heap Product Analytics Buyer's User Guide

What is Product Analytics?
Product analytics captures, measures, and visualizes user engagement across digital experiences, helping businesses understand user behavior and improve their products.

Benefits of Product Analytics

Understand and improve complex user journeys
Drive engagement and prevent churn
Optimize product investments with data-driven decisions

Who Needs Product Analytics?

Startups: To create a quality product and accelerate the process of finding a product-market fit.
Mid-market companies: To scale by increasing user retention, maximizing conversion rates, and focusing on desired features.
Enterprise companies: To stay nimble, adapt to customer demands, and align teams around a single view of the customer journey.

Frequently Asked Questions (FAQ)

Q: What is the goal of product analytics?
- A: The goal is to provide businesses with insights into user behavior and engagement to improve digital experiences and drive business outcomes.
Q: How does product analytics benefit startups?
- A: Product analytics helps startups create quality products and accelerate the process of finding a product-market fit by providing actionable feedback on what’s working and what’s not.
Q: What types of companies can benefit from product analytics?
- A: All kinds of companies can benefit from product analytics, including startups, mid-market companies, and enterprise organizations.

Who needs product analytics—and why?

The goal of this guide is to give you a thorough overview of product analytics and the advantages it will give your business.
In chapter 2 of this guide, we’ll also explain why combining product analytics with experience analytics and experience monitoring is the best way to understand your customers.
But let’s not get ahead of ourselves. First, let’s start with the basics.

What is product analytics?
Product analytics is a set of functionalities that capture, measure, and visualize all the ways users engage with your digital experience—from acquisition to retention—across multiple sessions, channels, and devices.
A good product analytics solution shows you every user’s activity on your website, mobile app, or any other digital product, across a lifetime of interactions. It also empowers you to use this information to improve your digital experience.

With product analytics, you can see

How users interact with your products over time and across devices
Which parts of your experience users love—and where they get stuck
The routes different users take through your experience
How different groups (or cohorts) of users behave
How behavior changes on different platforms
And much, much more…

When you truly understand your users’ behavior, you’ll understand their needs—and evolve your product to anticipate those needs.

This understanding is what makes it possible for you to improve complex user journeys, drive engagement and retention, and optimize your product investments.

What is product analytics good for?
The insights product analytics gives you are essential for building a great digital experience. That’s true whether that experience happens on desktop, mobile, tablet, IoT, or some combination of those.

On a business level, product analytics is good for answering questions that relate to 3 key business outcomes

Understanding and improving complex user journeys to (among other things) drive conversion

How many sessions does it typically take before your site users purchase a luxury item? Do they search and compare? Which other elements of your site do purchasers tend to interact with?
Which activities do users like to perform on mobile, and which on desktop? How do they move back and forth between those platforms?
How do users navigate through long processes, such as loan requests? Where do they mostly get stuck?

Driving engagement (by understanding its key drivers) and preventing churn

How are new users adopting the product? Is onboarding effective?
How do I know that our activation metric accurately reflects long-term value?
What activities bring users back, for repeat purchases or ongoing usage?
What features drive long-term revenue per user?

Optimizing product investments by enabling data-driven decisions

Of all the activities our team should work on, which will have the greatest impact on the business?
Which successful features should we optimize, and which underperforming ones should we deprecate?
Was our hypothesis about changing the user flow correct? Why or why not?

What kind of companies need product analytics?
In short? All kinds of companies need product analytics.

Startups need product analytics to create a quality product in the first place
Product analytics gives you the quickest and most actionable feedback on what’s working in your product and what’s not, helping you to accelerate the process of finding a product-market fit.
Mid-market companies need product analytics to scale
Product analytics is the key to growth. It gives you the ability to increase user retention, maximize conversion rates, and focus on the new features that customers really want. (Not just the features they tell sales they want.)
Enterprise companies need product analytics to stay nimble
Larger organizations need to adapt to evolving customer demands to stay ahead of competitors looking to disrupt the market. And to do that, they need a single view of the customer journey around which every team within the organization (however disparate) can align.
Product analytics keeps organizations in touch with their customers and their teams in touch with each other. It also gives them user data they can blend with other sources, such as finance, HR, supply chain, retail, sales, or marketing, to gain a holistic view of the entire business.

What industries tend to get the most from product analytics?
Any industry that requires companies to offer a great digital experience in order to be competitive (which, these days, is most industries) also demands the use of product analytics.

