July 13, 2025 • 28 mins
Tim Waterton, CRO at HappyOrNot, shares how their iconic smiley face terminals pioneered the micro-feedback approach that has collected over 2 billion pieces of customer feedback across 4,000 brands in 100+ countries. Their in-the-moment data collection method delivers higher response rates and more actionable insights than traditional surveys.
• HappyOrNot started with simple four-button terminals and has evolved to include tablet kiosks, digital options, and intelligent signage
• Real-time alerts notify staff when customer satisfaction drops, enabling immediate operational intervention rather than just retrospective insights
• Micro-feedback approach uses emoji responses followed by maximum 1-2 follow-up questions, making it accessible and easy
• Customers frequently provide positive verbatim feedback that identifies and recognizes excellent staff performance
• Terminals placed at strategic points (like high-margin specialty counters) help protect revenue while improving experience
• The system is designed for easy self-management with pre-configured devices that require minimal setup
• AI applications include verbatim analysis, sentiment categorization, and correlation with sales, staffing and foot traffic
• Future developments will enable organizations to interrogate connected data sources through conversational AI agents
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Welcome to the Productivity Podcast.
Today, I'm delighted to bejoined by Tim Waterton, cro, at
Happy or Not.
Hi, tim.
Speaker 2 (00:09):
Hi there Simon.
Great to be here.
Speaker 1 (00:12):
Good.
Thank you for taking the timeout.
I know we're all busy.
We were talking off air aboutwe wouldn't have placed bets on
where we are in the world withall those bits, so I appreciate
you taking the time out to chat.
It's an absolute pleasure thosebits.
So I appreciate you taking thetime out to chat.
It's an absolute pleasure, good.
So, tim, we're gonna find out abit more about happy or not
today all those exciting thingsyou're doing with collecting
(00:32):
data from customers and howpeople can take actions on them.
But before that, tell us a bitabout yourself.
Where's your career been andhow's it led you to happy or not
?
Speaker 2 (00:43):
yeah for sure, I guess it's.
Uh, it's really interesting toget into the customer data space
.
I've always been in the dataspace, it's fair to say.
Probably I've been a little bitof a data nerd.
The interesting bit when youget into the customer experience
data in the feedback market isthe data becomes a little bit
more interesting, it's a bitmore grounded, it's a bit more
relatable.
(01:04):
Some of the more abstract dataforms I've worked with in the
past are probably not quite asinteresting there, but it's um.
It's one of those areas where Iactually worked for another
finished company immediatelybefore happy or not so, a
company called m files and weworked in document and
information management, um andcore processes.
So that kind of introduced meto working with finished
companies and helping theFinnish company.
(01:26):
So I don't know the joys ofFinnair and Helsinki, also
Finnish train services which, bythe way, are amazing if you
have any experience with Britishtrain services.
But I still find it quiteamusing that the inception of
this and Happy or Not really wasthe first company that came up
with the smiley face terminals,but a smiley face terminal
(01:48):
company specializing inemotional feedback actually came
out of Finland stillastoundingly, given the natural
Finnish propensity for notproviding emotional feedback
perhaps, yeah, it's a greatplace to be and it's a great set
of data to be working with.
Speaker 1 (02:04):
But, yeah, it's a great place to be and it's a
great set of data to be workingwith.
So you talked about the smileyfaces.
People probably recognize thoseterminals when they've been in
train stations, various shops,outlets, airports, et cetera.
Tell us a bit about how youcapture the data, the terminals
themselves, which are thebuttons we press or the screens
we press now, and how all thatflows through.
Yeah, sure.
Speaker 2 (02:28):
So I mean Happy or Not started probably 10 years
ago with the kind of inceptionof, I guess, the iconic terminal
four buttons, you know foursmiley emojis green to red
customer feedback capture.
The goal was really clear rightto make it easy for customers to
care that share their thoughtsHappy or Not.
Now, I think, serves just over4 000 brands, 100 plus countries
(02:50):
, and we've collected 2 billionplus amounts of feedback.
Obviously, over that time thecollection methods have evolved
right.
So we've gone from that simpleterminal button price device and
we've added in tablet-basedkiosk options, basically a touch
range which allows a slightlymore richer set of questions but
(03:12):
I'll come back to that a bitlater because it's important
that you don't go too far withthe with complexity.
And then we've added otherthings to that as well.
So we've added digital options,we've added embeddable options
so people can drop stuff intotheir own kiosks, and we now
move, you know, further andfurther towards intelligent
signage options as well.
So the key is being able tocapture the right stuff in the
(03:36):
right place at the right timeand to have a collection fabric
that's varied enough to allowyou to fit in a form factor for
most deployment scenarios.
Speaker 1 (03:45):
Yeah, so that's kind of the front end and the capture
side and then, as ever withthese things, I assume there's a
portal behind that's gotanalytics, got data in for the
more, I suppose, serious datapeople that aren't capturing on
the front end to understand thatdata and do something with it
100%.
Speaker 2 (04:03):
So one way of looking at it.
We have, obviously, our own webapp, our own mobile app.
We've got our own analyticsenvironment that typically gets
deployed by the small to mediumbusinesses and by the frontline
and the operational teams in thefield.
For larger organizations.
(04:25):
Many of our customers pull ourdata out via API and pull it
into their own BI platforms andthey slice and dice it every
which way from Sunday andcorrelate it with other business
metrics, operational metricsthat are really important and
some of the events that we canjust touch on this again, I
guess, a little bit later, butsome of the events that we
generate are very relevant toreal-time action.
(04:48):
So we're able to integrate withlots of other applications for
alerting mechanisms.
So, for example, if you see aparticular set of conditions
occur that are deviating fromthe norm in a particular
location, we can generate analert and pipe back to anything,
generate a troubled ticket,pipe it to Slack, pipe it to
(05:08):
Teams.
What we generally find is weappeal tremendously now to the
operational side of the businessrather than just the insight
side of the business.
So it's not about retrospectiveinsights and an analysis of a
report of data surfaced in areport for the last three months
(05:28):
.
It's quite often real-timenotification of something that
happened in the last threeminutes, so that's why we tend
to tap into the operational sideof it more.
So that's the generalconsumption model.
Speaker 1 (05:39):
Yeah, that makes sense that operations are
becoming more involved.
You know an operator myself ina previous life.
