What are Product Analytics Frameworks?
At its core, a product analytics framework is a set of guidelines and processes that businesses should follow to collect, analyze, and act on data related to their products.
It includes a range of tools and techniques, such as data visualization, A/B testing, and constant monitoring.
All of that helps businesses to understand various things. For example, how customers are using their products, what features are most popular, and where there are opportunities for improvement.
Product Analytics Frameworks: Why Your Business Should Care
Product analytics frameworks are incredibly valuable for businesses. Why? Well, they provide a structured approach to analyzing product data and gaining insights into customer behavior.
With the right framework in place, your company can make informed decisions that drive growth and profitability.
And isn’t that one of the biggest (if not the biggest) business objectives you have?
7-Phase Product Analytics Framework
Getting started with product analytics can be confusing.
Like, what should you even begin with?
To help you get on the right tract fast, we have designed a product analytics framework for you.
Follow these 7 phases if you want to nail product analytics:
👉 Phase I: Goals and KPIs
First things first, you must have clearly defined business goals. In other words, you have to know why you want to engage with product analytics in the first place.
Examples of goals:
👉 Increase customer retention
👉 Improve customer acquisition
👉 Boost revenue
👉 Reduce churn
👉 Increase product engagement
👉 Optimize pricing
👉 Streamline product development
Next to that are key performance indicators (KPIs). These will help measure progress towards what you want to achieve. Each objective you set should have KPIs next to it:
👉 Increase customer retention:
📊 customer churn rate 📊 retention rate 📊 customer lifetime value
👉 Improve customer acquisition:
📊 conversion rate 📊 click-through rate 📊 bounce rate
👉 Boost revenue:
📊 monthly recurring revenue 📊 gross margin 📊 customer acquisition cost
👉 Reduce churn:
📊 customer churn rate 📊 renewal rate 📊 customer satisfaction score
👉 Increase product engagement:
📊 daily/monthly active users 📊 time spent in app 📊 user engagement score
👉 Optimize pricing:
📊 average revenue per user 📊 price sensitivity score 📊 customer lifetime value
👉 Streamline product development:
📊 number of new features released 📊 customer feedback score 📊 bug resolution time
👉 Phase II: Data Sources
Moving forward, your focus should lay on identifying relevant data sources to be used in the analysis.
This phase involves gathering a wide range of data sources, both internal and external.
Examples of internal data:
- product usage data (frequency, volume, most popular features)
- customer data (demographics, behaviors, preferences, purchasing habits)
- marketing data (email open rates, click-through rates, and social media engagement)
- operational data (inventory levels, production output, logistics data)
Examples of external data:
- public data sets
- industry reports
- social media data
👉 Phase III: Data Collection
Congratulations, 2 Phases are already behind you!
In Phase III of your product analysis project, you need to establish processes and tools for collecting the data we mentioned in Phase II.
What do we mean by that?
Your task is to define the data collection methods. You should also set up the infrastructure necessary to collect and store the data in a way that can be analyzed.
In general, we could distinguish 4 types of data:
Qualitative Behavioral
- usability testing
- contextual inquiry
- experience mapping
Qualitative Attitudinal
- interviews
- focus groups
- diary studies
Quantitative Behavioural
- rage and error clicks
- heatmaps and clickmaps
- A/B testing
Quantitative Attitudinal
- surveys
- panels
- card sorting
Usually, it’s best to work with a mix of different data types instead of limiting yourself to just one.
👉 Phase IV: Data Storage and Management
As we move forward, you need a place to store and manage data from. This is the focus of phase IV of a product analysis process. Storing and managing data.
Any type of information you gain ought to be kept in an easily accessible and understandable manner.
How could you do this?
This involves establishing a framework for data storage and management. It can include the use of a data warehouse or other data management tool.
What’s more, the tool you use for data collection may also guarantee data storage features. For example, LiveSession does this. Keep this in mind.
👉 Phase V: Data Analysis & Visualization
During Phase V of a data analysis project, your main goal is to examine data to find important information. Next, you have to present it visually through reports and charts.
To do this, different methods and software tools are used. Thanks to them, you can investigate the data, spot patterns and trends, and share the results with people who need to know.
For example, you could use LiveSession for this purpose. All thanks to useful and powerful features like metrics and dashboards.
