What is Product Category Analysis?

Product category analysis is a catch-all term, referring to any analysis of product categories that you carry out in order to understand what drives purchases. Starting with an overview of product categories, you parse through the data in order to glean more insight using techniques such as product development analysis and operations analysis, alongside competitor analysis and customer experience analysis.

Product category analysis isn’t a simple thing that just anyone can do, as it requires careful interpretation of the data and even data cleaning in order to get the most relevant results. The analysis must be automated by a computer due to the sheer volume of data.

Why You Should Use Product Category Analysis

Product category analysis can give you deep insights into how your products are faring in the current market, from the individual level all the way up to the category level. It condenses high volumes of data down into easy to digest forms for use, meaning you’ll be able to tell what’s happening at just a glance.

The analysis brings insight on various factors, and you can focus on one particular area that you think needs improving to gather, say, a product category attractiveness analysis. With the reports such an analysis produces you can easily compare your brand to your competitors, spot areas that could be improved and also identify your strongest products.

Product category analysis can answer key questions you have about the state of your business, such as:

• Who is/are my main competitor(s)?
  This one might seem obvious but there are always those that you can miss.
• What products do my competitors sell?
  Following on from the above, you can compare your products to theirs and see what areas they outrank you in and need to look to improve.
• What is the range of products offered within the category?
  Given a certain category, are you a standout or simply one among many? Is there a niche or a gap that you could potentially fill?
• What do your customers think about you?
  Customer experience and subsequent opinion drives returning customers and is a big factor in new sales. If you’re not getting the reach you want on a product, you need to invest more in advertisement.

What Are The Stages of Product Category Insights Analysis?

Product category analysis is a step-by-step process, one in which you should complete each step as thoroughly as you can before moving on to the next.  What you need to do at the beginning is simple — define your category. Some categories are subdivisions of others, resembling a tree structure or expanding flow chart.

Defining Data Requirements

Category analysis begins with data, and the more specific your data the more specific the results. What data you use is up to you and your goals, do you want to analyze on a monthly basis? Quarterly? In a general sense you can define your data requirements by asking yourself the following questions:

• What sources will you use, internal or external to your business?
• What time period are you looking at?
• How often do you want to collect data during that time period?
• What other brands are you looking at for comparison?
• What information do you want to see in the report?

While the first four are easy to decide, the last question might be a bit tricky if you’re first starting out. Beginning with general analysis and moving on to more specific trends can help if you’re not sure where your problems lie.

Collecting Your Data

Once you’ve defined what data you’re looking at, the next step is to collect it. Where this data comes from will vary depending on what platforms you use to collect it, e.g. social media, emails, website reviews, etc. Any data you have stored can be used at this point.

You can also use website scraping tools to collect useful information from other online sources such as forums or open databases, however your tool needs to be aware of sentiment analysis in order to properly interpret the data.
It’s helpful to diversify your sources to reduce bias and get a more representative view of consumer sentiment. Make sure to document where your data comes from to maintain transparency and traceability in your analysis.

Processing the Data

When you obtain raw data, it’s not going to be what is referred to as “clean.” Essentially, you’re going to have incorrect formats, duplicates, errors, white spaces and such that will mess with your analysis and leave you with incorrect analysis. There’s also the factor of irrelevant information, in this case meaning information not related to what you want to see in the report, which should also be removed.

Data cleaning should always be performed before any analysis is done.
Even small inconsistencies in your dataset can skew results and lead to false conclusions. A repeatable cleaning process ensures reliability and reduces time spent troubleshooting downstream.

Cleaning the Data

When you obtain raw data, it’s not going to be what is referred to as “clean”. Essentially, you’re going to have incorrect formats, duplicates, errors, white spaces and such that will mess with your analysis and leave you with incorrect analysis. There’s also the factor of irrelevant information, in this case meaning information not related to what you want to see in the report, which should also be removed.

Data cleaning should always be performed before any analysis is done.
Clean data ensures your insights are trustworthy and actionable. Automating this step can save hours of manual work and reduce the risk of human error in large datasets.

Data Analysis

Once your data is cleaned, you’re ready to analyze it. This is done through various mathematical models and formulae, also known as algorithms. Which algorithm you use is entirely up to you, but there are a few common ones that are aimed at customer satisfaction. These include:

• MoSCoW
  A prioritization algorithm that splits factors based on their desirability, with must-have, should-have, could-have and won’t-have categories.
• Kano
  An algorithm that splits factors into five “drivers”, from those that are taken for granted to those that consumers are completely indifferent on.
• Eisenhower Matrix
  Another prioritization algorithm that sorts factors into urgent/not urgent and important/not important categories, allowing you to focus on the most time-sensitive issues.

