Product Analytics area tremendous opportunity for software companies to gain competitive advantage achieving the best market-fit for their products. But to fully achieve these promises, companies, besides embrace analytics technologies, must also transform their processes, organization and culture.
What are product analytics?
The importance of the market-fit concept is extremely well expressed by Breg Cohen in his “Learn Product Management” ebook (https://280group.com/), where he redefined the whole product management scope as a function of product-market-fit.
“Product management scope is achieving optimal product-market fit in record time and fewer resources”
Until not long time ago, product-market fit was measured during the different product stages using a variety of different methods, such as interviews and focus groups, beta tester groups, Net Promoter Score, survey, etc. These mechanisms were time consuming, often reactive, expensive and, last but not least, they worked on small samples of users not on the whole user population.
Nowadays, as digital products can be instrumented to track every interaction that users have with them, market fit can be measured using this data. From the analysis of these interactions, product manager and product teams can identify what features work well and what not, how the users engage with the products and the level of user’s satisfaction. This kind of analysis is called Product Analytics.
Product analytics are
therefore the activity of analyzing product data (usage data) to identify and
testing opportunities for new product concepts, new product features or
modification of existing features.
Why are product analytics important?
It is easy to see that product analytics provide a tremendous mechanism to monitor and improve product-market fit. But in the hyper-competitive digital world, product analytics importance goes much beyond that: it can help systematically improve the product development. So, product analytics have become a necessary, integral and mission-critical component of any digital product development, as it can drives the whole product strategy. Indeed, using product analytics, product teams can:
- track interactions to identify successful, unsuccessful and missing product features, features usage frequency, etc.
- test new features or changes on existing ones to understand if they can produce benefits or increase user satisfaction.
- estimate the impact of new developments to define priorities in product budget allocation
- Identify segments and cohort of users based on their product usage profile
- …..
These examples show how product analytics can provide relevant insights in any product development phase.
Infusing analytics into any product decision-making process allow product team to create a new insight-driven development paradigm, where decisions are based on their impact on product-market fit or – in other words – their value to the users.
This new paradigm, where data and analytics are integral parts of the product development process, not only provides decision framework based on facts, it also provides a competitive advantage. Indeed, better market-fit means happier customers and ultimately better commercial results.
How to use insight in the product development process
|
Product phase |
Example of possible Insights |
|
Idea generation |
The analysis of how the product is used can provide important insights about areas to improve and features to add. Moreover, analytics can be used to estimate the benefits of the new developments |
|
Define and design of the Minimum Viable Product (MVP)
|
User behavior analysis allows to tailor MVP (Minimum Viable Product) on user’s desires only to maximize the market-fit of the product. |
|
Features prioritization
|
The estimation of new features benefits in conjunction with the relative costs provides an effective prioritization mechanism based on cost-benefit analysis |
|
Features build |
Analytics can be used to monitor and optimize the development process (e.g. reducing waiting time) |
|
Features Release (aka product launch) |
A/B tests are mandatory to test the “goodness” of any feature released and to verify the original estimation on the benefits of these features |
|
Post-production |
Users behavior analysis is a fundamental source of information for further product development. Product performance and usage analysis allow to proactively identify possible performance or usage bottlenecks |
How to implement product analytics
The importance of a measurement framework
Implementing product analytics is not just instrumenting products with analytics. The real problem is knowing how to structure the collected data to get the most out of them and to allow us to tell stories. Metrics not only need to make sense individually, they also need to make sense collectively. This is why product analytics are (or should be) built around measurement frameworks (MFs).
A measurement framework is a coherent way of structuring the relationships among:
- Goals or Objectives. These are the results that product teams want to achieve
- Metrics and Key Performance indicators (KPIs). These characterize, in a measurable way, all relevant aspects of the objectives
- Levers. These are the actions or activities that can be performed to influence the objectives
|
Goals |
Metrics |
Levers |
|
Acquisition (e.g. gain new users) |
· % of new users · % of users converting demo to pay version · % of subscriptions vs licenses |
· Change pricing policy · Introduce demo version · Subscription model · New marketing campaigns · Discounts and promotions |
|
Engagement (e.g. increase the usage of the product) |
· Features used vs features not used · Most used features · Most common sequence of features · User session time per feature · # of transactions x user |
· Improvement of the less used feature · Activity sequences optimizations · Promotions for sharing contents with the community |
|
Retention (e.g. ensuring existing customers continue to use the products) |
· # of failed or aborted transactions or operations · Average product use frequency · % of churn |
· Push notification or emailing of new features · Loyalty program |
|
Monetization (e.g. convert product usage in revenues) |
· Revenue volume · Costs · Margins |
· Price per feature · Product development optimizations · Support optimizations |
The importance of a cultural shift
Even if analytics-based insights are considered the top drivers of product innovation and development, their adoption is still far from optimal. Software companies struggle to infusing analytics into their decision-making processes and to organize analytic capabilities across the organization.
Indeed, product analytics are not only a technology: culture and processes are important too, even more than technology itself. Operationalizing product analytics requires:
- A good technology infrastructure such as application activity tracker code deployed inside the product, unified product data collection, strong data governance, sandboxes, standardized test procedure, analytical toolset
- a strict discipline where each team follows a standard process of instrumenting, collecting, measuring and testing any new features or changes.
- A cultural environment where experimenting is encouraged, there is no fear to fail and decisions are based on facts and data
Conclusions
Digital products are a very competitive market where entry barriers can be very low. In such market, the success – or failure – of a product is mainly due to the continuous and systematic improvement process to achieve the maximum product-market fit.
It is important to notice that achieving high market-fit is not an isolated task, it is a continuous and incremental process along the whole product lifecycle: it starts with the product concept and ends with the product post-launch. Product analytics can make the difference in this process, as they allow to:
- make decisions based on fact (insight)
- accelerate all the decisions processes
- increase decision “goodness”.
Indeed, product analytics collect and analyze data in almost real time, so the insight-based decision process is extremely fast. Moreover, product-analytics-driven decisions are more precise as they are based on data from all users, not on samples only.
In conclusion, product analytics can be a real competitive advantage for digital product companies, but to fully leverage them, companies must start a cultural transformation.
Related articles and books
- “A quick guide to product management” article by Andrea Fabrizi
- “Lean-Analytics Better Startup Faster” book by Alistair Croll and Benjamin Yoskovitz
- “Lean Product Management” ebook by Greg Cohen