ReThink Productivity Podcast
In this exciting podcast, Simon Hedaux from ReThink Productivity shares his insights and strategies for improving productivity and efficiency in the retail and hospitality industries. With the help of clients, partners, and the ReThink team, Simon covers everything from measuring and tracking productivity to developing and implementing effective strategies.
Whether you're a business owner, manager, or employee, this podcast is a must-listen for anyone who wants to learn how to get more done and improve their bottom line.
Here's what you can expect to learn:
- How to measure and track productivity
- Proven strategies for improving efficiency and reducing waste
- How to create a culture of productivity and innovation
- Tips for motivating and engaging your team
- Real-world examples of how other businesses have used ReThink Productivity to achieve success
Don't miss out on this opportunity to learn from the experts and get ahead of the curve with your own business.
ReThink Productivity Podcast
The Power of Micro-Feedback: How HappyOrNot Transforms Customer Experience
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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Welcome to the Productivity Podcast. Today, I'm delighted to be joined by Tim Waterton, cro, at Happy or Not. Hi, tim.
Speaker 2:Hi there Simon. Great to be here.
Speaker 1:Good. Thank you for taking the time out. I know we're all busy. We were talking off air about we wouldn't have placed bets on where we are in the world with all those bits, so I appreciate you taking the time out to chat. It's an absolute pleasure those bits.
Speaker 1:So I appreciate you taking the time out to chat. It's an absolute pleasure, good. So, tim, we're gonna find out a bit more about happy or not today all those exciting things you're doing with collecting data from customers and how people can take actions on them. But before that, tell us a bit about yourself. Where's your career been and how's it led you to happy or not?
Speaker 2:yeah for sure, I guess it's. Uh, it's really interesting to get into the customer data space. I've always been in the data space, it's fair to say. Probably I've been a little bit of a data nerd. The interesting bit when you get into the customer experience data in the feedback market is the data becomes a little bit more interesting, it's a bit more grounded, it's a bit more relatable.
Speaker 2:Some of the more abstract data forms I've worked with in the past are probably not quite as interesting there, but it's um. It's one of those areas where I actually worked for another finished company immediately before happy or not so, a company called m files and we worked in document and information management, um and core processes. So that kind of introduced me to working with finished companies and helping the Finnish company. So I don't know the joys of Finnair and Helsinki, also Finnish train services which, by the way, are amazing if you have any experience with British train services. But I still find it quite amusing that the inception of this and Happy or Not really was the first company that came up with the smiley face terminals, but a smiley face terminal company specializing in emotional feedback actually came out of Finland still astoundingly, given the natural Finnish propensity for not providing emotional feedback perhaps, yeah, it's a great place to be and it's a great set of data to be working with.
Speaker 1:But, yeah, it's a great place to be and it's a great set of data to be working with. So you talked about the smiley faces. People probably recognize those terminals when they've been in train stations, various shops, outlets, airports, et cetera. Tell us a bit about how you capture the data, the terminals themselves, which are the buttons we press or the screens we press now, and how all that flows through. Yeah, sure.
Speaker 2:So I mean Happy or Not started probably 10 years ago with the kind of inception of, I guess, the iconic terminal four buttons, you know four smiley emojis green to red customer feedback capture.
Speaker 2:The goal was really clear right to make it easy for customers to care that share their thoughts Happy or Not. Now, I think, serves just over 4 000 brands, 100 plus countries, and we've collected 2 billion plus amounts of feedback. Obviously, over that time the collection methods have evolved right. So we've gone from that simple terminal button price device and we've added in tablet-based kiosk options, basically a touch range which allows a slightly more richer set of questions but I'll come back to that a bit later because it's important that you don't go too far with the with complexity. And then we've added other things to that as well. So we've added digital options, we've added embeddable options so people can drop stuff into their own kiosks, and we now move, you know, further and further towards intelligent signage options as well. So the key is being able to capture the right stuff in the right place at the right time and to have a collection fabric that's varied enough to allow you to fit in a form factor for most deployment scenarios.
Speaker 1:Yeah, so that's kind of the front end and the capture side and then, as ever with these things, I assume there's a portal behind that's got analytics, got data in for the more, I suppose, serious data people that aren't capturing on the front end to understand that data and do something with it 100%.
