July 14, 2026 • 45 mins
Metrics shape the way support teams work, but not every metric tells the full story.
In this episode, we’re joined by Meghan Gallagher, Support Operations Consultant and Founder of MG Consulting, to talk about how support teams can measure what actually matters. We discuss why common metrics like CSAT, first response time, and average handle time can be misleading when they’re used on their own, how speed-focused goals can create bad habits, and why support leaders should use data as a starting point for better conversations.
We also dig into better ways to measure support’s impact, including recontact rate, replies to resolve, customer sentiment, retention, product feedback, and team bandwidth.
Plus, Meghan shares practical advice for telling a clearer story to leadership, motivating your team without burning them out, and revisiting your metrics as your support team grows and changes.
We want to hear from you! Share your support stories and questions with us at happytohelp@buzzsprout.com!
To learn more about Buzzsprout visit Buzzsprout.com.
Thanks for listening!
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Priscilla (00:00):
Welcome to Happy to Help, a podcast about customer
support from the people atBuzzsprout.
I'm your host, PriscillaBrooke.
Today we're taking a closerlook at how we measure customer
support.
We'll unpack the metrics mostteams currently rely on, what we
should be measuring, and how touse that data to tell a clear
story about your team's impact.
Thanks for joining us.
Let's get into it.
(00:21):
All right, so today we'retalking all about metrics.
I feel like there's a lot ofdiscussion right now about what
we're measuring, what we shouldbe measuring, whether the things
we're currently measuring areactually helping us or if
they're hindering our teams,because at the end of the day,
what you're measuring isimpacting the support you're
(00:41):
doing, and it's impacting howyour team feels about the work
they do.
And so it's just a reallyimportant thing to get right.
And it is something that allsupport teams are doing in some
way, whether we're educated onit or not.
Jordan (00:52):
And in this day and age, there's so many statistics on
like every software that you useday to day.
Priscilla (00:58):
And so it's hard to sort through it.
So this is gonna be reallygreat.
Yeah.
So I'm excited because we havesomeone on the show today who is
coming to us with this balanceof passion for metrics and also
expertise in metrics.
And so I'm really excited totalk through what metrics we
should and shouldn't bemeasuring.
So joining us today is MeghanGallagher.
Meghan is a support operationsconsultant with over 10 years of
(01:21):
experience in customer supportand five years in support ops.
She helps B2B SaaS teams turnunreliable support systems into
something that leadershipactually trusts, which is a big
accomplishment.
Yeah.
She has spent years buildingsupport operations from scratch,
and her work has helped teamssignificantly improve response
(01:42):
times, deflection rates, andcommunicate customer feedback in
a way that actually benefitsthe product and leads to product
updates and development.
She's based in Valencia, Spain,and she is currently building
her consultancy, MG Consulting,and she brings this unique
perspective because she hashistory in finance and history
(02:03):
in customer support and has alove for numbers.
So thanks for being here,Meghan.
I'm really excited to talkabout metrics.
Meghan (02:09):
Oh my God.
It's my pleasure.
It's something that I've alwayswanted to nerd out about, and
I'm happy to spread the wealthbecause I want people to learn
more.
Priscilla (02:18):
Yeah, I'm really excited.
I feel like metrics is one ofthose places where I'm gonna
really learn a lot from thisconversation.
So I'm really pumped.
Yes.
So before we get into aconversation about metrics, we
always like to kick our episodesoff by sharing a story of
someone who's made our dayrecently.
So do you have someone who'smade your day recently, Meghan?
Meghan (02:35):
I do.
My two-year-old daughter.
Her name's Alessia.
We're currently visiting inlines in Argentina right now.
And she's just she's a greattraveler.
She's very adaptable, like evenwhen she's not in her own home.
And yesterday I was making somefood and she's starting to
develop her own taste intothings.
Like I was preparing someempanadas.
(02:57):
I didn't have the empanadaready, but I gave her the
filling.
She didn't want the filling,she wanted the real deal.
And so for but the fact thatshe ate the empanada, I was just
like, oh, thank God.
Like that, I know it'ssomething so small, but like at
the same time, like it brings meso much joy.
Her defining her own personalpreferences.
Priscilla (03:18):
She's becoming a person.
That's great.
Meghan (03:20):
Yes.
Priscilla (03:20):
It's so fun.
Yeah.
I do think there's like a coolparallel to like new customer
support agents that are comingon a team.
You're watching her see whatshe likes and what she doesn't
like.
And in the same way, supportleaders can watch their new
support hires find that, youknow, this is the way I'm going
to handle this work.
Meghan (03:59):
Absolutely.
I honestly compare being like aleader, a support manager to
being a parent all of the time.
It's about listening and it'snot necessarily about solving
problems.
It's about creating anenvironment where people can
actually thrive.
And that translates a lot toleadership as well.
Priscilla (04:16):
Yeah, it definitely does.
So before we get into themetrics of everything, I want to
learn a little bit more aboutyou.
Can you share with us a littlebit more about your experience
in support and in finance andhow you kind of ended up in
this, you know, metrics-focusedplace?
Meghan (04:31):
Yeah.
So I think all of us in thesupport world has been in a
customer-facing role sinceforever.
It started at a dry cleaner, iswhen I was 16.
Um, I had no desire of likepicturing where I am today.
And so obviously, like when Iwas at the cleaners, you know,
I'm I'm in high school and I waslike, you know, I'm really good
(04:53):
at math.
And I like people too.
Like finance set felt like theperfect like segue into that.
So I studied finance.
I I even had like a fewinternships under my belt.
Well, after college, I waslike, life's too short.
I have my whole life to work.
I'm gonna take a gap yearbefore actually starting work
and travel.
And so that's exactly what Idid.
(05:15):
Now, with shocker when I cameback, even though I had like
volunteer experience on my tripand everything, not a single
finance like company wanted tohire me.
So I um found a role at a callcenter that was actually a
travel agency.
Priscilla (05:32):
Okay.
Meghan (05:33):
I was in my 20s, so I got married, I moved to
Argentina.
Uh remote work wasn't really athing, but of course, being
here, I was like, okay, I'mstill gonna lean into this
customer support thing becauseit's something that I'm really
good at, and I know that theremight be more opportunities for
me in a remote capacity.
And so that's when I have myfirst real like customer support
(05:56):
job.
I was a sole, it was just me onthe support team.
I was the sole leader, the soleperson handling tickets, and
was for an actual Amazon sellerwho was selling tea and stuff
like that.
One of the things that I wasbeing measured on was actually
the average product review forlike the rating.
(06:17):
What interesting.
That was one of my KPIs.
But you weren't in the productdevelopment.
No, I wasn't.
I wasn't.
That the thing is, is that Iinherited a bunch of really
crappy products as well.
So like it's not like thesewere new products.
They had, you know, anythingbelow a four is sauce on Amazon.
(06:41):
Like, and so I was dealt somelike products that had 3.4
average review, and basically itwas my job to either have
people update their negativereviews or delete them.
Priscilla (06:55):
So it was like support and it was like
reputation management at thesame time.
Meghan (06:59):
Yes, weird, very and and it was a very small segment of
like my actual the actual workthat I was doing.
Like I built the foundation forthe support team.
It didn't grow eventually, butI built the foundation.
I got us on a help desk.
We weren't even on a help deskbefore.
I was doing all these otheraccomplishments, and I always
(07:20):
felt like my metrics that I hadto painstakingly enter into a
Google Sheet every Friday.
I'm like, this is notreflecting what I'm doing here.
Like, this is not an accuraterepresentation of how I'm
performing.
And most of the time, thesenegative reviews were about
faulty like products.
(07:41):
Yeah.
And even though, like, I was ahuge believer in like bridging
that gap between support andproduct because I knew that you
know, this type of informationwasn't surface, but it wasn't an
accurate representation on howI was doing as a performer.
Right.
And so that's what kind ofinstigated my curiosity to be
(08:01):
like, there are better numbersout there that this isn't it.
Priscilla (08:04):
Yeah.
So at what point did you kindof leave that role and move into
a consulting perspective wherenow you're helping other
companies learn what to measurebased on like obviously that was
an early job, but thatexperience of clearly not
measuring the right things.
Meghan (08:21):
After leaving this job, I started my first job in tech
in 2019 for Whereby, which wasformerly known as a peer-in.
We love Whereby, we use it allthe time.
It's such a great interface.
It is.
And so I was hired as the thirdsupport hire in 2019.
I went in as a junior, my boss.
She obviously wanted people togrow either within support or
(08:45):
beyond their role, um, not be aticket cruncher.
And so she knew that she sawthat potential in me and really
helped me um, you know, learn.
Like I've attended so manywebinars and, you know, and and
just trying to give myself asmuch information as possible.
And I think in that type ofenvironment, when it came around
(09:08):
to actually looking at ourmetrics, like I was part of
those conversations about like,how do we do this?
What is this?
That was very basic because Ithink even in 2020, the
conversation about metrics ishas changed a lot.
It has.
Yeah.
It's changed so much.
I started measuring CSATbecause everybody else was doing
(09:29):
it.
Like I still was stillmeasuring first response time
because it what it was just thestandard, you know, and there
wasn't so much of a big libraryor anything else that you were
looking at.
Priscilla (09:41):
Right.
Meghan (09:42):
And so I started there, and then now that I'm like
working, you know, with otherteams, I'm like, okay, there are
so many different ways that youcan tell a story and what is it
that you're trying to prove.
Um, and you know, I I just havea hunger for learning and I
just love reading stuff onLinkedIn.
Priscilla (10:00):
There's so much stuff on LinkedIn about metrics, and
so I'm similar to you in that Ialso started as like the first
support person on a team, and Ihad no idea what to measure.
And I was like, well, responsetime, people always say that
they like things when they'refast.
So I'm gonna assume responsetime is really important.
And then, you know, you alwaysfeel good when you're getting
(10:23):
positive reviews.
And so that's another way tonaturally, you know, you're
gonna go, okay, well, then itmust be good because everyone's
saying it's great.
And so, you know, those werereally what I measured a lot.
And I think it's been cool inthe last five years to see so
many conversations about metricspop up on different, you know,
different uh communities and onLinkedIn and things like that.
