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Practical Data Analysis Examples Across Industries

Any example of data analysis is predominantly based on the most crucial need of a business. This can be customer retention, supply chain management, market research, industry-trend analysis, optimizing business operations, and so on. The data analysis examples shown in this article are taken in the context of practical business cases, wherein you will see how a machine learning approach can easily derive meaningful insights from vast and scattered data. Furthermore, you will see that an intelligent AI-based data analysis solution can easily extract these insights from across industries without disruption.

Why Do Companies Need Data Analysis?

Data analysis is crucial in the decision-making process of a business. Businesses rely on analyzing data for various needs such as understanding industry trends, customer relationship management, sales and marketing strategies, improvement in operations, and other essential functions.

For example, a company relies on data analysis to ensure its logistics and supply chain is running smoothly, or a clothing manufacturer may analyze data to review workload, downtime, and work queues. The bottom line is, that data analysis is not only elementary in customer-oriented processes but also daily operations of a business. That’s why you should be able to analyze data from diverse sources such as social listening for Instagram, emails, review forums, personalized customer surveys, etc.

The next section gives practical data analysis examples of how different industries leverage data analysis for operational and business intelligence purposes.

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Practical Data Analysis Applications With Examples

Data analysis, when conducted using an intelligent, AI-driven sentiment analysis software, gives you actionable insights that are more useful than dry, numerical metrics. Qualitative insights tell you not only what is trending in the market but why, and what you can do to improve your own performance for sustained growth. To understand this better, let’s look at these real-life data analysis examples across various industries.

  • Customer Feedback Analysis - Hospitality

A very prominent example of data analysis is the one carried out by Repustate IQ in response to, what seemed to be a trending sentiment across media channels and review websites about Disney World Resorts. Customer feedback analysis showed that people were unhappy with the services they received at Disney resorts and were taking to review platforms and social media to convey their disappointment.

An interesting development we noticed during our review sentiment analysis was that customer sentiment varied to a great extent based on the data sources, namely Google reviews, and Reddit. On further analysis, we found that this was because the type of customers is equally important when extracting insights. This is because casual customers tend to be more easy-going when providing feedback, while customers who are more passionate and most probably return-customers, tend to hold a business to a higher standard.

However, in this case, both types of customers complained that the high ticket prices, customer service, hospitality, general inconvenience, and several other factors left them less than satisfied.

Read the detailed data analysis.

  • Marketing Strategy - Finding Influencers for the cosmetics industry

Another great practical example of data analysis is the way social media comments analysis is used to find the right TikTok Influencer to boost a business’s social media marketing strategy. To do so, Repustata IQ analyzed data through TikTok social listening. For this particular use, we used the hashtag “beautyhacks” on TikTok and analyzed all the videos that followed. We chose the videos with the highest levels of engagement and the highest followers, and identified the top 5 authors.

We then analyzed how frequently these TikTokers posted videos, what kind of content they posted, the time taken for engagement to be initiated on the videos, audience sentiment, and so on. Repustate IQ received insights for each aspect individually and ultimately, we were able to identify the right Influencers for the cosmetics industry.

Read the detailed data analysis.

  • Market Research - Understanding public opinion from financial news sources

We conducted market research on the metaverse in response to the hype around it following Facebook’s decision to change its name to Meta. We found that there was additional excitement in the financial space also because of Facebook’s decision to invest USD 10 Billion in establishing a space for itself in the Metaverse similar to strategies by veterans like Roblox.

While there was much speculation on whether this was a good idea or not, there were many investors who were quite positive about Facebook’s decision. However, in this example of data analysis, we also found that many financial analysts and market watchers were unsure about investing in Facebook’s virtual economy. This fear came true when it was revealed in February 2022 that Facebook lost almost all of its USD 10 Billion investment in its ambitious new project.

Read the data analysis in detail.

  • Understanding Brand Experience

When MAC lipsticks announced that middle eastern artist Hindash would be designing a signature lipstick for MAC, it generated a buzz in the market. When we used Repustate IQ to analyze customer experience about the new product release, similar to other data analysis examples, we noticed that was a lot of sentiment related to the brand along with the product. This was not surprising, given MAC’s popularity within the LGBTQ community. However, a closer look showed that the reason was entirely different. While there was a very positive sentiment about the new lipstick, there seemed to be negative sentiment around MAC itself.

On further analyzing data to extract sentiment, we found that the negative sentiment was because stores went out of stock sooner than expected, and the customers blamed MAC for incorrectly predicting sales and mismanaging the supply chain. This implied, among other things, that most customers who bought MAC products were loyal to the brand and passionate about it, and held MAC to a higher standard than a casual customer would.

Read the detailed data analysis.

  • Analyzing Fan Experience in Sports & Entertainment

One of many brilliant data analysis examples is the fan experience analysis of the Maple Leafs matches and in-stadium game experience. On analyzing the hashtag #leafsforever on two social media sources -TikTok and Twitter - we saw that people loved the in-stadium experience of watching their favorite team play. However, high ticket prices, parking issues, the inconvenience of getting food and beverage, and other aspects left them dissatisfied.

To ensure that the analysis was balanced, a high number of comments and reviews were gathered - more than 4000. Thus, the insights we received were from across different customer demographics and gave practical insights as to what exactly could be done to satisfy all types of customs including improving in-stadium experiences as well as adding better revenue streams.

Read the detailed data analysis.

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Conclusion

These data analysis examples show how an AI and machine learning-based sentiment analysis process can give businesses clear and precise insights that can prove invaluable. You can improve customer experience, better brand reputation, increase brand perception, boost operational efficiencies, strategize more effective marketing and advertising campaigns, explore new sales and revenue streams, and more.

Repustate IQ allows you to do all this and more. The solution analyzes 23 languages seamlessly, without the need for translations. It is available as an API that can be integrated with an existing visualization tool like Power BI or Tableau. Or, if you are interested, it is also available as a complete solution with a comprehensive dashboard where you can see and manage your insights as in the above data analysis examples.

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