I'd say they're the heartbeatof most organizations and
clearly where the book stops aswell.
So you know that toilet you'regetting bad feedback on in X
environment, I assume then youcan push something to say.
You know, four customers in thelast Y have told you that
there's a problem here.
(05:59):
Can you send somebody to go andfix it?
And that stuff's invaluable,isn't it?
Because all you're going to dois just rack up more negative
sentiment the longer thatproblem goes on, certainly in an
airport environment wherethere's potentially nowhere else
to go.
Speaker 2 (06:16):
Yeah, 100%.
One example I tend to thinkabout is even in the grocery
environment is even in thegrocery environment.
We've got a grocery customer inthe US who actually instruments
the valuable areas of theirbusiness, so the specialty areas
, if you like, around the edgeof store, so their delicatessen
(06:39):
counter, their meat counter,their pastry counter, their
bakery, et cetera.
So those are the higher margingoods and they tend to be a
little bit more consultative,particularly kind of meat and
deli and fish.
That's where they generate ahuge amount of their margin.
So they actually instrumentthose areas and they use
real-time alerts.
So if they see peoplecomplaining about kind of
(07:01):
queuing situations at those highmargin stations, that's
something that they can jump inon straight away.
You're not really going to doit down down the center aisle,
but but in that area it makes awhole load of sense to do it.
So it's not just about thetoilets and with the full range
of the collection fabric nowwe're able to really instrument
(07:23):
the store.
So, particularly by usingsignage to complement devices,
depending on where you are, wecan actually really instrument
each particular department orarea of the store.
Speaker 1 (07:34):
And that that's back to those friction points for
customers, isn't it?
So we might not see it.
We're all customers and we wefeel it, but when we're in
business mode maybe we don't seeas much, certainly if you're in
their day-to-day.
So dealing with those frictionpoints, taking something, moving
that feedback forward, clearlymust be a competitive advantage
from a happier customers, assume, I assume, spend more or come
(07:56):
back yeah, I think come back isprobably the key um.
Speaker 2 (08:01):
So retention is actually lifetime value and
retention is the really reallykey one and and to that end,
customers respond really well tobeing hurt.
So they they feel they're beingheard if they're giving
feedback.
But what we notice more thananything else is the part of the
programs that we run out withour larger customer.
As you said, we did program, sowe encourage our large.
(08:23):
In fact we provide templatesfor doing this, where we'll
provide summaries of the itemsthat they should be addressing
and then they can pull thosekind of lists out and they've
usually responded to the top twoor three things.
And then then they typicallypost signage, sometimes digital,
sometimes physical.
That actually closes the loopwith customers and said you told
(08:46):
us that this particular itemwas an issue and this is what
we've done for you.
And I think when you close thatloop with your customers, that
really does reinforce the factthat you're listening.
You're not just askingquestions, because it sounds
like a good idea to ask thequestions, but you're asking
questions and you're takingaction on it and that really
(09:06):
cements it.
But I mean standard CX stuff, Iguess, but it's still important
to get done.
Speaker 1 (09:12):
Yeah, absolutely.
I mean, it's one of thoseclasses and if you can ask for
feedback, do something with itor don't.
Don't ask me for feedbackbecause it's a waste of
everybody's time, is the reality.
Yeah, and just talk to me aboutkind of response rates in
location, in moment feedback,because I know I'm sure like you
, I've shopped in lots of placeswhere at the end of the journey
I've got a scan, this code andfill me in 10, 20, 30 questions
(09:36):
that probably important to thatorganization, not necessarily
important to me, but a lot ofthat is based on my last point
of interaction, which istypically the checkout, so that
there can be quite a strong biasin some of those to how good
your checkout experience was orwasn't.
So just talk about becauseyou've kind of described yours
(09:56):
placed in various locations, youknow real quick mechanism to
capture that feedback, so thatemotional, in the moment micro
piece and does that tend todrive higher volumes for you?
Speaker 2 (10:11):
Absolutely it does.
Every single customer we workwith is shocked by the kind of
volume that we generate.
I'm shocked.
They expect to get that and Ithink that's because we don't
always replace traditionalsurveys.
In many cases we go alongsidetraditional surveys to
complement it.
(10:32):
But what people are not gettingfrom traditional surveys and
follow-up email surveys etc ispure volume and contextual
relevance.
So, as you said, if the lastthing you do is you leave the
store and then you get a bigsurvey to, quite often delivered
via email, you don't reallyremember an awful lot about what
(10:53):
happened in that shoppingexperience.
To be quite frank about it,it's almost the exit that you
kind of remember, whereas ifyou're actually capturing stuff
in the moment, at the point ofservice, you can ask a question
that's relevant to that pointand you can make it very, very
short and sharp and thereforeit's very, very easy to provide
relevant feedback.
So key one is point of serviceversus after the fact.
(11:16):
That's really key um in contextrather than out of line.
So if you ask a survey I don'tknow if you've noticed this, but
when you get email surveys inan era of like hyper
personalization or just let'scall it personalization, I'll
take the hyper out of it.
But in an era ofpersonalization, where we expect
that, have you noticed insurveys the number of times that
(11:37):
you get asked which, which ofour stores did you visit, what
day did you visit and whichdepartment did you visit and
what did you contemplate buying?
And actually the first five orsix questions are just
establishing some kind ofcontext so that they can put
business metadata on it and beable to interpret it.
Well, frankly, we don't have theattention span to deal with
(11:58):
that anymore.
Six questions and I'm done.
So.
I've not even answered one ofthe next 25 about what the
experience is like.
That I barely remember.
Most of it is just aboutcontext.
So for us, we already havecontext because we know exactly
where we are.
We know what that experiencepoint is.
The survey is very specific tothat experience point.
And then survey structure is keyfor us.
Um, we, we believe in microfeedback 100 and our definition
(12:24):
for that is the first question,which is simply respond via an
emoji, because it's we know howit is right.
It's just dead easy.
It's it's culturally agnostic,um, it's language agnostic.
And then, ideally, one follow-upquestion, possibly two
follow-up questions, with alwaysthe option for somebody to
provide a verbatim, and theverbatim is the real key to this
(12:48):
as well, because if you can getto that verbatim feedback and
people will type and shareverbatims that information is
free and unstructured and it'sthe closest thing to telling you
what kind of action you cantake to remediate something.
