You already know that to grow the users of your product, you’ll need to understand their behaviour and how they use your product. After all, this is a key tenet of product-led transformation. Instead of blindly throwing in random features, guessing what users would like, and hoping for one of your guesses to stick, you need to let your product guide you.
Do you know what problems your product is solving? Do you know who you’re solving for? Do you have the tools to answer these questions today?
If not, we at Mixpanel have created a nifty Product Analytics Maturity Self Assessment tool that you can use to quickly benchmark your current maturity. There’s also an accompanying guide that walks you through the steps of how you can build your product analytics maturity, and enable you to build products that deliver impact.
Building product analytics maturity
At its heart, product analytics maturity impacts your ability to answer questions about your users and how they use your product. Mixpanel’s framework for product analytics maturity is made up of four pillars.
- Data collection: How you gather and track data to later be analysed
- Depth of analysis: The process of inspecting, segmenting, and transforming data
- Collaboration: How accessible and usable the tool is for everyone
- Product metrics: Indicators of success measured from data
If you’ve taken our self-assessment, you’ll find yourself in one of the following maturity stages.
- Stage 0–Absent: Data is not used to make key product decisions. Often, early-stage companies that don’t generate much data or companies that are not digital-native will find themselves in this category.
- Stage 1–Novice: Data is not integral to product development or decision-making.
- Stage 2–Intermediate: Data is used to inform product decisions, but not yet to drive them forward in a systematic way.
- Stage 3–Advanced: The majority of decisions are made with product analytics in mind. The product team is empowered to analyze data and rely on it.
- Stage 4–Expert: All decisions are product-led. No product development decision is taken without the use of product analytics. The entire product team is encouraged and empowered to ask questions, identify issues, and understand the leading indicators of success.
If you find yourself in Stage 0 or Stage 1, no worries. You don’t need to transform yourself into a product analytics expert overnight to start understanding your users or optimise engagement and growth. Even moving incrementally, you can get valuable insights. The maturity assessment comes in useful in helping you understand which pillars you need to focus on.
Ultimately, at the Advanced / Expert stages, you’ll be able to answer key questions like:
- Which in-product actions correlate most strongly with high retention—a plan upgrade, or some other goal?
- How do engagement and retention vary by company or account?
- How do various combinations of user behaviours and user attributes affect engagement and retention?
- How do marketing launches cause changes in specific user behaviours or your primary metrics?
- How quickly do users reach your activation metric? What actions are likely to convert activated users to power users?
Confirming hypotheses and measuring impact using product analytics
Once you’ve begun to collect data, you can start digging into it to confirm or disprove your hypotheses and leverage the insights gleaned to make data-backed product decisions.
For example, food delivery platform Deliveroo leveraged the data they’d collected to understand how their daily active users were growing over time. In the process, they noticed an interesting spike in the DAU every few days. Initially, they hypothesized that this correlated with paydays when restaurant managers launch the app to check and download invoices and receipts.
When they dug into the data using Mixpanel, they realised that this assumption was off the mark. It turned out that restaurant owners logged on to Deliveroo’s app at the beginning of every week to check the previous week’s sales numbers and customer feedback, which they then used to customize their services and menus accordingly. Without proper data analysis, Deliveroo might’ve ended up implementing unneeded features.
When a product-led company builds a new feature, they make sure to measure the impact as well. Digital property portal Domain Group had identified a group of users who they termed “property-hunting parents” (users who want to buy/rent a property in the vicinity of a specific school). They built new features for this cohort, adding better and updated property information, improving UX and more. After launching, they tracked their key metrics in Mixpanel and saw that the new features were a success—users of the features retained more than 2x the average, and they also made 2x more inquiries.
Product analytics maturity as a key driver for product-led transformation
Improving product analytics maturity goes hand in hand with driving product-led transformation. Digital interactions are a wealth of insights that product analytics can help you to unlock, and in turn, your ability to access and act on these insights will drive the product-led transformation of your organization.
Keen to learn more about Product-Led Transformation? Check out Leading the Product’s 7Ts of Product – Led Transformation.