Product Analysis 101: Expert Insights and Tips

In business, there's always a new big thing. Cloud computing, the internet of things, blockchain, AI, you name it. Each has transformed how we operate, both in business and in life. Yet, all these trends share one crucial element — data. 

Data is the past, present, and future big thing.

Consider this. The data analytics market has skyrocketed from $189 billion in 2019 to $307 billion in 2023, as reported by Fortune Business Insights. Moreover, Deloitte notes that 68% of Chief Data Officers prioritize enhancing their use of data and analytics. The numbers stack up to confirm companies, by following all these trends, are actually doubling down on data. 

With the explosion of data and advancements in data science, Data-driven Product Managers now have unparalleled insights into their products. Thanks to data, product analysis provides valuable information on customer behavior and product performance. It’s a cornerstone of successful Product Launches, Data Stories, and Product Strategies in general.

In this guide, we’ll cover all the essentials of product analysis—what it is, why it matters, and how to do it step by step.

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What Is Product Analysis? 

Product analysis is the systematic examination of a product to understand its features, performance, and market position. It involves evaluating customer behavior, identifying strengths and weaknesses, and comparing the product against competitors. 

This process helps businesses optimize Product Development, Product Adoption, improve Product Marketing, Product Launch Strategies, and enhance Product Experience. Key elements of product analysis include market research, customer feedback, user research, and product metrics. 

By analyzing these factors, companies can make data-driven decisions to boost sales and customer satisfaction. In essence, product analysis provides crucial insights that drive innovation and ensure a competitive edge in the market.

Why Is Product Analysis Important?

To recognize the importance of Product Analysis you don’t have to look past your favorite streaming service and e-shopping platform — Netflix and Amazon. They set the standards for product data analysis globally. 

Netflix fine-tunes its user feed by analyzing behavior patterns. This data-driven approach gave birth to a hit show, House of Cards, as reported by the New York Times. Before production, Netflix identified three key insights: users love director David Fincher, adore Kevin Spacey, and enjoy the original British version of House of Cards. 

So, what do you get when you blend all three?  An American version of House of Cards and a massive win.

Amazon, a retail giant, leverages predictive data analysis to dominate. Their report shows that 92% of users prefer Amazon over other e-commerce sites. This success stems from their ability to meet customer needs precisely. Amazon maintains high retention rates by streamlining processes, swiftly resolving issues, and personalizing product suggestions — exactly what data says their customers want the most. 

Surveys reveal that 99% of consumers are satisfied with product variety, and 95% find Amazon's search results highly relevant. Their data collection and analysis fuel these remarkable outcomes.

Product analysis is crucial because it provides insights into customer behavior, highlights product strengths and weaknesses, and informs strategic decisions.

How to Conduct Step-by-Step Product Analysis

1. Define objectives and KPIs 

Clearly defining objectives and Key Performance Indicators (KPIs) is critical for a successful product analysis. 

Objectives (1) provide direction and align business goals with customer needs. Examples of objectives include improving Product Experience, increasing market share, decreasing churn rates, and enhancing product features. These objectives set the stage for targeted analysis and actionable insights.

Here are some examples of well-defined, specified goals:

1. Improve User Experience: Increase user satisfaction score from 75% to 85% within the next six months.

2. Increase Market Share: Grow market share by 10% in the next year by introducing three new features that competitors lack.

3. Boost Customer Retention: Reduce customer churn rate from 8% to 5% over the next quarter through improved customer support and loyalty programs.

4. Enhance Product Features: Increase the adoption rate of the new feature from 20% to 50% within the next three months through targeted user education and marketing campaigns.

KPIs (2) are the measurable, quantifiable values that indicate progress toward your set objectives. They need to align neatly with your objectives, otherwise they’re irrelevant and costly.

Here are some KPIs you could take into consideration:

1. User Satisfaction Scores: Measure user happiness with the product.

2. Customer Churn Rates: Track the rate at which customers stop using the product.

3. Market Penetration Percentages: Monitor the product's presence in the market compared to competitors.

4. Feature Adoption Rates: Measure how many users are adopting new or existing features.

2. Gather data

Collecting data is fundamental to product analysis. It’s what most people first associate with product analysis. For a good reason, of course — there’s no comprehensive understanding of the product's performance without it. 

Data collection involves gathering quantitative and qualitative data from various sources:

• User Testing: Direct interaction feedback.

• Heatmaps: Visual representation of user engagement.

• Reviews and Testimonials: Qualitative user experiences or data quantification.

• Product Usage Data: Metrics on feature usage and user behavior.

• Surveys: Quantitative feedback on user satisfaction.

• A/B Testing: Comparative analysis of product variations.

• Market Research: Competitive landscape analysis.

