Feedback Analysis

How to Get Started With Review Analysis

This is part of our complete Product Review Strategy Course. Click here to access the full course materials and develop your brand’s review strategy and tactics.

Picture yourself as an archaeologist, excavating the hidden stories and sentiments within online product reviews. With review analysis, you become a modern-day Indiana Jones, unearthing valuable consumer insights that guide your brand's journey to greatness. 

Online product review analysis is a powerful tool that can help consumer goods brands gain deeper insights into their customers' needs, preferences, and opinions. By analyzing online reviews and ratings, brands can quickly identify areas where their products are performing well and areas where they need improvement.

However, like digging for treasure, review analysis is often easier said than done. There are two options for carrying out the process of review analysis: manual analysis or AI-driven analysis.

As an AI review analysis tool, we’re the first to point out the benefits to using a platform like ours for the job. These include vastly increased efficiency and project scope, informative business visualizations, near-endless ways to slice-and-dice the data, automated sentiment categorization, and much more.

However, we still that recommend brands take a shot at manual review analysis before diving into an AI solution. Why? Having an understanding of the unique and nuanced nature of review analysis builds a foundation to extract the most value from customer feedback, whether you end up using an AI solution or not.

Here, we will discuss how to get started with manual product review analysis. We will go over the difference between manual versus AI-driven analysis, the necessary resources for manual analysis, and how you can get started today with just a spreadsheet. 

Limitations of Manual Analysis

Manual Analysis vs. AI-Driven Analysis

No matter how it’s conducted, product review analysis is one of the best ways to identify consumer preferences and customer sentiment as it provides real-world data from actual consumers at the product level, offering invaluable insights for informed decision-making. However, there are two distinct methods to choose from when you’re starting out: Manual analysis and AI-driven analysis.

Think of it this way, understanding manual analysis before using an AI tool is like learning to do long division before using a calculator. While it takes a bit of effort, it provides an understanding and appreciation for how to use the data and what types of insights you can gain from it. 

As suggested by the name, manual product review analysis is a non-automated method that is typically done using Excel or Google Sheets. It involves sorting through the data, assigning each review with a sentiment value, and searching for valuable information.

​​While this is a great solution to get started with a small sample of reviews, for brands that have high review volume or want insights on multiple competitors, manual analysis may not be practical or feasible. This is where AI solutions can provide a better alternative. AI-powered sentiment analysis tools can automatically categorize reviews and ratings as positive, negative, or neutral based on the language used in the review.

Like using a calculator, AI-powered sentiment analysis can analyze reviews at a scale that is impossible for a human to match. By analyzing a large volume of reviews and staying up to date with incoming reviews over time, AI-powered tools can identify trends and patterns in consumer sentiment toward your and your competitors, providing valuable insights that can inform business decisions.

What to Know Before Getting Started with Manual Analysis

Before you get started with manual product review analysis, it’s important to understand the resources and time commitment that you’ll need to generate powerful consumer insights using this method. 

The time commitment required for manual sentiment analysis will depend on the number of reviews and ratings you're analyzing. If you're analyzing a small number of reviews, such as a few hundred, you may be able to complete the analysis in a few hours. However, if you're analyzing a large number of reviews, such as hundreds or thousands, the analysis could take several days or weeks.

In addition to the time commitment, manual sentiment analysis requires careful attention to detail and a keen understanding of language and context. You'll likely need to read each review carefully and consider the context in which it was written. This can be especially challenging if you're analyzing reviews in multiple languages or from different cultural contexts.

Overall, performing manual online product review sentiment analysis requires a significant investment of time, but it can provide valuable insights into consumer sentiment towards your products or services. If you're willing to put in the time and effort, manual analysis can have an extremely positive return on investment.

If you’re interested in online product review analysis, you could be the next leader in Review Strategy at your organization. Join Yogi’s free Product Review Strategy Course and learn everything you need to know about using product reviews & customer sentiment to drive growth and innovation.

Getting Started with Manual Review Analysis

Step 1: Start with a Question

As we discussed in our previous article, it’s imperative to have a tactical point of entry to avoid overwhelm. With so much data at your fingertips, having a specific question helps to guide the process and avoid broad generalizations.

If you have a top-of-mind business question, such as the reason behind an underperforming SKU or campaign, this is a great place to begin. Otherwise, you can start with a simple question such as what drives the majority of one-star reviews.

