It wasn ’ t until we turned our attention to their Avaya ( then Lucent ) G3 PBX / ACD that SkyMall was cutting over from that we discovered it had something called a Short Abandon Filter .
It turned out that you could set this configuration to filter out abandon calls for up to 30 seconds . After we explained this to the customer and their operations team , they were much more accepting of the elevated abandon metric .
When these sorts of reporting differences between platforms come to light during the excitement of cutover , the experience can be anywhere from accepting like we had here up to a dumpster fire . Where you are instantly in a do-or-die battle to keep your platform from getting kicked to the curb .
The metric discrepancy causes you to compare apples to oranges and can drive you bananas if you have no previous experience . Here are some of the most common offenders :
1 . Abandons 2 . Service level agreements ( SLAs ) 3 . Occupancy and utilization
4 . Bonus : first call / contact resolution ( FCR )
I have played a part in multiple versions of this movie in different roles over the years and do not prefer any of them . The good news is , with a little forethought , some good questions and planning , these discrepancies can all be avoided or at least better managed .
DISCREPANCY CAUSES
How did we end up with the CX calculation discrepancy and what to do about it ?
Here are some factors that have contributed to it :
1 . Contact center metrics have continued to evolve along with the various vendors and platforms over the past 40 or so years .
2 . Vendors tend to use similar terms for the metrics .
3 . No standards emerged for the names of the metrics or the calculations themselves .
You can search to your heart ' s content , and you will find lots of articles from different contact industry resources on the metric guidelines . For example , you should generally target less than 5 % abandon for voice calls , but there has been no discussion on how abandon should be calculated or whether or not it should filter out false abandon / short abandon calls .
On the vendor side , if you search the documentation or ask the right question , or , in a rare case , are working with someone who is familiar and performing a deeper level of reporting discovery than most , you can determine how a specific version of a specific platform calculates “ abandon .” The same with SLA and the others we will discuss in further detail .
The problem is that all the platforms track a metric called “ abandon ,” and we assume it calculates the same outcome , but that is not always the case as we have seen .
Experience tells me that contact center and CX reporting is complex and nuanced . As much as I would love to blame the CX platform vendors , few of us in the industry , or those among us running contact centers , ever get down to this level of detail until they experience the problem firsthand like I did .
You , as the person who owns your contact center or CX reporting and analytics , also need to own the success for measuring similar outcomes as you migrate to your new contact center platform .
The burden is on you because it is rare that you will work with someone at a vendor , reseller , master agent , or a consultant who will know ( much less take the time ), to dig into the appropriate level of detail with your metric calculations as you consider your next platform or major upgrade .
Nobody selects their new CX platform based on reports and analytics , but everyone will complain about how difficult reporting is in their legacy platform .
I speculate that is because no business-to-business ( B2B ) platform ( CX or
CUSTOMER EXPERIENCE
otherwise ) solves reporting for everyone . They cannot solve for everyone because of the unique combination of customer requirements versus customer analytics capability versus vendor platform data and reporting capabilities .
So , your best hope is 80 / 20 , meaning your platform solves for 80 % of your reporting requirements . Make sure you also figure out how to solve the 20 % to meet your unique needs for data while matching your analytics capabilities and expertise .
NOBODY SELECTS THEIR NEW CX PLATFORM BASED ON REPORTS AND ANALYTICS , BUT EVERYONE WILL COMPLAIN ABOUT HOW DIFFICULT REPORTING IS IN THEIR LEGACY PLATFORM .
How do I know it ’ s rare to understand CX calculations at this level ? Because I work daily with prospects , resellers working on requests for proposals ( RFPs ), and vendors bringing us in to assist with CX reporting and analytics .
When I ask how they calculate their SLA they typically respond with the target , such as “ We target to answer 85 % of our calls in 25 seconds or less .”
And when I clarify with , “ Yes , I understand that is your target for service level , but what calculation do you use ? There are at least a handful of options .” More often than not , I get a blank stare , which does not indicate incompetence but rather underscores the obscurity of the problem .
Now that I have provided some context as to why the CX calculation discrepancy is a thing , in the next installment of this article I will explore the common metrics impacted by it .
Rick McGlinchey is co-founder and CEO of PureInsights LLC , an award-winning CX analytics and reporting platform . He has worked in enterprise B2B software for over 25 years , focused exclusively on contact centers . When it comes to contact center reporting , he ' s a CX data geek and has scars to prove it ! linkedin : https :// www . linkedin . com / in / rickmcglinchey / OCTOBER 2024 29