3. Use different product analysis frameworks

Employing diverse methods of data analysis is crucial to uncover insights. These methods help in identifying patterns, understanding user behavior, and pinpointing areas for improvement. Here are a few methods of data analysis:

• Statistical Analysis: Utilize statistical tools to identify trends, correlations, and patterns in quantitative data.

Example: Regression analysis to determine the impact of user engagement on retention rates.

• Thematic Analysis: Analyze qualitative data by identifying recurring themes and patterns.

Example: Coding user feedback from surveys to find common satisfaction and dissatisfaction points.

• SWOT Analysis: Evaluate the product’s Strengths, Weaknesses, Opportunities, and Threats.

Example: Analyzing market research data to identify potential opportunities for product improvement.

• A/B Testing: Compare two versions of a product feature to determine which performs better.

Example: Testing two different landing page designs to see which one leads to higher conversion rates.

• Cohort Analysis: Study the behavior of user groups segmented by time or specific actions to identify patterns and trends over time.

Example: Analyzing the retention rate of users who joined in a specific month versus those who joined in another.

• Root Cause Analysis: Identify the underlying reasons for specific issues or performance gaps.

Example: Investigating why a particular feature has low adoption despite high initial interest.

• Predictive Analytics: Use historical data to predict future trends and behaviors.

Example: Forecasting future user growth based on current usage patterns and market trends.

• KJ Analysis: A method named after Jiro Kawakita used to organize and prioritize information based on group consensus.

Example: Grouping and prioritizing customer feedback to identify the most critical issues to address.

• Value Chain Analysis: Identifies all activities within an organization that add value to the product and analyzes their efficiency.

Example: Assessing each step in the product development process to find areas for cost reduction and efficiency improvement.

• PESTLE Analysis: Examines external factors that could impact the product, including Political, Economic, Social, Technological, Legal, and Environmental aspects.

Example: Evaluating the impact of new regulations on product sales and market strategy.

• Business Model Canvas: A strategic management tool used to define and communicate a business model.

Example: Mapping out the value propositions, customer segments, and revenue streams to refine the product strategy and market approach.

4. Run competitor product analysis

Product Comparison Template

Winning products get to the core of a user need—and then solve it better than the competition. Use this template to identify your user need and evaluate other players in the market.

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Assessing competitor products is essential to set your product apart from competitors and improve. Competitive product analysis usually starts by analyzing their customer journey. It looks closely into different touchpoints. It assesses approaches and content types that enhance the experience on every single stop. 

For example, you can evaluate onboarding processes, support interactions, and feature tutorials to identify best practices and gaps. 

You can examine product reviews on sites like G2, Trustpilot, and Capterra to gather qualitative data on user experiences and pain points. Furthermore, by quantifying this data you can identify common trends and issues.

Examples of data sources and methods:

• Digital User Journey Analysis: Map out competitor touchpoints such as website navigation, signup processes, and customer support.

• Content Evaluation: Assess the types of content used at each touchpoint, like blogs, tutorials, FAQs, and social media.

• Product Reviews: Collect and analyze reviews from G2, Trustpilot, and Capterra to understand user satisfaction and complaints.

• Qualitative Data Collection: Extract detailed insights from user comments and testimonials.

• Quantitative Analysis: Summarize review ratings and feedback trends to identify recurring themes.

• Customer Surveys: Conduct surveys targeting your competitors’ users to gather direct feedback on their experiences.

You can learn so much about your product by thoroughly evaluating competitors. More importantly, you can uncover strengths and weaknesses, inform market positioning and enable your product to stand out in the crowded space.

5. Data optimization and monitoring

With the available data at hand, it's time to act. 

Start by pinpointing key areas for improvement based on user feedback, gathered data, and competitor analysis. Your eyes should be set on enhancing product experience, fixing pain points, and refining product features.

This is where the role of Data Product Manager comes in handy. They can help product teams build data packages, centralize data management for efficient product analysis, and manage large-scale data to enrich metrics with insights. 

Data-driven Product Managers can help you identify the right tools to keep the data accurate and insightful. Their know-how allows for good decisions when choosing a custom solution over commercial tools like Google Analytics, Mixpanel, or Hotjar. Finally, Data PMs regularly review the data to spot trends and adjust strategies accordingly.

Examples:

• Optimize Onboarding: The goal is to simplify steps based on user feedback. Data-driven product managers could pinpoint the right product onboarding software here like UserGuiding, SkyPrep, or Stonly. They can help integrate this data with central solutions to provide all-encompassing insights.

• Enhance Features: Add or improve features that users frequently request. Use tools like Jira or Trello to manage feature requests and development tasks. Implement customer feedback loops with platforms like Intercom or Productboard to ensure continuous user input and engagement. Regularly update users on new features and improvements to maintain transparency and trust.