By the way, I say remediatesomething, it's amazing how many
(13:09):
times people give positivefeedback.
So one of the biggestattributes that we find that
shocks people is people turnaround and call out members of
staff doing really good stuff.
Yeah, we're able to identifythat and go back and say, like
go to Sally and turn around andsay to her you did a great job
with this customer, you've gotsome really specific praise and
(13:32):
our customers love that bit.
Right, it's not the bit thatthey come to us for in the first
place, but suddenly realizejust how valuable it is in
closing the loop with our ownstaff when they've done good
stuff.
Speaker 1 (13:42):
I wouldn't say a nice byproduct is probably the wrong
terminology, but an unintendedconsequence of capturing the
data, but a positive onenevertheless.
And I find that reallyinteresting because I suppose at
a cynical level, if it was fouremojis I'd be saying okay, tim,
that's great.
So what?
So what?
You know, you tell me it's notgreat, you tell me it's great, I
(14:04):
can't do anything with it.
So that follow-up questions,those verbatim comments good,
bad, indifferent, I think colorthat picture in.
So at the top line we know it'sgood, bad, indifferent.
But then actually now we canstart to get to the richness of
the so what and I assume yourcustomers find that really,
really valuable to be able tothen turn those into action
(14:26):
orientated insights to the teams, whether things to the center,
whether that be buying,merchandising, pricing, whatever
it is.
Speaker 2 (14:35):
Yeah, and typically our customers will deploy a
hybrid.
So a really busy grocery store.
It makes a lot of sense just tohave a button terminal on the
exit where the question turnsaround, say, how do we do?
Um, that gives you a good ideaabout how you're doing generally
in serving customers across theday, across the days of the
(14:57):
week, but there's nothingactionable out of that.
But it's really good to be ableto calibrate roughly what
experience you're deliveringover time.
But it's the verbatim feedbackand the follow-up questions at
the other points in the storethat allow you to drive
immediate action from it.
So one's really good for timeseries deviation detection.
The other one's great fordriving the action.
(15:20):
But it's definitely a hybrid inthe collection fabric and
hybrid in the approach.
Speaker 1 (15:26):
And I want to touch on the future in a second and of
course, we'll talk about AI,because everybody's talking
about AI, but I just want tocircle back on so kind of things
, like if I was working aworking back in retail, the
things that be going through mymind would be okay.
So is this difficult to install?
Do I need to do a load ofwiring in the, in the stores,
because that's really tricky.
(15:47):
Is it simply plug in and play?
Is it easy to update questions?
Add a question, remove aquestion?
So if you just kind of againfill the fill in a bit of color
on that for us, yeah, well of.
Speaker 2 (15:58):
Well, of course I'm going to answer that it's
absolutely seamless.
You know I've been doing it,but actually it's as close to
that as you can get.
It is as close to that as youcan get.
The devices are shipped tostore, boxed, install
instructions et cetera.
They're actually pre-configured.
So they're pre-configured asexperience points with surveys
(16:27):
pre-loaded.
So it's case of an assemblywhich even takes some you know,
a fool like me um, less thanless than a couple of three
minutes to to put together andthen power up auto connection
survey.
Auto.
Low smiley faces come up.
You're good to go.
Um, the ongoing side is great.
I think there's a lot ofapproaches in the market.
We believe that customersshould be able to completely
self-manage if they need to.
Um, funny enough, our largestcustomers quite often
(16:48):
self-manage, um, and oursmallest customers tend to
self-manage, and in the middlewe tend to do quite a lot more
hand-holding.
Not too sure why that is, um,it's not entirely logical, but
tends to be the case.
Yeah, but people are completelyable to self-manage.
So they can set their schedules, they can set their surveys,
they can set surveys to changeaccording to particular
(17:09):
particular schedules etc.
They can attach surveys toparticular experience points or
groups um full academy trainingavailable and obviously customer
success to support behind that.
If people get a little bitstuck or they want some further
advice, or they want some bestpractice advice, so it's very
easy to literally grab it, go,power it up and just get up and
(17:32):
running and then you can moveinto kind of more advanced
functions self-serve by theacademy or with us helping you
out.
Speaker 1 (17:41):
So a myriad of options, but as close to plug-in
and play as practicallypossible.
Speaker 2 (17:46):
Yes, absolutely.
I mean, the one area that's kindof really interesting around
the signage space is and that'san area where we're kind of
heading exploring in more detailis people talk about QR codes
and and how you can kind of workin this space and assume that
if you give a large organizationsome QR codes, that they're
(18:09):
going to be able to come up withsome intelligent signage with
some good designs and the rightform factors in the right place
and deploy it.
But if you've never tried tryingto hook up with the marketing
department, the brand team, etcetera within a large
organization and turn around andsay I've got 25 QR codes here
and I really would like thesedeployed in this way, et cetera,
good luck with that, becauseyou'll be back 12 months.
(18:31):
Yeah, so one of the things thatwe've worked out the real skill
to that one is we've developedthat expertise in building, I
guess, the smart signage that wedo with our customers in a kind
of consultative way.
So that helps us expand thatcollection fabric in that
signage area, because firingpeople a few QR codes or
(18:53):
different mechanisms, it's sohard to get a project rolled out
in almost any organizationtoday.
Nobody's sitting around ontheir hands waiting for
something to do.
It's another area where weprovide a full fat service, if
you like, rather than, um, justjust give you something and say
get on with it in the futurethen.
(19:15):
So I assume ai features in theresomewhere yeah, I just I'll get
shot if I turn around.
So they didn't have no nicestrategy.
Um, yeah, everybody has it's.
It's a really I don't know.
It's a really interesting area.
There's AI without the abilityto influence the top line, or
(19:41):
the bottom line, quite honestly,is a bit of fun right now.
So so much AI capability, butwhere do you get the rubber on
the road?
I think that's the real key andwe're fortunate we're in a
place where it's quite naturalfor us.
Obviously the first place comesinto verbatim.
So suddenly something wasunbelievably complex and
difficult to deal with, likenatural language.
(20:01):
Processing beforehand becomesfar easier with access to.
So that's that's key for us.
We already post-process all ofour verbatim.
We categorize them based uponvertical market sensitivity
points.
(20:21):
So, for example, in a foodenvironment and we do quite a
lot in food service, which isessentially retail in the food
and beverage space it makessense when you're capturing
feedback there to be able toclassify your verbatim to price,
value, cleanliness, service andso.