However, traditionally product analytics has seen the most adoption in these 4 industries

B2B SaaS, in which product analytics is the primary way to learn how users use your digital product
Retail or ecommerce, where every increase in conversion makes a big difference to the bottom line
Financial services, where customers are making high-consideration purchases (necessitating repeat visits) and are often required to fill in complex forms
Travel and hospitality, where—again—purchases are high consideration, meaning users often engage on multiple platforms and over multiple sessions

IN SHORT: As this list of adopters suggests, any time you have repeat visits, cross-device journeys, high-consideration purchases, product-led growth (PLG), or where long-term retention is critical, product analytics is a major asset.

What roles tend to use product analytics?
Every team in a company can improve performance when they have an easily accessible product analytics platform that allows them to try out optimizations, quickly understand the impact of their optimization efforts, and document any improvements they make.

Product analytics is used by

Product managers who are looking for ways to increase activation, conversion, and retention, create captivating digital experiences, and tie feature usage to high-level business metrics
Product leaders who want to measure the effectiveness of their team, use data to prioritize their product roadmap, and demonstrate impact to the C-suite
Marketers who want to know the true effectiveness of their emails, social posts, and promotions, and who wish to improve the site and app experience to maximize conversion
Data teams who know that the success of the product is the success of the business, and who understand that sharing product analytics creates transparency across departments and a greater understanding of what’s happening company-wide

What about Google Analytics?

It’s analytics. By Google. What’s not to like?

A lot, actually. While GA4 is an improvement over the previous version of GA, we still don’t recommend it. Here’s why.

‘Codeless tracking’ is kind of a sham. While it’s better than pure manual tracking, with codeless tracking you still have to decide what events you want to track in advance. You’ll still end up with an incomplete, biased dataset. GA4 doesn’t offer retroactive data capture, so you’re limited to collecting data on events from the moment you decide to track them. Forget to track something? Too bad!
Integrations and customizations are limited. Very limited. Users will always have to interact with middleware. In GA4’s case, this is Big Query, Google’s data warehouse platform. While a free connection to Big Query will be included, you still have to pay for the data you use.
Mobile and web? No dice. Consumers now do 70% of their web browsing on mobile devices. With people visiting your site from multiple devices and platforms, the solution you choose should be able to link these visits. Even many advanced product analytics solutions don’t do this automatically. Google Analytics doesn’t do it at all.
Data thresholds. Because of privacy concerns, Google doesn’t want to show you everything it can. Google’s solution? Restrict your data. When thresholding happens (which you’re often unable to prevent), you miss out on tons of useful data and can end up reaching improper conclusions.

The basics of data tracking
There are 3 main ways to capture data for analytics.

Explicit (or manual) tracking requires engineers to insert a tracking code into each event. Data accumulates from the moment it’s installed. Any event that isn’t explicitly tracked doesn’t collect data for analysis. Google Analytics, Mix panel, and Amplitude are examples of platforms that offer explicit tracking.
Implicit tracking (also called automatic capture or retroactive capture) requires only inserting a single JavaScript snippet into the header of a site or application. After that, all activity is tracked automatically: every click, swipe, form fill, pageview and more. (Heap by Content square is powered by auto capture.)
Hybrid tracking is a new premise that pairs the infrastructure of an explicit platform with codeless, retroactive data collection. It claims to be the best of both worlds. In practice, this approach fails to deliver proactive insights, because only predefined events are available to the system. Without a mechanism for handling raw events that haven’t been explicitly ‘tagged’ within the data capture tool, the onus is still on teams to figure out what the data is trying to say.

Heap by Content square allows you to automatically capture user interactions—crucially, however, it doesn’t prevent you from designing explicit tracking experiments.

Instead, it gives you the advantages of both the automatic and manual tracking methods without the deficiencies of hybrid tracking.

The extent to which you use either method is entirely up to you. With Heap by Content square, you can tinker with and tune your tracking as much—or as little—as you need to.

The new winning formula: product analytics plus experience analytics

Let’s say you’re looking for a product analytics solution but keep hearing about ‘experience analytics’—and wonder if that’s what you really need to understand your customers.
Or you’re already using experience analytics, and are interested in finding out how it might work with product analytics.
In either case, this section is for you.
Thanks to recent developments in the analytics marketplace, you’re now able to combine product analytics and experience analytics on a single platform.

That’s great news for you and your customers. Let’s explore why.

What is experience analytics— and why does it matter?
According to Salesforce, 88% of consumers say the experience a company provides is just as important as its products or services. It’s imperative, then, for your business to understand your entire customer experience in as much depth as possible.

