.

The data utilized to train the sentiment analysis model is the first and most crucial component. Therefore, the model is trained using this pre-processed, hand-labelled dataset. 

 

Following receipt of the results, they are contrasted with a validation dataset, which consists of data that has been accurately categorized. Then, the model is ready to use.

 

In the subsequent steps, an AI-driven customer experience platform assesses brand experience utilizing a variety of ML subtasks such as named entity recognition (NER), natural language processing (NLP), etc.

 

1. Data gathering

Data on the customer experience is initially acquired from relevant sources. This can involve directly utilizing Live APIs for social media platforms like Facebook, Instagram, TikTok, etc. 

 

Alternatively, you can manually upload information as an excel file to the sentiment analysis site. The pertinent information is compiled and then annotated.

2. Data processing 

All the data prepared and cleaned in the first stage is processed in the second step. Numerous ML algorithms collaborate to process the data, which might be in many different formats, to extract the necessary information. 

  • Audio

All podcast information and videos from social media are converted from audio to text using speech-to-text software.

  • Captions

ML algorithms use video content analysis to recognize and extract text from caption overlays that may appear in the data for captions.

  • Images

 The platform uses optical character recognition (OCR) to extract images from the data, such as those from Facebook or Instagram, or text data, which it then correlates with its knowledge graph to determine relevance.

  • Logos

All potential logos are found, examined, and extracted in a video or image.

  • Text

All text information, such as comments, reviews, and transcribed audio recordings, is ingested. Notably, hashtags and emojis are extracted from social media sites like Twitter and TikTok by a sentiment analysis engine for customer experience analytics.

 

Emojis are never ignored during analysis, as this could result in misleading positives or negatives.

3. Data analysis

Analyzing brand experience data involves several components, the most important of which are summarised below. 

 

  1. a) Training the model

The data utilized to train the sentiment analysis model is the first and most crucial component. Therefore, the model is trained using this pre-processed, hand-labelled dataset. 

 

Following receipt of the results, they are contrasted with a validation dataset, which consists of data that has been accurately categorized. Then, the model is ready to use.

 

  1. b) Analysis of multilingual data  

Part-of-speech taggers are utilized for each language that the algorithms automatically identify in the data. Speech taggers are available for each of the 23 languages for sentiment analysis. 

 

Since the semantics are not lost in translation when using native language models to evaluate multiple languages, the results are more accurate.

 

  1. c) Custom tagging

Custom tags are produced for various elements and themes in the client brand experience data. Once trained, the model will automatically separate text based on these uniquely constructed tags.

 

  1. d) Topic classification

 The topic classifier assigns a theme, such as a cost, convenience, or food, to a text.

 

  1. e) Sentiment analysis

The sentiment analysis API isolates each topic and aspect before analyzing the sentiment. An emotion score between -1 and +1 is then given. 

 

The brand experience’s total sentiment score is ultimately established in percentile form once all the aspects have been assessed for the sentiment.

 

4. Data visualization 

On a customer experience dashboard, all the brand experience insights obtained from the previous processes are displayed in this step in the form of graphs and charts. 

 

If you need to watch a brand for Instagram sentiment analysis, you may set alerts in the dashboard for any specific keywords. 

 

If this is the case, the dashboard will notify you through email or text message each time the mention or keyword appears to have increased.

This is why conducting a voice of the consumer research can help you pinpoint significant chances for brand amplification.

 

The customer’s feelings toward a brand—such as joy, enthusiasm, or familiarity ultimately result in sales conversions and constitute customer brand experience on a general commercial level.

The data utilized to train the sentiment analysis model is the first and most crucial component. Therefore, the model is trained using this pre-processed, hand-labelled dataset. 

 

Following receipt of the results, they are contrasted with a validation dataset, which consists of data that has been accurately categorized. Then, the model is ready to use.

Also Read: Top 4 Ways To Improve Your Digital Customer Experience

Conclusion

Customer brand experience is a potent tool for swaying consumers’ purchasing choices. You may gain detailed brand experience insights from AI-driven sentiment analysis of user experience data to raise the value and recognition of your brand.

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