So we're really good atcategorization.
(20:44):
That's a natural thing for usto do.
So we provide summaries, weprovide monthly summaries, lots
of different variations there.
Ascorisation is really key andI think the other bit that
becomes really interesting isonce you start to use AI to
correlate your time series datawith your qualitative feedback
(21:05):
as well.
So using time series anomalydetection to pick up deviations
and then match deviations backto what's happening, to what are
the statements being made invarious parts of the store at
that particular point in time,which kind of brings you on to
the concept of correlation withsales data, staffing data,
football data, et ceteracertainly in a world where
(21:33):
everybody's experienced costchallenges with ni.
Speaker 1 (21:34):
So any any employer's got though that double whammy
of the when we've talked aboutit a lot on the podcast.
But they've experienced thethreshold dropping and then the
percentage going up.
So two hits correlating thatwith your rotors schedules to
see, is there a correlation of,you know, an uplifting sentiment
in happiness of customers atcertain points.
A downshift is that becauseyou've got back staffing too
(21:55):
much, there's lack ofavailability, you've closed
checkouts, you've changed youroffer.
I think that that's the golddust that some organizations on
the edge of some are starting tocreep into it.
Others probably haven't evenconsidered it because there's
too much other stuff going on.
But when we're in a world ofdealing with, and probably
(22:15):
ongoing, less colleagues on thefloor in food service, in retail
, wherever these fine marginalgains are going to be so much
more valuable because all theeasy stuff's been done.
Speaker 2 (22:30):
Absolutely.
It's really easy to sit thereand turn around and say what
does one point mean?
But one point means a heck of alot.
Um, really really means a lotwhen, when you're resource
constrained, and I think it'sgoing to get and this is a
little bit more of an out therestatement, so I wouldn't take it
as being being anything closeto gospel, but I think what's
(22:54):
held people back in correlatingthe various types of data that
have been available is it's hardwork.
Right, building warehouses ishard work.
Data quality is hard work.
Hyping it from various sourcesand integrating it is hard work,
and then even visualizing it isactually quite hard work.
(23:15):
I'm not too sure, but mostpeople in most organizations
kind of have got charter phobiabecause they just see so many
charts all the time and itdrives you mad.
(23:35):
See a situation, probably overthe next year or two, where data
is going to be exposed throughagents, and I think we see here
so much about agentic, ai andand agents cooperating and that
makes perfect sense.
But I think we'll also see thatorchestrated.
So the larger organizations aregoing to be investing in kind
of orchestration platforms thatwill be there to get various
(23:57):
agents communicating, and I cansee an environment where our
data might be surfaced by anagent that will talk to a sales
data agent, that will talk to ascheduling agent, and actually
that's going to a schedulingagent that will talk, and and
actually that's going to beorchestrated.
So, instead of having to writeincredibly complex sql queries
(24:19):
with complex joins and starquery schemas and everything
being fixed, I can see a moredynamic data fabric where it's
going to be so much easier forpeople to ask questions of.
I have a drop in sales onThursday that is unaccountable.
Can you tell me what myscheduling looked like at that
particular point in time and canyou check in with customer
(24:42):
experience data to find outwhether we saw a drop in
sentiment as well?
At the same time, I could seethat we're talking a couple of
years out, but it's the pace atwhich that's moving.
Speaker 1 (24:53):
Uh, I could I could genuinely see that starting to
happen, and I think it'll openthat capability to companies
that didn't that just couldn'tget it done before because it
was just too much heavy liftingyeah, I agree, and I think, as
ever with those things, thefirst set of outputs are
probably things that are goingto shock, expose, make people
feel uncomfortable becausethey've never been able to get
(25:14):
there and join all those points,like you say, without an army
of people or big, big, bigsoftware budgets or investments.
So I'd encourage people you'vekind of got to ride that because
once you get through thoseinitial shock pain points,
you'll really then start to moveforward.
But yeah, I, I agree, it'scoming.
Every day you see somethingdifferent, don't you?
(25:35):
In terms of where the tech'sgoing, somebody new, excuse me,
a new name.
It is a world that people needto be on board with, not
necessarily be in it right away,but be watching very closely
when the time for them is to tojump in and start to utilize
yeah, and I'm kind of closingthe loop with something that you
said earlier.
Speaker 2 (25:56):
We we were talking about, or maybe I said I'm not
too sure, um, but but when wewere talking about positive
feedback versus criticalfeedback and and the fact that
we find that our customers tapinto that positive feedback and
see it as being incrediblyvaluable, even if it wasn't what
they were looking forbeforehand.
They were looking for problemsto fix and then they find people
(26:16):
to fix.
I think, as we see these moreadvanced data correlations come
out and there'll be some shocksin there in terms of what
they'll expose, as you kind ofhighlighted.
I think it will also show whatgood practice looks like and
then training out good practiceis just as important as looking
to fix bad practice.
(26:37):
And again, we find that quiteregularly that there's a natural
coupling between what we do andfrontline staff training and,
at the end of the day, frontlinestaff is where the rubber meets
the road, because that is whereyour brand gets its
personification, that's whereyou interact with the brand in
store.
So always got a massive amountof respect for frontline staff
(26:59):
and how much they have to getdone and how multidisciplined
they've got to be and what I'mlike, as a member of the general
public, to deal with howchallenging I am.
So, yeah, they obviously need alot of training.
Funnily enough, from apartnership side, we get
approached by and we'reexploring some really solid
partnerships with trainingcompanies, because that verbatim
(27:22):
data surfaces where there arelittle weak spots and weak spots
usually are not becausesomebody wanted to be weak, it's
just they didn't know what theywere supposed to do next, and
so they see it as an opportunityfor reinforcement training for
frontline staff, to actuallyguide the scheduling of training
into particular regions ordistricts on particular topics.
Speaker 1 (27:40):
Again, it makes complete sense in a world where
we've probably got to be moremulti-skilled because there's
just less offers.
So we need to know more to keepthat intelligence up to date,
and there'll be more new stuffcoming down the line as offers
grow, offers change, et cetera,et cetera, for people to keep
competitive.
Tim, it's been an absolutepleasure to chat.
If people want to find out more, interact with you.
(28:02):
Where's the best place for themto find you?