Speaker 2:So one way of looking at it. We have, obviously, our own web app, our own mobile app. We've got our own analytics environment that typically gets deployed by the small to medium businesses and by the frontline and the operational teams in the field. For larger organizations.
Speaker 2:Many of our customers pull our data out via API and pull it into their own BI platforms and they slice and dice it every which way from Sunday and correlate it with other business metrics, operational metrics that are really important and some of the events that we can just touch on this again, I guess, a little bit later, but some of the events that we generate are very relevant to real-time action.
Speaker 2:So we're able to integrate with lots of other applications for alerting mechanisms. So, for example, if you see a particular set of conditions occur that are deviating from the norm in a particular location, we can generate an alert and pipe back to anything, generate a troubled ticket, pipe it to Slack, pipe it to Teams. What we generally find is we appeal tremendously now to the operational side of the business rather than just the insight side of the business. So it's not about retrospective insights and an analysis of a report of data surfaced in a report for the last three months. It's quite often real-time notification of something that happened in the last three minutes, so that's why we tend to tap into the operational side of it more. So that's the general consumption model.
Speaker 1:Yeah, that makes sense that operations are becoming more involved. You know an operator myself in a previous life. I'd say they're the heartbeat of most organizations and clearly where the book stops as well. So you know that toilet you're getting bad feedback on in X environment, I assume then you can push something to say. You know, four customers in the last Y have told you that there's a problem here. Can you send somebody to go and fix it? And that stuff's invaluable, isn't it? Because all you're going to do is just rack up more negative sentiment the longer that problem goes on, certainly in an airport environment where there's potentially nowhere else to go.
Speaker 2:Yeah, 100%.
Speaker 2:One example I tend to think about is even in the grocery environment is even in the grocery environment.
Speaker 2:We've got a grocery customer in the US who actually instruments the valuable areas of their business, so the specialty areas, if you like, around the edge of store, so their delicatessen counter, their meat counter, their pastry counter, their bakery, et cetera. So those are the higher margin goods and they tend to be a little bit more consultative, particularly kind of meat and deli and fish. That's where they generate a huge amount of their margin. So they actually instrument those areas and they use real-time alerts. So if they see people complaining about kind of queuing situations at those high margin stations, that's something that they can jump in on straight away. You're not really going to do it down down the center aisle, but but in that area it makes a whole load of sense to do it. So it's not just about the toilets and with the full range of the collection fabric now we're able to really instrument the store. So, particularly by using signage to complement devices, depending on where you are, we can actually really instrument each particular department or area of the store.
Speaker 1: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 we feel it, but when we're in business mode maybe we don't see as much, certainly if you're in their day-to-day. So dealing with those friction points, taking something, moving that feedback forward, clearly must be a competitive advantage from a happier customers, assume, I assume, spend more or come back yeah, I think come back is probably the key um.
Speaker 2:So retention is actually lifetime value and retention is the really really key one and and to that end, customers respond really well to being hurt. So they they feel they're being heard if they're giving feedback. But what we notice more than anything else is the part of the programs that we run out with our larger customer. As you said, we did program, so we encourage our large. In fact we provide templates for doing this, where we'll provide summaries of the items that they should be addressing and then they can pull those kind of lists out and they've usually responded to the top two or three things. And then then they typically post signage, sometimes digital, sometimes physical.
Speaker 2:That actually closes the loop with customers and said you told us that this particular item was an issue and this is what we've done for you. And I think when you close that loop with your customers, that really does reinforce the fact that you're listening. You're not just asking questions, because it sounds like a good idea to ask the questions, but you're asking questions and you're taking action on it and that really cements it. But I mean standard CX stuff, I guess, but it's still important to get done.
Speaker 1:Yeah, absolutely. I mean, it's one of those classes and if you can ask for feedback, do something with it or don't. Don't ask me for feedback because it's a waste of everybody's time, is the reality. Yeah, and just talk to me about kind of response rates in location, in moment feedback, because I know I'm sure like you, I've shopped in lots of places where at the end of the journey I've got a scan, this code and fill me in 10, 20, 30 questions that probably important to that organization, not necessarily important to me, but a lot of that is based on my last point of interaction, which is typically the checkout, so that there can be quite a strong bias in some of those to how good your checkout experience was or wasn't. So just talk about because you've kind of described yours placed in various locations, you know real quick mechanism to capture that feedback, so that emotional, in the moment micro piece and does that tend to drive higher volumes for you?
