What is Product Analytics?
What is Product Analytics?
Product analytics is a robust set of tools that allow product managers and product teams to assess the performance of the digital experiences they build. Product analytics provides critical information to optimize performance, diagnose problems, and correlate customer activity with long-term value.
If you’re responsible for creating a great website or digital product, product analytics can tell you who is using your product … and how … and where … and when.
Product analytics software gives you to tools to see exactly what is happening in their products. You can assess users’ digital experiences, optimize performance of product usage, diagnose problems in your customer experience, correlate customer activity with LTV, and more.
You want to know which features customers use—and which they ignore. Where users experience friction. How to best reduce churn. And where you can personalize interaction for users.
Why are Product Analytics important?
Products are complex! There are a million decisions when building them. But of all the ‘correct’ choices, which one is BEST?
Over 100 years ago, John Wanamaker said “Half of my ad budget is wasted—the problem is I don’t know which half.” Yet companies today still measure the effectiveness of their software with speculation and guesswork.
With respect to instinct and experience, data science gives us a smarter, more accurate path to decision-making rather than relying on guesses or customer interviews. For product teams, analytics give you a superpower.
See in real time how well you’re meeting user needs.
Measure the exact success of individual features.
Base product decisions on metrics that support business goals.
Transform your ability to develop ideas and design user experiences.
Now you’re making solid supportable decisions!
Who uses Product Analytics?
Anyone who needs to make better decisions!
Analytics can answer inquiries from stakeholders everywhere in your organization.
If you’re a product manager: You can understand what your users are really doing, in order to craft digital experiences that captivate them. Now you’ll be able to make data-driven decisions, measure and run experiments, and increase activation, conversion, and retention,
If you’re a marketer: You can see which programs bring in the most visitors. You’ll be able to identify the users most likely to convert, discover factors for long-term retention, and direct your efforts to improve product development.
If you’re a dev team leader: You’ll be able to eliminate bugs, fine-tune features, and resolve friction in the user journey while you save precious engineering resources.
If you’re a UX designer: You’ll learn in detail how people navigate feature sets. You can see what’s popular (and what’s confusing), identify roadblocks, and pinpoint key moments of abandonment
If you’re a growth manager: You’ll get a complete view of user engagement to help you define and refine retention strategies and optimize them according to business needs.
If you’re a business intelligence analyst:You’ll be empowered to create faster and more accurate processes that consolidate disparate insights into cohesive plans, turn insights into stories that inspire action, and answer questions in minutes, not months.
If you’re on a customer success team: You can track the health of your different accounts in real-time and see what customers are doing. Now you’ll know which features users favor and how often they use them, as well as make onboarding easier and more intuitive.
Of course, your customers benefit the most with products that are intuitive, easy, and a pleasure to use!
What can you do with Product Analytics?
Product analytics are great in many use cases.
For any team, product analytics data is crucial for measuring and systematically improving main product metrics: AARRR, aka the Pirate Metrics.
Acquisition: Knowing where your customers come from. Which channels do they favor? Which users are the best prospects? What are optimal costs for converting each user?
Activation: The product experiences on the journey to becoming a paying customer. Each breadcrumb along this trail is a “micro-conversion”. The point at which users fully connect and realize value is their “Aha! moment”. Product analytics can help you optimize these steps.
Retention: Are customers staying or leaving?
This is perhaps THE most important metric to understand. Figure out who’s satisfied (as well as who isn’t) and know WHY. Product analytics gives clues on how to win them over and make them happier
Referral: The only thing better than a satisfied customer….is a whole bunch of them! Are your purchasers talking up your product? Do they post on social media, or do they drop off? Product analytics lets you measure your customer loyalty by tracking their actions.
Revenue: Ultimately, it’s all about making more money with your product.
Streamlining your sales funnel will reduce acquisition costs and increase the value of the customers you retain.
How do you use a Product Analytics Platform?
