How To Analyze Data Using AI
Knowing how to analyze data to get the most accurate and insightful results for marketing campaigns, sales drivers, and long-term growth strategies need not be a complicated process. Technology has become so advanced and user-friendly that once you understand how data analysis works and what you can do to ensure high-precision results from the data analysis tool that you use, you can achieve all the business goals that you’ve set out towards.
This article gives you a breakdown of how data analysis works, the most advanced data analysis techniques today, and also practical examples of data analysis insights obtained from a variety of sources.
What Is Data Analysis?
Data analysis is the machine learning (ML)-based process of analyzing qualitative and quantitative data to extract meaningful insights. In the business sense, data analysis helps marketers and product teams gather insights from customer experience data found in surveys, social media listening, review forums, websites, and other sources. Data analysis also helps organizations understand employees better and nurture them.
Organizations can track emerging industry trends, conduct review sentiment analysis, monitor brand perception and performance, improve customer care on data analytics, and even get insights from TikTok and other social media platforms.
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What Are The Most Advanced Techniques For Data Analysis?
Advanced data analytics techniques are built on artificial intelligence and machine learning algorithms that oftentimes work in close coordination to process, analyze, and extract meaningful information from vast, scattered data. Some of the most advanced data analysis techniques are as follows.
1. Sentiment Analysis
Sentiment Analysis is an advanced data analysis technique that comprises many machine learning tasks such as natural language processing (NLP), named entity recognition (NER), and semantic analysis to identify and measure positive, negative, and neutral sentiments in data.
This technique is used heavily in marketing and business operations in order to analyze customer experience or obtain actionable insights from brand experience, and employee experience data.
2. Cluster Analysis
Cluster Analysis is a non-supervised ML-based statistical approach to identifying and grouping similar data points into categories. ML algorithms scan data to identify patterns and begin extracting similar and semantically similar data points that can be further processed to find predictive patterns. Cluster analysis can be used for determining factors that can lead to the success or failure of a project, differences between different consumer types, the performance of organizations or products, etc.
Clustering should not be confused with Classification. While Classification comprises instances that are grouped as per class labels, Clustering is based on grouping occurrences in data without predetermined labels.
3. Data Mining
Data Mining refers to the machine learning task of identifying trends, patterns, correlations, and irregularities in large data sets. Data mining consists of collecting, cleaning, and analyzing data, as well as reporting the insights thus extracted.
This technique is immensely helpful in business applications to find industry trends and patterns in consumer behavior from comments analysis, and extract sentiment from a vast array of data sources.
4. Predictive Modeling
Predictive Modeling is the process of using data mining and neural networks to predict future outcomes based on current and historical data. Neural networks are artificial intelligence mathematical models that mimic the way the human brain works in order to remember patterns in information.
These artificial neural networks (ANNs) are what make predictive modeling a very clever way of forecasting the future, whether it’s for stock market predictions, weather forecasts, industry trends, or consumer behavior, based on certain periods.
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What Is The Process Of Analyzing Data?
The process of how to analyze data begins with first identifying your business goals or objectives the insights will be used towards. This part is the one that is completely dependent on human decision-making. Once the goals are set, the process of analyzing data is mostly run by ML algorithms. Let us take a closer look at the steps involved.
Step 1 - Identify the End Objective
It is very important to evaluate why a particular task of data analysis is being carried out because it will help you decide what data sources will be the best to get accurate results. For example, you might want to gather data from all online data sources for an in-depth customer feedback analysis such as social media listening, news articles and videos, review forums, etc. Or you might just want to concentrate on surveys.
Similarly, you might want to decide whether you want only internal data sources for employee satisfaction or employer-brand insights, or should also include external data sources such as employer review websites such as Glassdoor or Indeed.
Step 2 - Determine Guidelines and Benchmarks
The next thing you need to decide is how you are going to measure the results. If you have certain benchmarks you have decided upon keeping in mind dynamic factors such as market conditions, events, etc. it will help you have realistic expectations.
You also need to decide whether you want to analyze data that is quantitative such as NPS scores, star ratings, customer effort scores (CES), social media likes, dislikes, shares etc. Or whether you want to also take into account qualitative data such as open-ended content in comments, reviews, videos, etc.
This is critical because you will see that often numerical analysis and quantitative analysis can show different results.
Step 3 - Data Gathering
Now that the two important parts of the steps in how to analyze data have been covered, data gathering becomes easy. Based on what you have decided, you can choose any number of data sources that can give you the most granular analysis for your objective. Or, you can narrow in and focus only on a data source that you know will give you the most optimal results, such as insights from social listening on Instagram or just surveys.
Step 4 - Data Analysis
Now that the data has been collected, the machine learning tool will process it methodically. Based on what you want from the analysis, several data analysis techniques, as mentioned in the above section, will work together in the background to extract for you the insights you are looking for.