Step 2: Establish Your Sample

Now that you have a goal in mind, you’ll need to decide which reviews to cover and how large your sample size will be. For accurate insights and data, we recommend a sample size no smaller than a few hundred.

It is recommended to include the highest percentage of total reviews as possible, however, if you’re dealing with tens or hundreds of thousands of reviews, you may want to start smaller.

To keep the process as simple as possible, you may want to start with a single product or product variation. You’ll also want to consider the time frame. For a product with a high review volume, a great sample might only include reviews from the past year to two years. 

Step 3: Aggregate & Export Reviews 

Performing manual online product review sentiment analysis involves manually sorting through and analyzing online reviews and ratings. To get started with this process, you'll first need to export the reviews and ratings from the platform where they're hosted, such as Amazon, Best Buy, or Target. If your organization uses an aggregation tool like Bazaarvoice or PowerReviews, this is a great place to more easily source the reviews.

Step 4: Remove Duplicates

Once you have the reviews in an exportable format, such as a CSV or Excel file, you’ll need to clean your review data. The first step is to remove any duplicate reviews, as these can skew your analysis. You can do this by using a simple plugin or function in Excel or Google Sheets that identifies and removes duplicate entries.

Step 5: Sort the Reviews

Next, you'll want to sort the reviews by SKU or product category, as well as by date, and finally, by star rating. This will allow you to analyze trends and changes in sentiment over time and identify any narratives that are taking place. You can use the sort and filter functions in Excel or Google Sheets to quickly and easily sort the reviews by category and date.

Step 6: Assign Sentiment to Each Review

Once you've sorted the reviews, you can begin analyzing them for sentiment. One approach is to use a sentiment analysis plugin, such as Azure Machine Learning for Excel, which can automatically categorize reviews as positive, neutral, or negative based on the language used in the review. However, keep in mind that these tools may not always be accurate and may miss important nuances in the language used in the reviews.

Alternatively, you can perform manual sentiment analysis by reading through each review and manually categorizing it as positive, neutral, or negative. This approach can be more time-consuming, but it allows you to capture the nuances and context of the reviews that an algorithm might miss.

Once you have assigned a sentiment value to each review, you may want to sort your sheet by sentiment. This way, you’ll have an easier time finding commonalities and trends among reviews with the same sentiment.

Step 7: Identify Trends & Themes

As you analyze the reviews, make note of common themes or issues that are mentioned repeatedly. You may even want to record the number of times a specific topic of conversation is mentioned so that you can later report the percentage of reviews that mentioned it.

These can provide valuable insights into areas where products may be performing well or need improvement. You can use these insights to inform marketing, product development, and eCommerce strategies.

Step 8: Turn Statistics into Insights and Recommendations

Once you’ve identified themes and trends within your review data, you can turn your findings into actionable insights by adding context and making recommendations. Consider using metrics such as review count, star rating, source, and time frame to put trends into context.

The most impactful way to deliver an insight is by making a recommendation. Whenever you can, include an actionable suggestion to go along with an insight to further increase ROI for your time spent on analysis.

Step 9: Refresh Data Over Time

Understanding trends over time and catching any potential issues early on is one of the most useful parts of manual review analysis. Although the process may become arduous, creating a resource that can be continually updated with new reviews and analyzed for emerging insights will provide the most value in the long run.


While both manual and AI-driven product review analysis methods have their merits, manual analysis can be a great starting point to get a deeper understanding of how review data works. It allows you to take a hands-on approach, capture nuanced feedback, and gain insights that might be missed without a thorough analysis process.

However, it's important to recognize that manual analysis can be time-consuming, requiring careful attention to detail and language nuances. The time commitment increases with larger review volumes, making it less feasible for larger companies. In such cases, AI-powered sentiment analysis tools can provide a more efficient and scalable solution.

We’ve discussed the step-by-step process for getting started with manual review analysis using just a simple spreadsheet. But keep in mind, the key to successful review analysis is turning statistics into actionable insights. Contextualize your findings, consider metrics like review count and star rating, and make recommendations based on your analysis.

Whether you choose manual or AI-driven analysis, harnessing the power of online product reviews and ratings can significantly impact your marketing, product development, and eCommerce strategies. By understanding consumer sentiment, you can make informed decisions and drive growth in your business. So, start analyzing those reviews today and unlock the valuable insights they hold!

Ready to see what AI-driven review analysis can do for your brand? Request a sample analysis with Yogi.