Product analytics will give you the quantitative data you need to understand user behavior at a high level, understand the full customer journey across sessions and devices, and to pin-point where to start deeper analysis.
But to conduct that deeper analysis and uncover the ‘why’ behind those quantitative metrics, you need the qualitative insights brought by experience analytics.
Experience analytics provides you with capabilities like session replay and page analysis with which you can analyze the behavior of your website and mobile app users within individual sessions in depth.

Experience analytics capabilities Heap by Content square gives you access to

Session replay
Session Replay enables you to watch replays of user behavior (in a single session or over multiple) that are relevant to quantitative insights. This lets you take a user’s-eye view of your experience, and uncover hidden issues and opportunities in the process.
For example, if your product analytics platform alerts you to a high number of users dropping off a demo sign-up page, you can watch session replays from that page to see where and why things have gone wrong.
And you won’t even have to skip to the relevant part—the replays will be automatically cued up at the exact point(s) of drop off.

Heatmap analysis
Heatmaps give you visually compelling insights into your users’ on-page and on-screen behaviors, letting you see what customers click on (and don’t click on), how far they scroll down your pages, and where they’re moving their cursors.
Again, product analytics will often provide you with the initial steer on which pages and screens you need to examine. Heatmaps adds the qualitative context to understand the ‘why’ behind the ‘what’.

Completing the experience analytics puzzle
Experience analytics gives you a lot more insights than product analytics alone. It lets you understand how your customers feel about your digital customer experience—and what’s driving engagement and abandonment on your site and mobile app.
However, to get the fullest possible insights into your user experiences (and users), you’ll need to combine product and experience analytics with 2 other analytic solutions.

Experience monitoring, which is dedicated to locating technical errors and UX design flaws causing user frustration within your journeys—so you can fix them as quickly as possible.
Voice of customer analytics, which is about leveraging AI to collect, organize, analyze, share and act upon customer feedback across all possible digital channels.

The best way to do this is by implementing a platform that offers all four capabilities. Which brings us to Content square…

Understand your brand’s entire user experience with Content square
You can now use Heap by Content square as part of Content square’s all-in-one Experience Intelligence platform.
Content square’s AI-powered all-in-one platform provides you with a comprehensive understanding of your customers’ behaviors, intent, and feelings by seamlessly uniting Product Analytics (courtesy of Heap by Contentsquare) with.

Experience Analytics: use tools like Session Replay, Heatmaps, and Journey Analysis to investigate user behavior and understand what’s motivating it, attribute revenue to content, and optimize your web and app journeys from end-to-end.
Experience Monitoring: quickly uncover, understand, and prioritize issues to reduce the impact of errors and user experience friction on your business. Real-time AI-powered alerts identify JavaScript, API, and custom errors or crashes and reduce your time to triage. Speed Analysis ensures your site and app are performing at their best.
Voice of Customer: bridge the gap between user behavior and user feedback so you can discover the root cause of the former and what’s driving the latter. Ask your customers anything and surface insights in seconds.

As well as adding context to your product analytics data, these products help remove the silos that traditional analytics create within companies, and ensure that optimization efforts across your different teams, such as product, marketing, and IT, are aligned.

4 ways product analytics improves experience analytics

What product analytics adds to experience analytics is the ability to see what users or groups of users do over time.

Product analytics also shows you how users behave across devices and sessions. For example, you can compare their behavior on mobile versus their behavior on desktop, or their behavior on desktop versus on IoT.
If you’re already using experience analytics, adding product analytics will let you understand end-to-end user journeys across sessions, devices, and time—and you’ll be able to prove ROI on even the most complex experiences.

Analyze user behavior across sessions
As powerful as it is, experience analytics mostly focuses on individual sessions.
By adding product analytics, you can see how users behave over time, across multiple sessions, and as they switch between devices.

That empowers you to ask questions like

How and where are users switching devices in their journey?
Do multiple ‘browsing’ sessions eventually lead to conversion?
How many browsing sessions usually take place before the prospects convert?
Which paths do most users take before converting?
How has traffic to this product page changed over time?

Crucially, product analytics—unlike experience analytics—lets you track retention rate: the percentage of users that return for another session after viewing a page or screen.

Use data to align the whole organization

Traditionally, product analytics and experience analytics have been owned and deployed by different teams. This creates data silos, stymies communication, and results in a patchwork approach to optimizing the end-to-end user journey.
By putting product and experience analytics together, you ensure that your product and marketing teams are working from the same robust dataset.
This will add context to the data they work with, enriching their understanding of how optimizations fit into the bigger picture, and ensuring that everybody’s pulling in the same direction.