Speaker 2 (28:04):
On LinkedIn or Happy or Not website or speak to any
of us.
Literally Anybody at Happy ornot is more than happy to have a
conversation with anybody so uhyeah, linkedin's probably the
best one, okay so, yeah, we'llpost your profile.
Speaker 1 (28:18):
We'll post your profile.
We'll put a link to the websiteon the show notes.
People can easily find you.
Thanks once again, fascinatingchat and we'll catch up soon uh
fit, really enjoyed it.
Speaker 2 (28:28):
Thank you very much, simon.
Welcome to the Productivity Podcast.
Today, I'm delighted to bejoined by Tim Waterton, cro, at
Happy or Not.
Hi, tim.
Speaker 2 (00:09):
Hi there Simon.
Great to be here.
Speaker 1 (00:12):
Good.
Thank you for taking the timeout.
I know we're all busy.
We were talking off air aboutwe wouldn't have placed bets on
where we are in the world withall those bits, so I appreciate
you taking the time out to chat.
It's an absolute pleasure thosebits.
So I appreciate you taking thetime out to chat.
It's an absolute pleasure, good.
So, tim, we're gonna find out abit more about happy or not
today all those exciting thingsyou're doing with collecting
(00:32):
data from customers and howpeople can take actions on them.
But before that, tell us a bitabout yourself.
Where's your career been andhow's it led you to happy or not
?
Speaker 2 (00:43):
yeah for sure, I guess it's.
Uh, it's really interesting toget into the customer data space
.
I've always been in the dataspace, it's fair to say.
Probably I've been a little bitof a data nerd.
The interesting bit when youget into the customer experience
data in the feedback market isthe data becomes a little bit
more interesting, it's a bitmore grounded, it's a bit more
relatable.
(01:04):
Some of the more abstract dataforms I've worked with in the
past are probably not quite asinteresting there, but it's um.
It's one of those areas where Iactually worked for another
finished company immediatelybefore happy or not so, a
company called m files and weworked in document and
information management, um andcore processes.
So that kind of introduced meto working with finished
companies and helping theFinnish company.
(01:26):
So I don't know the joys ofFinnair and Helsinki, also
Finnish train services which, bythe way, are amazing if you
have any experience with Britishtrain services.
But I still find it quiteamusing that the inception of
this and Happy or Not really wasthe first company that came up
with the smiley face terminals,but a smiley face terminal
(01:48):
company specializing inemotional feedback actually came
out of Finland stillastoundingly, given the natural
Finnish propensity for notproviding emotional feedback
perhaps, yeah, it's a greatplace to be and it's a great set
of data to be working with.
Speaker 1 (02:04):
But, yeah, it's a great place to be and it's a
great set of data to be workingwith.
So you talked about the smileyfaces.
People probably recognize thoseterminals when they've been in
train stations, various shops,outlets, airports, et cetera.
Tell us a bit about how youcapture the data, the terminals
themselves, which are thebuttons we press or the screens
we press now, and how all thatflows through.
Yeah, sure.
Speaker 2 (02:28):
So I mean Happy or Not started probably 10 years
ago with the kind of inceptionof, I guess, the iconic terminal
four buttons, you know foursmiley emojis green to red
customer feedback capture.
The goal was really clear rightto make it easy for customers to
care that share their thoughtsHappy or Not.
Now, I think, serves just over4 000 brands, 100 plus countries
(02:50):
, and we've collected 2 billionplus amounts of feedback.
Obviously, over that time thecollection methods have evolved
right.
So we've gone from that simpleterminal button price device and
we've added in tablet-basedkiosk options, basically a touch
range which allows a slightlymore richer set of questions but
(03:12):
I'll come back to that a bitlater because it's important
that you don't go too far withthe with complexity.
And then we've added otherthings to that as well.
So we've added digital options,we've added embeddable options
so people can drop stuff intotheir own kiosks, and we now
move, you know, further andfurther towards intelligent
signage options as well.
So the key is being able tocapture the right stuff in the
(03:36):
right place at the right timeand to have a collection fabric
that's varied enough to allowyou to fit in a form factor for
most deployment scenarios.
Speaker 1 (03:45):
Yeah, so that's kind of the front end and the capture
side and then, as ever withthese things, I assume there's a
portal behind that's gotanalytics, got data in for the
more, I suppose, serious datapeople that aren't capturing on
the front end to understand thatdata and do something with it
100%.
Speaker 2 (04:03):
So one way of looking at it.
We have, obviously, our own webapp, our own mobile app.
We've got our own analyticsenvironment that typically gets
deployed by the small to mediumbusinesses and by the frontline
and the operational teams in thefield.
For larger organizations.
(04:25):
Many of our customers pull ourdata out via API and pull it
into their own BI platforms andthey slice and dice it every
which way from Sunday andcorrelate it with other business
metrics, operational metricsthat are really important and
some of the events that we canjust touch on this again, I
guess, a little bit later, butsome of the events that we
generate are very relevant toreal-time action.
(04:48):
So we're able to integrate withlots of other applications for
alerting mechanisms.
So, for example, if you see aparticular set of conditions
occur that are deviating fromthe norm in a particular
location, we can generate analert and pipe back to anything,
generate a troubled ticket,pipe it to Slack, pipe it to
(05:08):
Teams.
What we generally find is weappeal tremendously now to the
operational side of the businessrather than just the insight
side of the business.
So it's not about retrospectiveinsights and an analysis of a
report of data surfaced in areport for the last three months
(05:28):
.
It's quite often real-timenotification of something that
happened in the last threeminutes, so that's why we tend
to tap into the operational sideof it more.
So that's the generalconsumption model.
Speaker 1 (05:39):
Yeah, that makes sense that operations are
becoming more involved.
You know an operator myself ina previous life.
I'd say they're the heartbeatof most organizations and
clearly where the book stops aswell.
So you know that toilet you'regetting bad feedback on in X
environment, I assume then youcan push something to say.
You know, four customers in thelast Y have told you that
there's a problem here.
(05:59):
Can you send somebody to go andfix it?
And that stuff's invaluable,isn't it?
Because all you're going to dois just rack up more negative
sentiment the longer thatproblem goes on, certainly in an
airport environment wherethere's potentially nowhere else
to go.
Speaker 2 (06:16):
Yeah, 100%.
