Brand Experience Analytics - A Guide For Brand Marketers

Brand experience is the overall sentiment a customer feels with regard to his engagement with a brand. It is one of the biggest contributors to customer acquisition and brand loyalty. In this article, we talk about what brand experience means in new-age marketing techniques and what machine learning (ML) processes are involved in brand experience analytics. We will also explore four customer brand experience case studies that showcase the strategic insights you can extract with the help of an AI-driven sentiment analysis platform for driving business growth through effective branding.

What Is Brand Experience?

As the name suggests, brand experience is the sum of all the feelings that entail during a consumer’s engagement with a brand during the buying cycle. As a marketing function, brand experience is based on product experience, customer experience, and brand management, and therefore is often-times used to predict consumer behavior. That’s why you can identify key opportunities for brand amplification through voice of customer analysis.

On an overall business level, customer brand experience constitutes brand involvement, brand personality, the customer’s personal feelings - delight, passion, or familiarity - with a brand, which ultimately translates into sales conversions.

Why Is Brand Experience Important?

A powerful and engaging brand experience can lead to key advantages for both a growing business and an established one. Some of them are as below.

1. Better sales conversions

A great brand experience leads to increased sales conversions. Take, for example, the iconic Lufthansa. Word-of-mouth referrals yield better results as people talk about their positive experience with a brand and this encourages other customers to trust the brand and make purchases.

2. Increased brand awareness

When a customer experiences the brand in a way that touches them, they are happy to leave positive remarks on your social brand channels, and refer friends and family. Some even post comments and user-generated videos on their social profiles to mention their experience, thus creating a buzz for your brand.

3. Nurturing brand loyalty

A meaningful brand experience encourages customer loyalty. Customer experience analysis can tell you how connected a consumer feels to a brand, which can give you the direction to develop strategies to build a brand strategy that fosters brand loyalty.

4. Raise in brand equity

When a customer has a powerful brand experience with a product or service, they use that as a benchmark when engaging with other brands, whether in the same category or otherwise. Thus a great customer brand experience raises your brand’s perceived value, which ultimately leads to higher brand equity which is instrumental in helping a company hike product prices, while still being competitively priced.

5. Brand differentiation

It is said that people forget many things but never how something made them feel. A brand experience can speak to a customer in such a way that it builds a persona in itself that immediately differentiates your product from the competition. This is something that brands like Old Spice have leveraged, and gained from.

Learn more about Brand Experience driven Marketing.

How Can We Analyze Brand Experience?

We analyze customer brand experience by identifying and analyzing sentiment in customer experience data through machine learning algorithms. This data is gathered through various sources like social media listening, review platforms, blogs, and ofcourse, survey data.

An AI-driven customer experience platform analyzes brand experience using many ML subtasks such as natural language processing (NLP), named entity recognition (NER), etc., in the following steps.

Step 1 - Data collection

Customer experience data is first gathered from relevant sources. This could be directly using Live APIs for social media such as for Instagram, Facebook, TikTok, etc. Or, you can also manually upload it onto the sentiment analysis platform in an excel file. Once the relevant data is collated, it is annotated.

Step 2 - Data processing

In the second step, all the data that has been cleaned and prepped in the previous step is processed. Since the data can be in many formats, various ML algorithms work in tandem to process it in order to extract relevant information. These are, as follow.

• Audio - All podcast data and social media videos are transcribed through speech to text software so that it is all turned into a text format
• Captions - ML algorithms engage in identifying and extracting text from any caption overlays that may occur in the data using video content analysis
• Images - The platform captures images in the data, say from Facebook or Instagram, or text data through optical character recognition (OCR), which it maps with its knowledge graph for relevancy
• Logos - All logos that may occur in a video or image are identified, analyzed, and extracted
• Text - All text data from comments, reviews, etc. is assimilated including all the audio files that were transcribed. It is important to note that Repustate IQ’s sentiment analysis platform for customer experience analytics also extracts emojis and hashtags from social media like Twitter or TikTok as well. This ensures that emojis are never ignored during analysis as that could lead to false positives or negatives.

Step 3 - Data analysis

There are various elements to analyzing the brand experience data, the key of which are listed below.

