Become great at product analytics
Make data-driven decisions with product analytics
By Matthew Brandt,
Interim Head of Business Insights @ Penta
Course length: 7h 00min
How do you turn product insights into action?
It’s all about gathering, measuring, analyzing and communicating product milestones so your company can make data-driven decisions.
Through real-world examples, case studies and a look at what not to do, this course will teach you how to make product analytics a core competency and driver of growth at your company.
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After 8 sessions, you’ll:
- Be a master of collecting and analyzing product-level data accurately
- Understand how to implement and track important product metrics
- Know how to compare multiple segments of users with distinct traits and consolidate data from multiple users into single profiles
- Be a pro at applying useful Google Sheets functions like VLOOKUP and COUNTIF and clustering data as well as apply common MySQL arguments such as COUNT, LIKE and CASE
Google Analytics. Mixpanel. Adobe Analytics.
We’ve all heard of them — they’re great for websites and apps with predefined goals and metrics.
But your product is much more complex and often lacks clear targets or paths to success.
This course will demystify your product data and teach you the methods and techniques you need to turn that data into actionable insights.
This course had excellent quality and density of material, no bull****
This course is right for you if…
- …you’re a Product Owner and need to understand more about your product
- …you’re an Analyst in a product-driven organization
- …you’re a UX Researcher and want to understand more about the product
This course is probably not for you if…
- …you have no experience with traditional web analytics
- …you already have 2+ years of experience in Product Analytics
- …you’re not working with a product
Skills you should have before taking this course
- Some familiarity with data collection, data storage (databases)
- Good knowledge of common analytics tools such as Google Analytics, Kissmetrics, Webtrekk, Adobe Analytics
- Experience using a web product (e.g. Trello, Dropbox, Salesforce, etc.)
A digital analyst for more than six years, Matthew has done end-to-end analytics for over 50 companies from the ground up by tracking concepts, overseeing implementations, building reporting and performing analyses of the data collected.
The last 2 years Matthew has been delivering deep insights to all different departments and teams of a SaaS-company with a web-based accounting product.
Your full course curriculum
This class is about getting familiar with product analytics and its associated terms, common questions and pitfalls, as well as differences to other types of analytics (such as web analytics). You’ll also learn about what the difference is between a “classic” website and a web product.
- Product analytics definition
- Differences between product and “classic” web analytics
- Common questions and mistakes
Collecting data correctly, at the right time and in the right format is a prerequisite of being able to do any kind of analysis afterwards. Knowing what (and what not) to collect is usually quite difficult, as many questions will not be known at the time data collection needs to be set up. This class covers all of these areas and sets you up to be as future-proof as possible with your data collection setup.
- Where, when and how to collect data correctly
- Data formatting and standards
- Implications of incorrect data
Mixpanel, Heap Analytics, Google Analytics, Amplitude, Snowplow - many tools exist on the market today that allow you to collect data and analyze it. Tying in with the previous class about data collection, you will learn about how that data is used in the different tools and most importantly, the core differences in their data collection and analysis strategies in regards to product analytics specifically.
- Overview of the tools available and their good & bad sides
- Gathering requirements and defining use cases
- Evaluating tools for your use cases
All products are different and yet, there is a set of common metrics that can be applied universally to each product. This allows comparison across different industries as well as for analysts to compare with previous experiences. Learn some of these core metrics and especially why they are useful in your case.
- 3 core metrics: Customer Acquisition Cost, Customer Lifetime Value, Churn Rate
- Leading vs. lagging metrics
- Benefits and drawbacks of core metrics
In order to understand how well a product is performing, users must be understood as much as possible. In this class you will learn more about how to look at users and, specifically, subsets (known as cohorts) of these users for different purposes. Additionally, you will see what kind of metrics can be used to understand your users and their behaviour better.
- How to understand users
- Cohort creation and analysis
- Sample user-based metrics used in product analytics
This class addresses many questions that arise when analyzing products. In addition to looking at sample questions, you will also see many challenges that can arise due to, among other reasons, the nature of product analytics being very retroactive.
- Example questions in product analytics
- Common challenges in product analytics
- Tactics for addressing challenges
In previous classes, multiple metrics are shown as examples that can be used in product analytics. In this class, you will learn methods about how to report and monitor on these metrics and more. You will also learn some basic steps to visualizing data.
- A selection of metrics for product analytics
- Setting up reporting and fundamental data visualisation principles
- Setting up monitoring
In this class, you will take all that you have learned about product analytics and learn how take your analysis to the next level by augmenting your data with data from other sources and systems. The real insights happen when you’re able to combine data from multiple places, giving you a more complete view of the users, group of users or aspect of the product you’re focused on.
- Data fusion how-to, do’s and don’ts
- Examples of data fusion
- Course wrap-up
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