(10:43):
And so I'm excited because Iwant to talk a little bit more,
you know, throughout thisepisode about what people are
measuring currently, like whatthose popular things are, but in
your opinion, what we should bemeasuring.
And then, like you said, how totake those metrics and present
the story to leadership in a waythat, you know, shows your
value as a team in an accurateway.
(11:05):
So when you think aboutcustomer support and support
metrics, what do you think mostteams are getting right right
now?
And then what do you thinkthey're just you know, they're
missing the mark on?
Meghan (11:16):
Yeah, I think in terms of right, like like we've been
talking about, there is acuriosity to learn more about
metrics and what's appropriatefor their team.
And I've made a post and a lotof people reached out to me
personally on LinkedIn, andthey're like, hey, I, you know,
I always like asking questions.
I'm like, what drew you toconnect?
And people are like, I thinkwhat we're measuring is not
(11:39):
accurately depicting how we'redoing in support and how we can
be making an impact.
So I think like that personallyis such a great thing to see
that people have the motivationto learn more and go beyond the
status quo.
And so that people canself-serve that information
however they please.
And so I think that's one thingthat people are doing
(12:02):
correctly.
Yeah.
Priscilla (12:03):
Yeah.
Meghan (12:04):
Now we're talking about like following the herd and
doing the status quo.
And I think a lot of thesemetrics that, you know, um that
are being currently measured arebuilt into our help desks.
And so that's why I think a lotof people are only using that,
those metrics that are surfacedfrom help desks, which can be
(12:25):
quite limiting to actuallydepicting how things are going,
um, and not and not going beyondthat.
And so, and a lot of thosemetrics are cost-based metrics
when when we think about speed,when we think about number of
touches per ticket, like theycan still be used.
I'm not saying it's not on mylike no-no list.
(12:48):
Um, but I think like a lot ofpeople don't realize that these
cost-based metrics ultimately dosupport the theory that support
is a cost center.
Like your metrics are reallydoing that.
So, yeah, those are like fewthings that I've been noticing
about metrics lately.
Priscilla (13:05):
Yeah, there's been a lot of conversations about
metrics over the last couple ofyears.
A lot of people talking aboutCSAT and response time.
In addition to those, when itcomes to measuring support, what
are the most popular metricsyou're seeing?
Meghan (13:18):
Average handle time is probably a very popular metric.
Average handle time is whensomebody opens a ticket, it's
the amount of time that it takesfor them to open it and send
that final response.
So, like I said, a lot ofmetrics that we're gonna be
talking about today, the onlyone that I say don't use is
CSAT, but um, the ConserverSatisfaction score.
(13:41):
But all these other ones, theycan be used but with caution.
And we'll kind of dive intothat because I'm not saying that
you shouldn't be tracking that,but you shouldn't be taking it
as face value.
So average handle time isdefinitely one of those that's
up there and also contactresolution.
So it's the percent of ticketsthat are resolved after one
(14:04):
response.
I don't necessarily think thatthis is accurate.
It's hard to dictate if it'sactually resolved or not.
Priscilla (14:12):
Yeah, I mean, that's a really good point because you
look at that and you go, oh, 50%of our emails are resolved with
one response.
But who's to say that responseisn't going to someone who read
it and goes, I have no idea whatthey're talking about.
I'm just giving up on this.
Like that's not a resolution.
That's a bad experience.
Yeah.
And they are so apathetictoward your product that they're
(14:32):
not even going to respond andtry to get to a right answer.
Yes.
And I'm not saying that that'sall the time, but it's
definitely a percentage of yourresponses.
And so I think that's a reallygood thing to point out that
it's not that these metrics arebad to track.
It's just that they don't tella complete story of what's going
on.
And you can't say, well, wehave a 50%, you know, one
(14:54):
response resolution and that bethe entire story because that's
not the story.
Meghan (14:58):
No, it's not.
And that's when ticket taxonomyis massive.
Because let's say your firstresponse rate is down.
Yeah.
Okay.
That triggers us toinvestigate.
And then we can kind of seelike, how does this connect with
ticket tags?
Like, is there a particularpart of the product that is
having first response times thatare really high?
(15:21):
Is that like documentationknowledge gap or just bad
product design?
And so that's where like youcan use these types of metrics
to combine it with tickets,other metrics.
You should never just take onemetric for face value,
especially like the firstcontact rate.
Priscilla (15:38):
Yeah.
Let's talk about CSAT for aminute.
I don't want to spend a ton oftime on CSAT because I think we
all agree that CSAT is not theright way to measure support.
And for those of you listeningwho might not know what CSAT is,
Jordan, I don't know if youknow CSAT.
I'm raising my hand here.
It's customer satisfaction.
Great.
Okay.
Can you share a little bitabout why CSAT is not?
Because I think it's probablythe most, maybe right there with
(16:01):
response time, the most uh likeshiny thing to measure and the
easiest thing to measure.
Yes.
Okay.
So why should we not measureit?
Meghan (16:09):
CSAT was this was a metric that was created in the
1980s.
And customer satisfaction istypically measured, like the
score is measured with a survey.
Yes.
Okay.
So like people have to opt into give a response on how the
satisfaction was, if they weresatisfied or not.
(16:30):
So it is not something that'sautomatically calculated.
It is something that peoplehave to opt in to give a score,
which um that honestly seguesinto one of the many reasons why
we shouldn't use CSAT isbecause most of the time I see
this constantly that people arestruggling to get survey
responses.
Jordan (16:50):
Yeah.
Meghan (16:51):
And also, like, let's say a specialist is following
their, you know, standardoperating procedure as expected,
and the customer doesn't likethat.
Or if the customer is actuallysaying, like, oh, I really wish
you would implement this featurerequest, it's like, uh, that's
not really accurately describinghow I handled the ticket.
(17:11):
Yeah.
And so, like I said, thismetric was created in the 80s,
and it was customer service backthen.
It was not customer support.
And so when you think aboutcustomer service back in the
80s, it was always thecustomer's always right.
You know, it was very much likemaking sure that we're
measuring that in this landscapeof how much support has
(17:36):
changed.
Like I think this metric isjust literally outdated.
Yeah.
And now that we have kind oflike sentiment analysis where we
could automatically pull thatfrom the ticket itself without
somebody having to opt in.
This is sentiment, is for thepeople that don't know, a lot of
people use like AI to kind ofscrape ticket content and be
(17:58):
able to judge if somebody isangry or upset or frustrated.
And I always like tracking likethe final sentiment to see if
people are happy or even thechange between that.
So that gives us a much moreaccurate picture of how
customers are actually feelingand if they're satisfied with
the support.
Yeah.
Priscilla (18:17):
I understand why a lot of support teams do focus on
that because it is easy to seebecause people are putting it on
a platter for you, very easy toaccess, even if it is just 8%
of your customers that arereaching out to support, but at
least you're getting actualwords from customers.
And it does make you feel good.
As a person on a support team,when you read a positive review,
(18:40):
it makes you feel like the workyou're doing is really good.
The flip side of that is a badreview, especially a bad review
on a good email, can make youfeel really terrible.
And it's something that earlyon, as our team was growing over
here, I told people on my team,like, do not look at CSAT
because it is encouraging untilit's really not encouraging.
(19:02):
Yes.
And then it can sit with you alot longer than it should.
It's like podcast reviews.
Yeah, it's like Instagramcomments.
It's like anything, you know,like they're great until they're
not.
Yeah.
And I remember probably threeor four weeks into working
support at all.
And we have like a great, anokay, and a bad rating kind of
system.
And I got an okay rating and Ihappened to see it.
(19:25):
You know, I was new in thisjob.
I was about to go to bed.
It was late at night.
I happened to pull my phone upand see an email pop up that
said that so-and-so gave me anokay rating.
And I was like, oh no, this isterrible.
This couldn't be any worse.
And I was just so stressed outabout it.
Yeah.
And it's so funny because Ilook back on it now and I like,
(19:45):
first of all, why are youlooking at your email right
before you go into bed?
Like, that's not healthy.
Don't do that.
Don't do that.
Don't do that.
But also, like to let that kindof a negative thing impact you,
it's not healthy.
So I think that there's so manyreasons why CSAT shouldn't be
put at the top of the pedestalor really anywhere when you're
trying to measure support.
It can be really beneficial toencourage your team.
(20:08):
Like, we'll pull out reallygood reviews that are about
really good cases and use thatas a motivation and as an
encouragement.
I love that.
But other than that, it'sreally not a good way to measure
your work.
Meghan (20:20):
No.
Susanna De Sosa just postedsomething about how she changed
the question in because youknow, you can change the
question.
It like help scout.
I know you can change thequestion for CSAT.
And she changed the question tosomething about like how did
Loom's support set you up forfuture success?
Like, did you take away moreinformation?
(20:42):
And do you feel like you cansucceed with this?
And even just changing thenarrative of that question, even
though it's still like a CSAT,you know, measured, you still
select yes, okay, or no?
Yeah, it can still change alot.
Just that small little tweak.
Priscilla (20:59):
And it might encourage people to actually
fill it out.
Yeah.
If it's a more unique questionthan if it's just the standard
run-of-the-mill, how was theservice today?
Question.
Yeah.
Yeah, I agree.
Have you seen or hadexperiences where focusing on
the wrong metrics haveencouraged bad behavior in your
teams?
Meghan (21:17):
1000% of like the first response rate.
And it's how quickly a ticketis created and how quickly you
can send that first response.
There's different frameworksthat you can look at this speed
or bandwidth.
Like I like using the bandwidthone if you're trying to measure
like how your team's bandwidthis, but most people default to
(21:39):
speed.
Yeah, they're not thinkingabout that.
I was on a team where theywould actually report the first
response time every single weekin sync without any like context
as to why it was red or green.
You know, it's not a goodfeeling.
Priscilla (21:55):
No.
Meghan (21:55):
When it is in the reds.
And so what I noticed is that alot.
People were sending like kindof like non-answers, just
confirming that hey, I'mchecking with this internally,
I'll get back to you.
Now, what's happening is thatevery industry is different.
Like some people do valuespeedy responses, but in the
(22:16):
cases that I've seen, customersvalue thorough and mindful
responses to reduce that backand forth because it, you know,
I we've all been there.
I personally don't likeinteracting with support over a
majillion emails.
