Tracking Events
- iOS Tracker
- Android Tracker
The mobile trackers capture two types of events, automatically captured and manual events.
Auto Tracking
Automatically captured events in the iOS Tracker are:
- App Lifecycle Tracking
- Captures application foreground and application background events
- Screen View Tracking
- Captures each time a new "screen" is loaded
- Exception Tracking
- Captures any unhandled exceptions within the application
- Installation Tracking
- Captures an install event which occurs the first time an application is opened
These are enabled in the tracker configuration. In this example, some helpful automatic contexts and all Autotracking is enabled:
let trackerConfig = TrackerConfiguration()
.sessionContext(true)
.platformContext(true)
.screenContext(true)
.applicationContext(true)
.lifecycleAutotracking(true)
.screenViewAutotracking(true)
.exceptionAutotracking(true)
.installAutotracking(true)
Custom Event Context
Custom context can be used to augment any standard Snowplow event type, including self describing events, with additional data. We refer to this custom context as Event Entities.
Custom context can be added as an extra argument to any of Snowplow's track..()
methods and to addItem
and addTrans
.
Each custom context is an array of self-describing JSON following the same pattern as an self describing event. As with self describing events, if you want to create your own custom context, you must create a JSON schema for it and upload it to an Iglu repository using the Snowplow BDP UI, Data Structures API, igluctl or one of the other supported Iglu clients. Since more than one (of either different or the same type) can be attached to an event, the context
argument (if it is provided at all) should be a non-empty array of self-describing JSONs.
Important: Even if only one custom context is being attached to an event, it still needs to be wrapped in an array.
Here are two example custom context JSONs. One describes a screen:
{
schema: 'iglu:com.example/screen/jsonschema/1-2-1',
data: {
screenType: 'test',
lastUpdated: '2021-06-11'
}
}
and the other describes a user on that screen:
{
schema: 'iglu:com.example/user/jsonschema/2-0-0',
data: {
userType: 'tester'
}
}
Tracking events with Custom Context
How to track a screen view with both of these contexts attached:
let event = ScreenView(name: "DemoScreenName", screenId: UUID())
event.contexts.add(
SelfDescribingJson(schema: "iglu:com.example/screen/jsonschema/1-2-1",
andDictionary: [
"screenType": "test",
"lastUpdated": "2021-06-11"
])!)
event.contexts.add(
SelfDescribingJson(schema: "iglu:com.example/user/jsonschema/2-0-0",
andDictionary: [
"userType": "tester"
])!)
tracker.track(event)
It is also possible to add contexts globally, so that they are applied to all (or a subset of) events within an application.
Manual Tracking
Self Describing
You may wish to track events in your app which are not directly supported by Snowplow and which structured event tracking does not adequately capture. Your event may have more than the five fields offered by Structured
events, or its fields may not fit into the category-action-label-property-value model. The solution is Snowplow’s self-describing events. Self-describing events are a data structure based on JSON Schemas and can have arbitrarily many fields.
To define your own custom event, you must create a JSON schema for that event and upload it to an Iglu Schema Repository using igluctl (or if a Snowplow BDP customer, you can use the Snowplow BDP UI or Data Structures API). Snowplow uses the schema to validate that the JSON containing the event properties is well-formed.
let data = ["targetUrl": "http://a-target-url.com" as NSObject];
let event = SelfDescribing(schema: "iglu:com.snowplowanalytics.snowplow/link_click/jsonschema/1-0-1", payload: data)
tracker.track(event)
A Self Describing event is a self-describing JSON. It has two fields:
- A
data
field, containing the properties of the event - A
schema
field, containing the location of the JSON schema against which thedata
field should be validated.
Structured
Our philosophy in creating Snowplow is that users should capture important consumer interactions and design suitable data structures for this data capture. You can read more about that philosophy here. Using trackSelfDescribingEvent
captures these interactions with custom schemas, as desribed above.
