Product Analytics can mean different things to different people. We, at Lantrns Analytics, came to equate it with providing insights into how a product is fulfilling its business model and growth strategy.
It serves to shed light on a business plan that can be summed up as: with this product, we want to serve this market, and this is how we plan to achieve product-market fit.
Our approach to product analytics focuses on those 3 components:
- Product-Market Fit
- Customer (aka market)
Below is an overview of those 3 components and their associated analytics.
Product-Market Fit Dashboard
Lean Analytics is a great framework to structure an analytical strategy to better understand the performance of a product and scale it. Without going into details (the book is worth more than 1 read), we want to provide insights on how a business is successfully (or not) achieving product-market fit. It shouldn’t be complacent (meaning no vanity metrics), it should provide a quick overview of progress, how it compares to historical performance, and how it trends towards future performance. In the context of the Lean Startup “philosophy”, the idea is to give business leaders a realistic view on if their growth strategy (engine) is working, or not (and consider pivoting if it’s not).
This first set of analytics keeps track of progress through the main stages defined in the Lean Analytics book (empathy, stickiness, virality, revenue and scale). We define objectives for each stage with an OMTM (one metric that matters) and the lead indicators that provides insights into the evolution of our OMTM.
As we’ve mentioned, there are 5 stages in the Lean Analytics framework:
- Empathy – “I’ve found a real, poorly-met need a reachable market faces.”
- Stickiness – “I’ve figured out how to solve the problem in a way they will adopt and pay for”
- Virality – “I’ve built the right product/features/functionality that keeps users around”
- Revenue – “The users and features fuel growth organically and artificially”
- Scale – “I’ve found a sustainable, scalable business with the right margins in a healthy ecosystem”
Those stages will guide the development of the right metrics (OMTMs and Lead Indicators). It’s not overly complicated, but it requires the commitment towards an overall growth strategy with gates that will define where a product is in its growth journey.
Below is a very basic example of a table that divides the growth strategy into its 5 stages and what are the objectives for those stages.
|Stage||Gate needed to move forward||Product-Market Fit Objective|
|Empathy||“I’ve found a real, poorly-met need a reachable market faces.”||Think tank senior analysts are taking interest in our newsletter and sharing our posts within and outside their organizations|
|Stickiness||“I’ve figured out how to solve the problem in a way they will adopt and pay for”||Think tank senior analysts start to request our paying subscription that provides added features and easier access to our main data pipeline|
|Virality||“I’ve built the right product/features/functionality that keeps users around”||Think tank senior analysts renew subscriptions and start inserting our brand within their own reports|
|Revenue||“The users and features fuel growth organically and artificially”||Even think tanks with important in-house data science groups are starting to use our data pipelines|
|Scale||“I’ve found a sustainable, scalable business with the right margins in a healthy ecosystem”||Our data pipelines are starting to be used in other verticals such as media groups, governments and NGOs.|
Here’s an example of a Product-Market fit dashboard for a product that is now in the second stage.
With Product Analytics, we are moving away from strategy and really getting into the performance of your product. It informs your tactics to achieve product-market fit.
You can evaluate the performance and evolution of key metrics such as the number of active users on the platform, engagement per cohort, as well as metrics segmented by types of users who are important for that platform.
Below is an example of such a dashboard taken from another project.
Finally, we have Customer Analytics, which is interested in user behaviour. We want to understand user journeys, how they are undertaking action which we deem valuable. This type of analytics helps us better understand how individual behaviours are influencing our OMTMs and lead indicators.
There are many ways to approach this. But first getting to the data can be a challenge, as we’re not only interested in transactional data, but behavioural data such as the pages visited, the time spent on site, frequency of visits, events triggered on their visits. There are many strategies to get to that data (Segment being a popular option), but this is worth an entire discussion on its own.
Below is a colourful visualization from Oliver Buchberger which gives a hint of what we mean by digging into a user’s journey.
This can all be implemented in-house, but there are also quite a few SaaS offerings to dig into that sort of data. Again, it’s first a matter of having the proper infrastructure to get to that data and push it towards the proper analysis channels.
Experimenting and Engaging
A few last words on where this sort of approach can/should lead.
The overall advantage of this type of approach is that based on results observed on those dashboards, we should be coming up with hypothesis and experimentations to influence lead indicator’s results, and eventually OMTMs.That means we come up with tweaks on the product to evaluate how users react to those changes.
One further step is to act on the insights you have at your disposal. The art of acting when needed – to the point of automating personalization with such a tool as Segment Personas or Snowplow React. Better understanding your product’s users should have for purpose to engage intelligently (and hopefully automatically) and help them move forward on their journey.