Like no, and you know, and evenlike reducing that one like
non-answer, like I already knowthat you got my email because I
(22:38):
probably got a confirmationemail saying that my ticket's
been created.
I don't need a non-answer.
And and it's just adding to thebloat.
And it clutters up your inbox.
And yeah, I've seen negativesentiment.
So that is where like firstresponse time, it's more about
speed, it's less about quality.
Priscilla (22:57):
Yeah, I will say when I was early on, I think we just
had two people on the team atthat point doing support here at
Bus Brow.
I remember, I mean, ourresponse time was fantastic.
And I was so proud of it.
It was like under 20 minutes.
There were two of us.
We were killing it.
We also were not taking lunchesand it was self-imposed.
(23:18):
It's funny.
I mean, I it was me going, Ineed to get this down, not
leadership saying you need toget this down.
But it was like, oh, can I makeit even lower next month?
You know, can I make it evenlower?
And I remember at some pointone of my leaders coming to me
and saying, Hey, I think weshould probably stop publishing
or sharing our response time forthe whole company as like this
(23:40):
thing we're so proud of, becausewe are proud of it.
Like it's great that that'sthat fast.
Meghan (23:43):
Yeah.
Priscilla (23:44):
But it makes me feel like we're just trying to get
faster and faster and faster.
And that's not a good thing.
And it was a like aneye-opening moment for me
because I thought, oh yeah,you're right.
I mean, there were moments in aconversation where we would
send something much faster thanwe probably should have, or we
wouldn't take the time tounderstand the full background
(24:05):
of the, you know, customer'saccount, things like that.
Yeah.
And so we pretty quickly tookthat out of our like main thing.
And it changed a lot aboutwhere we put our focus and where
we put our value as a team,that now it is a lot more
focused on a thorough response.
And the funny thing is, evenwhen like our response time is
(24:28):
higher now than it was backthen, but it's not crazy high.
It hasn't been affected so muchby adding in this like more
intentional quality focus onthat.
So I think it's, I think it'sjust interesting if you're
listening to this and you'relike, well, you know, response
time is the most importantthing.
You are probably not going tolose so much by adding, you
know, a little time on yourresponse time, but you're going
(24:49):
to gain a ton and your customersare going to feel way more
taken care of if they're gettinga good quality response, even
if they're waiting, you know, acouple hours longer to get it.
Yeah.
Meghan (25:00):
I mean, we're not brain surgeons.
Right.
Right.
Burnout is real.
And I think it's not somethingthat we should be busting our
heads about.
And honestly, if we have higherresponse times, then maybe it's
time to look at headcount.
Yes.
Or the tools we're using.
Yes.
Yes, exactly.
(25:20):
Or like knowledge gaps.
Like, you know, is there afeature that it's just so
convoluted and internally wedon't know about it?
That's what's leading to this.
So that's why like it doespoint to other things.
But I do think that taking itoff the main like KPI or the
main report, it does allow youto kind of like, okay, this is
(25:44):
not necessarily how I'm beingmeasured on, but it is still
being measured in thebackground.
Yeah.
So that's that's an excellentstory.
I love that.
Yeah.
Priscilla (25:51):
Well, and I like the way that you set it up in the
beginning, saying the bandwidthidea of it, because you're
right.
You can see that response timego up and you can think, okay,
I've either got a bunch of lazypeople on the team who are not
responding to emails quickly andthey're going and doing stuff
in the middle of the day.
Or you could say, what'sprobably actually happening is
that we don't have enough peopleto service the inbox that we
(26:13):
have and the traffic that wehave, or we don't have the tools
in place, or we don't have agood mission control set up to
give us a good idea of acustomer's account in order to
get an answer in a quick way.
We have to spend a ton of timedigging, or like you said, we
don't understand this facet ofthe product and no one's ever
taught it to us.
And so every time we get aquestion about that, we're all
(26:35):
fumbling over ourselves tryingto find an answer.
And it's taking a lot longer todo that.
Meghan (26:39):
Yes, exactly.
I'm working on a team right nowwhere they went through a
massive restructuring andnaturally our response times is
gonna go up.
Yeah, it's gonna happen.
But I said, okay, if we want totell a story on how we can
maybe reduce the response timesto automating some of these
really manual or overly designedsystems or really like trying
(27:01):
to even build tools forourselves to self-surface
information.
Like, let's do that and let'strack it from a bandwidth
perspective because that is avery interesting story for sure.
Priscilla (27:12):
And I think bandwidth, looking at it from
that perspective, takes a littlebit of like the personal weight
or guilt off of it.
Like if the response time, whenyou call it response time,
there's a personal aspect to itthat you're like, I'm not
responding fast enough.
That is the problem.
And this time is going up.
And so I am relating that backto myself and the fact that I am
not responding fast enough.
(27:32):
But if you talk about it forthe bandwidth perspective, it
takes off that personal guilt,which I think for a lot of us
who work in customer support,that like people pleasing,
taking on a lot of that, youknow, weight and shame and
stuff.
I think that's pretty normalfor these types of people.
Very and can be reallybeneficial.
Meghan (27:51):
When I was at Whereby, you know, I was there during the
height of the pandemic.
Our ticket volume increased by300%.
Of course.
Yeah.
That one weekend in March.
Priscilla (28:02):
Oh my gosh.
We were in the same boatbecause we were in podcasting.
So the April 2020 rise wasunbelievable.
Meghan (28:11):
Real.
Yes.
And our backlog of tickets wasprobably 2,000.
Um, like insane.
Oh my gosh.
Yeah, insane.
And so I'm a ticket cruncher.
Yeah, it's it's in my naturalspirit.
Yes.
I want to see that number godown.
It's stressful.
And my boss, you know, had totell me, slow down.
(28:32):
These people are probably gonnawait weeks to hear back from
us.
Let's make sure that we'retaking our and we didn't delete
a single email.
Priscilla (28:40):
I think that's a really good thing for your boss
to have said that if someone'sgonna wait a couple weeks for
you to respond, you better makesure that response is really
good.
Yeah.
And I think for peoplelistening who are in those kind
of situations where they don'thave enough bandwidth for the
traffic they're seeing insupport and they're like, none
of my metrics are gonna be goodbecause I'm, you know, so
underwater and I can't getleadership to get us either the
(29:03):
right tools or more people orwhatever it is.
That reminder of, hey, ifpeople are gonna wait two weeks
and that's something you can'tcontrol, yeah.
Then what you can control isthat your response can be really
good.
Like that you can have controlover.
So I think that could be reallyencouraging for someone who's
in that place right now, going,How do I handle this if I don't
have control over what we'remeasuring or, you know, make
(29:25):
sure that what you can control,you're controlling, which is the
response.
Take your time.
Yeah.
So I think it's important,obviously, to know what to
track.
But one thing you've talkedabout and mentioned a couple of
times so far is using data totell a story and to communicate
to your leaders.
And so I want to kind of moveinto that a little bit.
Jordan (29:44):
Yeah.
Priscilla (29:44):
What are the biggest challenges for support leaders
in showing that value beyond thequeue?
You know, how do you usemetrics to play a role in that?
Meghan (29:52):
When we talk about data and storytelling, the first
thing we have to do is know whowe're telling the story to.
And we should probably talk tothem.
Yeah.
We should probably ask them,what do you want to see?
Because I think a lot of peoplejust assume that leadership
wants certain aspects of it, butat the same time, like we need
(30:12):
to ask them, what is it thatyou're missing?
And what kind of information doyou want to see?
And there's no like rubric orinner bank of interview
questions.
You just have to start thatconversation.
Jordan (30:24):
That reminds me of what Priscilla was talking about
earlier.
She was reporting on herresponse time every time.
And then the leaders were like,Yeah, we don't really
interested in that so much.
Right.
Yeah.
Yeah.
Meghan (30:34):
Like typically one of the biggest challenges right now
is you know, support as a costcenter type views, do, like I
said, directly come frommetrics.
And sometimes leadership doeswant to see those metrics.
But the thing is, is that itdoes change the narrative a lot
on how the rest of the companyviews the support.
(30:55):
And so there are better metricsto measure impact.
And I think that if youcollaborate with leadership and
really talk them through andexplain why you want to use
these metrics.
So some of my favorite metricsare measuring impact.
Our person in particular arethe recontact rate.
(31:16):
So the recontact rate measureshow often customers reach out
again after their issue wasconsidered resolved.
There are kind of two sides ofthat story.
You know, maybe there's roomfor improvement, for support to
better learn a topic, or maybelearn why wasn't it resolved
truly the first time?
(31:37):
Or like I said, productusability.
Like, you know, is theresomething in the product that is
constantly generating thesetypes of questions?
So that's something that youcan see like where can we get
started on that?
Um, because we don't like docreating duplicate emails
essentially and more ticketvolume for something that should
have been resolved the firsttime.
(31:58):
And then another thing that Imentioned before is obviously
customer sentiment.
You know, this is typicallymeasured by AI, like because it
does measure sentiment feelingsbased off of the ticket context.
Most of our customers lovetelling us this is frustrating,
or I don't appreciate, I don'tunderstand, or you know, things
(32:19):
like that.
And so I think that like thiscan give us a lot more context
to how the customer is feelingbecause ultimately we are there
to support them and we are thereto uplift them.
And so it's important that wedo measure this type of
analysis.
It's like a qualitative type ofmetric as well.
Yeah.
Even though you can assignnumber to it, but it's still
(32:40):
very helpful.
Yeah.
Priscilla (32:42):
Yeah.
Specifically about sentiment,how would you go about measuring
that?
I mean, it's not, I use HelpScout, and I don't think there's
anything in there in HelpScout.
So how would you get thatsentiment?
Meghan (32:53):
So I think like you have to use external like AI, like
some people hire, you know, AIpeople or someone within your
team if they're looking to flextheir AI muscle and they want to
build more bots that arehelpful.
And like I've seen teams eventake it as far as building a
dashboard inversal, which is oneof those like no-code type of
(33:15):
like lovable-esque apps.
And so that you can surfacethat information.
I think that with sentiment,you could measure the change.
Let's say somebody starts offas frustrated and is actually
positive at the end.
That tells a great story.
Hopefully positive.
Yeah.
Right.
But even if it is negative,like you can still learn from
(33:36):
that.
Priscilla (33:36):
Yeah.
Yeah.
