To use the data that is injected into the [ExtendedUserAgent]
section by SessionAgentTLTRef, a Tealeaf administrator must create events that create charts for this data.
Tealeaf provides some objects to monitor user agents detected in the capture stream. You can create additional data objects to track all user agent information.
User agent evaluation logic
Self-reporting user agents can be identified through the user-agent
string value in the request. Tealeaf matches the values reported in this field to the values in the UserAgent
field in the public standard Browscap.csv
.
If matches are found, then the following fields in the agent's Browscap.csv
record are evaluated (true or false):
Crawler
SyndicationReader
MobileDevice
The evaluation logic is as follows:
- If
MobileDevice=true
, then the user agent is identified as amobile
device. - If
Crawler=true
ANDMobileDevice=true
, then the user agent is identified as amobile_bot
by Tealeaf. - If
Crawler=true
ORSyndicationReader=true
, then the user agent is identified as abot
by Tealeaf. - Otherwise, the user agent is identified as
browser
. - If the field is set to
UNKNOWN
, no data could be found for the UserAgent in any of the files. Session data from these user agents should be examined.
UserCap.csv
file.Editing or creating user agent entries
You can edit found entries or add new ones through UserAgentRevealer.exe
.
If the user agent entry cannot be identified, the Type value in the usersupplement.csv
file is set to UNKNOWN
. In these instances, you can add the user agent string to the usersupplement.csv
file with values that you define.
If the user agent entry in the usercap.csv
file contains errors or values that you would like to change, you can create an entry with the overriding data in usercap.csv
. These values are used instead of the values that are listed in the applicable public standard (browscap.csv
or WURFL.csv
).
All information that is entered into the fields is case-sensitive. Double quotation marks cannot be used and are stripped from the input.
- To edit an identified user agent entry or to create a new one, click Add UA.
- Select the appropriate Destination.
- Fill out all relevant fields.
- To commit the changes, click Commit.
The selected Destination file is updated and saved. When Tealeaf next refreshes its cache from the Destination file, the user agent information is applied to all subsequent hits.
Enable extended user agent detection
After the configuration files are downloaded and prepared for use, you can configure the Tealeaf Reference session agent to use them for user agent detection.
Before you extend user agent detection, you must configure the Tealeaf Reference session agent to enable extended user agent parsing. When enabled, captured user agent information is posted into the request in the [ExtendedUserAgent]
section by the Tealeaf Reference session agent.
By default, extended user agent parsing is enabled.
Locating a missing user agent
Some user agents may not be included in either the BrowsCap or WURFL user agent file.
To check if the identifying string for a user agent that you are trying to track is in the files, you can use the UserAgentRevealer utility.
If your tested string is not found in either file, custom user agent strings can be created and integrated with Tealeaf through the UserCap.csv
file.
Create user agent event value lists
To begin gathering user agent information, you must add or configure the corresponding dimensions to gather and store values. These functions are performed through the Event Manager.
You can use this method for capturing user agent information from the Browscap.csv
standard for fixed user agents and from the WURFL
standard for mobile user agents.
Adding values for reporting
For reporting purposes, Tealeaf provides the Traffic Type
dimension, which can be used to segment your reports based on the type of user agent. It pulls values from the Traffic Type
system hit attribute.
To create reports that operate on the data presented by BrowsCap and WURFL, you must acquire the captured values and store them in dimensions you create.
The best method for capturing this data is to create dimensions to store it, if necessary. When you enable logging on those dimensions, you can later download the dimension values, normalize them as needed, in a text file. This file can then be imported as a whitelist set of values back to the dimension through the Event Manager. These values can then be added to dimensions you create in the Event Manager.