Why do product teams need Product Analytics?
Product analytics is an integral component for the success of the product. It is about using data to understand and improve the way how the users engage with the product.
Product analytics helps product teams understand better the users and their behaviour, measure progress, validate if new ideas work, and make decisions of better quality.
Product analytics should be applied as early as possible in the product development, even before the new product or feature has been built.
Best product companies make data-informed and not data-driven decisions.
Product teams are making so many decisions every single day. Some are more strategic and determine the direction of the product, for example which new product or feature to build. Others are more tactical, for example a decision of how to design that new feature, or which colours or layout to use, etc. The success of the product is largely determined by the quality of these decisions.
If these decisions are based only on assumptions, there is a huge risk that the product will end up with a failure. We build the product with the assumption or the hope that it will bring value to the users. But would that actually be the case? Will the users find the product attractive and engaging? Will they be using the features in a way that you expected?
Wouldn't it be great if we can get such insights and read the minds of the users, so we can make better decisions on how to improve the product? This is where product analytics comes into play.
What is product analytics?
Product analytics is an integral component for the success of the product. It is about using data to understand and improve the way how the users engage with your product.
Product analytics can provide all kinds of information you need to understand how users use your product. Which features are they using, how do they interact with the product, what obstacles do they face when using the product.
Let’s take a user journey of an e-commerce application as an example. The user creates an account, signs in, then searches for a product, uploads a photo of a desired item, then searches a similar product, buys the product, and then leaves the application.
Using product analytics you could not only measure how many users buy a product at the end, but you could go in detail to learn how they engage with the product throughout their journey.
What products are most searched? Do they maybe use the price filter mostly so price matters most to them?
Are they using the new feature you’ve just created - searching by uploading a photo of a product similar to the desired one?
Do they spend a lot of time searching for the right product?
Do they find a product they like?
And you could answer such questions even for different segments, like user age, or user location.
Product analytics helps you get data for each of these touch points in the user journey. It enables you to maintain a dashboard of important metrics, so you can visualise the performance of your product, identify bottlenecks in the journey and work on resolving them.
Product analytics before the product or feature is live
Product analytics is valuable to understand how users are using your product features, so you can evolve and improve the product continuously. But it is even more valuable to use this technique to test new features before they are actually built. This is the key to successful product development. Testing and validating as early as possible, and failing fast before you spend time and money on building a feature that is not going to be adopted by the users.
This is how most successful companies and product teams work. Experimentation is the integral part of their culture and their processes. New ideas are tested first. Only if the data shows positive results, a solution for these ideas is built. Because most ideas don’t work when applied in practice. Even the ideas of the most successful product managers or business leaders are often wrong. This is why experimentation is crucial.
So how to involve product analytics during the product development? The process looks roughly like this.
The product team defines a clear measurable hypothesis.
Instead of building a complete solution, they would think creatively to find a simple way to experiment and test this hypothesis.
They would compare the results of the current product vs the product including this idea by using A/B testing.
If results are positive, they would continue implementing the full solution.
Should you start using product analytics for your product?
Yes, you should. Actually, you should have already started. Because otherwise you are targeting a goal with closed eyes.
It’s true that you can get much better data insights when product analytics is applied on a scale, when the product is more mature and the number of users is higher. But the earlier you start the more you can benefit. Think about how to use product analytics as early as possible, even from the Ideation phase, before you start with the actual delivery of the product feature.
Make data-informed and not data-driven decisions
While it's critical to use data and support decisions with data-driven techniques, following blindly the data can also be a risk. We also need intuition to design a data-driven test, to interpret the results of that test, or to know if we can trust the results. We need the opinion of our users, who can share their thoughts or emotions about why they are using the product in the way they are using, insights that not always can be captured by data.
Best decisions are not data-driven but are data-informed, they rely on a blend of both data and intuition.