And sometimes it's morebeneficial to know that someone
came in frustrated and theirsituation was turned around and
they left happy than coming inhappy and leaving happy.
Like it's a harder thing to do.
And so it's good to measurethat, to know that.
I think that's great.
And it's one of those things,you know, we talk about AI a
lot.
And it's one of those uses ofAI that can be really super
(33:58):
beneficial that is not justtaking away jobs.
You know what I mean?
That the worry that peoplehave.
It's like AI can be a reallybeneficial thing in scraping all
that content and giving youinformation on your customers
and on these interactions thatyou wouldn't have had before
without a ton of manual work.
Meghan (34:15):
Yes, exactly.
Priscilla (34:16):
And so I think that's a really great example.
What about retention revenueand product improvement?
Like, how do you take stats anddata and metrics and use them
in a way to influence thosethings?
Meghan (34:29):
With retention, you know, when I talk about
products, I mean like SaaS, youknow, like software as a service
product.
Most of us have trials, maybelike money back guarantee, like
refund periods where you're ableto ask for refund with no
questions asked.
And I think most of the timeour customers are reaching out
to us in that very openingwindow.
(34:51):
And I think like withretention, if you have that set
up, let's say you're at the endof that 30-day or two-week
period, you can say X percent ofpeople who got in touch with
support renewed for thefollowing month or something or
like renewed past that trialperiod.
(35:12):
I think when you have thosefirst 90 days, you know, I think
like every single SaaS productor even like service, I'm sure
the product team has calculatedlike what is that adoption
window when customers actuallyactivate and become like almost
like a fan and you know thatthey're not going anywhere.
I think it's important to askyour product team, like, what is
(35:33):
that window that we're hopingpeople are actually renewing
extra X amount of days?
And so when you say, like, forthe people that canceled, let's
say like 90% of them didn't evenreach out to support.
Yeah, you know, and that canhave a direct relationship with
being like, hey, X percent ofpeople that reached out to
support, they actually renewed.
(35:54):
And so that's one side of like,I know it it can be extremely
hard, especially diving intorevenue.
Like revenue, I think that oneof our best things is we know
maybe a customer is asking forspecific features for like a
high-ticket type of product.
We kind of like pre-qualifythem and support and pass them
(36:16):
over to sales.
How many of those have actuallyconverted?
You know, that's a greatbecause I know sales that
directly correlates to money.
Yeah.
But like imagine how manyinternal escalations, how many
handovers have actually resultedin a sale.
That's a number you can grablike easily.
Another thing is like withrefunds, it's a little tricky
(36:39):
because most of the time peoplelike they might churn for
whatever reason, but their churnreason isn't listed there, or
maybe there's multiple things,or they don't identify with it.
But it's always a variousfactors of things.
So that's always somethingsuper hard.
But in the support-driven slotcommunity, for those that are
(37:00):
not part of it, shoulddefinitely join.
Um, I'm in there quite a lot.
Somebody posted about this lastweek where they want to kind of
drive the needle on bugs, forexample.
And like, how can we show thatour customers are like leaving
us because of bugs?
Yeah.
And that's where you can kindof like find a bug that was a
(37:21):
high impact bug and workbackwards.
See how many customers haveactually reported that bug and
how many actually churned, eventhough it might be churning for
a different reason.
You can still show that in thereport of being like 30% of the
people that have reported thisbug since have left us.
Like that tells us maybe weshould fix this one.
(37:44):
Like so, kind of like workingbackwards and stuff.
It is very hard though to kindof connect the revenue and the
money part with support.
That's why it's notorious whywe're kind of like put on the
bottom as not impact drivers,um, because it is a little bit
harder, but not impossible toget those metrics.
Yeah.
(38:04):
And then with like productimprovements, like what I said
before, like having a goodticket taxonomy and showing that
what if we have negativesentiment about specific product
features, or if we notice thata lot of the support ticket
volume is coming from a specificfeature, hmm, that should raise
(38:25):
flags.
So, like I said, pairing theseother metrics like ticket
volume, how many tickets arecoming in about this specific
feature?
Maybe we should revisit it andsee what's going on.
Priscilla (38:35):
Yeah.
Yeah.
That just made me think of thetimes when someone walks up and
says, How many questions do weget about this issue?
Yes, exactly.
And you go, oh man, well, weget a bunch of questions about
that issue.
Yeah.
And then you go and you look itup to see how many you're
actually getting.
And it's actually not as manyas you thought, but it feels
like a lot.
Yeah.
That's another reason whymetrics are really important to
(38:55):
be very intentional about,because when you're in the inbox
working the queue, you cananswer four of the same
questions in a day and feellike, oh my gosh, a ton of
people run into this issue.
Meghan (39:06):
Yeah.
Priscilla (39:06):
But when you look at it in the whole grand scheme of
things, it's actually not as bigof an issue, or vice versa.
You're like, that's not really,I don't see that that much.
And then you go look at theactual numbers and you say, oh,
actually, it's a lot higher thanmy perception of it is.
And so it's just another reasonwhy being intentional about
your metrics and beingintentional about how you're
measuring things is really goodin more ways than just the
(39:29):
proving value, because thatdefinitely is, but the accurate
understanding of what yourcustomers are saying and how
they're interacting with yourproduct.
It's really important.
Yeah.
I think metrics can also play ahuge role in how we motivate
the people on our team.
And I think I said it up topthat the things that we measure
impact the emails that we sendor the phone calls that we take.
And so, you know, I want totalk a little bit about how we
(39:51):
can use the metrics that we aretracking to motivate and
encourage our team.
Meghan (39:58):
Yeah, I think for starters, I personally think
that metric should be revisitedevery quarter.
You know, I I don't think itshould be a set one and done
kind of situation.
And I like involving peopleearly on in the team to be like,
here are my proposed metricsthis quarter.
And like, because I kind offrame it as the like, how can we
(40:20):
show that um we're capable ofdoing X, Y, Z and do these
numbers accurately portray that?
Because your support departmentcan change every quarter, and
what story you're trying todepict or what proof you're
trying to show will change.
So having people have thoseconversations early on is super
(40:41):
motivating because people cansay, like, I don't think this is
a good idea, or I think this isa good idea, and then you know,
have a conversation about it.
Also, when you are introducinga new set of metrics, I think as
leaders, we have to be preparedto explain a motivation behind
them and also where we currentlystand.
Because I think for us, it'simportant to have a benchmark
(41:05):
that is realistic and where it'swhere we're standing right now
as the team capacity and thingslike that.
This is our benchmark.
And we're trying to go a littlebit lower, a little bit higher.
Jordan (41:16):
Yeah.
Meghan (41:17):
And I think having a real conversation in a meet and
like a team sync about thesetypes of metrics, like I think
is super important early on, youknow, just to get an
understanding of why we'recoming at it and why we're
measuring this.
For example, we talk aboutfirst response time a lot in
terms of bandwidth.
Like, you know, we're hopingthat this will go down because
(41:41):
we're in we're empowering you tomaybe automate some redundant
tasks or improve ourdocumentation.
And so try to show what kind ofimpact we're having beyond that
metric because it it relates itto almost like quality of life,
like workplace, um, and try toconnect it to that.
And I think that if you havethat conversation rather than
(42:03):
being like, these are ourmetrics, that will help um kind
of like plant those seeds earlyon.
Yeah.
And then also celebrate wins.
Like I I think a lot of peopledon't do this.
It's a big one.
It's a big one.
And even if there are losses,let's start a conversation just
because it's maybe in the red,that's okay.
(42:24):
Like, you know, let's haveconversations on how we can
improve it as a team or likewhat might be happening.
Like, I think open and honestcommunication is always the
best.
And yeah.
Priscilla (42:34):
And it makes it less scary when you have those
conversations regularly, andit's less like, oh my gosh,
something's bad.
So now we're talking about it.
It's more like this is aconversation we have
consistently.
And if the number goes up ordown, you know, we're still
having the conversation, we'retalking about it.
Yeah.
So as we wrap things up, I wantto give our listeners, you
know, some things they can takeaway into their work day
(42:55):
tomorrow if they're, you know,gonna go and start looking into
their digging into theirmetrics.
So if you could track just likeone or two things, what would
you tell people to track?
Meghan (43:06):
Another one that I didn't have time to mention is
replies to resolve.
I think that focusing less onthe back and forth, it's okay if
you follow up just to check inand something's being, you know,
it's not gonna skew theaverage, but I still think that
it gives us a better picturethan that first percent like
contact rate, like if the firstreply is resolved.
(43:27):
I think the number of repliesto resolve like gives us a
better story on like, hmm, thisticket had 16 replies, like from
the customer agent before theticket was resolved.
We should probably look intothat.
Yeah.
Priscilla (43:41):
Is there a mindset shift that you would like to see
people take when it comes tometrics?
Meghan (43:47):
Yeah, I think numbers do not define how you and your
team perform.
It is the starting point totrigger a conversation.
We are in support, we are notdefined by numbers.
It just is our gut feeling thatfeels validated by numbers, but
you should always look beyondthat number and try to
understand why that number goesup or go down.
(44:09):
And so it shouldn't define usat who how we support our
customers.
Priscilla (44:14):
I mean, I think that's the crux of all of it,
right?
Like we can use metrics, butyou know, the numbers are not
the definition.
Yes.
There's a story there and weneed to dig deeper and figure
out what the story is.
Well, thank you so much forjoining us, Meghan.
This was great.
I learned a lot about metrics.
I'm really excited to go andtake some things back into my
team and how we measure and whatwe share.
So I'm really excited aboutthat.
If there is anyone listening tothis who wants to learn more
(44:37):
about you or maybe use you as aconsultant for their team, how
can they get in touch with you?
Meghan (44:42):
LinkedIn.
LinkedIn is the best way to getin touch with me.
I also have linked a calendarlink where you can book a free
strategy session with me and wecan kind of just talk about, you
know, your sport infrastructuremetrics, what it is that you're
trying to do, and start theconversation there.
Priscilla (44:59):
Yeah.
And if you want to book thatstrategy session, we'll link
that in the show notes of thisepisode.
So you can just scroll down onyour iPhone and click that link
and set up a time because thatis gonna be, I mean, that's a
great resource that peopleshould definitely use.
Please, I love talking.
