How to Use Granular Segmentation with Feature Flags

These days, SaaS has become an integral part of running many businesses. So rolling out new features that resonate with the user base is key to a business’s growth.

Imagine a feature that promises to enhance user experience but that ends up resonating with only a small subset of users. This scenario underscores the importance of precision in feature rollouts.

Fortunately, feature flagging management tools like Flagsmith can help with granular segmentation. This process helps your team make sure that new features are introduced to the most relevant audiences. Granular Segmentation makes it easier to understand your user base, leading to higher engagement and satisfaction.

In this article, we will be focusing on the concept of granular user segmentation and its significance in enhancing feature rollouts. We’ll also explore some best practices, pitfalls to avoid, and will look at how Flagsmith facilitates granular segmentation with feature flags.

Here’s what we’ll cover:

What is Flagsmith?

Flagsmith is an open-source feature management platform that helps teams control feature rollouts with precision. It helps developers toggle features on or off for specific users, environments, or groups without redeploying code.

Ideal for A/B testing, phased rollouts, and remote configuration, Flagsmith ensures real-time adjustments and seamless feature delivery. With flexible deployment options – hosted, private cloud, or on-premises – it adapts to the needs of organizations of all sizes.

The Importance of Granular Segmentation

What is Granular Segmentation?

Granular segmentation is a process in which the user base is divided into groups based on unique attributes such as behavior demographics or engagement levels. These groups can be identified as segments of users of a platform, and each segment is based on several traits that help teams tailor feature rollouts to meet the needs of each segment.

This granular level of control over rollouts helps product teams release features that resonate with their user base. This creates a more personalized experience for the end user that can improve the effectiveness of the feature.

Now, let’s discuss what kind of an impact feature rollouts might have.

Impact of Feature Rollouts

The advantages of granular segmentation in feature rollouts include:

Targeted relevance: Features are delivered to users who will benefit most from them, making the updates more relevant and useful. This targeted approach increases the likelihood of user engagement.
Optimized user experience: Because of this targeted approach, businesses can prevent rollouts of features that overwhelm their users in any way. This means that users would receive updates according to their interests, leading to a better user experience.
Higher adoption rates: All this would also lead to higher adoption rates. An increased adoption rate is a sign of good engagement from a business’s users as well as of business growth
Less risk when rolling out new features: Segmenting your user base and releasing new features to, say, a select 10% of users reduces risk. Teams can see how features do with those users and adjust accordingly before rolling out to the next segment. Or they can roll back if the impact is negative, which helps them avoid incidents like the latest high-publicity one we saw with CrowdStrike.

To put things into perspective, let’s discuss an example of an e-commerce store.

The MART Example

The MART is an online store that sells various products. They want to introduce an AI-powered recommendation engine, but only to a subset of their user base that shows less engagement on the platform buying products. AI-powered recommendation engines would target this user segment to generate more sales from the platform and increase business growth.

Here we see the concept of segmentation in practice where a feature is dedicatedly exposed to a user group’s explicit attributes, thus leading to increased relevance and user satisfaction.

If the feature proves to be successful with the targeted segment, the next phase would be to expand its availability to other user groups.

How Feature Flags Enable Granular Segmentation

You can integrate Flagsmith into your development workflow by using SDKs. The user segmentation adds a layer of granular control to the product teams with over-feature releases. This control helps product teams to minimize the risk of degradation of a new feature. They can leverage the GUI to interact with Flagsmith and roll out/roll back features according to their needs.

What are Segments in Feature Rollouts?

A segment is a subset of identities, defined by a set of rules based on traits associated with identities. So a single identity can be a part of many segments and is associated with an environment, such as staging or production.

You might be wondering – how can product teams use segments in their feature rollouts?

You can use segments to create ‘overrides’ on any number of features in your application. This allows you to control the state and/or value of a feature for a selection of your users, as defined by the segment.

Now that you understand segments, let’s discuss what key features allow you to use detailed user segmentation.

User attributes: Flagsmith allows you to define and manage user attributes, such as location, behavior, subscription levels, or platform activity. These are attributes you can use to create highly specific user segments.
Segment definitions: You can create custom segment definitions from these user attributes. For instance, you can define a segment for users who have been highly active on the platform since last month or users who live in a different region than most of your user base. This granularity ensures that you can target features to the most relevant user groups.
Dynamic targeting: Dynamic targeting can help you adjust feature rollouts on the basis of user attributes. This means that you can progressively roll out features to segments of users, monitor their performance, and make adjustments to the feature accordingly.

Flexibility and Control

Flexibility and control are a rare combination when it comes to such tools, but with Flagsmith you get the best of both worlds. User segments and feature management ensure you have precision and control over your feature rollouts:

Granular control: Multiple segment creation and control access are available in Flagsmith with a variety of criteria, allowing feature rollouts that cater to specific user needs.
Analytics and feedback: Analytics and feedback are an integral part of the feature testing loop. They provide tracking of how different segments interact with new features. It’s invaluable for understanding the user’s behavior on the platform which helps you make informed decisions for further rollouts.

So now you’ve learned what segments are, what you can do with them, and how segments help in granular control over rollouts. Now, let’s move on and see how you can implement segmentation using Flagsmith.

How to Implement Granular Segmentation in Flagsmith

Set Up Flagsmith in Your Project

You can integrate Flagsmith into your application using the available SDKs for the language of your choice. For example, to integrate the SDK in Node.js, you’ll first need to install the npm package as follows:

npm i flagsmith-nodejs --save

After installing the package, you will use the following code to initialize Flagsmith in your project:

const Flagsmith = require('flagsmith-nodejs');
const flagsmith = new Flagsmith({ environmentKey: 'FLAGSMITH_SERVER_SIDE_ENVIRONMENT_KEY',});

Once it’s integrated, configure your Flagsmith instance by creating a new project. We’ll go through this below.

