1 00:00:00,160 --> 00:00:02,510 Hello and welcome to the Retail Podcast. 2 00:00:02,740 --> 00:00:05,750 Today we're joined by Tim Waterton from Happy or not. 3 00:00:05,760 --> 00:00:09,030 Rather than keep him hanging on the stream, let me bring Tim 4 00:00:09,080 --> 00:00:12,210 onto the stream saying welcome to the podcast. 5 00:00:12,360 --> 00:00:16,810 I'd be so surprised if someone hasn't seen your product in a 6 00:00:16,820 --> 00:00:19,990 retail airport. You name it, your your product 7 00:00:20,780 --> 00:00:23,510 and service has been there. But for those who don't know, 8 00:00:23,620 --> 00:00:26,570 you just give us an overview what what Happy or not is all 9 00:00:26,580 --> 00:00:29,030 about. Yeah, you're right. 10 00:00:29,200 --> 00:00:31,690 We do tend to get seen typically in airports. 11 00:00:32,259 --> 00:00:34,390 London Heathrow is the classic one for me. 12 00:00:35,360 --> 00:00:39,710 We've deployed I think probably across 130 plus countries now 13 00:00:40,510 --> 00:00:43,480 4000 brands. A few of the retail brands I 14 00:00:43,490 --> 00:00:48,140 guess that would pop out would be Little Spa, Inditex, Zara. 15 00:00:48,210 --> 00:00:50,680 So lots of varieties of bread, yeah. 16 00:00:51,780 --> 00:00:56,270 But essentially people will have seen smiley terminals and 17 00:00:56,280 --> 00:00:58,120 they'll understand how that kind of works. 18 00:00:58,130 --> 00:01:03,530 But the the real principle behind what we do is very light 19 00:01:03,620 --> 00:01:10,580 in the moment, richness, capture or feedback at scale, and 20 00:01:10,670 --> 00:01:14,320 probably shouldn't be confused, if you like, by the simplicity 21 00:01:14,330 --> 00:01:16,780 of the interface, I mean the Smileys are very engaging, 22 00:01:17,670 --> 00:01:20,450 culturally agnostic, They're language agnostic. 23 00:01:20,460 --> 00:01:21,890 That's kind of why they work that way. 24 00:01:23,030 --> 00:01:25,480 But there are follow up questions behind that on 25 00:01:25,490 --> 00:01:30,180 different ranges of our devices and we're able to get that data 26 00:01:30,240 --> 00:01:35,760 back to the core in real time, slice it and dice set, match it 27 00:01:35,770 --> 00:01:39,020 with other stuff and come up with a whole bunch of insights. 28 00:01:39,030 --> 00:01:43,100 So the subtlety and the magic really happens behind the scenes 29 00:01:43,210 --> 00:01:47,160 rather than a point of capture. But obviously there's really 30 00:01:47,230 --> 00:01:51,880 quite a lot to making sure that we stand out as a brand and pop 31 00:01:51,890 --> 00:01:54,810 out, if you like, from the background and generate the 32 00:01:54,820 --> 00:01:57,250 volumes of feedback that give us the data to cure. 33 00:01:58,160 --> 00:02:01,080 So I mean, look, let's let's let's just unpack that cause I 34 00:02:01,090 --> 00:02:04,330 think there's a you, you, you, there's a lot of competition out 35 00:02:04,340 --> 00:02:08,570 there in this space and and people take a different approach 36 00:02:08,639 --> 00:02:12,560 to to feedback. So if I'm one of your, I mean 37 00:02:12,950 --> 00:02:16,230 obviously the use cases will vary whether you're a grocer and 38 00:02:16,240 --> 00:02:19,310 where, where you put the terminals and what you're hoping 39 00:02:19,320 --> 00:02:20,860 to get. But what? 40 00:02:20,870 --> 00:02:23,030 What do you find? What's the number one reason 41 00:02:23,040 --> 00:02:26,300 customers come to you and choose yourselves? 42 00:02:26,350 --> 00:02:27,240 What? What what? 43 00:02:27,250 --> 00:02:31,270 What's their thinking and what just to add to that what did 44 00:02:31,280 --> 00:02:34,060 they not think that sort of surprised them once you sort of 45 00:02:34,410 --> 00:02:36,840 they got to know you a little bit better or they started 46 00:02:36,850 --> 00:02:39,780 deploying the because everyone has the perception of I'm going 47 00:02:39,790 --> 00:02:43,260 to get immediate feedback for example at point of sale if 48 00:02:43,270 --> 00:02:47,320 someone had a good experience through their path to purchase, 49 00:02:47,530 --> 00:02:49,700 right. I assume that that's that's top 50 00:02:49,710 --> 00:02:53,220 of mind for a lot of folks, but what are the things that they 51 00:02:53,230 --> 00:02:55,470 don't expect that they get from you? 52 00:02:57,500 --> 00:03:03,130 That's a very good question. I think that there's so many 53 00:03:03,140 --> 00:03:06,450 examples of deployment even within retail, because retail in 54 00:03:06,460 --> 00:03:08,210 itself is so multifaceted, right? 