Meghan (45:15):
So yeah, please book me.
Priscilla (45:17):
I love it.
If you have a question or atopic that you would like us to
cover on a future episode, youcan shoot us a text using the
send us a text link in ourepisode description.
As always, if you like thisepisode, please review it on
Apple Podcasts or share it withsomeone who works in customer
support.
That would be a huge benefit tous.
Thank you again to MeghanGallagher for joining us on
(45:38):
today's episode.
Thank you for listening.
Now go and make someone's day.
Welcome to Happy to Help, a podcast about customer
support from the people atBuzzsprout.
I'm your host, PriscillaBrooke.
Today we're taking a closerlook at how we measure customer
support.
We'll unpack the metrics mostteams currently rely on, what we
should be measuring, and how touse that data to tell a clear
story about your team's impact.
Thanks for joining us.
Let's get into it.
(00:21):
All right, so today we'retalking all about metrics.
I feel like there's a lot ofdiscussion right now about what
we're measuring, what we shouldbe measuring, whether the things
we're currently measuring areactually helping us or if
they're hindering our teams,because at the end of the day,
what you're measuring isimpacting the support you're
(00:41):
doing, and it's impacting howyour team feels about the work
they do.
And so it's just a reallyimportant thing to get right.
And it is something that allsupport teams are doing in some
way, whether we're educated onit or not.
Jordan (00:52):
And in this day and age, there's so many statistics on
like every software that you useday to day.
Priscilla (00:58):
And so it's hard to sort through it.
So this is gonna be reallygreat.
Yeah.
So I'm excited because we havesomeone on the show today who is
coming to us with this balanceof passion for metrics and also
expertise in metrics.
And so I'm really excited totalk through what metrics we
should and shouldn't bemeasuring.
So joining us today is MeghanGallagher.
Meghan is a support operationsconsultant with over 10 years of
(01:21):
experience in customer supportand five years in support ops.
She helps B2B SaaS teams turnunreliable support systems into
something that leadershipactually trusts, which is a big
accomplishment.
Yeah.
She has spent years buildingsupport operations from scratch,
and her work has helped teamssignificantly improve response
(01:42):
times, deflection rates, andcommunicate customer feedback in
a way that actually benefitsthe product and leads to product
updates and development.
She's based in Valencia, Spain,and she is currently building
her consultancy, MG Consulting,and she brings this unique
perspective because she hashistory in finance and history
(02:03):
in customer support and has alove for numbers.
So thanks for being here,Meghan.
I'm really excited to talkabout metrics.
Meghan (02:09):
Oh my God.
It's my pleasure.
It's something that I've alwayswanted to nerd out about, and
I'm happy to spread the wealthbecause I want people to learn
more.
Priscilla (02:18):
Yeah, I'm really excited.
I feel like metrics is one ofthose places where I'm gonna
really learn a lot from thisconversation.
So I'm really pumped.
Yes.
So before we get into aconversation about metrics, we
always like to kick our episodesoff by sharing a story of
someone who's made our dayrecently.
So do you have someone who'smade your day recently, Meghan?
Meghan (02:35):
I do.
My two-year-old daughter.
Her name's Alessia.
We're currently visiting inlines in Argentina right now.
And she's just she's a greattraveler.
She's very adaptable, like evenwhen she's not in her own home.
And yesterday I was making somefood and she's starting to
develop her own taste intothings.
Like I was preparing someempanadas.
(02:57):
I didn't have the empanadaready, but I gave her the
filling.
She didn't want the filling,she wanted the real deal.
And so for but the fact thatshe ate the empanada, I was just
like, oh, thank God.
Like that, I know it'ssomething so small, but like at
the same time, like it brings meso much joy.
Her defining her own personalpreferences.
Priscilla (03:18):
She's becoming a person.
That's great.
Meghan (03:20):
Yes.
Priscilla (03:20):
It's so fun.
Yeah.
I do think there's like a coolparallel to like new customer
support agents that are comingon a team.
You're watching her see whatshe likes and what she doesn't
like.
And in the same way, supportleaders can watch their new
support hires find that, youknow, this is the way I'm going
to handle this work.
Meghan (03:59):
Absolutely.
I honestly compare being like aleader, a support manager to
being a parent all of the time.
It's about listening and it'snot necessarily about solving
problems.
It's about creating anenvironment where people can
actually thrive.
And that translates a lot toleadership as well.
Priscilla (04:16):
Yeah, it definitely does.
So before we get into themetrics of everything, I want to
learn a little bit more aboutyou.
Can you share with us a littlebit more about your experience
in support and in finance andhow you kind of ended up in
this, you know, metrics-focusedplace?
Meghan (04:31):
Yeah.
So I think all of us in thesupport world has been in a
customer-facing role sinceforever.
It started at a dry cleaner, iswhen I was 16.
Um, I had no desire of likepicturing where I am today.
And so obviously, like when Iwas at the cleaners, you know,
I'm I'm in high school and I waslike, you know, I'm really good
(04:53):
at math.
And I like people too.
Like finance set felt like theperfect like segue into that.
So I studied finance.
I I even had like a fewinternships under my belt.
Well, after college, I waslike, life's too short.
I have my whole life to work.
I'm gonna take a gap yearbefore actually starting work
and travel.
And so that's exactly what Idid.
(05:15):
Now, with shocker when I cameback, even though I had like
volunteer experience on my tripand everything, not a single
finance like company wanted tohire me.
So I um found a role at a callcenter that was actually a
travel agency.
Priscilla (05:32):
Okay.
Meghan (05:33):
I was in my 20s, so I got married, I moved to
Argentina.
Uh remote work wasn't really athing, but of course, being
here, I was like, okay, I'mstill gonna lean into this
customer support thing becauseit's something that I'm really
good at, and I know that theremight be more opportunities for
me in a remote capacity.
And so that's when I have myfirst real like customer support
(05:56):
job.
I was a sole, it was just me onthe support team.
I was the sole leader, the soleperson handling tickets, and
was for an actual Amazon sellerwho was selling tea and stuff
like that.
One of the things that I wasbeing measured on was actually
the average product review forlike the rating.
(06:17):
What interesting.
That was one of my KPIs.
But you weren't in the productdevelopment.
No, I wasn't.
I wasn't.
That the thing is, is that Iinherited a bunch of really
crappy products as well.
So like it's not like thesewere new products.
They had, you know, anythingbelow a four is sauce on Amazon.
(06:41):
Like, and so I was dealt somelike products that had 3.4
average review, and basically itwas my job to either have
people update their negativereviews or delete them.
Priscilla (06:55):
So it was like support and it was like
reputation management at thesame time.
Meghan (06:59):
Yes, weird, very and and it was a very small segment of
like my actual the actual workthat I was doing.
Like I built the foundation forthe support team.
It didn't grow eventually, butI built the foundation.
I got us on a help desk.
We weren't even on a help deskbefore.
I was doing all these otheraccomplishments, and I always
(07:20):
felt like my metrics that I hadto painstakingly enter into a
Google Sheet every Friday.
I'm like, this is notreflecting what I'm doing here.
Like, this is not an accuraterepresentation of how I'm
performing.
And most of the time, thesenegative reviews were about
faulty like products.
(07:41):
Yeah.
And even though, like, I was ahuge believer in like bridging
that gap between support andproduct because I knew that you
know, this type of informationwasn't surface, but it wasn't an
accurate representation on howI was doing as a performer.
Right.
And so that's what kind ofinstigated my curiosity to be
(08:01):
like, there are better numbersout there that this isn't it.
Priscilla (08:04):
Yeah.
So at what point did you kindof leave that role and move into
a consulting perspective wherenow you're helping other
companies learn what to measurebased on like obviously that was
an early job, but thatexperience of clearly not
measuring the right things.
Meghan (08:21):
After leaving this job, I started my first job in tech
in 2019 for Whereby, which wasformerly known as a peer-in.
We love Whereby, we use it allthe time.
It's such a great interface.
It is.
And so I was hired as the thirdsupport hire in 2019.
I went in as a junior, my boss.
She obviously wanted people togrow either within support or
(08:45):
beyond their role, um, not be aticket cruncher.
And so she knew that she sawthat potential in me and really
helped me um, you know, learn.
Like I've attended so manywebinars and, you know, and and
just trying to give myself asmuch information as possible.
And I think in that type ofenvironment, when it came around
(09:08):
to actually looking at ourmetrics, like I was part of
those conversations about like,how do we do this?
What is this?
That was very basic because Ithink even in 2020, the
conversation about metrics ishas changed a lot.
It has.
Yeah.
It's changed so much.
I started measuring CSATbecause everybody else was doing
(09:29):
it.
Like I still was stillmeasuring first response time
because it what it was just thestandard, you know, and there
wasn't so much of a big libraryor anything else that you were
looking at.
Priscilla (09:41):
Right.
Meghan (09:42):
And so I started there, and then now that I'm like
working, you know, with otherteams, I'm like, okay, there are
so many different ways that youcan tell a story and what is it
that you're trying to prove.
Um, and you know, I I just havea hunger for learning and I
just love reading stuff onLinkedIn.
Priscilla (10:00):
There's so much stuff on LinkedIn about metrics, and
so I'm similar to you in that Ialso started as like the first
support person on a team, and Ihad no idea what to measure.
And I was like, well, responsetime, people always say that
they like things when they'refast.
So I'm gonna assume responsetime is really important.
And then, you know, you alwaysfeel good when you're getting
(10:23):
positive reviews.
And so that's another way tonaturally, you know, you're
gonna go, okay, well, then itmust be good because everyone's
saying it's great.
And so, you know, those werereally what I measured a lot.
And I think it's been cool inthe last five years to see so
many conversations about metricspop up on different, you know,
different uh communities and onLinkedIn and things like that.
(10:43):
And so I'm excited because Iwant to talk a little bit more,
you know, throughout thisepisode about what people are
measuring currently, like whatthose popular things are, but in
your opinion, what we should bemeasuring.
And then, like you said, how totake those metrics and present
the story to leadership in a waythat, you know, shows your
value as a team in an accurateway.
(11:05):
So when you think aboutcustomer support and support
metrics, what do you think mostteams are getting right right
now?
And then what do you thinkthey're just you know, they're
missing the mark on?