In general, a good analytics tool will give you the following capabilities:
Tracking can be automatically applied to user actions across your sites and apps
Segmentation teaches you who users are, where they came from, and when
Profiles let you establish user categories around criteria of your choice
Notifications permit you to alert product teams and communicate with users
A/B Testing compares versions of messaging and features for effectiveness
Dashboards allow you to visualize data in useful and revealing ways
Funnels can be set up to explore different paths to conversions
Measurement tools allow you to evaluate each feature’s user engagement
What are some great Product Analytics Tools? Find out in our complete guide!
How do Product Analytics platforms work?
By tracking the actions users take on your site—all of the clicks, pageviews, formfills, swipes, and other activities involved in navigating a digital product. In our opinion, there are three requirements for a truly great product analytics platform.
Automatic Data Capture. Ingesting and processing every bit of user data from the beginning is key, because manual tracking requires advance planning and uses valuable engineering time. You don't want to be playing catch-up with your data! Without automatic capture, your dataset will never be fully complete.
A data science layer that brings your analytics efforts to the next level. It predictively indicates hotspots, logjams, and red flags in the user flow, steers your teams towards insightful questions, and shows you the invisible steps between the steps users take. This means you can surface insights even where you are NOT looking! Data science encourages you to make effective decisions with real information for maximum gains.
Integrated Session Replays, so you can see exactly what customers see.
Now you can identify and reproduce any situation where users encounter friction. You’ll learn whether users are finding your highest-value features easily, see where people are getting stuck, and understand why.Session replays add rich visual context to traditional product analytics metrics, so your quantitative data and qualitative data support each other. And fully integrated means no wasting time going back and forth between different tools.
In short, a good product analytics platform should let you answer any questions you have—even ones you haven’t thought of yet.
What other features matter for Product Analytics?
Any good product analytics tool will also have the following features:
Virtual Events provide endless flexibility with the data you’ve autocaptured. Put events into any context that’s important, and answer any question you need—even retroactively. Prove and disprove multiple hypotheses without having to rewrite code. Different teams can assign multiple labels to the same interactions, so your dataset stays clean yet accessible.
Cohort Analysis lets you see how many users are returning within specific time periods. Behavioral analysis helps you understand how your customers react to your product, so you can determine how and when to reach them to cultivate the greatest rates of retention. This is especially useful for finding your product-market fit.
Data Governance keeps your information safe, clean and organized. You can easily categorize events, segments, reports, personal sandboxes, and staging environments, while role-based project access and default user roles give you total control over who views and modifies your dataset.
Behavioral Segmentation offers much greater refinement than demographic segmentation. You can sort users according to the actions they take in your site, see who your best customers are, and learn what they like to do.
Integrations allow you to enrich your dataset to answer more complex questions. You can connect to external sources (CRM, email marketing, testing, accounting, payment systems, etc.) and run analysis on their information. The more integrations your product analytics can accommodate, the better.
Data warehousing is important! It’s the ease with which you can set up and maintain data access. Many systems require engineering time and resources to create this pipeline, but Heap Connect gives you an easy connection to your data warehouse. All data entering or leaving our infrastructure is securely encrypted with TLS/HTTPS. This is especially valuable for enterprise companies with lots of data and scarce engineering resources.
Security and compliance procedures are critical to handling your sensitive information. Each account’s data must be logically separated, with access protected by authentication and authorization controls, and all cloud databases encrypted at rest.
When a tool has most of these, it lets you give customers more of what they respond to—and less of what they don’t. The result? You can create experiences that are most enjoyable for users—and most profitable for you.
Is the new Google Analytics (GA4) a good tool for Product Analytics?
Google Analytics was one of the first analytics tools, and best of all it was free. But it was never built for the depth and sophistication of a modern customer journey. GA4 brings major changes: event-based models, more views, new engagement metrics, more detail across the board, and “codeless tracking” that doesn’t require engineers to set up.
However, you’ll still have to decide what events you want to track in advance. So you still end up with an incomplete, biased dataset. And the other limitations are significant:
No way to surface things you’re not already tracking.
There’s no way to see user behavior as it actually happens.
No retroactive data capability.
You’re limited to collecting data on events from the moment you decide to track them.
