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..
At its most powerful, product analytics tell you exactly what is happening in your product, giving you the how and when and what and where—and even the who—about your users’ behavior. For product teams, product analytics provides critical information and valuable insights. Who is using your product, and how. Which features they use…and don’t use. Where they experience friction. How to diagnose problems, reduce churn, and personalize interaction for users. And also ways to correlate user behaviors with long-term value.
Why are Product Analytics important?
Products are complex. There are a million decisions when building them. How do you discern which are wrong and which are right? And of all the correct choices, which one is superior?
In the heyday of retail, product marketing claimed to be scientific, but John Wanamaker’s quote from that era tells a different story: “Half of my ad budget is wasted—the problem is I don’t know which half.” Now, light years later, companies still are measuring the effectiveness of their software with speculation and guesswork.
While millions of successful products have certainly been created with gut instinct and experience, today data science and analytics gives us a smarter, more accurate path to decision-making. For product teams, analytics is a godsend. Rather than having to guess, or to rely on customer interviews when making decisions, you can see in real time how well you’re meeting user needs.
You can measure the success of individual features. You can base product decisions on metrics that support your larger business goals. Used properly, analytics can transform your ability to develop ideas and design user experiences. With analytics you’re making solid supportable decisions. At Heap, we like to say that analytics give your product a superpower.
Who uses Product Analytics?
Anyone who needs to make better decisions. Since the data is behavior-based and you’re collecting 100% of the user activity from your website, analytics can provide answers to inquiries from stakeholders everywhere in your company. Simply put: once you have all of the information you need, what questions would you like to ask?
Product managers can understand what their users do, make data-driven decisions, measure and run experiments, increase activation, conversion, and retention, and craft captivating digital experiences.
Marketers can see not only which marketing programs (emails, promotions, social posts, campaigns) bring in the most visitors, but which marketing programs bring in the visitors most likely to convert, and most likely to exhibit long-term retention. This information helps marketers direct their efforts and improves product development.
Dev team leaders can eliminate bugs, fine-tune features, and resolve user friction without deploying additional engineering effort.
UX designers can learn in detail how people navigate feature sets, see which are popular and which are confusing, and identify roadblocks and key points of abandonment.
Growth managers can get a complete view of user engagement, so they can define and optimize retention strategies according to business needs.
And of course, it’s your customers who ultimately benefit the most, as the thoughtful application of product analytics results in 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, and for any team, they’re crucial for measuring and systematically improving main product metrics: AARRR, aka the Pirate Metrics.
Acquisition: Knowing where your customers come from: which channels they favor, which users are the best prospects, and your optimal costs for acquiring each user as a customer.
Activation: The experiences that a user has with your product on the journey to becoming a paying customer. Each of the breadcrumbs along this “trial trail” is known as a micro-conversion. The point at which users fully connect with your offering and realize its value is called the “Aha! moment”. Product analytics can help you optimize these steps.
Retention: This is perhaps the most important metric to understand: are customers staying or leaving? Product analytics can help you track users to figure out who’s satisfied, and how to make them happier—as well as who isn’t, and give you clues on how to win them over.
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 create a tracking plan to measure your customer loyalty through their actions.
Revenue: Ultimately, it’s all about how you make money with your product. Streamlining your sales funnel with product analytics will help you 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
How do Product Analytics platforms work?
A product analytics tool works by tracking the actions users take on your site – the clicks, pageviews, formfills, swipes, and other activities involved in navigating a digital product.
In general, a good product analytics tool will have the following features:
- Automatic Data Capture is critical for getting the most use out of any product analytics tool. Manual tracking requires advance planning and uses valuable engineering time. Without automatic capture, you will always be playing catch-up with your data and the dataset will never be fully complete.
- Virtual events give you endless flexibility in taking advantage of all the data you’ve gathered through autocapture. Different teams can assign multiple labels to the same autocaptured interactions, even retroactively, without modifying that data. This allows you to put events into any context that’s important, to answer any question you need, and prove and disprove multiple hypotheses without having to go back to the source and rewrite code. Your dataset stays clean yet accessible
Cohort Analysis tells you how many users are returning within specific time periods. Acquiring customers is important, but over the long term, keeping them matters more. Behavioral analysis helps you understand how your customers react to your product, and determine how and when to reach them to cultivate the greatest rates of retention. This is especially useful for determining your product-market fit.
- Data Governance is a key component for keeping your information safe, clean and organized. With Heap, events, segments, reports, personal sandboxes, and staging environments are easily categorized. Role-based project access and default user roles let you expand access with total control over who views and modifies your dataset.
