This blog post is about optimizing for product growth and success and how we are planning to do it at Swimm as an early stage startup. We will cover:
- The transition from making decisions to making hypotheses
- Product analytics - what it is, why we need it and who benefits from it
- The product analytics platform we chose at Swimm
- What we have achieved so far using this platform
- What's next?
This post is based on our bi-weekly Swimminars (Swimm webinars), a tradition we hold at Swimm from day one by which each team member gets to dive deep into a new topic and share it with the team. To learn more about what we do, click here.
From Making Decisions to Making Hypotheses
Swimm is an early stage company, and as such, some of our decisions about what needs to be done in the product are based on our instincts. We mainly look at data after we’ve already decided on features.
As we work towards our Open Beta version of our SaaS application, we expect an increase in the number of users. Therefore, we are striving to reach a level of growth, where it's no longer sufficient to make gut-level decisions. We want to become more hypothesis oriented, meaning that we want to move from shipping a feature and hoping it will make users happy, to testing whether changing this feature will have a metric impact, and why.
We were looking for a way to achieve this shift by becoming more data-driven and measuring outcomes. So we entered the world of product analytics.
What is Product Analytics?
Product analytics has many different synonyms. One of them is product adoption. Another is digital insights. There is also behavior analytics, customer analytics... all of these pretty much mean the same as product analytics. Different companies are using these different synonyms, but they basically talk about the same topic.
For those unfamiliar with the field, product analytics is a set of tools that tells you exactly what is happening inside your product. This includes insights about:
- Who is using your product
- How they're using it
- Which features they are using or they are not using
- Where they are experiencing friction with the product
- How to correlate user behaviors with long term value
Why is Product Analytics Important?
Product analytics is important because it:
- Provides you with insights on what your customers are doing with your product
- Helps the company develop better ways to reward and reinforce desired behaviors
- Helps keep customers coming back (customer retention) and help with conversion optimization (acquisition)
- Measures the success or failure of individual features
Who Benefits from Product Analytics
There are many stakeholders who can benefit from product analytics:
- Product - can understand what the users of the product do, make data-driven decisions, measure and run experiments on the product. This helps increase activation, conversion or retention efforts inside the product.
- Developers - can find and resolve implementation flaws because they are aware of user experiences within the product. They can also eliminate bugs, fine tune features and resolve user friction with the data they can see from the analytics platform.
- Designers - can find and resolve design flaws when they are aware of specific user experiences within the product. They can also learn in more detail how users navigate between the different features, see which ones are popular and which ones confusing, and then iterate on that. Finally, they can also identify roadblocks and key points of abandonment.
- Marketing - by knowing exactly what the users are doing with the product, they can determine what users do within the product and how they behave. They can also use app analytics to tailor their messaging on the product, website and marketing campaigns.
- Sales - can use product analytics to better identify the right time to contact either a prospect or an existing customer.
- Finance - can understand revenue, alongside behavioral data that we get about our users.
- Customers - the application of what we do with product analytics will result in products that are more intuitive, easier to use and more delightful.
Product Analytics Platforms
These platforms allow you to track any user interaction, such as a page view or a click event.
Why Not Google Analytics?
Google Analytics is free, so why not use Google Analytics? Google Analytics uses a page view driven paradigm. This is a concept from the year 2000, when the focus was on helping answering questions, such as how many users are coming to your website, how many or what pages they visit, as well as how they found out about your website.
But Google Analytics is not able to figure out which specific actions a user did perform on any given page of your website. For example, it doesn't give clarity on an entire user journey. The bottom line is that Google Analytics by itself is insufficient to figure out how your users are really using the product, although it can be useful on top of an event-based product and analytics platform.
Our Chosen Product Analytics Platform: Heap
Heap is the product analytics tool that we've decided to use at Swimm. It is an event-based product analytics tool. It powers over 8,000 companies worldwide from different industries, both B2B and B2C, like Splunk and Freshworks.
Fun fact: Heap in computer science is a tree based data structure, which is probably where their name was taken from.
Heap helps early stage startups:
- Understand how and why customers engage with our product, and understand who they are
- Assess the performance of the app experience we are building
- Automatically track and capture user interactions in our app (without requiring initial dev resources)
- Organize this data to let us answer questions, run experiments and explore insights
- Push out customer events of interest to external tools for further actions and analysis
- Augment Heap data with external data
- Retroactively get insights on things that were tracked but were previously not of interest
What’s Next with Heap?
We use Heap for auto-tracking of events, meaning that we still haven't manually tracked application events. So far, we’ve created a few segments inside Heap to differentiate between leads and active users.
We want to help provide value and refine the user journey, and Heap can help with planning what to focus on per every new feature.
As our Beta product evolves, it’s important for us to dive deeper with our product analytics so we can understand what is happening inside our product and benefit our users and to make more data-driven decisions across all departments.
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