Limited integrations and customizations. This is a big problem when trying to do more things to make your data useful.
Middleware is mandatory. While a free sample of BigQuery is included, you still have to pay for the data.
“AI and predictive metrics” are basically alerts.
You still have to manually define conditions you deem important or critical.
No way to analyze hidden behaviors. Automated results may save some time, but they don’t qualify as true insights.
Together, we think these make GA4 less than ideal for building a great user experience. To do that right, you need the deepest insights and actionable information about your users.
You can only fix non-performing features and optimize the effective ones by understanding in detail how users interact with what you build, and what users like best. This way you can engage them longer, upsell them more, and keep users happily coming back again and again.
When should my company invest in Product Analytics?
Anytime you wish to affect and improve user experience!
When you’re curious about the experiences users have on your site. You’re concerned with making interactions smoother. You want to build a product people like. Or better yet, love!
If you're a small company:
Discover your product-market fit
Conduct user and market tests
Develop your MVP into an optimal offering
If you're a mid-market company:
Ensure that you scale properly
Develop your data value chain
Increase your retention and conversion rates
Lower your customer churn
If you're an enterprise company:
Perfect your data storytelling
Ensure that you continue to evolve with the market
Beat your competition (who also uses product analytics)
Avoid disruption by the hungry startups out to eat your lunch!
How do I choose the right Product Analytics for my situation?
The best way to get maximum insights is to start with the end in mind. Autocapture will do the hard work of collecting data for you, but the real value in product analytics comes from how, and how often, you use it to develop thorough processes within your organization.
The more ways you flex your data, the more insights you will get.
Be laser-focused on the outcome. What are your business goals, and how will data help you achieve them?
Set your KPIs. What are important milestones? How will you know when your situation has improved or your problem has been solved?
Establish targets. Product analytics can even help you figure out where to aim. See how many (and which kinds of) customers use different elements of your product. Notice what’s performing well and what isn’t.
Have objectives for your objectives. You can’t be too granular when tracking data. Get as specific as you can with each step. As performance increases, make sure you know what it means.
Be open to exploring. Keeping spreadsheets of all the events and properties you want to track is tedious. Heap tracks all your data and keeps it organized so you can ask any question you want, at any point in the process.
How should I approach implementation?
Don’t set your strategy in stone. Keep implementation a fluid process, and evolve with every new milestone:
Changes to the product
New ideas produced by your experimentation
Make sure you review your plan regularly to update goals, metrics, and reports. This is easiest with a product analytics system that has the most flexibility around events and tracking, in order to fast track your implementation and avoid delaying the time to value.
Ideally you can choose the product analytics solution that does the engineering for you.
What are the major platforms in Product Analytics?
What makes Heap’s Product Analytics stand out?
Heap approaches product analytics differently, beginning with our philosophy of exploration. We believe that product-market fit is the ultimate measure of success for companies of every size.
Why stumble into this balance with guesswork, when you can map the territory? With Heap, you can create any hypothesis you want to test, at any point in the dev process, and the data will be there for you.
Why is Heap the ideal Product Analytics solution?
Heap offers the most complete and systematic way to test, measure, and improve product-market fit. Heap frees you to be both an artist and a scientist with your data. And it’s super easy to implement!
A single snippet autocaptures every click, swipe, tap, pageview, and fill, offering complete access to your historical data.
Powerful data science highlights events you never thought to track, to surface insights you didn't even know you were looking for!
Fully integrated session replays let you step directly into the shoes of any and every customer you choose.
No manual tracking, advance planning, or engineer time is required.
You can also add new behaviors for Web, iOS, and Android without engineering. Enrich data with sources like A/B testing, CRM systems, and email providers. Audit, verify, and modify behaviors and events. And relax knowing PII privacy is protected.
You have lots of choice when it comes to choosing product analytics, but just having the tools is not enough. You need to use them wisely, with support from a first-class team.
Your data is a vast world to explore. Heap gives you a treasure map.
Interested in a demo of Heap’s Product Analytics platform? We’d love to chat with you!