One example I tend to thinkabout is even in the grocery
environment is even in thegrocery environment.
We've got a grocery customer inthe US who actually instruments
the valuable areas of theirbusiness, so the specialty areas
, if you like, around the edgeof store, so their delicatessen
(06:39):
counter, their meat counter,their pastry counter, their
bakery, et cetera.
So those are the higher margingoods and they tend to be a
little bit more consultative,particularly kind of meat and
deli and fish.
That's where they generate ahuge amount of their margin.
So they actually instrumentthose areas and they use
real-time alerts.
So if they see peoplecomplaining about kind of
(07:01):
queuing situations at those highmargin stations, that's
something that they can jump inon straight away.
You're not really going to doit down down the center aisle,
but but in that area it makes awhole load of sense to do it.
So it's not just about thetoilets and with the full range
of the collection fabric nowwe're able to really instrument
(07:23):
the store.
So, particularly by usingsignage to complement devices,
depending on where you are, wecan actually really instrument
each particular department orarea of the store.
Speaker 1 (07:34):
And that that's back to those friction points for
customers, isn't it?
So we might not see it.
We're all customers and we wefeel it, but when we're in
business mode maybe we don't seeas much, certainly if you're in
their day-to-day.
So dealing with those frictionpoints, taking something, moving
that feedback forward, clearlymust be a competitive advantage
from a happier customers, assume, I assume, spend more or come
(07:56):
back yeah, I think come back isprobably the key um.
Speaker 2 (08:01):
So retention is actually lifetime value and
retention is the really reallykey one and and to that end,
customers respond really well tobeing hurt.
So they they feel they're beingheard if they're giving
feedback.
But what we notice more thananything else is the part of the
programs that we run out withour larger customer.
As you said, we did program, sowe encourage our large.
(08:23):
In fact we provide templatesfor doing this, where we'll
provide summaries of the itemsthat they should be addressing
and then they can pull thosekind of lists out and they've
usually responded to the top twoor three things.
And then then they typicallypost signage, sometimes digital,
sometimes physical.
That actually closes the loopwith customers and said you told
(08:46):
us that this particular itemwas an issue and this is what
we've done for you.
And I think when you close thatloop with your customers, that
really does reinforce the factthat you're listening.
You're not just askingquestions, because it sounds
like a good idea to ask thequestions, but you're asking
questions and you're takingaction on it and that really
(09:06):
cements it.
But I mean standard CX stuff, Iguess, but it's still important
to get done.
Speaker 1 (09:12):
Yeah, absolutely.
I mean, it's one of thoseclasses and if you can ask for
feedback, do something with itor don't.
Don't ask me for feedbackbecause it's a waste of
everybody's time, is the reality.
Yeah, and just talk to me aboutkind of response rates in
location, in moment feedback,because I know I'm sure like you
, I've shopped in lots of placeswhere at the end of the journey
I've got a scan, this code andfill me in 10, 20, 30 questions
(09:36):
that probably important to thatorganization, not necessarily
important to me, but a lot ofthat is based on my last point
of interaction, which istypically the checkout, so that
there can be quite a strong biasin some of those to how good
your checkout experience was orwasn't.
So just talk about becauseyou've kind of described yours
(09:56):
placed in various locations, youknow real quick mechanism to
capture that feedback, so thatemotional, in the moment micro
piece and does that tend todrive higher volumes for you?
Speaker 2 (10:11):
Absolutely it does.
Every single customer we workwith is shocked by the kind of
volume that we generate.
I'm shocked.
They expect to get that and Ithink that's because we don't
always replace traditionalsurveys.
In many cases we go alongsidetraditional surveys to
complement it.
(10:32):
But what people are not gettingfrom traditional surveys and
follow-up email surveys etc ispure volume and contextual
relevance.
So, as you said, if the lastthing you do is you leave the
store and then you get a bigsurvey to, quite often delivered
via email, you don't reallyremember an awful lot about what
(10:53):
happened in that shoppingexperience.
To be quite frank about it,it's almost the exit that you
kind of remember, whereas ifyou're actually capturing stuff
in the moment, at the point ofservice, you can ask a question
that's relevant to that pointand you can make it very, very
short and sharp and thereforeit's very, very easy to provide
relevant feedback.
So key one is point of serviceversus after the fact.
(11:16):
That's really key um in contextrather than out of line.
So if you ask a survey I don'tknow if you've noticed this, but
when you get email surveys inan era of like hyper
personalization or just let'scall it personalization, I'll
take the hyper out of it.
But in an era ofpersonalization, where we expect
that, have you noticed insurveys the number of times that
(11:37):
you get asked which, which ofour stores did you visit, what
day did you visit and whichdepartment did you visit and
what did you contemplate buying?
And actually the first five orsix questions are just
establishing some kind ofcontext so that they can put
business metadata on it and beable to interpret it.
Well, frankly, we don't have theattention span to deal with
(11:58):
that anymore.
Six questions and I'm done.
So.
I've not even answered one ofthe next 25 about what the
experience is like.
That I barely remember.
Most of it is just aboutcontext.
So for us, we already havecontext because we know exactly
where we are.
We know what that experiencepoint is.
The survey is very specific tothat experience point.
And then survey structure is keyfor us.
Um, we, we believe in microfeedback 100 and our definition
(12:24):
for that is the first question,which is simply respond via an
emoji, because it's we know howit is right.
It's just dead easy.
It's it's culturally agnostic,um, it's language agnostic.
And then, ideally, one follow-upquestion, possibly two
follow-up questions, with alwaysthe option for somebody to
provide a verbatim, and theverbatim is the real key to this
(12:48):
as well, because if you can getto that verbatim feedback and
people will type and shareverbatims that information is
free and unstructured and it'sthe closest thing to telling you
what kind of action you cantake to remediate something.
By the way, I say remediatesomething, it's amazing how many
(13:09):
times people give positivefeedback.
So one of the biggestattributes that we find that
shocks people is people turnaround and call out members of
staff doing really good stuff.
Yeah, we're able to identifythat and go back and say, like
go to Sally and turn around andsay to her you did a great job
with this customer, you've gotsome really specific praise and
(13:32):
our customers love that bit.
Right, it's not the bit thatthey come to us for in the first
place, but suddenly realizejust how valuable it is in
closing the loop with our ownstaff when they've done good
stuff.