Bring segmentation into your experience analytics analyses

With product analytics, you can segment your user data to create user cohorts. These are groups of users that share a common demographic, experience, or behavioral pattern.
For example, you might create a cohort of particularly high-value users (those who convert quickly or repeatedly, or who use your product on a daily basis).
You can then feed this segmented data into the rest of your tech stack—including your experience analytics solution—to get a more precise understanding of what’s driving the behavior of a particular user group.
How do high-value users behave differently on your landing page to comparatively low-value users? Cohort analysis, in combination with experience analytics, will help you answer such questions.

Import offline data into your analyses

Product analytics makes it easy to ingest offline events. We’re talking store purchases, physical touchpoints, in-person marketing campaigns, and more. Because of product analytics’ data structure, it’s simple to bring this data into the platform. It lets you track the effects that changes in your digital experience have on in-person behavior, and vice versa.
In short, when product analytics, experience analytics, experience monitoring and voice of the customer capabilities occupy the same platform, you get all potential lenses on the customer experience in one place.
This gives you a complete view into what users are doing, everywhere—and shows you how to drive the behavior you need to see to grow your business.

Product analytics vs experience analytics

Which features are typically in product analytics, which are in experience analytics, and which are in both? Glad you asked. Here’s a handy chart.

Product analytics	Experience analytics and monitoring
✅	✅	Page and site optimization	See which elements on your site drive purchases, engagement, and lifetime value (LTV)
	✅	Site errors and site performance monitoring	Keep your site or product performing at an elite level
✅	✅	Behavioral analytics	Understand what every user does, everywhere
✅		Cross-device tracking	See what users do on web, mobile, tablet, IoT, and all other digital platforms
	✅	Session replays*	See your digital experience through the eyes of your users
	✅	Heatmaps analysis*	Easily visualize how users navigate your pages or site and understand which elements on a page are performing well, to optimize page layout
✅		Marketing campaign data and attribution	See which campaigns perform the best, and how users get to your site or product (the substitute for GA4)
✅	✅	Customer journey mapping	Visualize the routes customers take through your product or site
✅		Adoption, retention, and churn analysis	Know which behaviors predict adoption, retention, and churn, so you can structure your site or product around them
	✅	A/B testing optimization	Analyze A/B test performance to understand which tests perform best and why

How to choose the best product analytics solution?

So, now you know something about product analytics. But how do you choose the right product analytics solution?
We propose that whichever solution you choose, you follow these 9 considerations…

If your product analytics solution is making you do all the work, it’s wasting your time

It should save time and resources, not make life more complicated. The most important thing to know upfront is whether or not tracking is automatic. If it’s not, it will your tool give your developers extra work.
Manual tracking takes up engineering time and may not give you all the data you need. Likewise, a solution might be able to deliver all the data you need, but if it makes you sift through and find what you need, it’ll never deliver the value you want.
A good solution gives you the data. A great solution helps you find the information and insights you need in it.

How Heap by Content square helps

As well as automatically tracking user behavior, our Heap by Content square’s AI Co Pilot makes analysis easy for all, whatever their experience level.
All you have to do is ask Co Pilot what you want to know (ex: “How many users completed our checkout process last week?”) and it will run an analysis for you. You’ll get easy-to-understand answers that are enriched with charts and visuals to help inform your decisions and optimize your experiences.
Plus, if you want to dig deeper, just ask follow-up questions—if you can’t think of any, Co Pilot will suggest some for you.

It should allow you to follow the full customer journey

Prospects and customers don’t just magically appear and disappear. They come and go because how they use your product over time is part of their journey.
Do purchasers visit many times before clicking ‘buy’? What activities correlate with long-term usage?
To answer these questions, you need a solution that shows you how users engage over time. If your solution can’t do that, then it isn’t really capturing the impact of your product on your business.

It should track users across multiple devices

Doing everything on your laptop is so 2010. Today customers move from web to mobile to tablet to watch to… well, whatever device will show up next. And they (that is we) expect those transitions to be seamless.
Lots of product analytics solutions only track web or mobile behavior. But the real insight lies in tracking both, and seeing how users move from and between different platforms.

It should find the needles in the haystack

If your team were in a race (and, let’s face it, they are) would you rather they have a paper map to the treasure, or a live GPS feed?
A powerful data science layer makes all the difference between having information in front of you, and being able to take meaningful actions with it to make effective progress.