Product Category Competition Analysis

Understanding how your product compares to competitors is essential in fast-moving categories. Once your data is structured, you can analyze customer feedback, sentiment trends, and feature mentions across competing products. This shows not just how your product performs—but why.

Common models include:

• SWOT Analysis
  Categorizes strengths, weaknesses, opportunities, and threats based on consumer feedback—highlighting what sets products apart or holds them back.
• Sentiment Scoring
  Uses natural language processing to measure how positively or negatively consumers talk about key features, helping identify areas where you lead or fall behind.
• Share of Voice by Topic
  Reveals which brands dominate conversations around specific features or pain points—useful for spotting messaging gaps or market white space.
• Feature Benchmarking
  Compares review volume and satisfaction by feature to show what matters most to consumers—and how your brand stacks up.

These tools turn raw feedback into competitive insights, helping teams prioritize improvements, sharpen positioning, and innovate smarter.

Category Insights Analysis

Category insights help you understand the broader context in which your product competes—what consumers care about across the entire category, not just your brand. By analyzing aggregated feedback from multiple brands and products, you can identify common expectations, emerging trends, and shifting priorities.

Key approaches include:

• Trend Surfacing
  Spot rising topics, concerns, or desires within the category—often before they hit mainstream. This helps brands stay ahead of changing consumer expectations.
• Attribute Aggregation
  Measure sentiment and frequency around key product attributes (like “hydration,” “fit,” or “durability”) across all competitors to uncover what truly matters to consumers at scale.
• Gap Analysis
  Identify where consumer needs are not being met across the category—highlighting whitespace opportunities for product innovation or differentiation.
• Customer Priority Mapping
  Rank category-level topics by importance and satisfaction, helping brands see which features are table stakes and which can be differentiators.
• These insights provide a high-level view of the category landscape—helping you align product strategy, messaging, and innovation with what consumers actually value.

Compiling Your Report

The final stage is translating your analysis into a clear, digestible format. This typically takes the form of a report that includes visualizations like charts, graphs, and tables to highlight key findings. Spreadsheets and presentations are also useful for sharing detailed data and summaries across teams.

At the heart of the report are category drivers—the factors that influence why consumers choose or reject products in your category. These could be product attributes (e.g., scent, durability), emotional motivators (e.g., trust, social proof), or functional needs (e.g., ease of use, price).

It’s also important to highlight barriers to purchase, which may include unmet expectations, negative sentiment, or confusion in the customer journey. Identifying these gives you a roadmap for where to focus improvements or innovations.

Strong reports often include trend summaries, competitor benchmarks, and priority recommendations based on what matters most to your target audience. The ultimate goal isn’t just to describe the market—it’s to guide decisions that drive growth.

Case-In-Point: Drones

With our product insights engine, you can use our product and category analysis tools to gain great insight into whatever category you wish. There’s no need to collect data, sort it, analyze it etc., we’ve done it all for you and have it ready to present in an easy to read format.

With that in mind, let’s look at a fun and interesting category, drones.

Revuze lets you see the top products in a category and any new launches that have recently been released, as well as rising stars that have had a high increase in customer sentiment.

There’s also our competitive landscape tool, which lets you analyze either the top ten products or top ten brands within a category in any given month, comparing them on a chart of customer sentiment vs verified review volume. Using this, you can the category leaders and have links right to their product pages for further, individualized analysis.

The above is real data taken from our product insights engine, showing the top ten products in the Drones category for April 2022. 

Looking at the chart on the right hand side, you can clearly see that the DJI Mini 2 Fly More Quadcopter is a leader in both review volume and consumer sentiment, indicating that it is both a top-seller and a high quality product. 

Over on the left hand side, you can see the Maetot 1080P HD Camera Quadcopter, which while not having as great a number of reviews has a higher overall customer sentiment. This can be an indicator of a more specialized, high quality product, that while overall makes less sales will make up for it by having a higher price tag.

Using the individual product analysis tools you can track backwards across time, seeing where products stand on an individual level as well as how they have improved over time for a more detailed analysis. You can track a product’s SWOT, compare it to others in its category and even see the top drivers and star ratings analysis.