Speaker 1 (13:42):
I wouldn't say a nice byproduct is probably the wrong
terminology, but an unintendedconsequence of capturing the
data, but a positive onenevertheless.
And I find that reallyinteresting because I suppose at
a cynical level, if it was fouremojis I'd be saying okay, tim,
that's great.
So what?
So what?
You know, you tell me it's notgreat, you tell me it's great, I
(14:04):
can't do anything with it.
So that follow-up questions,those verbatim comments good,
bad, indifferent, I think colorthat picture in.
So at the top line we know it'sgood, bad, indifferent.
But then actually now we canstart to get to the richness of
the so what and I assume yourcustomers find that really,
really valuable to be able tothen turn those into action
(14:26):
orientated insights to the teams, whether things to the center,
whether that be buying,merchandising, pricing, whatever
it is.
Speaker 2 (14:35):
Yeah, and typically our customers will deploy a
hybrid.
So a really busy grocery store.
It makes a lot of sense just tohave a button terminal on the
exit where the question turnsaround, say, how do we do?
Um, that gives you a good ideaabout how you're doing generally
in serving customers across theday, across the days of the
(14:57):
week, but there's nothingactionable out of that.
But it's really good to be ableto calibrate roughly what
experience you're deliveringover time.
But it's the verbatim feedbackand the follow-up questions at
the other points in the storethat allow you to drive
immediate action from it.
So one's really good for timeseries deviation detection.
The other one's great fordriving the action.
(15:20):
But it's definitely a hybrid inthe collection fabric and
hybrid in the approach.
Speaker 1 (15:26):
And I want to touch on the future in a second and of
course, we'll talk about AI,because everybody's talking
about AI, but I just want tocircle back on so kind of things
, like if I was working aworking back in retail, the
things that be going through mymind would be okay.
So is this difficult to install?
Do I need to do a load ofwiring in the, in the stores,
because that's really tricky.
(15:47):
Is it simply plug in and play?
Is it easy to update questions?
Add a question, remove aquestion?
So if you just kind of againfill the fill in a bit of color
on that for us, yeah, well of.
Speaker 2 (15:58):
Well, of course I'm going to answer that it's
absolutely seamless.
You know I've been doing it,but actually it's as close to
that as you can get.
It is as close to that as youcan get.
The devices are shipped tostore, boxed, install
instructions et cetera.
They're actually pre-configured.
So they're pre-configured asexperience points with surveys
(16:27):
pre-loaded.
So it's case of an assemblywhich even takes some you know,
a fool like me um, less thanless than a couple of three
minutes to to put together andthen power up auto connection
survey.
Auto.
Low smiley faces come up.
You're good to go.
Um, the ongoing side is great.
I think there's a lot ofapproaches in the market.
We believe that customersshould be able to completely
self-manage if they need to.
Um, funny enough, our largestcustomers quite often
(16:48):
self-manage, um, and oursmallest customers tend to
self-manage, and in the middlewe tend to do quite a lot more
hand-holding.
Not too sure why that is, um,it's not entirely logical, but
tends to be the case.
Yeah, but people are completelyable to self-manage.
So they can set their schedules, they can set their surveys,
they can set surveys to changeaccording to particular
(17:09):
particular schedules etc.
They can attach surveys toparticular experience points or
groups um full academy trainingavailable and obviously customer
success to support behind that.
If people get a little bitstuck or they want some further
advice, or they want some bestpractice advice, so it's very
easy to literally grab it, go,power it up and just get up and
(17:32):
running and then you can moveinto kind of more advanced
functions self-serve by theacademy or with us helping you
out.
Speaker 1 (17:41):
So a myriad of options, but as close to plug-in
and play as practicallypossible.
Speaker 2 (17:46):
Yes, absolutely.
I mean, the one area that's kindof really interesting around
the signage space is and that'san area where we're kind of
heading exploring in more detailis people talk about QR codes
and and how you can kind of workin this space and assume that
if you give a large organizationsome QR codes, that they're
(18:09):
going to be able to come up withsome intelligent signage with
some good designs and the rightform factors in the right place
and deploy it.
But if you've never tried tryingto hook up with the marketing
department, the brand team, etcetera within a large
organization and turn around andsay I've got 25 QR codes here
and I really would like thesedeployed in this way, et cetera,
good luck with that, becauseyou'll be back 12 months.
(18:31):
Yeah, so one of the things thatwe've worked out the real skill
to that one is we've developedthat expertise in building, I
guess, the smart signage that wedo with our customers in a kind
of consultative way.
So that helps us expand thatcollection fabric in that
signage area, because firingpeople a few QR codes or
(18:53):
different mechanisms, it's sohard to get a project rolled out
in almost any organizationtoday.
Nobody's sitting around ontheir hands waiting for
something to do.
It's another area where weprovide a full fat service, if
you like, rather than, um, justjust give you something and say
get on with it in the futurethen.
(19:15):
So I assume ai features in theresomewhere yeah, I just I'll get
shot if I turn around.
So they didn't have no nicestrategy.
Um, yeah, everybody has it's.
It's a really I don't know.
It's a really interesting area.
There's AI without the abilityto influence the top line, or
(19:41):
the bottom line, quite honestly,is a bit of fun right now.
So so much AI capability, butwhere do you get the rubber on
the road?
I think that's the real key andwe're fortunate we're in a
place where it's quite naturalfor us.
Obviously the first place comesinto verbatim.
So suddenly something wasunbelievably complex and
difficult to deal with, likenatural language.
(20:01):
Processing beforehand becomesfar easier with access to.
So that's that's key for us.
We already post-process all ofour verbatim.
We categorize them based uponvertical market sensitivity
points.
(20:21):
So, for example, in a foodenvironment and we do quite a
lot in food service, which isessentially retail in the food
and beverage space it makessense when you're capturing
feedback there to be able toclassify your verbatim to price,
value, cleanliness, service andso.
So we're really good atcategorization.
(20:44):
That's a natural thing for usto do.
So we provide summaries, weprovide monthly summaries, lots
of different variations there.
Ascorisation is really key andI think the other bit that
becomes really interesting isonce you start to use AI to
correlate your time series datawith your qualitative feedback
(21:05):
as well.
