It is important to understand that data is valuable but it's much more vital to understand that end results are the key here - to make the data easily actionable.
Looking at the end results, the data in Pico is structured in an easy-to-use way. A simple filter applied to the fans database, according to the required target group, is all that’s needed, to push an offer or send a message to, through one of the open communication channels with them.
i.e. If the team wants to push a ticket offer to fans, they would like to create a specific target group which includes fans who are not season ticket holders, who did attend games during the season and did show interest in last-minute tickets
In order to allow this filtering process, our data is structured with personal information columns (name, gender, location, email, age, etc.) and insights columns. The insights columns are basically a yes/no indicator to whether the fan is part of this group indicator.
i.e. If a fan asked to buy tickets on FB messenger, and then, while participating in an activation on Twitter clicked an offer to buy the merchandise we will have a column named "asked_about_tickets" with the value TRUE and A column named "intersted_in_merchandise" with the value TRUE.
Under the hood, when we are building the target groups, we will send the ticket offer push message only to fans who have the value TRUE in “asked_about_tickets”.