Stop adding features to your product. Start crafting behaviors.

As a data scientist that has been working in many different areas, starting in agro-tech, mobile games, and now in the no-code industry at Bravo Studio, I faced a common issue in companies that I have been so far: the fever of adding features and features to the product, waiting for the users to come.

Check here the first medium post about this series about how to apply effectively data science to UX/UI .

Once the MVP stage is finished, many companies take the decision of adding new and exciting features for increasing the user's activity and other main metrics (mostly revenue). Sometimes, those features have been asked by the users and sometimes are cool ideas that we have. However, this is a common mistake.

In Bravo Studio, we don’t think in this way. We care a lot about the usage of our Product and therefore we are always thinking about how to provide the maximum value to our users.

We believe that the best way for increasing the usage and value of the product is to craft the product from a behavioral perspective instead of feature perspective.

The perfect product is the one which its features are crafted around its users behavior.

Shifting to a Behavioral Perspective mindset

When you are building a product it is not easy changing the mindset to a behavioral perspective. Adding new features is always easier than understanding how your users think.

The best way for changing this mindset is asking to yourself the following questions:

  1. Which are the use-cases of your product? Is it a product for a specific profile of users or can be handled by anybody?
  2. Which is the life-cycle of those use-cases?
  3. Which are the behaviors that the users need to do in order to perceive the product as valuable?

Once you answered those main 3 questions, it’s time to design a strategy to get the users to the target behaviors effectively.

The coffee shop example

As I said before, if we want to show the value of the product to our users, we need to drive the users to certain behavior or behaviors. This is called target behavior funnel.

As an example, let’s think that we are the owners of a coffee shop. The idea here is to drive our users through different behaviors in order to make it easier to find the value of our coffee shop. Take into account that sometimes, this value is not directly related to revenue.

One product might have several stages where find the value for each of one use-cases. That valuable stage or point might lead directly to revenue or to other successful targets like engagement or increment of customers.

So for this example, our coffee shop might have the different use cases:

  • People that want to have a coffee with friends.
  • People that are looking for a quiet and inspirational place for working while they are drinking coffee.

The behavioral target is to make them to drink coffee (or buy something) in our store.

In order to do that, we identify the multiple behaviors of each use-case

Last but not least, when you are building the life-cycles, it is important to detect how the user can drive us more users (reproduction of the product), after they have reached the valuable point or the target behavior.

So continuing with this example, instead of buying new types of coffee or adding a free piece of cake for improving the business, we need to focus on the current behaviors and optimize them.

For example, knowing that some people want to use the place for working, let’s add several accessible plugs for them or let’s create a good vibe for relaxing and working. Another option would be to have tables for individual customers and other tables for bigger groups with a separation between them.

Applying Analytics to the Behavioral Funnel

This coffee shop example can be extrapolated to other products like measuring the behaviors of a certain video-game or to the usage of an app.

There are many ways of measuring a behavioral funnel. It is possible to check the main metrics like how many users are doing X actions successfully. However, looking at isolated metrics tells us nothing. We need a better strategy for getting a holistic vision of the product.

In my experience as a Data Scientist related to digital products, the best way for seeing the big picture is using Sankey Diagrams.

Sankey diagrams were originally used for visualization and the analysis of energy flows but they are a great tool to depict the flow of money, time, and resources (source)

There are many ways for building this kind of chart. In my case I would use Python + Plotly Libraries + R Shiny as a dashboard platform.

The cool thing of this kind of chart is that:

  1. We are able to see the users flow: direction and amount.
  2. It is possible to attach additional metrics to each node: time until reaching the step, % of errors…
  3. Finally, it’s a good tool for detecting anomalies in our user flow like behaviors that we didn’t design but exist or pain points in the users journey.

Optimizing the funnel

Once that we have analyzed the Sankey diagram it is time for optimization. The best way of doing that is making A/B testing experiments. As a quick examples:

  • Removing paths that are too long for the users.
  • Fixing common bugs in the product before reaching certain behaviors.
  • Making valuable points more accessible for our users.

And as I mentioned before, creating different features for specific life-cycles.

Once we have detected and optimized the different behaviors, the impact of adding new features will be much higher than before.

This new behavioral perspective allows us to understand our users behaviors and needs. Now, every time you design a new feature you will know what feature and where must be implemented in order to bring value effectively to your users.

Wrapping up

  • The best way for increasing the usage and value of the product is crafting the product from a behavioral perspective instead of feature perspective.
  • The best way for changing this mindset is asking simple questions about your users and what behaviors you want to create for bringing them value.
  • Sankey Diagrams are awesome tools for measuring these behaviorals funnels.
  • Once we have detected and optimized the different behaviors, the impact of adding new features will be much higher than before.

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Juan

Juan

Lead Data Scientist 📊 at Bravo Studio. Start-up growth advisor 🚀 He/Him — 🏳️‍⚧️🏳️‍🌈 ally. Opinions are my own