• Training the model - The first and foremost element is the data that is used to train the sentiment analysis model. This dataset is pre-processed, manually labeled, and then applied to train the model. Once the results are received, they are compared against a validation dataset, which is data that is correctly labeled. This process is repeated a couple of times, which eventually leads to the most optimal results. The model is now ready to be used.
• Multilingual data analysis - Part-of-speech taggers are used for every language that is automatically detected in the data by the algorithms. Repustate has speech taggers for each of the 23 languages it conducts sentiment analysis in. Using native language models to analyze different languages leads to more accurate insights as the meanings are not lost in translation.
• Custom tagging - Custom tags are created for various aspects and themes that are found in the customer brand experience data. Once the model has been trained, it will automatically segregate text based on these custom-created tags.
• Topic Classification - The topic classifier attaches a theme to a text such as price, food, convenience, etc.
• Sentiment Analysis - The sentiment analysis API isolates each aspect and theme, and processes it for sentiment. It then assigns a sentiment score in the range of -1 to +1. Ultimately, once all the aspects are analyzed for sentiment, an overall sentiment score of the brand experience is created in percentile form.

Sentiment scores gained through such a process can be in 3 types - aspect-based sentiment analysis, which is the most granular; topic-based sentiment analysis; and document-based sentiment analysis.

Step 4 - Data visualization

In this step, all the brand experience insights derived from the above steps are showcased on to a customer experience dashboard in the form of graphs and charts. The dashboard allows you to set alerts for any specific keywords, say, if you need to monitor a brand for Instagram sentiment analysis. In such a case, the dashboard will send you alerts via email or phone messenger every time there is a hike in the mention or the keyword appears.

Brand Experience Case Studies

1. MAC Cosmetics

We conducted a customer brand experience case study of MAC cosmetics where we analyzed sentiment for the brand’s viral YouTube video by international makeup artist Hindash. The analysis gave us deep insights into why the video had received such positive ratings and how it had single-handedly increased social mentions of the brand when it was launched.

The Repustate IQ sentiment analysis platform also showed the reasons behind the negative sentiment, which turned out to be caused not because there was anything wrong with the product. Interestingly, it was the opposite. The product had become so popular, thanks to the high brand engagement propelled by the video, that it was selling faster than anticipated, hence leading to a shortage in supply. And it was this fact that was leading to negative sentiment.

Read the MAC cosmetics brand experience case study.

2. McDonald’s

Social media video content analysis for a viral McDonald TikTok video project we did, highlighted the importance of sentiment analysis in social media analytics. Unlike sentiment analysis of comments that can give reasons behind buyer behavior, mere social media metrics, such as the total number of likes, comments, shares, videos, etc. only give a superficial view of social engagement.

As the case study showed, even though the video had gained popularity because it was driven by a humorous take on a self-inspired logo revamp by a graphic designer, sentiment analysis of comments showed that people were talking about much more than just the logo. More precisely, they were talking about onion rings. People hoped that McDonald’s would take note of their desire and add onion rings to their menu.

Read the McDonald’s brand experience case study.

3. Toronto Maple Leafs

Brand experience analytics can give us sentiment-driven insights into how we can increase sales conversions, infuse product differentiation, and drive brand loyalty. In a case study of the Toronto Maple Leafs, we analyzed the hashtag #leafsforever on TikTok and Twitter and gained insights into what fans were saying, and what could be done to improve their in-stadium experience.

From the 3936 comments that Repustate IQ pulled up from the two social data sources, elements in the aspects and entities showed granular details that were behind the 60% positive sentiment score. The platform showed how #vegasborn was a term trending in the comments for the original hashtag. Similarly, we saw aspect co-occurrence insights that told us that price was something that was mentioned several times in conjunction with tickets. And so was technology mentioned several times.

Read the Toronto Maple Leafs case study to know more.

4. Wendy’s

Wendy’s customer brand experience analysis showed that even though there was a high positive ranking for the brand, individual location-based sentiment was driven by many factors. Hence, it was fluctuating. Amongst one of the most popular quick-service restaurants (QSRs) in the region, when we compared two popular, high-traffic locations of Wendy’s in downtown Toronto in Google Reviews, we saw that sentiment for both locations was dependent on separate factors.

Brand experience as a whole is what your customers experience when they engage with you throughout the buying stages. Repustate IQ insights showed that in both cases, people loved the food, and did not mind waiting, yet were thrown off by bad customer service and the lack of a seating arrangement. This goes to show that even though a brand’s overall sentiment may be positive, there are many factors that can contribute to a changing sentiment trend.

Read the Wendy’s brand experience case study.

Conclusion

Customer brand experience can be a very powerful way to influence buying decisions amongst customers. AI-driven sentiment analysis of user experience data can give you granular brand experience insights to help improve your brand value and brand recognition.

Leverage Repustate IQ and boost your branding strategies with precise, targeted, actionable insights. With built-in capabilities like search inside video, semantic clustering, and multilingual data analysis across a wide array of sources including surveys, you get all the right marketing directions you need right at your fingertips.