Glossary
A quick reference guide to key SDK terms like events, outcomes, flows, and more.
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A quick reference guide to key SDK terms like events, outcomes, flows, and more.
Last updated
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This page contains a comprehensive reference for key terms used in our SDK.
This page provides clear and concise definitions for technical concepts and jargon, helping you understand and effectively implement our features. Whether you’re new to the SDK or need a quick refresher, this is your go-to guide for terms like outcomes, entry points, and user-initiated flows.
When ContextSDK deploys a machine learning model to your app, users are divided into two groups: Control and Treatment. This ensures accurate performance comparisons.
Users in this group experience the ML model’s effects. Their app experience is influenced by ContextSDK’s decisions, such as seeing upsell offers at optimal moments, receiving push notifications when they’re most relevant, etc.
This group is also commonly referred to as the ContextSDK Group.
Users in this group serve as a baseline. They experience your app as it was before integrating ContextSDK, without any influence from the ML model.
By comparing these groups under the same conditions, you can determine whether behavior changes are due to ContextSDK and measure improvements in conversions, sales, revenue, or other success metrics.
During calibration phase, ContextSDK gathers interaction data to refine its machine learning model for your application. This phase continues until approximately 1,000 positive user interactions have been collected for a given flow. Once sufficient data is collected, ContextSDK transitions to optimized decision-making, ensuring that in-app prompts are delivered at the most effective moments.
Also, during the calibration phase, your app behaves the exact same way as it used to. If you are also adding a new #activation-opportunity, we will disable the it during the calibration phase. For more information, see .
See Custom Signals to learn more.
See Adding Entry Points to learn more.
A moment being captured in the user's journey, which includes the user's context. See also Capturing Context In Key Moments.
Also known as Project.
When capturing a context, you’ll need to define a flow name, which acts as the context’s unique identifier for internal differentiation. A single user journey may include multiple distinct flows.
Each flow you track must include both Positive outcome and Negative outcome. If either is missing, the flow is considered incomplete and may not function as intended.
This refers to a machine learning (ML) model. It's the artifact produced by our team based on the context alongside the outcomes collected. Models enable ContextSDK to make decisions about which actions to take in your app.
When optimizing a flow, you focus on achieving a specific goal. For example, if you’re improving the conversion rate, you might track a conversion as a positive outcome and track the offer being dismissed as a negative outcome.
If an outcome isn’t logged for a given context, the ML model can’t be trained to improve the prompt. Without this data, it’s unclear whether the user converted after the context was captured.
A desired result that indicates success, such as a user completing a purchase or signing up.
An undesired result, such as a user closing an offer or abandoning the flow.
A project, previously referred to as an experiment, represents a flow within your app and defines a specific goal. For example, you might create a project to:
Increase revenue from the post-onboarding upsell prompt
Reduce push notification opt-out rates
Improve conversion rates for app rating prompts
Your app may have multiple projects, as needed. Each project consists of one or more flows (see Flow), and for each flow, a single Model is active. The active model may be the same across flows or differ for each flow.
When training a machine learning model for your flow, its goal is to determine whether to display an upsell offer based on the user’s context. The model generates a score between 0 and 1, where 0 represents the least favorable moment to show the offer, and 1 represents the most favorable.
Prompt intensity controls how often an offer appears by setting a threshold for the best moments. The intensity is expressed as a percentage, for example:
At 30%, the offer appears only in the top 30% of the best moments:
At 50%, the offer is shown only in the top 50% of the most favorable moments:
A lower intensity results in fewer but more optimally timed prompts, while a higher intensity increases the likelihood of displaying the prompt across a broader range of moments.
This is a visual prompt (e.g. an alert, a modal, a sheet, etc.) shown in your app that offers something, usually a premium subscription, a consumable, an app rating request, or an app permission request. What is shown depends on your app's specific goals and monetization strategies.
Also known as Upsell Offer.
User-initiated flows are actions intentionally taken by users that require immediate on-screen feedback. For example, when a user navigates to their profile and selects “Purchase Pro Plan”, they expect a subscription screen to appear instantly. In such cases, you can capture the context using the calibrate method. However, decision-making isn’t applied here, as it could lead to inaction — such as nothing happening when the button is pressed — if the ML model determines it’s a “bad moment.”
Chance-initiated flows, on the other hand, are triggered by chance or predefined mechanisms rather than user actions. Examples include:
Displaying an ad every five game levels completed.
Showing an upsell screen on app launch for users without an active subscription.
Suggesting an upgrade after the user completes a key positive action in the app.
In these scenarios, users aren’t actively seeking to open the associated screens. This means that if the SDK decides not to show a prompt during a “bad moment,” users remain unaware, preserving their experience.
Custom signals allow you to provide additional context when capturing user interactions. ContextSDK processes more than 200 built-in signals, but you can enhance its predictions by including app-specific data — such as in-game progress, friend count, or prior interactions like ad views or feature usage. Any past event that may influence a user’s decision-making process is valuable to include.
Important: Never send personally identifiable information (PII) to the SDK. See to learn more.
Entry points are triggers in your app that may show an upsell to your users.
In those moments, your app invokes ContextSDK to capture and analyze the user’s context.
These contexts allow the ML model to determine the next action - such as show an upsell screen, an ad, an app rating prompt, or skip the flow completely. The actions are tailored to your app’s specific use case.