• Monitor Performance: Track user satisfaction and engagement post-optimization. Utilize analytics tools such as Google Analytics, Mixpanel, or Amplitude to monitor key performance indicators (KPIs) like user satisfaction scores, feature adoption rates, and customer retention metrics. Set up regular reporting and dashboards to visualize data trends. Use this data to make informed decisions and iterate on product improvements continuously.

6. Share your new product analysis findings

First, know who you’re sharing your insights with. Tailor messaging based on their literacy, interests, and goals. For example, when presenting to the marketing team, focus on campaign metrics with interactive dashboards. For the executive team, emphasize big-picture strategy and revenue impact.

Choosing the right format for communication can significantly impact how your message is received. (1) Written reports offer detailed and thorough information. (2) Slide decks are visual and engaging. (3) Dashboards provide interactive, real-time data insights. (4) Videos and (5) webinars create a personal, replayable experience. (6) Infographics are visual and shareable, perfect for a quick data snapshot.

Whatever format you choose, use clear and simple language:

• Avoid jargon. (No one likes it)

• Use concise, active sentences. (You want to enforce action)

• Maintain logical flow. (Show them how organized you are!)

Try not to get too repetitive with the way you visualize your data. Mix between:

• Bar Charts: Compare categories.

• Line Charts: Show trends over time.

• Heat Maps: Display density and concentration.

• Pie Charts: Show proportions.

• Scatter Plots: Identify relationships.

To tell a compelling story, start with the problem. Show how your analysis addresses it and highlight the benefits. Keeping the business question at the center ensures your analysis is relevant. Align every step with business goals, use appropriate visualizations, and provide actionable insights.

Most importantly, connect data to real-world scenarios. Everyone, technical and non-technical folks, will love you for it. Finally, continuously evaluate and iterate your analysis to keep it aligned with evolving business needs.

Product Analysis Examples

Product analyses come in different shapes and sizes. Some companies, depending on their needs, may focus more on the marketing side while others may want to report feature performance to their dev teams. Product analysis tends to twist and turn since needs are seldom one-size-fits-all.

Product Feature Analysis Template

Understand how your Product’s features stack up to the competition. Identify core features needed to compete against industry-standard products. Then go above and beyond!

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Here are examples of how they vary depending on what is at focus.

1. Product Feature Analysis: Involves evaluating specific features of a product to determine their usefulness and appeal to users. Benefits include refining product design and prioritizing feature improvements based on user needs and preferences. 

2. Product Market Analysis: Focuses on understanding the market landscape, including competition, customer needs, and market trends. Benefits include identifying opportunities for growth and improving market positioning. It helps companies tailor their products to meet market demands.

3. Product Marketing Analytics: Analyzes the effectiveness of marketing strategies and campaigns related to a product. It focuses on metrics like click-through rates, conversion rates, and ROI. The main benefit is optimizing marketing efforts to increase product awareness and sales.

4. Product Line Analysis: Examines the performance of different products within a product line. This analysis helps in identifying the most and least successful products, aiding in inventory management and strategic planning.

5. Product Sales Analysis: Tracks and evaluates sales data to identify trends and patterns. Benefits include forecasting sales, setting realistic sales targets, and understanding customer buying behavior.

6. Product Review Analytics: Analyzes customer reviews and feedback to gauge product satisfaction and identify common issues. This helps in improving product quality and customer service.

7. Product Performance Analysis: Measures how well a product meets its intended goals and user expectations. Key benefits include ensuring product reliability, improving user satisfaction, and guiding future product enhancements.

8. Product Category Analysis: Focuses on analyzing products within a specific category to understand their market performance. Benefits include identifying bestsellers, understanding category trends, and making informed decisions on product development and marketing strategies.

10 Time-tested Product Analysis Tools

• Mixpanel. Offers advanced analytics to track user interactions and behaviors within your product.

• Amplitude. Provides detailed insights into user journeys and product usage patterns.

• Heap. Automatically captures and organizes user interactions for comprehensive behavioral analytics.

• Pendo. Combines product analytics with user feedback and in-app messaging.

• FullStory. Records and analyzes user sessions to understand their experience and identify issues.

• PostHog. Open-source product analytics platform that provides complete control over user data.

• Hotjar. Visualizes user behavior through heatmaps, recordings, and surveys.

• Google Analytics: Tracks website traffic and user interactions, offering extensive data insights.

• Kissmetrics. Focuses on tracking user behavior to help optimize the customer lifecycle.

• Crazy Egg. Provides heatmaps and A/B testing tools to improve website performance.

Out With Educated Guesses, in with Product Analysis

It's time to move beyond educated guesses and embrace the power of product analysis. By leveraging data-driven insights, you can make informed decisions that drive product success and customer satisfaction. 

Implement the strategies and tools discussed in this guide and you’ll be well on your way to building great products.

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Are you struggling to translate data into decisions? Become the data-savvy Product Manager every team need with our free micro-certification in Product Analytics.

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