Speaker 2:Absolutely it does. Every single customer we work with is shocked by the kind of volume that we generate. I'm shocked. They expect to get that and I think that's because we don't always replace traditional surveys. In many cases we go alongside traditional surveys to complement it.
Speaker 2:But what people are not getting from traditional surveys and follow-up email surveys etc is pure volume and contextual relevance. So, as you said, if the last thing you do is you leave the store and then you get a big survey to, quite often delivered via email, you don't really remember an awful lot about what happened in that shopping experience. To be quite frank about it, it's almost the exit that you kind of remember, whereas if you're actually capturing stuff in the moment, at the point of service, you can ask a question that's relevant to that point and you can make it very, very short and sharp and therefore it's very, very easy to provide relevant feedback. So key one is point of service versus after the fact. That's really key um in context rather than out of line. So if you ask a survey I don't know if you've noticed this, but when you get email surveys in an era of like hyper personalization or just let's call it personalization, I'll take the hyper out of it. But in an era of personalization, where we expect that, have you noticed in surveys the number of times that you get asked which, which of our stores did you visit, what day did you visit and which department did you visit and what did you contemplate buying? And actually the first five or six questions are just establishing some kind of context so that they can put business metadata on it and be able to interpret it.
Speaker 2:Well, frankly, we don't have the attention span to deal with that anymore. Six questions and I'm done. So. I've not even answered one of the next 25 about what the experience is like. That I barely remember. Most of it is just about context. So for us, we already have context because we know exactly where we are. We know what that experience point is. The survey is very specific to that experience point.
Speaker 2:And then survey structure is key for us. Um, we, we believe in micro feedback 100 and our definition for that is the first question, which is simply respond via an emoji, because it's we know how it is right. It's just dead easy. It's it's culturally agnostic, um, it's language agnostic.
Speaker 2:And then, ideally, one follow-up question, possibly two follow-up questions, with always the option for somebody to provide a verbatim, and the verbatim is the real key to this as well, because if you can get to that verbatim feedback and people will type and share verbatims that information is free and unstructured and it's the closest thing to telling you what kind of action you can take to remediate something. By the way, I say remediate something, it's amazing how many times people give positive feedback. So one of the biggest attributes that we find that shocks people is people turn around and call out members of staff doing really good stuff. Yeah, we're able to identify that and go back and say, like go to Sally and turn around and say to her you did a great job with this customer, you've got some really specific praise and our customers love that bit. Right, it's not the bit that they come to us for in the first place, but suddenly realize just how valuable it is in closing the loop with our own staff when they've done good stuff.
Speaker 1:I wouldn't say a nice byproduct is probably the wrong terminology, but an unintended consequence of capturing the data, but a positive one nevertheless. And I find that really interesting because I suppose at a cynical level, if it was four emojis I'd be saying okay, tim, that's great. So what? So what? You know, you tell me it's not great, you tell me it's great, I can't do anything with it. So that follow-up questions, those verbatim comments good, bad, indifferent, I think color that picture in. So at the top line we know it's good, bad, indifferent. But then actually now we can start to get to the richness of the so what and I assume your customers find that really, really valuable to be able to then turn those into action orientated insights to the teams, whether things to the center, whether that be buying, merchandising, pricing, whatever it is.
Speaker 2:Yeah, and typically our customers will deploy a hybrid. So a really busy grocery store. It makes a lot of sense just to have a button terminal on the exit where the question turns around, say, how do we do? Um, that gives you a good idea about how you're doing generally in serving customers across the day, across the days of the week, but there's nothing actionable out of that. But it's really good to be able to calibrate roughly what experience you're delivering over time. But it's the verbatim feedback and the follow-up questions at the other points in the store that allow you to drive immediate action from it. So one's really good for time series deviation detection. The other one's great for driving the action. But it's definitely a hybrid in the collection fabric and hybrid in the approach.
Speaker 1: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 to circle back on so kind of things, like if I was working a working back in retail, the things that be going through my mind would be okay. So is this difficult to install? Do I need to do a load of wiring in the, in the stores, because that's really tricky. Is it simply plug in and play? Is it easy to update questions? Add a question, remove a question? So if you just kind of again fill the fill in a bit of color on that for us, yeah, well of.
