Product Analysis in SaaS: Types, Steps, and Tools
What is product analysis and what are its benefits?
These are just a couple of questions we answer in the article. We also explore:
- Different types of product analysis
- The steps needed to analyze your product
- The tools you can use
Let’s get right to it!
- Product analysis is the process of collecting and analyzing data about various aspects of product performance.
- The aim of product analysis is to better understand its strengths and weaknesses, to evaluate how effective it is at satisfying user needs, and to identify areas for improvement.
- Product analysis benefits teams from across the organization, including your product, marketing, customer success, and UX design colleagues.
- To carry out product analysis, you need to track relevant product metrics, like Customer Acquisition Cost (CAC) and retention rate. Their choice depends on your goals.
- Competitive product analysis shows you how your product compares to competing offerings. You can use it to refine your marketing, differentiation, and pricing strategies.
- Thanks to segment analysis, you can identify patterns in feature adoption among different user groups.
- Journey analysis allows you to pinpoint friction and drop-off points.
- Attribution analysis is a technique used to identify the impact of different product aspects on customer experience. You can use it to find the touchpoints that are essential for customer success.
- Cohort analysis groups users based on the time they signed up for the product, among other similarities, and looks for common patterns in their behavior.
- Churn and retention analyses help teams understand why users leave or stay with the product.
- To conduct product analysis, choose clear goals and ways to measure them. Next, collect data from the right sources and analyze it. Finally, act on the findings and communicate all the changes to other teams.
- To carry out product analysis, you can use the data warehouse and BI tool combo. However, a product analytics tool will usually be cheaper and easier to implement and use.
- Heap is a standalone analytics platform for high-level product analysis while Hotjar provides features for granular user behavior analysis.
- Userpilot is a product adoption platform, so you can use it not only for product analysis but also to collect user feedback and improve in-app engagement.
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.
What is the purpose of product analysis?
It is to gain a deeper understanding of a product’s strengths and weaknesses, 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 you should be tracking
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.
Different types of product analysis to perform
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 to guide product marketing strategies
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. It can also help them adjust their differentiation and pricing strategy.
Overall, a competitive product analysis enables the team to create a marketing strategy that resonates with the users and drives new customer acquisition and account expansion.
Segment analysis for analyzing feature adoption patterns
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.
Journey analysis to identify friction and drop-off points
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 Amplitude’s Pathfinder to identify all the sequences leading up to events and turn them into funnels. That’s how you identify the individual stages in the journey.
Once you have your journey steps and milestones ready, track how users progress and move along. Pay attention to the steps which take a long time to complete or where users drop off. That’s where unnecessary friction can be hampering their progress.
Attribution analysis to identify touchpoints leading to success
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 for a granular understanding of customer behavior
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 to understand what keeps users from churning
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 that 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.
Churn analysis to determine why customers unsubscribe
Churn analysis is the opposite of retention analysis. Instead of looking for the reasons why users keep using a product, it tries to track down the reasons why they unsubscribe.
These reasons could be involuntary, like a declined credit card, or voluntary, like poor user onboarding, lack of value, or inadequate customer support.
Customer feedback analysis to understand user sentiment
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.
SaaS product analytics strategy step by step
Whichever type of product analysis you choose, conducting it involves the same sequence of steps.
Define product analysis objectives and KPIs
Start by defining your goals. This is the final 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,
- and 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 in-app engagement patterns and remove friction from the UI.
- User feedback – both quantitative, for example, the NPS or CSAT scores, and qualitative 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.
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 actually clickable? How far do they scroll down? Do they see the relevant information?
And 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 data analyzed
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.
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
Userpilot is a digital adoption platform. This means that apart from the analytics functionality, it also allows you to collect customer feedback and act on it with in-app experiences. This makes it a more comprehensive solution than standalone analytics tools.
Here are some of Userpilot analytics features:
- Advanced user segmentation (by user attributes, company data, tagged features, custom events, in-app experiences, and user feedback)
- Goal tracking
- Custom events
- Feature tagging (track clicks, hovers, and text input)
- Real-time usage data enabling you to trigger contextual in-app guidance
- Quantitative survey analytics
- Integrated NPS score analytics, including qualitative response tagging
- A/B testing for in-app experiences
- Checklist and resource center analytics
- Integrations with analytics tools (Mixpanel, Heap, Amplitude, and Segment)
- Funnel analysis and paths (coming soon)
Heap – Standalone product analytics platform
Heap offers analytics features that may not always be matched by adoption platforms.
Some of Heap’s features include:
- User segmentation and cohort analysis
- Autocapture – you can track events without the need to tag them first
- User journeys – to map out all the paths users take within the product
- Illuminate – to uncover friction points
- Funnel analysis
- Session replays
- Custom dashboards
- Integrations, including AWS Redshift and user onboarding tools, like WalkMe
Hotjar – User behavior analytics tool
Hotjar is an excellent tool for in-depth user behavior analysis that offers heatmaps and session recordings.
Heatmaps are visual representations of how users engage with different parts of the page. One quick look is all you need to identify the most popular features. The warmer the color of the spot on the screen, the more attention it attracts.
Session recordings are recordings of what users do on the page. Teams often use them for user testing, where they give users a specific task to perform, but you can also watch user interactions with the product without them knowing about it at a later time.
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!