Meghan (11:16):
Yeah, I think in terms of right, like like we've been
talking about, there is acuriosity to learn more about
metrics and what's appropriatefor their team.
And I've made a post and a lotof people reached out to me
personally on LinkedIn, andthey're like, hey, I, you know,
I always like asking questions.
I'm like, what drew you toconnect?
And people are like, I thinkwhat we're measuring is not
(11:39):
accurately depicting how we'redoing in support and how we can
be making an impact.
So I think like that personallyis such a great thing to see
that people have the motivationto learn more and go beyond the
status quo.
And so that people canself-serve that information
however they please.
And so I think that's one thingthat people are doing
(12:02):
correctly.
Yeah.
Priscilla (12:03):
Yeah.
Meghan (12:04):
Now we're talking about like following the herd and
doing the status quo.
And I think a lot of thesemetrics that, you know, um that
are being currently measured arebuilt into our help desks.
And so that's why I think a lotof people are only using that,
those metrics that are surfacedfrom help desks, which can be
(12:25):
quite limiting to actuallydepicting how things are going,
um, and not and not going beyondthat.
And so, and a lot of thosemetrics are cost-based metrics
when when we think about speed,when we think about number of
touches per ticket, like theycan still be used.
I'm not saying it's not on mylike no-no list.
(12:48):
Um, but I think like a lot ofpeople don't realize that these
cost-based metrics ultimately dosupport the theory that support
is a cost center.
Like your metrics are reallydoing that.
So, yeah, those are like fewthings that I've been noticing
about metrics lately.
Priscilla (13:05):
Yeah, there's been a lot of conversations about
metrics over the last couple ofyears.
A lot of people talking aboutCSAT and response time.
In addition to those, when itcomes to measuring support, what
are the most popular metricsyou're seeing?
Meghan (13:18):
Average handle time is probably a very popular metric.
Average handle time is whensomebody opens a ticket, it's
the amount of time that it takesfor them to open it and send
that final response.
So, like I said, a lot ofmetrics that we're gonna be
talking about today, the onlyone that I say don't use is
CSAT, but um, the ConserverSatisfaction score.
(13:41):
But all these other ones, theycan be used but with caution.
And we'll kind of dive intothat because I'm not saying that
you shouldn't be tracking that,but you shouldn't be taking it
as face value.
So average handle time isdefinitely one of those that's
up there and also contactresolution.
So it's the percent of ticketsthat are resolved after one
(14:04):
response.
I don't necessarily think thatthis is accurate.
It's hard to dictate if it'sactually resolved or not.
Priscilla (14:12):
Yeah, I mean, that's a really good point because you
look at that and you go, oh, 50%of our emails are resolved with
one response.
But who's to say that responseisn't going to someone who read
it and goes, I have no idea whatthey're talking about.
I'm just giving up on this.
Like that's not a resolution.
That's a bad experience.
Yeah.
And they are so apathetictoward your product that they're
(14:32):
not even going to respond andtry to get to a right answer.
Yes.
And I'm not saying that that'sall the time, but it's
definitely a percentage of yourresponses.
And so I think that's a reallygood thing to point out that
it's not that these metrics arebad to track.
It's just that they don't tella complete story of what's going
on.
And you can't say, well, wehave a 50%, you know, one
(14:54):
response resolution and that bethe entire story because that's
not the story.
Meghan (14:58):
No, it's not.
And that's when ticket taxonomyis massive.
Because let's say your firstresponse rate is down.
Yeah.
Okay.
That triggers us toinvestigate.
And then we can kind of seelike, how does this connect with
ticket tags?
Like, is there a particularpart of the product that is
having first response times thatare really high?
(15:21):
Is that like documentationknowledge gap or just bad
product design?
And so that's where like youcan use these types of metrics
to combine it with tickets,other metrics.
You should never just take onemetric for face value,
especially like the firstcontact rate.
Priscilla (15:38):
Yeah.
Let's talk about CSAT for aminute.
I don't want to spend a ton oftime on CSAT because I think we
all agree that CSAT is not theright way to measure support.
And for those of you listeningwho might not know what CSAT is,
Jordan, I don't know if youknow CSAT.
I'm raising my hand here.
It's customer satisfaction.
Great.
Okay.
Can you share a little bitabout why CSAT is not?
Because I think it's probablythe most, maybe right there with
(16:01):
response time, the most uh likeshiny thing to measure and the
easiest thing to measure.
Yes.
Okay.
So why should we not measureit?
Meghan (16:09):
CSAT was this was a metric that was created in the
1980s.
And customer satisfaction istypically measured, like the
score is measured with a survey.
Yes.
Okay.
So like people have to opt into give a response on how the
satisfaction was, if they weresatisfied or not.
(16:30):
So it is not something that'sautomatically calculated.
It is something that peoplehave to opt in to give a score,
which um that honestly seguesinto one of the many reasons why
we shouldn't use CSAT isbecause most of the time I see
this constantly that people arestruggling to get survey
responses.
Jordan (16:50):
Yeah.
Meghan (16:51):
And also, like, let's say a specialist is following
their, you know, standardoperating procedure as expected,
and the customer doesn't likethat.
Or if the customer is actuallysaying, like, oh, I really wish
you would implement this featurerequest, it's like, uh, that's
not really accurately describinghow I handled the ticket.
(17:11):
Yeah.
And so, like I said, thismetric was created in the 80s,
and it was customer service backthen.
It was not customer support.
And so when you think aboutcustomer service back in the
80s, it was always thecustomer's always right.
You know, it was very much likemaking sure that we're
measuring that in this landscapeof how much support has
(17:36):
changed.
Like I think this metric isjust literally outdated.
Yeah.
And now that we have kind oflike sentiment analysis where we
could automatically pull thatfrom the ticket itself without
somebody having to opt in.
This is sentiment, is for thepeople that don't know, a lot of
people use like AI to kind ofscrape ticket content and be
(17:58):
able to judge if somebody isangry or upset or frustrated.
And I always like tracking likethe final sentiment to see if
people are happy or even thechange between that.
So that gives us a much moreaccurate picture of how
customers are actually feelingand if they're satisfied with
the support.
Yeah.
Priscilla (18:17):
I understand why a lot of support teams do focus on
that because it is easy to seebecause people are putting it on
a platter for you, very easy toaccess, even if it is just 8%
of your customers that arereaching out to support, but at
least you're getting actualwords from customers.
And it does make you feel good.
As a person on a support team,when you read a positive review,
(18:40):
it makes you feel like the workyou're doing is really good.
The flip side of that is a badreview, especially a bad review
on a good email, can make youfeel really terrible.
And it's something that earlyon, as our team was growing over
here, I told people on my team,like, do not look at CSAT
because it is encouraging untilit's really not encouraging.
(19:02):
Yes.
And then it can sit with you alot longer than it should.
It's like podcast reviews.
Yeah, it's like Instagramcomments.
It's like anything, you know,like they're great until they're
not.
Yeah.
And I remember probably threeor four weeks into working
support at all.
And we have like a great, anokay, and a bad rating kind of
system.
And I got an okay rating and Ihappened to see it.
(19:25):
You know, I was new in thisjob.
I was about to go to bed.
It was late at night.
I happened to pull my phone upand see an email pop up that
said that so-and-so gave me anokay rating.
And I was like, oh no, this isterrible.
This couldn't be any worse.
And I was just so stressed outabout it.
Yeah.
And it's so funny because Ilook back on it now and I like,
(19:45):
first of all, why are youlooking at your email right
before you go into bed?
Like, that's not healthy.
Don't do that.
Don't do that.
Don't do that.
But also, like to let that kindof a negative thing impact you,
it's not healthy.
So I think that there's so manyreasons why CSAT shouldn't be
put at the top of the pedestalor really anywhere when you're
trying to measure support.
It can be really beneficial toencourage your team.
(20:08):
Like, we'll pull out reallygood reviews that are about
really good cases and use thatas a motivation and as an
encouragement.
I love that.
But other than that, it'sreally not a good way to measure
your work.
Meghan (20:20):
No.
Susanna De Sosa just postedsomething about how she changed
the question in because youknow, you can change the
question.
It like help scout.
I know you can change thequestion for CSAT.
And she changed the question tosomething about like how did
Loom's support set you up forfuture success?
Like, did you take away moreinformation?
(20:42):
And do you feel like you cansucceed with this?
And even just changing thenarrative of that question, even
though it's still like a CSAT,you know, measured, you still
select yes, okay, or no?
Yeah, it can still change alot.
Just that small little tweak.
Priscilla (20:59):
And it might encourage people to actually
fill it out.
Yeah.
If it's a more unique questionthan if it's just the standard
run-of-the-mill, how was theservice today?
Question.
Yeah.
Yeah, I agree.
Have you seen or hadexperiences where focusing on
the wrong metrics haveencouraged bad behavior in your
teams?
Meghan (21:17):
1000% of like the first response rate.
And it's how quickly a ticketis created and how quickly you
can send that first response.
There's different frameworksthat you can look at this speed
or bandwidth.
Like I like using the bandwidthone if you're trying to measure
like how your team's bandwidthis, but most people default to
(21:39):
speed.
Yeah, they're not thinkingabout that.
I was on a team where theywould actually report the first
response time every single weekin sync without any like context
as to why it was red or green.
You know, it's not a goodfeeling.
Priscilla (21:55):
No.
Meghan (21:55):
When it is in the reds.
And so what I noticed is that alot.
People were sending like kindof like non-answers, just
confirming that hey, I'mchecking with this internally,
I'll get back to you.
Now, what's happening is thatevery industry is different.
Like some people do valuespeedy responses, but in the
(22:16):
cases that I've seen, customersvalue thorough and mindful
responses to reduce that backand forth because it, you know,
I we've all been there.
I personally don't likeinteracting with support over a
majillion emails.
Like no, and you know, and evenlike reducing that one like
non-answer, like I already knowthat you got my email because I
(22:38):
probably got a confirmationemail saying that my ticket's
been created.
I don't need a non-answer.
And and it's just adding to thebloat.
And it clutters up your inbox.
And yeah, I've seen negativesentiment.
So that is where like firstresponse time, it's more about
speed, it's less about quality.
Priscilla (22:57):
Yeah, I will say when I was early on, I think we just
had two people on the team atthat point doing support here at
Bus Brow.