However, as part of a Snowplow implementation there may be interactons where custom Self Describing events are perhaps too complex or unwarranted. They are then candidates to track using Structured
, if none of the other event-specific methods outlined below are appropriate.
let event = Structured(category: "Example", action: "my-action")
.label("my-label")
.property("my-property")
.value(5)
tracker.track(event)
Timing
Use the Timing
events to track user timing events such as how long resources take to load.
let event = Timing(category: "timing-category", variable: "timing-variable", timing: 5)
.label("optional-label")
tracker.track(event)
Screen View
Track the user viewing a screen within the application. This type of tracking is typically used when automatic screen view tracking is not suitable within your application.
let event = ScreenView(name: "DemoScreenName", screenId: UUID())
tracker.track(event)
Consent
Consent Granted
Use the ConsentGranted
event to track a user opting into data collection. A consent document context will be attached to the event using the id
and version
arguments supplied.
let event = ConsentGranted(expiry: "2022-01-01T00:00:00Z", documentId: "1234abcd", version: "1.2")
.name("document-name")
.documentDescription("document-description")
tracker.track(event)
Consent Withdrawn
Use the ConsentWithdrawn
event to track a user withdrawing consent for data collection. A consent document context will be attached to the event using the id
and version
arguments supplied. To specify that a user opts out of all data collection, all
should be set to true
.
let event = ConsentWithdrawn()
.all(true)
.documentId("1234abcd")
.version("1.2")
.name("document-name")
.documentDescription("document-description")
tracker.track(event)
Ecommerce Transaction
Modelled on Google Analytics ecommerce tracking capability, Snowplow uses three steps that can be used together to track online transactions:
- Create a Ecommerce event. Use
Ecommerce
to initialize a transaction object. This will be the object that is loaded with all the data relevant to the specific transaction that is being tracked including all the items in the order, the prices of the items, the price of shipping and theorder_id
. - Add items to the transaction. Create an array of
EcommerceItem
to pass to theEcommerce
object. - Submit the transaction to Snowplow using the track() method, once all the relevant data has been loaded into the object.
let transactionID = "6a8078be"
let itemArray = [
EcommerceItem(sku: "DemoItemSku", price: 0.75, quantity: 1)
.name("DemoItemName")
.category("DemoItemCategory")
.currency("USD")
]
let event = Ecommerce(orderId: transactionID, totalValue: 350, items: itemArray)
.affiliation("DemoTransactionAffiliation")
.taxValue(10)
.shipping(15)
.city("Boston")
.state("Massachisetts")
.country("USA")
.currency("USD")
tracker.track(event)
Push Notification
To track an event when a push notification is used, it is possible to use the PushNotification
event which contains a NotificationContent
object:
let attachments = [["identifier": "testidentifier",
"url": "testurl",
"type": "testtype"]]
var userInfo = Dictionary<String, Any>()
userInfo["test"] = "test"
let content = NotificationContent(title: "title", body: "body", badge: 5)
.subtitle("subtitle")
.sound("sound")
.launchImageName("launchImageName")
.userInfo(userInfo)
.attachments(attachments)
let event = PushNotification(
date: "date",
action: "action",
trigger: "PUSH",
category: "category",
thread: "thread",
notification: content)
tracker.track(event)
The mobile trackers capture two types of events, automatically captured and manual events.
Auto Tracking
Automatically captured events in the iOS Tracker are:
- App Lifecycle Tracking
- Captures application foreground and application background events
- Screen View Tracking
- Captures each time a new "screen" is loaded
- Exception Tracking
- Captures any unhandled exceptions within the application
- Installation Tracking
- Captures an install event which occurs the first time an application is opened
These are enabled in the tracker configuration. In this example, some helpful automatic contexts and all Autotracking is enabled:
TrackerConfiguration trackerConfiguration = new TrackerConfiguration(appId)
.sessionContext(true)
.platformContext(true)
.applicationContext(true)
.screenContext(true)
.lifecycleAutotracking(true)
.screenViewAutotracking(true)
.exceptionAutotracking(true)
.installAutotracking(true);
Custom Event Context
Custom context can be used to augment any standard Snowplow event type, including self describing events, with additional data. We refer to this custom context as Event Entities.
Custom context can be added as an extra argument to any of Snowplow's track..()
methods and to addItem
and addTrans
.
Each custom context is an array of self-describing JSON following the same pattern as an self describing event. As with self describing events, if you want to create your own custom context, you must create a JSON schema for it and upload it to an Iglu repository using the Snowplow BDP Console UI, Data Structures API, igluctl or one of the other supported Iglu clients. Since more than one (of either different or the same type) can be attached to an event, the context
argument (if it is provided at all) should be a non-empty array of self-describing JSONs.
Important: Even if only one custom context is being attached to an event, it still needs to be wrapped in an array.