Speaker 2: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 you can get. The devices are shipped to store, boxed, install instructions et cetera. They're actually pre-configured. So they're pre-configured as experience points with surveys pre-loaded. So it's case of an assembly which even takes some you know, a fool like me um, less than less than a couple of three minutes to to put together and then power up auto connection survey. Auto. Low smiley faces come up. You're good to go.
Speaker 2:Um, the ongoing side is great. I think there's a lot of approaches in the market. We believe that customers should be able to completely self-manage if they need to. Um, funny enough, our largest customers quite often self-manage, um, and our smallest customers tend to self-manage, and in the middle we 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 completely able to self-manage. So they can set their schedules, they can set their surveys, they can set surveys to change according to particular particular schedules etc. They can attach surveys to particular experience points or groups um full academy training available and obviously customer success to support behind that. If people get a little bit stuck or they want some further advice, or they want some best practice advice, so it's very easy to literally grab it, go, power it up and just get up and running and then you can move into kind of more advanced functions self-serve by the academy or with us helping you out.
Speaker 1:So a myriad of options, but as close to plug-in and play as practically possible.
Speaker 2:Yes, absolutely.
Speaker 2:I mean, the one area that's kind of really interesting around the signage space is and that's an area where we're kind of heading exploring in more detail is people talk about QR codes and and how you can kind of work in this space and assume that if you give a large organization some QR codes, that they're going to be able to come up with some intelligent signage with some good designs and the right form factors in the right place and deploy it.
Speaker 2:But if you've never tried trying to hook up with the marketing department, the brand team, et cetera within a large organization and turn around and say I've got 25 QR codes here and I really would like these deployed in this way, et cetera, good luck with that, because you'll be back 12 months. Yeah, so one of the things that we've worked out the real skill to that one is we've developed that expertise in building, I guess, the smart signage that we do with our customers in a kind of consultative way. So that helps us expand that collection fabric in that signage area, because firing people a few QR codes or different mechanisms, it's so hard to get a project rolled out in almost any organization today. Nobody's sitting around on their hands waiting for something to do. It's another area where we provide a full fat service, if you like, rather than, um, just just give you something and say get on with it in the future then.
Speaker 2:So I assume ai features in there somewhere yeah, I just I'll get shot if I turn around. So they didn't have no nice strategy. 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 ability to influence the top line, or the bottom line, quite honestly, is a bit of fun right now.
Speaker 2:So so much AI capability, but where do you get the rubber on the road? I think that's the real key and we're fortunate we're in a place where it's quite natural for us. Obviously the first place comes into verbatim. So suddenly something was unbelievably complex and difficult to deal with, like natural language. Processing beforehand becomes far easier with access to. So that's that's key for us.
Speaker 2:We already post-process all of our verbatim. We categorize them based upon vertical market sensitivity points. So, for example, in a food environment and we do quite a lot in food service, which is essentially retail in the food and beverage space it makes sense when you're capturing feedback there to be able to classify your verbatim to price, value, cleanliness, service and so. So we're really good at categorization. That's a natural thing for us to do. So we provide summaries, we provide monthly summaries, lots of different variations there. Ascorisation is really key and I think the other bit that becomes really interesting is once you start to use AI to correlate your time series data with your qualitative feedback as well. So using time series anomaly detection to pick up deviations and then match deviations back to what's happening, to what are the statements being made in various parts of the store at that particular point in time, which kind of brings you on to the concept of correlation with sales data, staffing data, football data, et cetera certainly in a world where everybody's experienced cost challenges with ni.
Speaker 1:So any any employer's got though that double whammy of the when we've talked about it a lot on the podcast. But they've experienced the threshold dropping and then the percentage going up. So two hits correlating that with your rotors schedules to see, is there a correlation of, you know, an uplifting sentiment in happiness of customers at certain points. A downshift is that because you've got back staffing too much, there's lack of availability, you've closed checkouts, you've changed your offer. I think that that's the gold dust that some organizations on the edge of some are starting to creep into it. Others probably haven't even considered it because there's too much other stuff going on. But when we're in a world of dealing with, and probably ongoing, less colleagues on the floor in food service, in retail, wherever these fine marginal gains are going to be so much more valuable because all the easy stuff's been done.
