What is Product Analysis in SaaS: Types, Steps & Tools

What is product analysis?

Product analysis is the process of collecting, filtering, and analyzing data regarding all aspects of product performance to inform decisions related to product development, marketing, and sales.

In SaaS, product analysis focuses both on business and economic factors as well as product-market fit , UX and UI design, features, and usability.

Product analysis can help you understand current product performance assess how effective it is at satisfying customer needs, and identify opportunities for improvement.

This is possible thanks to an in-depth examination of customer interactions at all touchpoints along the customer journey.

Who can benefit from product analysis in your SaaS?

Detailed product analysis benefits teams from across the organization:

Product team: gives them insights into which aspects of the product are most valuable to users so that they can better prioritize their future development.
Product marketing team: allows them to understand how the product differentiates from the competitors and its unique value proposition.
UX designers: helps them understand how users interact with the product to improve its intuitiveness and usability.
Customer success and support teams: gives them information about the issues customers face so that they can provide them with better guidance and help them achieve their goals.

Product analytics metrics to track

There’s no product analysis without product analytics metrics. They are the quantifiable measures that organizations use to track and analyze product performance.

What metrics do you need to track?

This depends on your goals. Here are a few examples.

Customer Acquisition Cost (CAC) is the average cost needed to acquire a customer. Track it if you want to improve the effectiveness of your marketing and sales operations.
Trial to Paid Conversion Rate is the ratio of new customers who subscribe to the paid plans. It’s useful for optimizing your user onboarding and pricing plans.
Product Activation Rate is the percentage of new users who have reached the activation stage. It’s another metric that you should track to improve your user onboarding processes.
Customer Retention Rate is the percentage of users who continue using the product. It is an indication of the value that your product delivers over a period of time.
Customer Lifetime Value (CLV or LTV) is the average revenue a customer brings to the organization until they leave. It’s an indication of the overall value of the product to users, and the higher it is, the more effective your account expansion efforts are.

What are the steps in the product analysis process?

Product analysis can help you understand current product performance, in-app customer behavior, and what changes you need to make to elevate your product above the competition.

In this section, we look at how to do product analysis.

Define product analysis objectives and KPIs

Start by defining your goals. This is the outcome that you want to achieve. For example, this could be improving profit margins.

To help you choose a good goal, use a framework like OKR or Golden Circle framework.

Apart from the change that you want to achieve, focus on how you’re going to get there and how to measure your progress. So,

Break down the big goals into milestones.
Think about specific initiatives that will bring you closer to your goal.
Decide on the metrics that will tell you’ve reached your objective.

Choose the right sources of data collection

Once you decide on the goals and the metrics to track, look for the right data sources.

Here are a few possible options:

Product usage data – for example, feature usage rate or custom event completion to optimize user journey and improve user activation.
Heatmaps and session recordings – to analyze user behavior and remove friction from the UI.
User feedback – both quantitative and qualitative user feedback to identify areas for improvement.
Customer testimonials, reviews, and social media mentions.
Customer success and support, or sales interactions with customers – to better understand user needs and pain points.

Visualize collected data with product analytics tools

To analyze the collected data you need a tool that will make the visualization process easy for you.

When you build dashboards to monitor performance, you make it easy for everyone in your company to make informed decisions.

Each dashboard should match your initial objective and include key metrics showcasing that.

Analyze product analytics data to identify trends

Analysis of the collected data comes next.

For example, if you’re trying to make your UI design more intuitive, you could be looking at heatmaps and session recordings to find patterns in user interactions and answer some questions.

Where do they click? Are the UI elements clickable? How far do they scroll down? Do they see the relevant information?

If you want to improve feature engagement, feature usage analysis can help you identify the features that are popular with different user personas and those that are underutilized.

Make product decisions based on analyzed data

Next, it’s time to implement product analytics and leverage the insights to make your product better.

So if your users don’t scroll far enough to find important information, move it up the page. If they keep ignoring a CTA button you could change its look, move it to a different location, or use a tooltip to attract the user’s attention.

