Best Practice for Measuring Deflection Rate

drosenst2
Giga Contributor

Hi all. I have a quick question. How do you all measure deflection rate with knowledge? Are you using search data and comparing that to ticket data? Article views and calls? I feel like this process has always been some guessing and a lot of manual work. I'd love to find a more automated and accurate process going forward. Thanks as always. 

1 ACCEPTED SOLUTION

MaryG
ServiceNow Employee

Deflection has always been a proxy metric — you can't directly observe a ticket that was never created. But there are better and worse ways to estimate it.

 

The kb_use table captures article interactions including the "This resolved my issue" flag on the portal. It's the basis for most OOTB deflection reporting, but it's self-reported and most users who find what they need don't click anything — so it undercounts.

 

Combining a few sources gives you something more defensible:

  • Search sessions without ticket creation — queries that end without an incident or case submitted within a defined window (24–48 hours is common) are a reasonable deflection proxy
  • Portal session analysis — KB article views followed by session end without ticket submission
  • Ticket volume trending by topic — if knowledge coverage improves in an area and ticket volume drops, that's a meaningful signal even if not a precise count

 

Some estimation is unavoidable. What matters more than finding a perfect metric is agreeing on a consistent methodology with your stakeholders before you start measuring — otherwise the number gets challenged every time it's presented.

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MaryG
ServiceNow Employee

Deflection has always been a proxy metric — you can't directly observe a ticket that was never created. But there are better and worse ways to estimate it.

 

The kb_use table captures article interactions including the "This resolved my issue" flag on the portal. It's the basis for most OOTB deflection reporting, but it's self-reported and most users who find what they need don't click anything — so it undercounts.

 

Combining a few sources gives you something more defensible:

  • Search sessions without ticket creation — queries that end without an incident or case submitted within a defined window (24–48 hours is common) are a reasonable deflection proxy
  • Portal session analysis — KB article views followed by session end without ticket submission
  • Ticket volume trending by topic — if knowledge coverage improves in an area and ticket volume drops, that's a meaningful signal even if not a precise count

 

Some estimation is unavoidable. What matters more than finding a perfect metric is agreeing on a consistent methodology with your stakeholders before you start measuring — otherwise the number gets challenged every time it's presented.