Thus, through data mining, sentiment analysis steps, semantic clustering, text analysis, and several other techniques, you will be able to track customer sentiment trends, trends based on language or data sources, emerging industry trends, sentiment scores based on business, brand, or organizational aspects, and much more.
Step 5 - Insights Visualization
Data analysis insights need to be presented in a manner suitable for deciphering the information. That’s why they are shown in the form of charts and graphs on a visualization dashboard. Not only that, many business intelligence tools like Repustate IQ also show you finer details such as the text behind negative or positive sentiments, the authors behind the most number of highly engaging videos or tweets, or may even have drop-downs to filter the top posts sorted by views, likes, or comment counts.
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Practical Data Analysis Applications With Examples
Here are some practical data analysis examples based on various industries and business requirements.
1. Customer Feedback Analysis For A Brand
Objective - Understanding customer sentiment about Disney World Resorts
Data sources- Google reviews and Reddit
Customer feedback analysis is a very important function provided by data analysis. When we noticed critical articles quite frequently in news outlets and online magazines about Disney World Resorts, we decided to analyze data from two different sources that are distinct in their audience type - Google reviews and Reddit.
We noticed that there were many factors leading to customer dissatisfaction and even though Google reviews showed a higher positive sentiment score, on analyzing data from Reddit, we found that the positive sentiment was significantly less. Was it because the audiences on Google had a better time? Or was it that one of the two audiences held Disney Resorts to a higher standard?
Read the analysis in detail to find out more.
2. Finding Influencers For The Cosmetics Industry
Objective - Finding social media influencers for Cosmetics on TikTok
Data source - TikTok
How to analyze data is something that if done correctly and with a clever tool can not only give you customer insights but also prove very handy in your social media marketing strategy. To demonstrate how to achieve this goal, we searched for the hashtag “beautyhacks” using Repustate IQ on TikTok. When the videos were collected we set out to see how many videos had the same hashtag, which tended to have the highest levels of engagement, and which had the highest followers. We were also on the lookout for the kind of content they were posting because unscrupulous influencers are not uncommon.
Once we identified the top 5 video authors, we conducted further research. Through Repustate IQ we were able to see how frequently these authors posted the videos, how long it took for engagement to be initiated on the videos, the sentiments on relevant topics, how high quality the posts/videos were, and other factors. We found many authors who were highly influential in the area and fit the bill of what we were looking for.
Take a deep dive into how to find an Influencer through data analysis.
3. Understanding Public Opinion About A Brand
Objective - Determining public sentiment about Facebook Metaverse
Data sources - News
When news sources began buzzing about Metaverse, we wanted to understand why this was happening. After all, the Metaverse has been around for more than 30 years yet the mainstream news media were never so focussed on it before. Upon conducting Metaverse news data analysis, we discovered that most of the hype was because of Facebook’s decision to change its name to Meta, and also because of its decision to invest USD 10 Billion in the Metaverse itself.
Despite the euphoria surrounding the announcement, there was also negative sentiment, though not as much from Facebook enthusiasts as from investors and market watchers. These analysts and investors were being cautious about following suit and investing in Facebook’s Metaverse virtual economy. This fear was not unfounded as news reports earlier this year stated that Facebook has lost almost all of its USD 10 Billion investment in the Metaverse.
Read in detail the data analysis of Facebook and the Metaverse’s influence on the financial sector.
4. Brand Experience Sentiment
Objective - Understanding brand experience with a newly launched MAC lipstick
Data source - YouTube
When middle eastern artist Hindash won the MAC international competition to design a signature lipstick for MAC, there was a lot of excitement about it. Consumer sentiment was more than 80% based on his YouTube video debuting the lipstick. We saw engagement slow down in the later part of the year, but the sentiment seemed stable.
However, to understand why there was an 8% negative sentiment and the rest of the audience was neutral, we decided to further analyze all the negative data at a more granular level. Through data analysis using Repustate IQ, we found that the negative sentiment was not about the product i.e. the new lipstick but related to MAC as a brand. Consumers were disappointed that there was a shortage of the product and felt that the company did not predict sales correctly nor manage the supply chain efficiently. In effect, the negative sentiment was not about the subject - the newly launched product - but about the brand itself.
Read this interesting brand experience data analysis in detail.
Data analysis is a very intricate machine learning process but one that can prove invaluable for marketing teams and at an overall organizational level. Repustate’s data analytics solution Repustate IQ is available in all industry models including hospitality, healthcare, retail, banking, and more.
Powered by AI-driven sentiment analysis, text analytics, and multilingual semantic analysis, you are assured of high-precision results whatever your goals - customer experience, brand experience, or employee experience.
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