To drive feature adoption, a product manager may choose a secondary onboarding flow to help users discover the underutilized features that are relevant to their use cases.

Communicate insights with different teams

Once you validate the changes to the product, make sure to communicate them to other teams potentially through a product analysis report.

Your customer success and sales teams need to be on the same page so that they don’t get surprised by customer queries they can’t handle. They also need time to update the support, marketing, and sales materials.

Different types of product analysis

There are different approaches and techniques you can use for product analysis, depending on what aspect of the product you’re trying to improve. Let’s check out a few options.

Competitive product analysis

Competitive product analysis focuses on the product functionality and its UX compared to those of competing products.

It helps product marketing teams better understand the needs of their target customers and identify gaps in the market.

Overall, a competitive product analysis enables the team to create marketing strategies that resonate with the users and drive new customer acquisition and account expansion.

User segmentation analysis

Segment analysis allows you to identify patterns in how users adopt various features.

How do you go about it?

Start by choosing the criteria for segmentation. For example, you could choose users who have the same goals or jobs to be done.

Next, segment your users again based on how long they’ve been your customers or whether they’re paid or free users, to identify the most successful users within the segment with a specific job to be done.

Finally, look at their feature usage. Look at the features they use the most. If your analytics tool allows you to analyze paths leading up to specific events, check in what sequence they engage with different features to create funnels and inform your onboarding strategies accordingly.

Funnel analysis

Funnel analysis is the process of understanding the steps users take on your product to reach a particular goal, be it sign-up or onboarding. It is particularly helpful in identifying friction and drop-off points, enabling you to minimize churn and boost conversion rates.

User behavior analysis

User behavior analytics (UBA) is the monitoring, gathering, and analyzing of user actions and data using monitoring systems, allowing you to acquire useful insights into your users’ desires, concerns, and challenges.

It responds to questions like:

What attracts the attention of customers, and what goes unnoticed?
Which specific aspects of your website or app frustrate them?
What activities or patterns do customers exhibit just before they leave your website or app?
What specific content or elements are visitors looking for or failing to find as they go through the pages they visit?

Journey analysis

To carry out journey analysis, you first need to map it out.

To do so, start by identifying key activation events and set them as goals or milestones for each segment.

Then use a tool like Userpilot’s path analysis to understand the path users take from signing up to activation.

Trends analysis

Trend analysis is a crucial aspect of understanding patterns and changes in data over time.

In the context of product analysis, trend analysis involves examining historical data to identify recurring patterns, seasonal fluctuations, and long-term trends that can inform decision-making and strategy.

Continuously monitor trends in your product metrics and iterate on your analysis as new data becomes available. Stay proactive in identifying emerging trends and adapting your strategies accordingly.

Attribution analysis

This is a research method that helps you determine the factors contributing to a specific outcome. Product teams can use it to identify the customer touchpoints that increase the chances of customer success.

Attribution analysis involves analyzing user flow data, just like journey analysis. However, it focuses only on those users who have completed the journey. It looks back at the interactions of successful users with the product to single out those that contribute to their success the most.

For example, a product manager may use attribution analysis to determine that certain product feature usage increases the free trial conversion rate.

Cohort analysis

Cohort analysis, just like segmentation, looks at patterns in how different user groups behave inside the product. These cohorts share similar characteristics, like time.

Why is it relevant?

For starters, user behavior varies based on seasons. For example, certain SaaS products, like fitness apps, are more popular at the beginning of the year. This, however, doesn’t mean that users who sign up in January are the most successful at achieving their goals or staying for long.

Cohort analysis also helps you assess the impact of changes to the product functionality or onboarding practices. For example, you can analyze how user behavior changes after introducing a new feature or removing unnecessary steps from an onboarding flow.

Retention analysis

Retention analysis aims to understand why users stay with the company and keep coming back to use its product again and again.

It often uses cohort analysis to look at patterns among users who have joined at the same time to identify any seasonal influences on retention.

Retention analysis also uses funnel and milestone analysis to determine which users succeed or where in the customer journey they fail. On the other hand, feature usage tracking and heatmap analysis can be used to give insights into specific user behaviors that are linked with retention.