Speaker 2:Absolutely. It's really easy to sit there and turn around and say what does one point mean? But one point means a heck of a lot. Um, really really means a lot when, when you're resource constrained, and I think it's going to get and this is a little bit more of an out there statement, so I wouldn't take it as being being anything close to gospel, but I think what's held people back in correlating the various types of data that have been available is it's hard work. Right, building warehouses is hard work. Data quality is hard work. Hyping it from various sources and integrating it is hard work, and then even visualizing it is actually quite hard work. I'm not too sure, but most people in most organizations kind of have got charter phobia because they just see so many charts all the time and it drives you mad.
Speaker 2:See a situation, probably over the next year or two, where data is going to be exposed through agents, and I think we see here so much about agentic, ai and and agents cooperating and that makes perfect sense.
Speaker 2:But I think we'll also see that orchestrated. So the larger organizations are going to be investing in kind of orchestration platforms that will be there to get various agents communicating, and I can see an environment where our data might be surfaced by an agent that will talk to a sales data agent, that will talk to a scheduling agent, and actually that's going to a scheduling agent that will talk, and and actually that's going to be orchestrated. So, instead of having to write incredibly complex sql queries with complex joins and star query schemas and everything being fixed, I can see a more dynamic data fabric where it's going to be so much easier for people to ask questions of. I have a drop in sales on Thursday that is unaccountable. Can you tell me what my scheduling looked like at that particular point in time and can you check in with customer experience data to find out whether we saw a drop in sentiment as well? At the same time, I could see that we're talking a couple of years out, but it's the pace at which that's moving.
Speaker 1:Uh, I could I could genuinely see that starting to happen, and I think it'll open that capability to companies that didn't that just couldn't get it done before because it was just too much heavy lifting yeah, I agree, and I think, as ever with those things, the first set of outputs are probably things that are going to shock, expose, make people feel uncomfortable because they've never been able to get there and join all those points, like you say, without an army of people or big, big, big software budgets or investments.
Speaker 1:So I'd encourage people you've kind of got to ride that because once you get through those initial shock pain points, you'll really then start to move forward. But yeah, I, I agree, it's coming. Every day you see something different, don't you? In terms of where the tech's going, somebody new, excuse me, a new name. It is a world that people need to be on board with, not necessarily be in it right away, but be watching very closely when the time for them is to to jump in and start to utilize yeah, and I'm kind of closing the loop with something that you said earlier.
Speaker 2:We we were talking about, or maybe I said I'm not too sure, um, but but when we were talking about positive feedback versus critical feedback and and the fact that we find that our customers tap into that positive feedback and see it as being incredibly valuable, even if it wasn't what they were looking for beforehand. They were looking for problems to fix and then they find people to fix. I think, as we see these more advanced data correlations come out and there'll be some shocks in there in terms of what they'll expose, as you kind of highlighted. I think it will also show what good practice looks like and then training out good practice is just as important as looking to fix bad practice.
Speaker 2:And again, we find that quite regularly that there's a natural coupling between what we do and frontline staff training and, at the end of the day, frontline staff is where the rubber meets the road, because that is where your brand gets its personification, that's where you interact with the brand in store. So always got a massive amount of respect for frontline staff and how much they have to get done and how multidisciplined they've got to be and what I'm like, as a member of the general public, to deal with how challenging I am. So, yeah, they obviously need a lot of training. Funnily enough, from a partnership side, we get approached by and we're exploring some really solid partnerships with training companies, because that verbatim data surfaces where there are little weak spots and weak spots usually are not because somebody wanted to be weak, it's just they didn't know what they were supposed to do next, and so they see it as an opportunity for reinforcement training for frontline staff, to actually guide the scheduling of training into particular regions or districts on particular topics.
Speaker 1:Again, it makes complete sense in a world where we've probably got to be more multi-skilled because there's just less offers. So we need to know more to keep that intelligence up to date, and there'll be more new stuff coming down the line as offers grow, offers change, et cetera, et cetera, for people to keep competitive. Tim, it's been an absolute pleasure to chat. If people want to find out more, interact with you. Where's the best place for them to find you?
Speaker 2:On LinkedIn or Happy or Not website or speak to any of us. Literally Anybody at Happy or not is more than happy to have a conversation with anybody so uh yeah, linkedin's probably the best one, okay so, yeah, we'll post your profile.
Speaker 1:We'll post your profile. We'll put a link to the website on the show notes. People can easily find you. Thanks once again, fascinating chat and we'll catch up soon uh fit, really enjoyed it.
Speaker 2:Thank you very much, simon.
