This B2B use case shows you how to specify your data at an account level rather than at a person level for analysis. Account-level analysis can answer questions such as
You accomplish all this by bringing in the account-level information as a lookup dataset.
You first create a lookup schema in Adobe Experience Platform, then create a lookup table data set by ingesting .csv-based account-level data. Then you proceed to create a connection in Customer Journey Analytics (CJA0 that combines different datasets, including the lookup one you created. You subsequently create a data view and finally are able to utilize all this data in Workspace.
Lookup tables can be up to 1 GB in size.
Creating your own schema for the lookup table ensures that the dataset used will be available in CJA with the correct setup (record type). Best practice is to create a custom schema class called “Lookup”, empty of any element, that can be re-used for all lookup tables.
Once the schema has been created, you need to create a lookup dataset from that schema, in Experience Platform. This lookup dataset contains account-level marketing information, such as: company name, total number of employees, domain name, what industry they belong to, annual revenue, whether they are current customers of the Experience Platform or not, which sales stage they are in, which team inside the account is using CJA, etc.
CJA does not support integers in lookup datasets. If you add the integer fields in your XDM schema for your lookup dataset, you will not able to use those integers as metrics or calculated metrics. For example, if annualRevenue or totalEmployees are defined as integers, they will show as “0” in reporting in CJA. However, if you assign them as strings, you can use them as lookup information.
For example, annualRevenue or totalEmployees are defined as Integer in following example - that’s the reason it’s showing “0” in CJA.
Instructions on how to Map a CSV file to an XDM schema should help if you are using a CSV file.
Other methods are available as well.
On-boarding the data and establishing the lookup takes about 2 to 4 hours, depending on the size of the lookup table.
For this example, we are combining 3 datasets into one CJA connection:
|Dataset name||Description||AEP Schema class||Dataset details|
|B2B Impression||Contains clickstream, event-level data at the account level. For example, it contains the email ID and corresponding account ID, as well as the marketing name, for running marketing ads. It also includes the impressions for those ads, per user.||Based on XDM ExperienceEvent schema class||The
|B2B Profile||This profile dataset tells you more about the users in an account, such as their job title, which account they belong to, their LinkedIn profile, etc.||Based on XDM Individual Profile schema class||No need to select
|B2B Info||See “Create lookup data set” above.||B2BAccount (custom lookup schema class)||The relationship between
Here is how you combine the datasets:
accountIDkey that will be used in your lookup table. Then select its matching key (corresponding dimension), also
accountIDin your event dataset.
Follow instructions on creating data views.
You can now create Workspace projects based on the data from all 3 datasets.
For example, you can find answers to the answers posed in the introduction: