With recent advances in cloud computing, machine learning, and natural language processing, digital assistants are becoming a part of everyday life. Consumers are starting to talk to their devices and expecting them to understand and respond in human-like ways. As these platforms become more established, brands can present their services to consumers in these same realistic and lifelike ways. For example, consumers can ask things like:
This page provides an overview of how best to use Adobe Analytics to measure and optimize these types of experiences.
Most digital assistants today follow a similar high-level architecture:
One of the best places to implement Analytics is in the app. The app receives the intent and details from the digital assistant, then the app determines how to respond.
There are two times during a request that can be helpful to send data to Adobe Analytics.
If you are just interested in recording what happened with the customer for future optimization, send a request to Adobe Analytics after the response has been returned. You’ll have the full context of what the request was and how the system responded.
For some digital assistants, you get a notification when someone installs the skill, especially when authentication is involved. Adobe recommends sending an Install event by setting the context data variable
a.InstallEvent=1. This feature is not available on all digital assistants, but is helpful when it is present for looking at retention. The following code sample sends the Install event, Install Date, and AppID values into context data variables.
GET /b/ss/examplersid/1?vid=[UserID]&c.a.InstallEvent=1&c.a.InstallDate=2017-04-24&c.a.AppID=Spoofify1.0&c.OSType=Alexa&pageName=install HTTP/1.1 Host: <xref href="https://example.data.adobedc.net"> example.data.adobedc.net Cache-Control: no-cache </xref href="https:>
It is likely that your organization wants apps for multiple platforms. The best practice is to include an app ID with each request. This variable can be set in the
a.AppID context data variable. Follow the format of
[AppName] [BundleVersion], for example, BigMac for Alexa 1.2:
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Spoofify1.0&c.a.Launches=1&c.Product=AmazonEcho&c.OSType=Alexa&pageName=install HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Spoofify2.0&c.a.Launches=1&c.Product=GoogleHome&c.OSType=Android&pageName=install HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
Adobe Analytics uses the Adobe Experience Cloud Identity Service to tie interactions across time to the same person. Most digital assistants return a
userID that you can use to keep the activity for different users. In most cases, this value is what you can pass in as a unique identifier. Some platforms return an identifier that is longer than the 100 characters allowed. In these cases, Adobe recommends that you hash the unique identifier to a fixed length value using a standard hashing algorithm, such as MD5 or Sha1.
Using the ID Service provides the most value when you map ECIDs across different devices (for example, web to digital assistant). If your app is a mobile app, use the Experience Platform SDKs as-is and send the user ID using the
setCustomerID method. However, if your app is a service, use the user ID provided by the service as the ECID, as well as setting it in
GET /b/ss/examplersid/1?vid=[UserID]&pageName=[intent] HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
Because digital assistants are conversational, they often have the concept of a session. For example:
Consumer: “Ok Google, call a cab for me”
Google:: “Sure, what time would you like?”
Google: “Sounds good, the driver will be by at 8:30pm”
Sessions are important to keep context, and help collect more details to make the digital assistant more natural. When implementing Analytics on a conversation, there are two things to do when a new session is started:
GET /b/ss/examplersid/1?vid=[UserID]&c.a.LaunchEvent=1&c.Intent=[intent]&pageName=[intent] HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
Each of the digital assistants has algorithms that detect intents and then passes the intent down to the “App” so that the app knows what to do. These intents are a succinct representation of the request.
For example, if a user says, “Siri, Send John $20 for dinner last night from my banking app,” the intent might be something like sendMoney.
By sending in each of these requests as an eVar, you can run pathing reports on each of the intents for conversational apps. Make sure your app can handle requests without an intent as well. Adobe recommends passing in ‘No Intent Specified’ to the intent context data variable, as opposed to omitting the variable.
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Penmo1.0&c.a.LaunchEvent=1&c.Intent=SendPayment&pageName=[intent] HTTP/1.1 Host: example.sc.adobedc.net Cache-Control: no-cache
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Penmo1.0&c.a.LaunchEvent=1&c.Intent=No_Intent_Specified&pageName=[intent] HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
In addition to the intent, digital assistants often have a set of key/value pairs that give details of the intent. These can be called slots, entities or parameters. For example, “Siri, Send John $20 for dinner last night from my banking app” would have the following parameters:
There is typically a finite number of these values with your app. To track these values in Analytics, send them into context data variables and then map each of the parameters to an eVar.
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Penmo1.0=1&c.a.LaunchEvent=1&c.Intent=SendPayment&c.Amount=20.00&c.Reason=Dinner&c.ReceivingPerson=John&c.Intent=SendPayment&pageName=[intent] HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
Sometimes the digital assistant provides your app with inputs that it doesn’t know how to handle. For example, “Siri, Send John 20 bags of coal for dinner last night from my banking app”
When this situation happens, have your app ask for clarification. Additionally, send data to Adobe that indicates the app has an error state along with an eVar that specifies what type of error occurred. Be sure to include errors where the inputs are not correct and errors where the app had a problem.
GET /b/ss/examplersid/1?vid=[UserID]&c.a.AppID=Penmo1.0&c.Error=1&c.ErrorName=InvalidCurrency&pageName=[intent] HTTP/1.1 Host: example.data.adobedc.net Cache-Control: no-cache
While most platforms don’t expose the device that the user spoke to, they do expose the capabilities of the device. For example, Audio, Screen, Video, etc. This information is useful because it defines the types of content that can be used when interacting with your users. When measuring device capabilities, it is best to concatenate them (in alphabetical order).
Leading and trailing colons help when creating segments. For example, show all hits with
|Person||Device response||Action/Intent||GET request|
|Install Spoofify||No response||Install||
|Play Spoofify||“Okay, playing Spoofify”||Play||
|Change song||“Okay, what song do you want?”||ChangeSong||
|Play “Baby Shark”||“Okay, playing ‘Baby Shark’ by PinkFong”||ChangeSong||
|Change playlist||“Okay, what playlist do you want?”||ChangePlaylist||
|Play my favorite songs playlist||“Okay, playing your favorite songs playlist”||ChangePlaylist||
|Turn music off||No response, music turns off||Off||