Behavioral Segmentation offers much greater refinement than demographic segmentation by letting you sort users according to the actions they take in your site.You not only see who your best customers are, you see what they like to do. So you can reach them with more of what they respond to, and less of what they don’t. This lets you lead your users to an ecommerce experience that is most enjoyable for them and most profitable for your business.
- Integrations allow you to enrich your dataset to answer more complex questions. Connecting to external sources like your warehouse and third-party tools lets you run analysis on their information. For example, your CRM, email marketing, testing, accounting, and payment systems. The more integrations your product analytics can accommodate, the better.
- Data warehousing is an important element of product analytics—specifically, the ease with which you can set up and maintain access. Some analytics systems require engineering time and resources to create this pipeline, and others will handle it for you.
- 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.
Is Google Analytics a good tool for Product Analytics?
Google Analytics was one of the first analytics tools, and as a free option it’s become ubiquitous. But GA was developed when websites were basically electronic brochures—simple, static places to hold text. GA was built to analyze marketing spend, and never meant to accommodate the depth and sophistication of a modern customer journey. You can’t track much more than SEO and simple page metrics. In order to be measurable with Google Analytics, events must be specifically defined ahead of time. Even then, GA cannot process PII data, so it’s not possible to determine the value that specific users are getting from your product.
To fix your non-performing features and optimize the effective ones, you need to get deeper insights and actionable information about your users. And this can only come from knowing in detail how they interact with what you build. When you know what users are doing and what they like best, you can engage them longer, upsell them more, and keep them happily coming back again and again.
When should my company invest in Product Analytics?
The short answer: any time you wish to affect and improve user experience. When you’re curious about the experiences users have on your site, and concerned with making them better. If you want to build a product people like—or better yet, love.
Small companies need product analytics to achieve product-market fit by conducting user tests and developing their MVP into an optimal offering.
Mid-market companies need product analytics to scale properly, develop their data value chain, increase user retention and conversion rates, and lower customer churn.
Enterprise companies need product analytics to perfect their data storytelling, ensuring they continue to evolve with the market and not get disrupted by the hungry young companies out to eat their lunch (by using product analytics themselves.)
Your marketing and acquisition spends can also be useful yardsticks. More good information and guidelines are available here.
How do I choose the right Product Analytics for my situation?
When you’re engaging with product analytics, 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. The more ways you flex your data, the more insights you will get. And the way to get maximum insights is by incorporating thorough processes in your organization.
- Get laser-focused on the outcome. What are your business goals? 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 not only helps you hit targets, it can also help you figure out where to aim. This is because product analytics can show you how many (and which kinds of) people are using different elements of your product, so you can see what’s performing well and what isn’t.
- Have objectives for your objectives. You can’t be too granular when tracking data, so get as specific as you can with each step. As performance increases, make sure you know what it means.
- Be open to exploring. Standard analytics tools require a lot of tedious preparation, like keeping spreadsheets of all the events and properties you want to track. Since Heap tracks all your data and keeps it organized, you can ask any question you want, at any point in the process.
How should I approach implementation?
When implementing product analytics, don’t set your strategy in stone. Implementation should remain a fluid process that evolves with every new insight, sales target, business goal, website feature, change to the product, or new idea produced by experimentation. Make sure you review your plan regularly to update goals, metrics, and reports. This will be easiest to do if you select a product analytics system with the most flexibility around events and tracking. Choose a provider that does the engineering for you, in order to fast track your implementation and avoid delaying the time to value.
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. By accommodating both rigorous planning and serendipitous surprise, Heap frees you to be both an artist and a scientist with your data.
Why is Heap the ideal Product Analytics solution?
Unlike Mixpanel and Amplitude, Heap is easy to implement, with complete access to your historical data. Plus the ability to add new behaviors for Web, iOS, and Android without added engineering. And unlike Pendo, Heap lets you enrich data with third-party sources like A/B testing, CRM systems, and email providers. We’re also the only leading product analytics solution that gives you the flexibility to audit, verify, and modify behaviors and events.
With Heap, installing a single snippet lets you autocapture every click, swipe, tap, pageview, and fill from all of your users, while still respecting PII privacy. This complete dataset can answer any questions you have—even ones you haven’t thought of yet. Now your data becomes a place to go exploring. No manual tracking, advance planning, or engineer time required.
Heap Connect gives you an easy connection to your data warehouse, which is especially valuable for enterprise companies with lots of data to manage and scarce engineering resources. Heap Connect sets this up easily for you, with no internal resources required to build and manage the pipeline. And heap securely encrypts all data entering or leaving our infrastructure with TLS/HTTPS. All of our AWS databases are encrypted at rest. Each account’s data is logically separated, and access to data is protected by authentication and authorization controls.
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, and have support from a first-class team. Heap offers the most complete and systematic way to test, measure, and improve product-market fit.