How to Create Identities and Define User Traits and Segments

Now you’ll need to create the identities and traits you want to use for segmentation. These could include user profile information, behavior metrics, or any other relevant data.

So, let’s create a user named John Doe.

Now click on the created user and define a trait country.

In Flagsmith, creating a trait country involves defining a user attribute that specifies their geographic location. Traits are key-value pairs assigned to user identities, allowing for precise segmentation. For example, you can define the “country” trait with values like “USA,” “Canada,” or “Germany.”

This enables product teams to create segments based on location and target feature rollouts accordingly. For instance, a feature can be activated only for users with the “country” trait set to “USA,” facilitating controlled and region-specific rollouts.

Next, you’ll create some segments. You’ll use the Flagsmith dashboard to create custom segments based on these attributes. For example, you can create a segment for users who are from the USA. Define your segment, for example (western_users ), as below:

How to Create and Manage Feature Flags

Create a feature flag called ai_recommendation_engine in Flagsmith for the features you want to roll out. Each flag represents a specific feature or configuration option that can be toggled on or off.

Next, assign your feature flags to the segment you created. For instance, if you have a recommendation engine, you can target it specifically to users that match the segment created in the previous step. Use the Flagsmith dashboard to set these targeting rules and manage feature flag settings.

How to Target Segments for Rollouts

After configuring Flagsmith and setting up your segments and traits, you can start rolling out features to your defined segments.

First, you’ll want to do gradual rollouts. Using the percentage split operator, you can initially release the feature to a small percentage of users within the segment. Based on performance and feedback, you can gradually expand the rollout to a larger portion of the segment or additional segments, ensuring a controlled and data-driven approach.

Second, monitoring is a crucial part of feature rollouts and Flagsmith can help you with its analytics tools. You can track the performance of your feature flags and user segments, monitor how different segments interact with the new features, and make adjustments as needed.

For example, you might decide to increase the rollout percentage or adjust segment definitions based on user feedback.

Some best practices:

Start small: To test out segmentation, it’s a good idea to start small and create well-defined segments to test new features. This will help you gather valuable feedback and will prevent you from being overwhelmed in case of degraded performance or a rollback scenario.
Use data: Analytical tools are a great help in gathering data on how different segments interact with your features. You can use this data to refine your targeting and improve the user experience.
Iterate: You’ll likely make better decisions after several iterations. So remember that you should iterate your segmentation and rollouts based on metrics and user feedback.

Some common pitfalls:

Overlapping segments: Distinction between segmentations is the key to avoiding conflicts between feature targeting. Always be careful while defining segments for your user groups.
Ignoring feedback: The greatest mistake a product team can make is to overlook early user feedback. Early feedback is crucial for identifying issues and making informed decisions about a feature rollout.

By following these steps and best practices, you can effectively use this granular segmentation approach, ensuring that your feature rollouts are targeted, relevant, and successful.

Benefits of Granular Segmentation for User Engagement

Improved User Satisfaction

Granular segmentation helps your users out, as it gives them specifically personalized features according to their needs and inclinations. You can build more personalized experiences by aiming certain features at particular users that match their behavior or interest.

For example, a fitness app might launch an update that contains a workout feature for those users who have shown interest in strength building, rather than for all users. This targeted approach ensures that users receive updates related to and suitable to them, which leads to a positive experience, increased satisfaction, and better recognition of your product.

Increased Engagement

When users get features or updates that are targeted toward their specific needs, it’s more likely that they’ll engage with that feature. Granular segmentation helps maximize engagement by providing users with upgrades that are pertinent to their interests and usage patterns.

For example, an e-commerce platform could propose a new recommendation system and try it out on users who recurrently browse specific categories. This relevant targeting will likely increase the probability that those users will respond to those recommendations, leading to increased engagement and potentially higher conversions.

Enhanced Feature Adoption

Targeting specific segments of users with features that address their needs should lead to higher adoption rates. Presenting new features to users who are very likely to benefit from them, you increase the probability of these features being adopted and utilized.

For example, a software company introducing a new improved analytics tool would likely target power users who consistently use analytics features. After those users provide positive feedback and adopt the tool, it can be deployed on other segments. Then the team can be confident that the feature is approved and effective.

Data-Driven Insights

Granular segmentation offers valuable insights into how various user groups engage with new features. Analyzing this data can provide you insights into the behavior and inclinations your users as well as the overall impact of your features.

For example, you might realize that users are responsive to new features in a specific segment compared to other segments. Such information helps you refine your feature strategy, making rational decisions regarding future launches, and enhancing user engagement across different segments.

Optimized Resource Allocation

Centering on targeted segments lets you allocate resources more effectively. instead of investing in a broad, one-size-fits-all approach, you can direct your initiative towards segments that are likely to benefit from and engage with new features. This optimized allocation assures that your resources are utilized efficiently, leading to positive outcomes and a higher return on investment.

By leveraging granular segmentation, you can enhance user engagement, improve feature adoption, and gain valuable insights, all of which contribute to a more successful and user-centric feature rollout strategy.

Conclusion

In this article, we discussed the power of granular user segmentation in driving successful feature rollouts, highlighting how it can improve user satisfaction, engagement, and adoption rates. We also explored how Flagsmith enables this approach, offering tools to manage and target features with precision.

By leveraging these strategies, you can ensure that your product updates are more relevant and impactful. If you’re interested in optimizing your feature rollouts, consider exploring Flagsmith’s capabilities to start making data-driven decisions.

Source: freeCodeCamp Programming Tutorials: Python, JavaScript, Git & MoreÂ