5 Feature Management Use Cases in 5 minutes!

Optimizely Rollouts offers full featured feature flagging capabilities that unlock the power of Progressive Delivery allowing you to ship faster with more safety and confidence. But, what are all ways you can leverage Optimizely Rollouts to help your team?

Today, I’ll go through five powerful capabilities of Optimizely Rollouts and Full Stack all while using a cute Astronaut!

1. Feature Flags

With Feature Flags we can launch a feature, like our astronaut, with just a simple toggle of a switch in the Optimizely UI. With Optimizely feature flags, we can launch features to all of our users!

Before launching the Feature Flag
Rollouts - Before Launching Feature Flag

After switching the Feature Flag Toggle on
Rollouts - After Switching Feature Toggle on

Similarly, with Optimizely feature flags you can toggle the feature off and use it as a kill switch if you need to disable the feature quickly for users in case that release didn’t go as smoothly as intended.

After Kill Switch enabled
Rollouts - After Kill Switch Enabled

2. Random Rollouts

Instead of launching our feature to all our users, let’s launch our astronaut to only a random portion of our traffic – let’s say 25%.

Rolling out the Astronaut to 25%
Rollouts - Rolling out the Astronaut to 25%

You can use the slider to rollout to any percentage and use this to slowly up the percentage of users seeing your feature to gradually rollout a release and mitigate the risk of new changes.

3. Targeted Rollouts

Now, let’s target the Astronaut feature to just the top row of users. This can be done by creating an audience to define the segment of users and adding it to the rollout in the Optimizely UI.

Adding the Audience
Rollouts - Adding the Audience

Using Targeted Rollouts so only the top row sees the Astronaut! (Bam!)
Rollouts - Only Top Row Sees Astronauts

Targeted rollouts allow you to enable new features to specific users for things like private betas or granular control of feature access.

4. Feature Configuration

Instead of toggling the feature on and off, let’s configure the height of the Astronaut in Optimizely and change that to be smaller, let’s say 50% smaller. After implementing these remote variables in your application, when we save, we can change the height of the Astronaut without any additional code deploys!

Set the Feature Configuration
Rollouts - Set Feature Configuration

Smaller Astronauts!
Rollouts - Smaller Astronauts!

Feature configuration (a.k.a. remote configuration) allows you to iterate on and optimize a feature after it has been deployed.

5. Experimentation with Feature Tests

Now that we’ve configured the height of the astronaut, we can run an experiment where we give half our users small Astronauts and half full size Astronauts.

We can set up two variations, one with the configuration of 50% for Astronaut height and the other with the configuration of 100% for Astronaut height.

Setting up the variations
Rollouts - Set Up Variations

Launch the Experiment on Astronaut heights!
Rollouts - Launched Astronauts with Different Heights

Running a live experiment on our users allows you run data-driven A/B tests and use real customer data to make the right decisions for your customers.

What feature flag capabilities do you use the most? Message me in our Slack community or find me on Twitter at @asametrical.

If you like this content, check out my free e-book: Ship Confidently with Progressive Delivery and Experimentation which offers more best practices from just getting started to scaling these technologies organization-wide.

And if you are looking for a platform to get started, check out Optimizely’s free offering.