Data science enables you to

View the differing paths and optional steps users take on your product or site, so you can capture the differences in conversion rates between them
Quantify the amount of effort that steps in the journey take, so you understand the degree of friction your customers experience at every point in a given user flow
Recognize user interactions that you didn’t choose to track, but are having an impact on your conversion or retention, so you can build in appropriate steps in your funnels
Suggest user cohorts that correlate highly with the outcomes you’re trying to achieve

It should help your teams make smarter decisions
Data is rarely valuable on its own. It’s useful to the degree it can aid your decision-making, by answering questions like

How many “invisible steps” are occurring between 2 points in a funnel?
What behaviors most predict long-term retention?
How do power users navigate our site, and how can we nudge other users to take those actions?
Which channel brings in the people who purchase our large-ticket items?
At which part of the funnel do people drop off? Which groups of people drop off more?

If your solution can’t help direct your larger investment decisions, it’ll rapidly run out of value for you.

It should keep your data clean and dependable

All the data in the world is no good if it’s impossible to use. For your data to be maximally valuable, it needs to be clear, organized, and consistent. When the dataset is trustworthy to everyone in the organization, teams can work collaboratively across departments, and you can scale.
For this, you need things like naming conventions, collaborative workflows, a data dictionary, and more.
Because speed is critical to iteration, you can quickly answer questions and raise new ones. You can’t do this if your data is a pile of spaghetti.

It should return a clear ROI
For as many cool things as product analytics can show you, it’s useless unless those insights help quantify the effects of your decisions. A product analytics platform should help you answer questions like:

When we changed that user flow, how many more users converted?
How much did we save in support calls?
Did our decisions affect down-funnel behavior?

If your solution can’t clearly show what you’re doing right and what not, you’ll continue to make decisions based on your gut alone.

It should easily connect to your data warehouse
The larger your organization, the more important it is to centralize your dataset and blend product information with data from across the business, all while using minimal engineering resources.
A system that automatically pushes behavioral data to your data warehouse while keeping it organized means your data teams can spend less time munging data, and more time generating insights.

It should be geared toward increasing conversion and retention
Product analytics is critical for measuring and systematically improving AARRR (aka ‘the pirate metrics’).

Acquisition: where do your customers come from?
Which users are the best prospects, which channels do they favor, and what are your optimal costs for acquiring each user?
Activation: what steps does a user take in your product?
Each step on their journey to becoming a paying customer is known as a micro-conversion. Wouldn’t it be a great idea to optimize the effectiveness of each one?
Retention: are your customers staying or leaving?
Product analytics helps you make happy users happier, and steer you towards ways to win dissatisfied users back.
Revenue: how do 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.
Referral: are purchasers talking up your product or disappearing?
Loyal customers are more likely to spread the good word about your product, and product analytics helps you measure customer loyalty through an assessment of their behavior—certain metrics
(which we call ‘activation metrics’) correlate with a higher retention rate.

Heap by Content square helps drive conversion
Today’s conversion journeys are often long and complex. But with Heap by Content square’s multi-session dashboard, you can understand your conversion journeys from first landing on your website to making a purchase—and beyond.
Find out how many sessions it takes to convert the typical user, whether app access makes them more or less likely to convert, what actions taken within the journey typically correlate to conversion, and much, much more.

Remember: not all tools give you this much freedom
In our opinion, if your product analytics solution is not prepared to do anything and everything you ask, then it’s useless.

Conclusion

Extraordinary digital experiences don’t just happen randomly. They’re intentionally created based on a deep, data-driven understanding of user needs and desires.
Product analytics is the means to this end. We believe that to be useful, a product analytics solution must have 2 critical capabilities: a comprehensive user data foundation, and an analytics environment that’s built to mine through it for hidden insights.
Heap by Content square ticks both boxes to bring you quantitative insights that will improve your products and drive better business results.
Plus, it includes experience analytics tools like Session Replay and Heatmaps that give you the qualitative insights you need to truly understand your journeys (and how to optimize them).
However, to get the best possible combination of qualitative and quantitative insights into both your product users and website visitors, you need a platform that seamlessly combines product analytics with experience analytics, experience monitoring and voice of customer analytics.
In other words? You need Content square’s Experience Intelligence platform—which Heap by Content square sits on.
If you’d like to find out more about Heap by Content square, and how it fits into Content square’s platform, contact our sales team today.

Learn more about how Heap can give your team the insights that drive business results.

Documents / Resources

heap Product Analytics Buyer's [pdf] User Guide
Product Analytics Buyer s, Product Analytics Buyer s, Analytics Buyer s, Buyer s

References

User Manual

heap Product Analytics Buyer’s

Product Information

Product Usage Instructions