So using time series anomalydetection to pick up deviations
and then match deviations backto what's happening, to what are
the statements being made invarious parts of the store at
that particular point in time,which kind of brings you on to
the concept of correlation withsales data, staffing data,
football data, et ceteracertainly in a world where
(21:33):
everybody's experienced costchallenges with ni.
Speaker 1 (21:34):
So any any employer's got though that double whammy
of the when we've talked aboutit a lot on the podcast.
But they've experienced thethreshold dropping and then the
percentage going up.
So two hits correlating thatwith your rotors schedules to
see, is there a correlation of,you know, an uplifting sentiment
in happiness of customers atcertain points.
A downshift is that becauseyou've got back staffing too
(21:55):
much, there's lack ofavailability, you've closed
checkouts, you've changed youroffer.
I think that that's the golddust that some organizations on
the edge of some are starting tocreep into it.
Others probably haven't evenconsidered it because there's
too much other stuff going on.
But when we're in a world ofdealing with, and probably
(22:15):
ongoing, less colleagues on thefloor in food service, in retail
, wherever these fine marginalgains are going to be so much
more valuable because all theeasy stuff's been done.
Speaker 2 (22:30):
Absolutely.
It's really easy to sit thereand turn around and say what
does one point mean?
But one point means a heck of alot.
Um, really really means a lotwhen, when you're resource
constrained, and I think it'sgoing to get and this is a
little bit more of an out therestatement, so I wouldn't take it
as being being anything closeto gospel, but I think what's
(22:54):
held people back in correlatingthe various types of data that
have been available is it's hardwork.
Right, building warehouses ishard work.
Data quality is hard work.
Hyping it from various sourcesand integrating it is hard work,
and then even visualizing it isactually quite hard work.
(23:15):
I'm not too sure, but mostpeople in most organizations
kind of have got charter phobiabecause they just see so many
charts all the time and itdrives you mad.
(23:35):
See a situation, probably overthe next year or two, where data
is going to be exposed throughagents, and I think we see here
so much about agentic, ai andand agents cooperating and that
makes perfect sense.
But I think we'll also see thatorchestrated.
So the larger organizations aregoing to be investing in kind
of orchestration platforms thatwill be there to get various
(23:57):
agents communicating, and I cansee an environment where our
data might be surfaced by anagent that will talk to a sales
data agent, that will talk to ascheduling agent, and actually
that's going to a schedulingagent that will talk, and and
actually that's going to beorchestrated.
So, instead of having to writeincredibly complex sql queries
(24:19):
with complex joins and starquery schemas and everything
being fixed, I can see a moredynamic data fabric where it's
going to be so much easier forpeople to ask questions of.
I have a drop in sales onThursday that is unaccountable.
Can you tell me what myscheduling looked like at that
particular point in time and canyou check in with customer
(24:42):
experience data to find outwhether we saw a drop in
sentiment as well?
At the same time, I could seethat we're talking a couple of
years out, but it's the pace atwhich that's moving.
Speaker 1 (24:53):
Uh, I could I could genuinely see that starting to
happen, and I think it'll openthat capability to companies
that didn't that just couldn'tget it done before because it
was just too much heavy liftingyeah, I agree, and I think, as
ever with those things, thefirst set of outputs are
probably things that are goingto shock, expose, make people
feel uncomfortable becausethey've never been able to get
(25:14):
there and join all those points,like you say, without an army
of people or big, big, bigsoftware budgets or investments.
So I'd encourage people you'vekind of got to ride that because
once you get through thoseinitial shock pain points,
you'll really then start to moveforward.
But yeah, I, I agree, it'scoming.
Every day you see somethingdifferent, don't you?
(25:35):
In terms of where the tech'sgoing, somebody new, excuse me,
a new name.
It is a world that people needto be on board with, not
necessarily be in it right away,but be watching very closely
when the time for them is to tojump in and start to utilize
yeah, and I'm kind of closingthe loop with something that you
said earlier.
Speaker 2 (25:56):
We we were talking about, or maybe I said I'm not
too sure, um, but but when wewere talking about positive
feedback versus criticalfeedback and and the fact that
we find that our customers tapinto that positive feedback and
see it as being incrediblyvaluable, even if it wasn't what
they were looking forbeforehand.
They were looking for problemsto fix and then they find people
(26:16):
to fix.
I think, as we see these moreadvanced data correlations come
out and there'll be some shocksin there in terms of what
they'll expose, as you kind ofhighlighted.
I think it will also show whatgood practice looks like and
then training out good practiceis just as important as looking
to fix bad practice.
(26:37):
And again, we find that quiteregularly that there's a natural
coupling between what we do andfrontline staff training and,
at the end of the day, frontlinestaff is where the rubber meets
the road, because that is whereyour brand gets its
personification, that's whereyou interact with the brand in
store.
So always got a massive amountof respect for frontline staff
(26:59):
and how much they have to getdone and how multidisciplined
they've got to be and what I'mlike, as a member of the general
public, to deal with howchallenging I am.
So, yeah, they obviously need alot of training.
Funnily enough, from apartnership side, we get
approached by and we'reexploring some really solid
partnerships with trainingcompanies, because that verbatim
(27:22):
data surfaces where there arelittle weak spots and weak spots
usually are not becausesomebody wanted to be weak, it's
just they didn't know what theywere supposed to do next, and
so they see it as an opportunityfor reinforcement training for
frontline staff, to actuallyguide the scheduling of training
into particular regions ordistricts on particular topics.
Speaker 1 (27:40):
Again, it makes complete sense in a world where
we've probably got to be moremulti-skilled because there's
just less offers.
So we need to know more to keepthat intelligence up to date,
and there'll be more new stuffcoming down the line as offers
grow, offers change, et cetera,et cetera, for people to keep
competitive.
Tim, it's been an absolutepleasure to chat.
If people want to find out more, interact with you.
(28:02):
Where's the best place for themto find you?
Speaker 2 (28:04):
On LinkedIn or Happy or Not website or speak to any
of us.
Literally Anybody at Happy ornot is more than happy to have a
conversation with anybody so uhyeah, linkedin's probably the
best one, okay so, yeah, we'llpost your profile.
Speaker 1 (28:18):
We'll post your profile.
We'll put a link to the websiteon the show notes.
People can easily find you.
Thanks once again, fascinatingchat and we'll catch up soon uh
fit, really enjoyed it.
Speaker 2 (28:28):
Thank you very much, simon.
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