Here are two example custom context JSONs. One describes a screen:
{
schema: 'iglu:com.example/screen/jsonschema/1-2-1',
data: {
screenType: 'test',
lastUpdated: '2021-06-11'
}
}
and the other describes a user on that screen:
{
schema: 'iglu:com.example/user/jsonschema/2-0-0',
data: {
userType: 'tester'
}
}
Tracking events with Custom Context
How to track a screen view with both of these contexts attached:
ScreenView event = new ScreenView("screen", UUID.randomUUID().toString());
event.customContexts.add(
new SelfDescribingJson("iglu:com.example/screen/jsonschema/1-2-1",
new HashMap<String, String>() {{
put("screenType", "test");
put("lastUpdated", "2021-06-11");
}})
);
event.customContexts.add(
new SelfDescribingJson("iglu:com.example/user/jsonschema/2-0-0",
new HashMap<String, String>() {{
put("userType", "tester");
}})
);
tracker.track(event);
It is also possible to add contexts globally, so that they are applied to all (or a subset of) events within an application.
Manual Tracking
Self Describing
You may wish to track events in your app which are not directly supported by Snowplow and which structured event tracking does not adequately capture. Your event may have more than the five fields offered by Structured
events, or its fields may not fit into the category-action-label-property-value model. The solution is Snowplow’s self-describing events. Self-describing events are a data structure based on JSON Schemas and can have arbitrarily many fields.
To define your own custom event, you must create a JSON schema for that event and upload it to an Iglu Schema Repository using igluctl (or if a Snowplow BDP customer, you can use the Snowplow BDP Console UI or Data Structures API). Snowplow uses the schema to validate that the JSON containing the event properties is well-formed.
Map<String, String> properties = new HashMap<>();
properties.put("targetUrl", "http://a-target-url.com");
SelfDescribingJson sdj = new SelfDescribingJson("iglu:com.snowplowanalytics.snowplow/link_click/jsonschema/1-0-1", attributes);
SelfDescribing event = new SelfDescribing(sdj);
tracker.track(event);
A Self Describing event is a self-describing JSON. It has two fields:
- A
data
field, containing the properties of the event - A
schema
field, containing the location of the JSON schema against which thedata
field should be validated.
Structured
Our philosophy in creating Snowplow is that users should capture important consumer interactions and design suitable data structures for this data capture. You can read more about that philosophy here. Using trackSelfDescribingEvent
captures these interactions with custom schemas, as desribed above.
However, as part of a Snowplow implementation there may be interactons where custom Self Describing events are perhaps too complex or unwarranted. They are then candidates to track using Structured
, if none of the other event-specific methods outlined below are appropriate.
Structured event = Structured("my-category", "my-action")
.label("my-label")
.property("my-property")
.value(5);
tracker.track(event);
Timing
Use the Timing
events to track user timing events such as how long resources take to load.
Timing event = new Timing("timing-category", "timing-variable", 5)
.label("optional-label");
tracker.track(event);
Screen View
Track the user viewing a screen within the application. This type of tracking is typically used when automatic screen view tracking is not suitable within your application.
ScreenView event = new ScreenView("screen", UUID.randomUUID().toString());
tracker.track(event);
Consent
Consent Granted
Use the ConsentGranted
event to track a user opting into data collection. A consent document context will be attached to the event using the id
and version
arguments supplied.
ConsentGranted event = new ConsentGranted("2018-05-08T18:12:02+00:00", "doc id", "1.2")
.documentDescription("doc description")
.documentName("doc name");
tracker.track(event);
Consent Withdrawn
Use the ConsentWithdrawn
event to track a user withdrawing consent for data collection. A consent document context will be attached to the event using the id
and version
arguments supplied. To specify that a user opts out of all data collection, all
should be set to true
.
ConsentWithdrawn event = new ConsentWithdrawn(true, "doc id", "1.2")
.documentDescription("doc description")
.documentName("doc name");
tracker.track(event);
Ecommerce Transaction
Modelled on Google Analytics ecommerce tracking capability, Snowplow uses three steps that can be used together to track online transactions:
- Create a Ecommerce event. Use
Ecommerce
to initialize a transaction object. This will be the object that is loaded with all the data relevant to the specific transaction that is being tracked including all the items in the order, the prices of the items, the price of shipping and theorder_id
. - Add items to the transaction. Create an array of
EcommerceItem
to pass to theEcommerce
object. - Submit the transaction to Snowplow using the track() method, once all the relevant data has been loaded into the object.
EcommerceTransactionItem item = new EcommerceTransactionItem("sku-1", 35.00, 1)
.name("Acme 1")
.category("Stuff")
.currency("USD");
List<EcommerceTransactionItem> items = new LinkedList<>();
items.add(item);
EcommerceTransaction event = new EcommerceTransaction("order-1", 40.00, items)
.shipping(5.00);
tracker.track(event);