This is what your product/ product user analytics dashboard may look like:
👉 Phase VI: Hypothesis Generation & Testing
Once you collect all the data, it’s time to come up with ideas for product improvements or new features. Then, testing those ideas through experiments and A/B tests comes into play.
And that’s what Phase VI is all about.
To begin, the data insights gained in previous phases are used to generate hypotheses about what changes might improve the product or create new opportunities.
This involves identifying patterns, trends, and relationships in the data that can suggest new ideas or areas for improvement.
Once hypotheses are developed, experiments and A/B tests can be used to test them in a controlled way.
These tests involve comparing the performance of different versions of a product, feature, or process. All this to determine which one performs better.
👉 Phase VII: Iterative Improvement
Finally, you have to remember that product analysis isn’t a one time task. Instead, it’s a journey that’s constantly evolving.
Thus, continuously improving the product based on data insights and testing your results is key.
This is how you can ensure that you are always up to date with all the trends.
3 Reasons Why You Should Use Product Analytics Frameworks
The 7-phase framework we’ve just shown you is great and all but…
Have you wondered why you should even use it in the first place?
Well, we have. And the conclusion is clear. We think you should take advantage of product analytics frameworks like our because they:
💯 enable data-driven decision-making
Such a framework provides a well-structured approach to product data analysis. This lets businesses like yours to make informed decisions based on real-world data. Sounds exciting, doesn’t it?
💡Practical example: your company might use a product analytics framework (phase III) to identify which features are most popular among users, and prioritize development accordingly.
💯 increase revenue and profitability
Once you start optimizing your products and services based on data-driven insights, you can increase customer satisfaction and loyalty. Ultimately, this will drive revenue and profitability.
💡Practical example: your company might use a product analytics framework (phase VII) to see that users who engage with a specific feature are more likely to become paying customers, and schedule creating that feature first to drive revenue.
💯 reduce wasted resources
When you have a clear framework to follow, it’s much easier (and faster) to get done with whatever you need to. This includes product analytics tasks. Thanks to our framework, you won’t get lost searching for the next right step to make. It’s already there waiting for you.
💡 Practical example: your company might use a product analytics framework to realize that a particular marketing channel is not driving significant traffic or conversions, and shift resources to more effective channels.
No time? Hire a Product Analyst who has:
- technical skills
- analytical abilities
- experimentation knowledge
- effective communication
- product-oriented mindset
You can also provide product analytics training for your team. Find out how to do this from this article.
… or let LiveSession do the job
LiveSession can be a great addition to your company's toolstack when it comes to product analytics.
Why? The answer is simple.
It’s because we provide a valuable product analytics solution that lets you understand how users interact with your products.
Our tool records user sessions and tracks user behavior, such as clicks, scrolls, and mouse movements. All this allows companies like yours to see how users navigate their product and where they encounter issues or challenges.
This data can be used to identify areas for improvement and optimize the user experience.
Additionally, LiveSession offers a range of analytical tools, including heatmaps, conversion funnels, and session replays.
Ultimately, they allow companies to dive deeper into user behavior and gain a better understanding of their customers.
By leveraging LiveSession as part of your product analytics toolstack, you will gain valuable insights into user behavior and product usage.
Sounds interesting? Sign up for a forever free plan today to get a taste of our superpowers.
Good luck!
Over to you
And… cut! That’s all for today. Thanks so much for your attention. We hope you enjoyed this post.
By implementing a comprehensive framework like the 7-phase we just presented, you can collect and analyze data at every stage of the product life cycle.
Get ready to make data-driven decisions, identify areas for improvement, and ultimately deliver better products to your customers.
We hope you’re as excited as we are.
Now, let’s continue our journey with another article on product analysis. Are you ready for some real life product analysis examples? Read it now here .
FAQ
Find the answers to the most important questions on product analytics frameworks here.
What is a product analytics framework?
Product analytics frameworks are guidelines and processes that businesses can use to collect, analyze, and act on product data.
Why should businesses use product analytics frameworks?
Businesses should use product analytics frameworks for various reasons. Examples include: making data-driven decisions, increasing revenue and profits, and reducing wasted resources.
How can businesses get started with product analytics frameworks?
Businesses can get started with product analytics frameworks by defining their key goals and metrics. Next, they gave 6 more steps to take in order to get through the whole process.
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