I remember, I mean, ourresponse time was fantastic.
And I was so proud of it.
It was like under 20 minutes.
There were two of us.
We were killing it.
We also were not taking lunchesand it was self-imposed.
(23:18):
It's funny.
I mean, I it was me going, Ineed to get this down, not
leadership saying you need toget this down.
But it was like, oh, can I makeit even lower next month?
You know, can I make it evenlower?
And I remember at some pointone of my leaders coming to me
and saying, Hey, I think weshould probably stop publishing
or sharing our response time forthe whole company as like this
(23:40):
thing we're so proud of, becausewe are proud of it.
Like it's great that that'sthat fast.
Meghan (23:43):
Yeah.
Priscilla (23:44):
But it makes me feel like we're just trying to get
faster and faster and faster.
And that's not a good thing.
And it was a like aneye-opening moment for me
because I thought, oh yeah,you're right.
I mean, there were moments in aconversation where we would
send something much faster thanwe probably should have, or we
wouldn't take the time tounderstand the full background
(24:05):
of the, you know, customer'saccount, things like that.
Yeah.
And so we pretty quickly tookthat out of our like main thing.
And it changed a lot aboutwhere we put our focus and where
we put our value as a team,that now it is a lot more
focused on a thorough response.
And the funny thing is, evenwhen like our response time is
(24:28):
higher now than it was backthen, but it's not crazy high.
It hasn't been affected so muchby adding in this like more
intentional quality focus onthat.
So I think it's, I think it'sjust interesting if you're
listening to this and you'relike, well, you know, response
time is the most importantthing.
You are probably not going tolose so much by adding, you
know, a little time on yourresponse time, but you're going
(24:49):
to gain a ton and your customersare going to feel way more
taken care of if they're gettinga good quality response, even
if they're waiting, you know, acouple hours longer to get it.
Yeah.
Meghan (25:00):
I mean, we're not brain surgeons.
Right.
Right.
Burnout is real.
And I think it's not somethingthat we should be busting our
heads about.
And honestly, if we have higherresponse times, then maybe it's
time to look at headcount.
Yes.
Or the tools we're using.
Yes.
Yes, exactly.
(25:20):
Or like knowledge gaps.
Like, you know, is there afeature that it's just so
convoluted and internally wedon't know about it?
That's what's leading to this.
So that's why like it doespoint to other things.
But I do think that taking itoff the main like KPI or the
main report, it does allow youto kind of like, okay, this is
(25:44):
not necessarily how I'm beingmeasured on, but it is still
being measured in thebackground.
Yeah.
So that's that's an excellentstory.
I love that.
Yeah.
Priscilla (25:51):
Well, and I like the way that you set it up in the
beginning, saying the bandwidthidea of it, because you're
right.
You can see that response timego up and you can think, okay,
I've either got a bunch of lazypeople on the team who are not
responding to emails quickly andthey're going and doing stuff
in the middle of the day.
Or you could say, what'sprobably actually happening is
that we don't have enough peopleto service the inbox that we
(26:13):
have and the traffic that wehave, or we don't have the tools
in place, or we don't have agood mission control set up to
give us a good idea of acustomer's account in order to
get an answer in a quick way.
We have to spend a ton of timedigging, or like you said, we
don't understand this facet ofthe product and no one's ever
taught it to us.
And so every time we get aquestion about that, we're all
(26:35):
fumbling over ourselves tryingto find an answer.
And it's taking a lot longer todo that.
Meghan (26:39):
Yes, exactly.
I'm working on a team right nowwhere they went through a
massive restructuring andnaturally our response times is
gonna go up.
Yeah, it's gonna happen.
But I said, okay, if we want totell a story on how we can
maybe reduce the response timesto automating some of these
really manual or overly designedsystems or really like trying
(27:01):
to even build tools forourselves to self-surface
information.
Like, let's do that and let'strack it from a bandwidth
perspective because that is avery interesting story for sure.
Priscilla (27:12):
And I think bandwidth, looking at it from
that perspective, takes a littlebit of like the personal weight
or guilt off of it.
Like if the response time, whenyou call it response time,
there's a personal aspect to itthat you're like, I'm not
responding fast enough.
That is the problem.
And this time is going up.
And so I am relating that backto myself and the fact that I am
not responding fast enough.
(27:32):
But if you talk about it forthe bandwidth perspective, it
takes off that personal guilt,which I think for a lot of us
who work in customer support,that like people pleasing,
taking on a lot of that, youknow, weight and shame and
stuff.
I think that's pretty normalfor these types of people.
Very and can be reallybeneficial.
Meghan (27:51):
When I was at Whereby, you know, I was there during the
height of the pandemic.
Our ticket volume increased by300%.
Of course.
Yeah.
That one weekend in March.
Priscilla (28:02):
Oh my gosh.
We were in the same boatbecause we were in podcasting.
So the April 2020 rise wasunbelievable.
Meghan (28:11):
Real.
Yes.
And our backlog of tickets wasprobably 2,000.
Um, like insane.
Oh my gosh.
Yeah, insane.
And so I'm a ticket cruncher.
Yeah, it's it's in my naturalspirit.
Yes.
I want to see that number godown.
It's stressful.
And my boss, you know, had totell me, slow down.
(28:32):
These people are probably gonnawait weeks to hear back from
us.
Let's make sure that we'retaking our and we didn't delete
a single email.
Priscilla (28:40):
I think that's a really good thing for your boss
to have said that if someone'sgonna wait a couple weeks for
you to respond, you better makesure that response is really
good.
Yeah.
And I think for peoplelistening who are in those kind
of situations where they don'thave enough bandwidth for the
traffic they're seeing insupport and they're like, none
of my metrics are gonna be goodbecause I'm, you know, so
underwater and I can't getleadership to get us either the
(29:03):
right tools or more people orwhatever it is.
That reminder of, hey, ifpeople are gonna wait two weeks
and that's something you can'tcontrol, yeah.
Then what you can control isthat your response can be really
good.
Like that you can have controlover.
So I think that could be reallyencouraging for someone who's
in that place right now, going,How do I handle this if I don't
have control over what we'remeasuring or, you know, make
(29:25):
sure that what you can control,you're controlling, which is the
response.
Take your time.
Yeah.
So I think it's important,obviously, to know what to
track.
But one thing you've talkedabout and mentioned a couple of
times so far is using data totell a story and to communicate
to your leaders.
And so I want to kind of moveinto that a little bit.
Jordan (29:44):
Yeah.
Priscilla (29:44):
What are the biggest challenges for support leaders
in showing that value beyond thequeue?
You know, how do you usemetrics to play a role in that?
Meghan (29:52):
When we talk about data and storytelling, the first
thing we have to do is know whowe're telling the story to.
And we should probably talk tothem.
Yeah.
We should probably ask them,what do you want to see?
Because I think a lot of peoplejust assume that leadership
wants certain aspects of it, butat the same time, like we need
(30:12):
to ask them, what is it thatyou're missing?
And what kind of information doyou want to see?
And there's no like rubric orinner bank of interview
questions.
You just have to start thatconversation.
Jordan (30:24):
That reminds me of what Priscilla was talking about
earlier.
She was reporting on herresponse time every time.
And then the leaders were like,Yeah, we don't really
interested in that so much.
Right.
Yeah.
Yeah.
Meghan (30:34):
Like typically one of the biggest challenges right now
is you know, support as a costcenter type views, do, like I
said, directly come frommetrics.
And sometimes leadership doeswant to see those metrics.
But the thing is, is that itdoes change the narrative a lot
on how the rest of the companyviews the support.
(30:55):
And so there are better metricsto measure impact.
And I think that if youcollaborate with leadership and
really talk them through andexplain why you want to use
these metrics.
So some of my favorite metricsare measuring impact.
Our person in particular arethe recontact rate.
(31:16):
So the recontact rate measureshow often customers reach out
again after their issue wasconsidered resolved.
There are kind of two sides ofthat story.
You know, maybe there's roomfor improvement, for support to
better learn a topic, or maybelearn why wasn't it resolved
truly the first time?
(31:37):
Or like I said, productusability.
Like, you know, is theresomething in the product that is
constantly generating thesetypes of questions?
So that's something that youcan see like where can we get
started on that?
Um, because we don't like docreating duplicate emails
essentially and more ticketvolume for something that should
have been resolved the firsttime.
(31:58):
And then another thing that Imentioned before is obviously
customer sentiment.
You know, this is typicallymeasured by AI, like because it
does measure sentiment feelingsbased off of the ticket context.
Most of our customers lovetelling us this is frustrating,
or I don't appreciate, I don'tunderstand, or you know, things
(32:19):
like that.
And so I think that like thiscan give us a lot more context
to how the customer is feelingbecause ultimately we are there
to support them and we are thereto uplift them.
And so it's important that wedo measure this type of
analysis.
It's like a qualitative type ofmetric as well.
Yeah.
Even though you can assignnumber to it, but it's still
(32:40):
very helpful.
Yeah.
Priscilla (32:42):
Yeah.
Specifically about sentiment,how would you go about measuring
that?
I mean, it's not, I use HelpScout, and I don't think there's
anything in there in HelpScout.
So how would you get thatsentiment?
Meghan (32:53):
So I think like you have to use external like AI, like
some people hire, you know, AIpeople or someone within your
team if they're looking to flextheir AI muscle and they want to
build more bots that arehelpful.
And like I've seen teams eventake it as far as building a
dashboard inversal, which is oneof those like no-code type of
(33:15):
like lovable-esque apps.
And so that you can surfacethat information.
I think that with sentiment,you could measure the change.
Let's say somebody starts offas frustrated and is actually
positive at the end.
That tells a great story.
Hopefully positive.
Yeah.
Right.
But even if it is negative,like you can still learn from
(33:36):
that.
Priscilla (33:36):
Yeah.
Yeah.
And sometimes it's morebeneficial to know that someone
came in frustrated and theirsituation was turned around and
they left happy than coming inhappy and leaving happy.
Like it's a harder thing to do.
And so it's good to measurethat, to know that.
I think that's great.
And it's one of those things,you know, we talk about AI a
lot.
And it's one of those uses ofAI that can be really super
(33:58):
beneficial that is not justtaking away jobs.
You know what I mean?
That the worry that peoplehave.
