Since I’m still developing my product, I don’t yet have any data to work with. I want to begin learning product analytics. Could you kindly share some tips and resources on how I can? I believe it includes analytics and SQL.
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Identifying the metrics that will be most crucial for you to track would be the first step. The best route can then be decided.
Learning SQL wouldn’t harm, but you might want to consider integrating alternative analytic tools into your environment depending on what you’re doing and what you want to track. There are many free tools available on Google, or you can look at programs like MixPanel, Amplitude, Pendo, and others if you have the money.
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The booklet on this topic from Mixpanel is an excellent place to start, in my opinion.
As others have stated, analytics is concerned with:
- Articulating the objectives of the business and the product (as per OKRs or KPIs). Typically, we divide them into three categories: NPS for customers’ satisfaction, business goals like revenue, and product goals like activation/adoption.
- Mapping those to the product’s levers choosing the appropriate product metrics that relate to broad objectives
- Separating such metrics into metrics at the feature level.
Some of the tasks included are:
- Making sure you have the necessary instruments to measure the aforementioned 3
- Understanding common metrics to measure (for e.g., E-Commerce marketplaces look at average cart size, but SaaS may care about trial to paid conversion ratios)
- Recognizing constraints (e.g., GA samples data in several reports)
- Removing bias and, if necessary, cleansing the data
- Understanding how to assess data statistically, such as when to utilize means, averages, and correlation ratios.
- Gaining knowledge from them to inform judgments about products and to frame foresight to support strategy.
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This is a great insight, @NathanEndicott.
Everyone frequently focuses on the engineering involved in measuring things, such as big data, data pipelines, visualization tools, etc. Executives enjoy chatting about the cutting-edge technologies they’re investing in.
Even if you achieve technically, what counts more is whether you took meaningful measurements. And why was it crucial to measure it rather than something else in the first place?
A flawlessly constructed KPI can be produced for a lot of money and time, but it will be useless.
On the other hand, avoid believing that employing superior technology would magically improve KPIs that were previously flawed when replacing older, less effective technology. I removed and replaced an analytics system for 15 months. Everyone believed that the new one was so much superior because it was made with glitzy Amazon tools.
The unsavory truth is that we changed our KPIs’ metrics in order to make them clearer and more accurate. If we hadn’t chosen such an overly complicated KPI definition, the outdated technology would have performed properly.
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It actually depends on the success criteria for the product, for which you’ll require alignment between business and design. Because the primary KPI may occasionally change in the short and long terms (for instance, a new product or feature may not be able to significantly increase income merely a few weeks after debut). I occasionally observe that design has UX objectives to gauge attributes like usability and demands for some tracking.
I usually have a few product iterations in mind, so I formulate hypotheses about those iterations and put tracking in place that can assist in addressing those hypotheses.
In conclusion, have your questions prepared. equip the tracking strategy > create dashboards to quickly obtain the queries > share.
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Here’s an educational video that teaches Analytics with simple words. Pretty sure it’s the easiest way for you to learn concepts of product analytics 
In this first video Mckenna shares what users you should consider active in your product.
- twitter:
- linkedin:
cheers
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To perform analytics, you don’t need a lot of data. You can start with market research and note the expected breakpoints of the metric (for example, mobile games should have 5% retention on the 30th day; I made that up), then move on to user research that includes interviews, pre-launch advertisement analytics, and so forth.
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The duties of a product analyst include analyzing market data to choose which items to introduce and speaking with customers to learn about their needs. In the end, you will collaborate with our customers and other organizational leaders to help us choose which goods to introduce in order to increase profitability. So basically, you will need to equip yourself with proper tools and knowledge of product analytics. I would recommend you choose a short-term course (from plenty of courses available online by Udemy, CourseEra, etc.) or you can go through the vast resource at your disposal right here on Prowess’ community’s learning resources page.
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Wow! How did I miss that? It just slipped out of my mind that Prowess has such wonderful short courses. Thanks for reminding me @FelipeRibeiro.
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Just keep a few things in mind while choosing a course. The course must include these topics:
- How to look at user metrics & business KPIs
- Look at your data and get insights
- Take action on your data
- Track feature success
- Communicate on data with your stakeholders
- Use tools like A/B test to drive Data-Driven Decisions
BTW, There is a book called ‘Lean Analytics’ which I can recommend as a starting point.
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Making judgments when working for a software firm can be tricky and challenging. Articles don’t really provide a comprehensive grasp of what it means, even while many courses and conferences on product management discuss being data-driven, “take data-driven decisions,” “use data to drive decisions,” etc.
What ought to you monitor?
How can I analyse business KPIs and gather insights?
How to respond to the data
Work with data departments: How to?
How to use A/B testing and other data analytic tools
etc.
When I was there six years ago, I saw right away that I needed to become a data analytics expert if I wanted to be a proactive PM who made data-driven decisions.
After feeling confident about it for a year, I made the decision to compile all of my knowledge from articles and my experiences as a PM into the most comprehensive course on data analytics for product managers.
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