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Mind the Product

Podcast

How to prioritise your work for impact – Nacho Bassino (Product Leadership Coach)

AI governance

Perplexity lawsuit sends warning to product managers

B2B product management

Artificial Intelligence (AI) & Machine Learning (ML)

Building Product Teams

Business Strategy

Data Driven Product Management

Digital Transformation

Diversity and Inclusion

Minimum Viable Product

OKRs

Prioritisation

Product Design

Product Research

Product Development Process

Product Ethics

Product Discovery

Product Leadership

Product Management Career

Product Strategy

Stakeholder Management

🌎 <a href="https://www.mindtheproduct.com/world-product-day/" style="color:#FFFFFF;text-decoration:underline;">Happy World Product Day!</a> Be sure to celebrate product, yourself, and your peers today! 🎉

Why teams often struggle with OKRs: A conversation with Jeff Gothelf

Lessons from my first year as a Prod Ops Manager

How Google makes AI work at Enterprise scale – Miku Jha (Director, AI/ML and Generative AI, Google Cloud)

5 ways to invent and simplify as an AI/ML product manager

Andres Phillips

<p>In this <span style="text-decoration: underline;"><a href="https://www.mindtheproduct.com/mtpcon/london/" target="_blank" rel="noopener">#mtpcon London+EMEA</a></span> session, sponsored by <span style="text-decoration: underline;"><a href="https://mixpanel.com/?utm_medium=blog&amp;utm_source=mixpanel&amp;utm_campaign=mtp-london&amp;utm_content=&amp;utm_term=" target="_blank" rel="noopener">Mixpanel</a></span>, 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.</p>
<p>Watch the session in full to see their talk or read on for an overview of the key points:</p>
<ul>
<li>The levels of product analytics can be split into five different levels of maturity; non-existent, novice, intermediate, advanced, and expert</li>
<li>The pillars of product analytics maturity include; data collection, depth of analysis, collaboration, and product metrics</li>
<li>Understanding the pillars of product analytics maturity can help you propel your organization through the stages</li>
<li>Once you understand the pillars, you can work to improve each one</li>
</ul>
<h2>Product analytics maturity</h2>
<p>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:</p>
<ul>
<li>collect data</li>
<li>gain access to data</li>
<li>use data to make decisions about product releases, product changes, experiments, tests</li>
</ul>
<p>Using the information gathered, they were able to split the levels of product analytics into five different levels of maturity:</p>
<h4><span style="font-weight: bold;">Stage 0 &#8211; Non-existent</span></h4>
<p>Here product analytics is non-existent, and development is guided by something other than <span style="text-decoration: underline;"><a href="https://mixpanel.com/content/guide-to-product-metrics/full-report/">product metrics</a></span>. There is limited customer feedback, intuition, or assumptions about user behavior.</p>
<h4><span style="font-weight: bold;">Stage 1 &#8211; Novice </span></h4>
<p>At this level, the organization is committed to using data to some extent to guide its product, but it is very reactive.</p>
<h4><span style="font-weight: bold;">Stage 2 &#8211; Intermediate</span></h4>
<p>Analytics is important at this level, but the organization is not quite sure how to prioritize metrics and use them to guide product development.</p>
<h4><span style="font-weight: bold;">Stage 3 &#8211; Advanced</span></h4>
<p>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.</p>
<h4><span style="font-weight: bold;">Stage 4: Expert</span></h4>
<p>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.</p>
<h3>Using the product analytics maturity model</h3>
<p>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.</p>
<h2>Things to consider</h2>
<p>If you want to propel your organization to the next stages, you need to consider a few things:</p>
<ol>
<li>What’s the endgame?</li>
<li>Who will be using the tool?</li>
<li>Have you put significant thought into what stage of product analytics maturity you’re in?</li>
<li>Where are you headed?</li>
<li>Who’s on board?</li>
</ol>
<p>However, you can expect non-linear progression through the stages.</p>
<h2>Pillars of product analytics maturity</h2>
<p>Understanding the pillars of product analytics maturity can help you propel your organization through the stages.</p>
<ul>
<li><span style="font-weight: bold;">Data Collection</span>: How you gather and track data to later be analyzed</li>
<li><span style="font-weight: bold;">Depth of Analysis</span>: The process of inspecting, segmenting, and transforming data</li>
<li><span style="font-weight: bold;">Collaboration</span>: How accessible and usable the tool is for everyone</li>
<li><span style="font-weight: bold;">Product Metrics</span>: Indicators of success measured from data</li>
</ul>
<h3>Data Collection</h3>
<p>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:</p>
<ul>
<li>Leveraging a customer data platform (CDP) for multiple sources of data</li>
<li>Maintaining a robust event tracking plan</li>
<li>Not launching a feature without tracking</li>
<li>The combination of front and back end data</li>
<li>Integration across the data stack</li>
</ul>
<h3>Depth of Analysis</h3>
<p>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.</p>
<p>Typically:</p>
<ul>
<li>Novice-level companies focus on traffic. For example, how many visitors</li>
<li>Intermediate level focus on acquisition. For example, how many purchases?</li>
<li>Advanced level focus on journeys. For example, where are the top user paths?</li>
<li>Expert level focus on deep engagement. For example, is the product getting stickier?</li>
</ul>
<h3>Collaboration</h3>
<p>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.</p>
<h3>Product metrics</h3>
<p>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.</p>
<p>You should also use a framework to understand if all metrics are feeding into the same funnel. Some popular frameworks include <a href="https://www.reforge.com/blog/growth-loops" target="_blank" rel="noopener">Growth Loops</a>, <a href="https://www.mindtheproduct.com/aarrr-vs-rarra-pirate-metrics-explained/">Pirates (AARRR or RARRA)</a>, and <a href="https://uxdesign.cc/googles-heart-framework-choosing-the-right-metrics-for-your-product-112bd7300d55" target="_blank" rel="noopener">Google HEART</a>.</p>
<h2>What’s next?</h2>
<p>Finally, product managers need to do is take each of the four pillars and determine how to improve each one.</p>
<ul>
<li>Data collection: Ensure that you have clean data, multiple sources of data, one source of truth, and determine what tools can be added.</li>
<li>Depth of analysis: Ensure that there is no siloed data and connected tools for user journey and engagement understanding.</li>
<li>Collaboration: Ensure you have self-service analytics, save time for analysts and data scientists, and ensure you can upskill teams in data literacy.</li>
<li>Product metrics: Take note of actionable metrics, including team KPIs, and use a measurable framework.</li>
</ul>
<h2>How does your product analytics strategy stack up?</h2>
<p>See how your product analytics strategy stacks up by taking this quick quiz from Mixpanel. Using your results, they&#8217;ll provide you with a rating and share tips to advance.</p>
<p><a class="button btn btn-primary" href="https://mixpanel.com/content/product-analytics-strategy/quiz/" target="_blank" rel="noopener">Take the quiz</a></p>

core/freeform

Product Management Metrics and KPIs

Measuring the user experience by Tomer Sharon

Why product managers should not be data-driven by Jens-Fabian Goetzmann

Metrics don’t have all the answers: a human-centered approach to business strategy by Samihah Azim

Your ticket to product analytics expertise by Ross Walker and Matt Smith

Andres Phillips