In this #mtpcon London+EMEA session, sponsored by Mixpanel, Ross Walker and Matt Smith, Senior Solutions Consultant and Strategic Customer Success Manager at MixPanel, break down some of the characteristics needed to consider your organization an expert in product analytics and explain how MixPanel can help you focus on product analytics maturity.
Watch the session in full to see their talk or read on for an overview of the key points:
- The levels of product analytics can be split into five different levels of maturity; non-existent, novice, intermediate, advanced, and expert
- The pillars of product analytics maturity include; data collection, depth of analysis, collaboration, and product metrics
- Understanding the pillars of product analytics maturity can help you propel your organization through the stages
- Once you understand the pillars, you can work to improve each one
Product analytics maturity
Ross begins by explaining how they ran a survey at MixPanel to understand where product maturity sits within the organizations that they work for. From it, they aimed to better understand how the organizations:
- collect data
- gain access to data
- use data to make decisions about product releases, product changes, experiments, tests
Using the information gathered, they were able to split the levels of product analytics into five different levels of maturity:
Stage 0 – Non-existent
Here product analytics is non-existent, and development is guided by something other than product metrics. There is limited customer feedback, intuition, or assumptions about user behavior.
Stage 1 – Novice
At this level, the organization is committed to using data to some extent to guide its product, but it is very reactive.
Stage 2 – Intermediate
Analytics is important at this level, but the organization is not quite sure how to prioritize metrics and use them to guide product development.
Stage 3 – Advanced
Organizations at this level are guided by data for most decisions and analysis is driving product development as they constantly consider what works for who and why.
Stage 4: Expert
At this level, the product team has access to a full set of accurate data and uses it throughout the product development process. Company goals are unified, and everyone is focused on them.
Using the product analytics maturity model
At this point in the talk, Ross Invites the audience to use the product analytics maturity model to find out what level they’re at. From their original survey, 4% of companies were non-existent, 12% novice, 59% intermediate, 24% advanced, and under 1% expert.
Things to consider
If you want to propel your organization to the next stages, you need to consider a few things:
- What’s the endgame?
- Who will be using the tool?
- Have you put significant thought into what stage of product analytics maturity you’re in?
- Where are you headed?
- Who’s on board?
However, you can expect non-linear progression through the stages.
Pillars of product analytics maturity
Understanding the pillars of product analytics maturity can help you propel your organization through the stages.
- Data Collection: How you gather and track data to later be analyzed
- 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
When it comes to data collection, the more sources of data you have, the more questions can be answered. Examples of advanced data collection might include:
- Leveraging a customer data platform (CDP) for multiple sources of data
- Maintaining a robust event tracking plan
- Not launching a feature without tracking
- The combination of front and back end data
- Integration across the data stack
Depth of Analysis
Also keep in mind that understanding your customers is harder than ever, and you need to move towards more advanced data collection methods to increase your product analytics maturity.
- Novice-level companies focus on traffic. For example, how many visitors
- Intermediate level focus on acquisition. For example, how many purchases?
- Advanced level focus on journeys. For example, where are the top user paths?
- Expert level focus on deep engagement. For example, is the product getting stickier?
In order to collaborate on data analytics, you need a central data source, to ensure that you can have self-serve insights, do complex analysis, and facilitate continuous learning to get company-wide buy-in.
Of course, you will need to know what types of metrics you are tracking. Having the right metrics enables you to develop the key insights necessary to make better decisions for your product. Aligning a focus metric to customers’ active usage isn’t enough either. Additional metrics that address the full customer within your product will help drive the execution of strategy.
Finally, product managers need to do is take each of the four pillars and determine how to improve each one.
- Data collection: Ensure that you have clean data, multiple sources of data, one source of truth, and determine what tools can be added.
- Depth of analysis: Ensure that there is no siloed data and connected tools for user journey and engagement understanding.
- Collaboration: Ensure you have self-service analytics, save time for analysts and data scientists, and ensure you can upskill teams in data literacy.
- Product metrics: Take note of actionable metrics, including team KPIs, and use a measurable framework.
How does your product analytics strategy stack up?
See how your product analytics strategy stacks up by taking this quick quiz from Mixpanel. Using your results, they’ll provide you with a rating and share tips to advance.