How can CPGs effectively mine data from product reviews and ratings?
Companies have started to rely on sentiment analysis using product review data. This helps them find useful insights from customer feedback. Listening to your customer base ensures the success of your brand.
That’s because people love to talk about the products they buy and use. Not only to their friends but to thousands of internet strangers.
A decade ago, you would have called that free marketing. But today, customer reviews and ratings can serve an even bigger purpose.
With advanced technology, brands need to to stay current with the trends. This helps them continue to create successful products. The customer-generated data at their disposal offers the most valuable resource for market research.
Keep reading to learn more about sentiment analysis and why Yogi can make a huge impact on your metrics.
The Increasing Relevance of User-Generated Content
Analytics technology shows brands how customers feel about their products or services. Some ways that companies gather anecdotal information from their customers include:
- Usability tests
- Surveys and response emails
- Customer or test subject interview
- Social media
- Customer Reviews
For the first three methods, the companies need to know what questions to ask in advance. In many cases, researchers will come up with specific leading questions because they want certain results in order to validate marketing claims. They don’t lead the participants too much, but they nudge them towards a range of responses, making the ultimate information less trustworthy.
What’s more, running these studies takes significant time and money. So does asking to complete surveys and setting up interviews. Also, the results you seek are never guaranteed. These methods sometimes generate helpful results. In the end, however, these studies require excessive resources and continue to have blind spots.
Customers can leave reviews on several sites, including Amazon, Target, or Walmart. As a result, companies have an endless source of data to draw from. In a sense, customer review data serves as a focus group of tens of thousands of real purchasers.
This makes sentiment analysis a necessary tool for generating fast and reliable business insights. Customer sentiment platforms function as a source of useful data for large companies, delivering unbiased business insights that are especially useful for Marketing, eCommerce, and Product teams.
The Difference Between Data and Insights
Big Data dominates the conversations around new analytical technologies. You can assume that data functions as both the ends and the means of your business solutions.
Yet, data makes one key component of the equation. Companies now have access to massive amounts of unfiltered information straight from their customers.
On its own, review and ratings data doesn’t help companies decide what actions to take or how to create strategies. Data without organization, without a system, is chaos. Especially because the internet produces so much of it every second.
You take the combination of data and analytics tools to create value known as insights. Companies grow from the information that insights provide them. Insights serve as the backbone of business intelligence in a tech-savvy era.
The problem: companies have access to an enormous number of customer reviews from many sources. These sources will generate more data over time. Too much information for any single human can sift through on their own.
The goal: to find useful, actionable insights from the information in the data. But how do businesses find that from unfiltered data?
Artificial intelligence uses various analytics methods to sift through large quantities of data. Sentiment analysis offers an effective tool to help companies understand their customer base.
Using Natural Language Processing to Navigate Text-Based Data
Natural Language Processing (NLP) describes how computers learn and understand human languages. NLP functions as a specific type of machine learning that processes text-based data. CPG companies with large sources of user-generated content need these processes to mine data effectively.
Examples of user-generated data include written communications found in:
- Customer forums
- Online product reviews
- Discussion groups
- Blogs
- Survey responses
- Social media posts
NLP algorithms perform the following tasks to find useful patterns effectively:
- Analyze large blocks of text
- Allow computers to understand human languages
- Learn from patterns
- Extract relationships in words and phrases
- Organize similar texts into groups
- Find meanings in text-based data
Natural language processing has made many advances in recent years. As AI becomes more sophisticated, it can be used to process qualitative tasks. NLP can meaningfully interpret text-based data through specific methods, like sentiment analysis.
Sentiment analysis helps categorize general feelings and reactions. Companies find this very useful for product review analytics.
But what is sentiment analysis?
Learn more about the NLP method used by Yogi’s product review insights software, K means clustering.
Sentiment Analysis Using Product Review Data
Sentiment analysis refers to how analysts or analysis tools look for meaning in the context of text. This information then helps businesses observe conversations about their brand, product, or service. Sentiment analysis relies on customer review data to understand how consumers socially engage.
Sentiment analysis classifies text data as positive, negative, or neutral. It relies on data from online forums and reviews.
It searches for an understanding of how consumers feel at the base level about a company or product. When done correctly, customer sentiment analysis provides an an in-depth look into brand perception across a variety of attributes.
Yogi uses customer sentiment analytics to help Marketing, eCommerce, and Product teams improve their claims and offerings. On top of it’s advanced analysis abilities, Yogi leverages in-app business visualizations and BI integrations to easily pinpoint what customers are saying.
Generating analysis of keywords, themes, NPS, and other customer patterns allows for highly specific insights. This leads to improved workflows, better campaigns, and increased sales.
Studies show how to apply sentiment analytics methods to product review data. This particular study relied on customer review data from Amazon.
The researchers demonstrated their findings using two levels of categorization:
- Sentence-level categories
- Review-level categories
Analysts and analysis tools assess the meanings and intent behind customer content at these levels. Oftentimes the context surrounding keywords provides the most accurate meanings.
People do take words literally, but allowing open access for anyone to post opens the floodgates. Things like sarcasm and posting in bad faith might muddle overall results if the analyst or analysis tool is not sensitive enough.Thus we can see the possible shortcomings of low-quality sentiment analysis.
The Limitations of Sentiment Analysis
Some sentiment analysis tools do have limitations. Sometimes they can’t read between the lines when people use sarcasm or irony. To take a text at face value, one might interpret a comment as “good” if it uses typically positive phrasing. But the commenter actually displayed negative statement using sarcasm.
Advanced sentiment analysis tools detect the nuance of what someone says with context. This is where additional machine learning methods on top of sentiment analysis come into play.
You need tools that will help you understand customer intent and their desires.
With advancements in machine learning, researchers have many tools to accurately analyze product reviews in context. These methods provide different insights that work together to paint an overall picture:
- Sentiment Analysis
- Intent Analysis
- Contextual Semantic Analysis
A combination of machine learning analysis solutions are able to provide the most accurate business insights, this is how Yogi product review software functions. Grouping text-based data allows more clarity in understanding outcomes. It shows business what works well. Negative groupings allow for opportunities to think of solutions and make improvements.
Human communication contains endless complexities. Written languages express thoughts and feelings in less obvious ways. With more data, software tools can develop increasingly accurate insights.
With more data, computers learn more about nuances in communication. Analytical tools have a larger pool of information to work from. Sentiment analysis tools work with advanced processes to mine large datasets.
You can read more details from this article about how several natural language processing tools work together. Sentiment analysis provides one aspect of insight. But it acts more like a broader analysis, like seeing the bigger picture.
Use Product Reviews to Improve Your Business
The more buzz your company can generate, the more information you have to work with. We believe that sentiment analysis using product review data has great potential to provide brands with the most intimate level of visibility into the thoughts and feelings of their customers.
Customers can inspire brands and also guide the direction that a product takes. Product review analytics tools give the most value and results over time, turning consumers into thought leaders.
Ready to find out what your brand can do with Sentiment Analysis from Product Review Data?