It's like AI can be a reallybeneficial thing in scraping all
that content and giving youinformation on your customers
and on these interactions thatyou wouldn't have had before
without a ton of manual work.
Meghan (34:15):
Yes, exactly.
Priscilla (34:16):
And so I think that's a really great example.
What about retention revenueand product improvement?
Like, how do you take stats anddata and metrics and use them
in a way to influence thosethings?
Meghan (34:29):
With retention, you know, when I talk about
products, I mean like SaaS, youknow, like software as a service
product.
Most of us have trials, maybelike money back guarantee, like
refund periods where you're ableto ask for refund with no
questions asked.
And I think most of the timeour customers are reaching out
to us in that very openingwindow.
(34:51):
And I think like withretention, if you have that set
up, let's say you're at the endof that 30-day or two-week
period, you can say X percent ofpeople who got in touch with
support renewed for thefollowing month or something or
like renewed past that trialperiod.
(35:12):
I think when you have thosefirst 90 days, you know, I think
like every single SaaS productor even like service, I'm sure
the product team has calculatedlike what is that adoption
window when customers actuallyactivate and become like almost
like a fan and you know thatthey're not going anywhere.
I think it's important to askyour product team, like, what is
(35:33):
that window that we're hopingpeople are actually renewing
extra X amount of days?
And so when you say, like, forthe people that canceled, let's
say like 90% of them didn't evenreach out to support.
Yeah, you know, and that canhave a direct relationship with
being like, hey, X percent ofpeople that reached out to
support, they actually renewed.
(35:54):
And so that's one side of like,I know it it can be extremely
hard, especially diving intorevenue.
Like revenue, I think that oneof our best things is we know
maybe a customer is asking forspecific features for like a
high-ticket type of product.
We kind of like pre-qualifythem and support and pass them
(36:16):
over to sales.
How many of those have actuallyconverted?
You know, that's a greatbecause I know sales that
directly correlates to money.
Yeah.
But like imagine how manyinternal escalations, how many
handovers have actually resultedin a sale.
That's a number you can grablike easily.
Another thing is like withrefunds, it's a little tricky
(36:39):
because most of the time peoplelike they might churn for
whatever reason, but their churnreason isn't listed there, or
maybe there's multiple things,or they don't identify with it.
But it's always a variousfactors of things.
So that's always somethingsuper hard.
But in the support-driven slotcommunity, for those that are
(37:00):
not part of it, shoulddefinitely join.
Um, I'm in there quite a lot.
Somebody posted about this lastweek where they want to kind of
drive the needle on bugs, forexample.
And like, how can we show thatour customers are like leaving
us because of bugs?
Yeah.
And that's where you can kindof like find a bug that was a
(37:21):
high impact bug and workbackwards.
See how many customers haveactually reported that bug and
how many actually churned, eventhough it might be churning for
a different reason.
You can still show that in thereport of being like 30% of the
people that have reported thisbug since have left us.
Like that tells us maybe weshould fix this one.
(37:44):
Like so, kind of like workingbackwards and stuff.
It is very hard though to kindof connect the revenue and the
money part with support.
That's why it's notorious whywe're kind of like put on the
bottom as not impact drivers,um, because it is a little bit
harder, but not impossible toget those metrics.
Yeah.
(38:04):
And then with like productimprovements, like what I said
before, like having a goodticket taxonomy and showing that
what if we have negativesentiment about specific product
features, or if we notice thata lot of the support ticket
volume is coming from a specificfeature, hmm, that should raise
(38:25):
flags.
So, like I said, pairing theseother metrics like ticket
volume, how many tickets arecoming in about this specific
feature?
Maybe we should revisit it andsee what's going on.
Priscilla (38:35):
Yeah.
Yeah.
That just made me think of thetimes when someone walks up and
says, How many questions do weget about this issue?
Yes, exactly.
And you go, oh man, well, weget a bunch of questions about
that issue.
Yeah.
And then you go and you look itup to see how many you're
actually getting.
And it's actually not as manyas you thought, but it feels
like a lot.
Yeah.
That's another reason whymetrics are really important to
(38:55):
be very intentional about,because when you're in the inbox
working the queue, you cananswer four of the same
questions in a day and feellike, oh my gosh, a ton of
people run into this issue.
Meghan (39:06):
Yeah.
Priscilla (39:06):
But when you look at it in the whole grand scheme of
things, it's actually not as bigof an issue, or vice versa.
You're like, that's not really,I don't see that that much.
And then you go look at theactual numbers and you say, oh,
actually, it's a lot higher thanmy perception of it is.
And so it's just another reasonwhy being intentional about
your metrics and beingintentional about how you're
measuring things is really goodin more ways than just the
(39:29):
proving value, because thatdefinitely is, but the accurate
understanding of what yourcustomers are saying and how
they're interacting with yourproduct.
It's really important.
Yeah.
I think metrics can also play ahuge role in how we motivate
the people on our team.
And I think I said it up topthat the things that we measure
impact the emails that we sendor the phone calls that we take.
And so, you know, I want totalk a little bit about how we
(39:51):
can use the metrics that we aretracking to motivate and
encourage our team.
Meghan (39:58):
Yeah, I think for starters, I personally think
that metric should be revisitedevery quarter.
You know, I I don't think itshould be a set one and done
kind of situation.
And I like involving peopleearly on in the team to be like,
here are my proposed metricsthis quarter.
And like, because I kind offrame it as the like, how can we
(40:20):
show that um we're capable ofdoing X, Y, Z and do these
numbers accurately portray that?
Because your support departmentcan change every quarter, and
what story you're trying todepict or what proof you're
trying to show will change.
So having people have thoseconversations early on is super
(40:41):
motivating because people cansay, like, I don't think this is
a good idea, or I think this isa good idea, and then you know,
have a conversation about it.
Also, when you are introducinga new set of metrics, I think as
leaders, we have to be preparedto explain a motivation behind
them and also where we currentlystand.
Because I think for us, it'simportant to have a benchmark
(41:05):
that is realistic and where it'swhere we're standing right now
as the team capacity and thingslike that.
This is our benchmark.
And we're trying to go a littlebit lower, a little bit higher.
Jordan (41:16):
Yeah.
Meghan (41:17):
And I think having a real conversation in a meet and
like a team sync about thesetypes of metrics, like I think
is super important early on, youknow, just to get an
understanding of why we'recoming at it and why we're
measuring this.
For example, we talk aboutfirst response time a lot in
terms of bandwidth.
Like, you know, we're hopingthat this will go down because
(41:41):
we're in we're empowering you tomaybe automate some redundant
tasks or improve ourdocumentation.
And so try to show what kind ofimpact we're having beyond that
metric because it it relates itto almost like quality of life,
like workplace, um, and try toconnect it to that.
And I think that if you havethat conversation rather than
(42:03):
being like, these are ourmetrics, that will help um kind
of like plant those seeds earlyon.
Yeah.
And then also celebrate wins.
Like I I think a lot of peopledon't do this.
It's a big one.
It's a big one.
And even if there are losses,let's start a conversation just
because it's maybe in the red,that's okay.
(42:24):
Like, you know, let's haveconversations on how we can
improve it as a team or likewhat might be happening.
Like, I think open and honestcommunication is always the
best.
And yeah.
Priscilla (42:34):
And it makes it less scary when you have those
conversations regularly, andit's less like, oh my gosh,
something's bad.
So now we're talking about it.
It's more like this is aconversation we have
consistently.
And if the number goes up ordown, you know, we're still
having the conversation, we'retalking about it.
Yeah.
So as we wrap things up, I wantto give our listeners, you
know, some things they can takeaway into their work day
(42:55):
tomorrow if they're, you know,gonna go and start looking into
their digging into theirmetrics.
So if you could track just likeone or two things, what would
you tell people to track?
Meghan (43:06):
Another one that I didn't have time to mention is
replies to resolve.
I think that focusing less onthe back and forth, it's okay if
you follow up just to check inand something's being, you know,
it's not gonna skew theaverage, but I still think that
it gives us a better picturethan that first percent like
contact rate, like if the firstreply is resolved.
(43:27):
I think the number of repliesto resolve like gives us a
better story on like, hmm, thisticket had 16 replies, like from
the customer agent before theticket was resolved.
We should probably look intothat.
Yeah.
Priscilla (43:41):
Is there a mindset shift that you would like to see
people take when it comes tometrics?
Meghan (43:47):
Yeah, I think numbers do not define how you and your
team perform.
It is the starting point totrigger a conversation.
We are in support, we are notdefined by numbers.
It just is our gut feeling thatfeels validated by numbers, but
you should always look beyondthat number and try to
understand why that number goesup or go down.
(44:09):
And so it shouldn't define usat who how we support our
customers.
Priscilla (44:14):
I mean, I think that's the crux of all of it,
right?
Like we can use metrics, butyou know, the numbers are not
the definition.
Yes.
There's a story there and weneed to dig deeper and figure
out what the story is.
Well, thank you so much forjoining us, Meghan.
This was great.
I learned a lot about metrics.
I'm really excited to go andtake some things back into my
team and how we measure and whatwe share.
So I'm really excited aboutthat.
If there is anyone listening tothis who wants to learn more
(44:37):
about you or maybe use you as aconsultant for their team, how
can they get in touch with you?
Meghan (44:42):
LinkedIn.
LinkedIn is the best way to getin touch with me.
I also have linked a calendarlink where you can book a free
strategy session with me and wecan kind of just talk about, you
know, your sport infrastructuremetrics, what it is that you're
trying to do, and start theconversation there.
Priscilla (44:59):
Yeah.
And if you want to book thatstrategy session, we'll link
that in the show notes of thisepisode.
So you can just scroll down onyour iPhone and click that link
and set up a time because thatis gonna be, I mean, that's a
great resource that peopleshould definitely use.
Please, I love talking.
Meghan (45:15):
So yeah, please book me.
Priscilla (45:17):
I love it.
If you have a question or atopic that you would like us to
cover on a future episode, youcan shoot us a text using the
send us a text link in ourepisode description.
As always, if you like thisepisode, please review it on
Apple Podcasts or share it withsomeone who works in customer
support.
That would be a huge benefit tous.
Thank you again to MeghanGallagher for joining us on
(45:38):
today's episode.
Thank you for listening.
Now go and make someone's day.
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