Glossary¶
Quick definitions for all the terms you'll encounter in FlagPal.
A¶
Actor¶
It represents an entity in your application that receives feature flag values. In most cases, an Actor is a person — but it can also be an organisation, an AI agent, an API client, or any other entity that your app uses. It is an optional feature of FlagPal: If you're not storing your actor Feature Flags on your platform (locally), you can use our Actor feature to store them remotely on our servers. Imagine this a remote database that saves your provided list of features together with your user's identifier.
A/B Test¶
A controlled experiment where users are split into two or more groups (for example A and B), each receiving a different version of a feature. Results are measured to find out which version performs better. FlagPal calls these Experiments.
B¶
Boolean¶
A data type with only two possible values: true or false. The most common type for Feature Flags (on/off). Also used for Metrics that track whether something happened (yes/no).
C¶
Control (Variant)¶
In an Experiment, the Control is typically the existing/unchanged version of the feature — the baseline you're testing against. Other variants represent the new versions being tested.
Conversion Rate¶
A conversion rate is the percentage of users who completed a specific action (like a purchase).
For example, if 12 of 100 users who participated in an experiment completed a purchase, the conversion rate is 12%.
E¶
Experience¶
A configuration in FlagPal that delivers a specific set of Feature Flag values to users matching certain criteria (targeting rules). Used for feature rollouts and targeted feature delivery. Has one Feature Set (unlike an Experiment which has multiple).
Experiment¶
A controlled A/B test in FlagPal. Users who match targeting rules are split into multiple variants, each receiving different Feature Flag values. Metrics measure which variant performs best.
F¶
Feature Flag¶
A named value (Boolean, String, Integer, Array, or Date) that your application reads to determine what to show users or how to behave. Feature Flags are the core building block of FlagPal.
Feature Set (Variant)¶
A collection of Feature Flag values that belong to a single variant in an Experience or Experiment. In an Experience, there is one Feature Set. In an Experiment, each variant has its own Feature Set.
Feature Value¶
A specific value carried by a Feature Flag. When creating a Feature Flag, you define its type (and possibly rules). Depending on the type, you can set its value, for example:
- Boolean:
trueorfalse - String:
"example" - Integer:
123 - Array:
[1, 2, 3] - Date:
2023-09-20
Funnel¶
The internal term FlagPal uses for both Experiences and Experiments. You'll see more of this term in case you are integrating the API manually.
I¶
Impressions¶
A commonly used CRO (Conversion Rate Optimization) term: The number of distinct users counted in an experiment variant. Used as the denominator when calculating conversion rates and averages. You can decide this to be any Metric that is tracked in FlagPal.
M¶
Metric¶
A measurable outcome attached to an Experiment. FlagPal supports three types: Boolean (did it happen?), Count (how many times?), and Money (how much revenue?).
Multi-tenant¶
FlagPal's architecture separates data by Project — each project's data is completely isolated from other projects. This is called multi-tenancy.
O¶
Operator¶
The comparison method used in a targeting rule. Examples: equals, greater than, contains, before. The operator sits between the property and the value in a rule: [property] [operator] [value].
P¶
Project¶
Your workspace in FlagPal. All Feature Flags, Experiences, Experiments, and Metrics belong to a specific project. Projects are isolated from each other.
R¶
Rule¶
A condition used in Experience or Experiment targeting. Each rule specifies a Feature Flag, an operator, and a value. Multiple rules use AND logic — all must match for the target to apply.
S¶
Statistical Significance¶
A measure of confidence that the difference between experiment variants is real and not due to random chance. Higher statistical significance means you can be more confident in the results. It's generally recommended to wait for significance before declaring a winner.
T¶
Traffic Percentage¶
The proportion of total users who are eligible to enter an Experiment. Setting this to 50% means only half your users can be in the experiment; the other half see the default.
V¶
Variant¶
One of the versions being tested in an Experiment. Each variant has a Feature Set (a collection of Feature Flag values). Also called a Feature Set in FlagPal's interface.
W¶
Weight¶
A relative number that controls how much traffic goes to a particular variant or Experience. Equal weights create an equal split; unequal weights create an unequal split.
Example: Variant A weight 1, Variant B weight 3 → Variant A gets 25%, Variant B gets 75%.