I’m trying to optimize our routing by measuring the ‘True Efficiency’ of our self-service Architect flows. We’ve defined ‘Flow Outcomes’ for successful tasks (like ‘Password Reset Complete’).
My hypothesis is that some of our ‘Successes’ are actually ‘Soft Abandons’ where the customer gets frustrated and hangs up after hitting the outcome block but before being transferred. I want to correlate the ‘Flow Outcome Success Rate’ with the ‘Abandon Rate’ of the subsequent Queue.
Has anyone found a clean way to join these two datasets? The Flow Outcome record in Analytics doesn’t naturally ‘link’ to the Queue segment in a single observation query.
This is a great use case for gamification metrics! We track ‘Deflection Quality’ as a KPI.
To join the data, you need to use the conversationId as your primary key. Pull the POST /api/v2/analytics/conversations/details/query and look at the flow segments and the queue segments for the same ID. If a flowOutcome of ‘Success’ is followed by a disconnectType of ‘Peer’ (customer hangup) while the routingStatus is still ‘JOINING’, then you’ve found a ‘Soft Abandon’. It means they finished the bot flow but didn’t want to wait for the agent.
In my agency experience building flows for multiple clients, we often find that ‘Success’ is a lie if the customer doesn’t stay on the line!
One trick I use: in Architect, immediately after the ‘Flow Outcome’ block, set a Participant Data attribute like FlowOutcomeAttained = "True". Then, if the call hits the Queue and abandons, you can easily filter your ‘Queue Abandon’ report by that attribute to see how many abandoned calls thought they were successful. It’s much faster than trying to parse the nested Analytics segments manually!