Sentiment analysis

Customer feedback analysis is an easy way to understand how users are feeling about your product.

For best effects, it relies both on quantitative and qualitative data to track trends and gain insights into factors that affect them.

For example, an NPS survey gives you a quantifiable metric to track, and if followed by an open-ended question, it helps you understand why users give specific answers when collecting customer feedback.

The best product analytics tools for SaaS companies

One way to carry out product analysis is by using a data warehouse, like Amazon Redshift, and business intelligence tools, like Tableau. Such a combo gives you full control over how the data is stored and displayed.

However, it also requires expertise in data engineering, it’s more difficult to set up, cumbersome to operate (requires SQL knowledge), and usually works out quite pricey.

Alternatively, you can use product analytics software that tends to be more affordable, and easier to implement and maintain. Most importantly, it’s accessible to team members without technical expertise thanks to visual UI.

Let’s check out a few product analytics platforms.

Userpilot – Ultimate engagement and product analytics tool

Product analytics lets you collect and analyze data about how users interact with your product so you can extract actionable insights. Userpilot lets you look at granular product analytics, such as which features have the highest adoption rates, and big-picture insights like trend reports.

Here are Userpilot’s top product analytics features:

Feature tagging: Userpilot’s click-to-track feature tagger lets you view how many times a feature has been used and by how many users to measure its adoption. Users on the Starter plan can add up to 15 feature tags while those on the Growth or Enterprise tier can create unlimited tags.

Trends and funnels: Userpilot’s trends and funnels report lets you extract actionable insights from big data. You’ll be able to see which stage of an onboarding/conversion funnel most users drop out on and create trend reports with detailed breakdowns by user or period.

Saved reports: The saved reports analytics dashboard shows you all the reports you’ve created and lets you filter, export, or delete any reports in a single click. In addition to adding filters or sorting by report type, you can also edit or duplicate reports to help you review analytics.

Analytics dashboards (Product Usage, New Users Activation, Core Feature Engagement, User Retention): These dashboards enable you to keep track of your key product performance and user behaviour metrics at a glance, without any technical setup required.
Analytics integrations: Userpilot integrates with some of the most popular analytics tools like Amplitude, Mixpanel, Segment, Google Analytics, and more. This makes it possible to sync product analytics both ways between the tools in your tech stack (two-way integration is only available for Hubspot at the time of writing, more to come).

Heap – Standalone product analytics platform

As a cutting-edge product analytics tool, Heap is packed with features that help you monitor product usage, activation, and adoption. Insights from Heap can help you identify points of friction in user journeys and optimize the user experience to drive product growth.

Here’s how Heap facilitates seamless product analytics:

Once you install Heap’s code snippet into your product, it automatically starts tracking user actions. You can access this raw data on your Heap dashboard and label the most relevant events.

In-depth analysis on Heap

You can use the Live data feed for a glimpse of how users move through your product in real time. This makes it easier to identify and eliminate roadblocks.

Real-time data on Heap

You can use Session Replays to monitor the exact journey a user takes within your product. It’ll help you identify points where they struggle to complete an action or leave without taking the desired action.

Session replays feature on Heap

Head to the “Usage over time” chart in the Analyze section for an overview of different events, user behavior, and conversion rates. Heap provides numerous filters for these events too.

Usage tracking on Heap

Other product analytics features that come in handy include Journey Maps, Funnel analysis, Retention analysis, and Heatmaps.

Hotjar – User behavior analytics tool

Hotjar’s heatmaps only gather user experience insights rather than product analytics. This means it can help you identify trends, patterns, and areas for improvement based on how users behave within your website or web application but can’t track product usage metrics or KPIs.

Conclusion

Product analysis allows teams from across the organization to better understand product strengths and weaknesses, evaluate how well it meets user needs, and identify ways to add value.

If you want to see how you can use Userpilot to conduct your product analysis, book the demo!

What is Product Analysis in SaaS: Types, Steps & Tools