There’s also the product map, offering a comparison of the individual product’s consumer sentiment vs the average of the category, topics of discussion and where the product fares in each of them. Our product topic map sets out these topics, both positive and negative, in an easy-to-read format that correlates the amount of chatter a particular topic has with the area it takes up on the map. Below you’ll find the product topic map for the DJI Mini 2 Fly More Quadcopter, arguably the number one product in the Drones category right now.

What Kind Of Data Can Be Used?

Data comes in all shapes and sizes, and the more channels you have available to gather said data the more informative it’s going to be. Some of the main sources of data include:

• Sales reports
• Customer emails
• Chatbot logs
• Customer surveys
• Social media posts
• Forum posts

While some might be more relevant than others, all data that you can gather will be useful providing it passes through the data cleaning process. 

What Do You Get Out Of Product Category Analysis?

What information you get out of your product category analysis depends entirely on what you put in. That being said, there are a few key pieces of information that you will always get to some degree

• Category Trends
  Category trends refers to any information on trends of customer behavior towards a certain category. Emerging trends that customers want to see, things that turn them away and add-on options that boost sales are all examples of such trends.
• Category Drivers
  Category drivers are anything that prompts consumers to buy specific products when compared to alternatives. What these are will depend entirely on the category in question, but a good example would be the iPhone vs Android rivalry and which side a consumer stands on.
• Consumer Beliefs
  Consumer beliefs can be tricky. Not only are they specific to an individual but they can vary wildly across different locations. That being said, generalizations can be useful, with one example being the public’s aversion to 5G towers and thus any 5G technology. Remember, it doesn’t have to be true for consumers to believe it, and that which is perceived to be true is often just as important or more so as what actually is.
• Brand Equity 
  Brand equity is a measure of your brand’s performance, specifically its performance in the public markets. It’s essentially the boost that a brand receives due to being well known, and therefore the worth of the brand name itself. A great example of this is Coca-Cola, who have a great brand equity when compared to a generic brand of the soft drinks industry. If you have high brand equity, your brand is trusted.

With the above in mind, there’s plenty of insights that we can gain from product category analysis, forming the basis for your strategies moving forwards. While these aren’t an exhaustive list, a few of them are:

• Product improvements
• Promotion/Advertisement strategies
• Product Category Marketing
• Positioning strategies
• Price adjustments
• Finding market gaps
• Learning which features of your products to emphasize
• Altering your business Model
• Finding new distribution channels
• Expanding production/processing

In Conclusion

Product category analysis can be complex and time-consuming, often requiring thousands of individual data points to generate a reliable, accurate view—not just of one product, but of the full competitive landscape. From sourcing and cleaning data to uncovering sentiment and surfacing category drivers, the process demands a careful balance of structure, context, and interpretation.

But the payoff is significant. A well-executed category analysis reveals not just what’s happening in the market, but why—enabling brands to prioritize the right features, communicate more effectively, and uncover whitespace opportunities before the competition. It helps bridge the gap between what consumers are saying and what businesses are doing.

In a marketplace where customer expectations evolve quickly and loyalty is hard-won, the ability to continuously understand and respond to your category is no longer a luxury—it’s a strategic necessity.

Frequently Asked Questions (FAQ’s)

1. How often should businesses perform category analysis?
Category analysis should be continuous. Consumer preferences and competitive landscapes evolve quickly, and brands that rely on quarterly or annual reviews risk falling behind. With automated tools like Revuze, businesses can track category shifts in real time and stay aligned with emerging trends, sentiment changes, and white space opportunities.

2. What are the key outputs of product category analysis?
Key outputs include emerging trends, consumer sentiment by feature, competitive positioning, and unmet needs. Category analysis reveals what matters most to consumers and where your brand stands. Revuze automates this process, providing a clear view of category dynamics without manual effort—enabling faster, insight-driven decisions across teams.

3. How does customer journey mapping fit into category analysis?
Category analysis provides context for the customer journey by uncovering what influences decisions at each stage. It highlights key friction points, unmet expectations, and moments of delight. Revuze analyzes real customer feedback across the journey, helping brands optimize messaging, product design, and experience based on actual behavior and sentiment.

4. What tools help with automated category analysis?
AI-powered platforms like Revuze are built for automated category analysis. They mine unstructured data—such as reviews and customer feedback—to deliver real-time insights. Unlike manual methods, these tools identify trends, track sentiment, and benchmark competitors at scale, giving brands a continuous edge in fast-changing markets.

Emily Louise Spencer

Emily Louise Spencer is an in-house content writer at Revuze. She is a graduate of the University of York with a master's degree in Chemistry. A published scientific author, she now works as a content writer and copy editor.