55 00:03:08,220 --> 00:03:13,660 So, yeah, you know it's high value electronics or whether 56 00:03:13,670 --> 00:03:18,280 it's kind of very fast flow convenience store, fundamentally 57 00:03:18,290 --> 00:03:23,220 different businesses for sure. So one thing is the real time 58 00:03:23,230 --> 00:03:27,560 nature, so real time nature. And I've talked about Heathrow 59 00:03:27,670 --> 00:03:30,700 in many situations, in convenience stores, in league 60 00:03:30,710 --> 00:03:34,640 dietas, etcetera. Some about the voices actually 61 00:03:34,650 --> 00:03:38,620 popped into the washing, right, Because nobody likes about 62 00:03:38,630 --> 00:03:39,940 washing. That's a fact of life. 63 00:03:40,200 --> 00:03:44,200 A lot of that that was employed in washroom, immediate, real 64 00:03:44,210 --> 00:03:46,940 time feedback allows people to get in there and deal with any 65 00:03:46,950 --> 00:03:48,700 situations they've actually need to deal with. 66 00:03:50,130 --> 00:03:54,160 Terminals be real lot like live. As in if you are hitting you're 67 00:03:54,170 --> 00:03:59,210 getting negative sentiment. Is it feedback live or is it a 68 00:03:59,260 --> 00:04:02,550 collated? That I there's there is a 69 00:04:02,560 --> 00:04:06,070 latency involved obviously, but we're talking like a couple of 70 00:04:06,080 --> 00:04:11,620 minutes rather than talking about hourly chunk of data, 71 00:04:11,630 --> 00:04:13,830 right. So it's not real time in the way 72 00:04:13,840 --> 00:04:15,950 of kind of low latency trading on Wall Street. 73 00:04:16,190 --> 00:04:18,550 We're not at that level. We're not going to invest at 74 00:04:18,560 --> 00:04:21,850 that level. But it's it's meaningfully real 75 00:04:21,860 --> 00:04:24,480 time in the context of an operational team on ground. 76 00:04:25,860 --> 00:04:29,260 So things that people are surprised about, they're 77 00:04:29,270 --> 00:04:34,380 surprised about when they're busy hours are the surprised at 78 00:04:34,470 --> 00:04:40,080 the difference in perception of service from hour to hour. 79 00:04:41,290 --> 00:04:45,230 I mean good examples attention example actually I think sees 80 00:04:45,240 --> 00:04:48,120 them at Spar actually had an interesting experience with us. 81 00:04:49,050 --> 00:04:54,820 She I familiar with Spa, right. Obviously I I grace a some 82 00:04:54,830 --> 00:04:58,140 significant scale. First example that they found 83 00:04:58,150 --> 00:05:00,190 out was, ironically, a broken fridge. 84 00:05:01,100 --> 00:05:04,850 Right. So what actually happened was 85 00:05:04,860 --> 00:05:10,590 they started to get a cluster of negative feedback as people were 86 00:05:10,680 --> 00:05:16,010 leaving the store having come back to return mill that was off 87 00:05:16,810 --> 00:05:18,640 ohh wow. And they were able to look at 88 00:05:18,650 --> 00:05:21,800 the open feedback and sit there and realise that there was a 89 00:05:21,810 --> 00:05:23,930 spike of people complaining about the milk. 90 00:05:24,640 --> 00:05:27,960 They went down that aisle, checked the fridges and found 91 00:05:27,970 --> 00:05:30,910 that one of the fridges would break and actually the milk in 92 00:05:30,920 --> 00:05:32,630 that refrigeration unit was warm. 93 00:05:34,140 --> 00:05:37,670 So guess you could find that any bunch of ways, but they got to 94 00:05:37,680 --> 00:05:41,640 it quickly. Couple of things obviously you 95 00:05:41,650 --> 00:05:45,290 know fridge back, yeah but apart from getting the unit fixed, 96 00:05:45,470 --> 00:05:47,790 they changed their operational procedures to make sure that 97 00:05:47,800 --> 00:05:50,470 they did a walk around and check the bridges on a regular basis. 98 00:05:51,830 --> 00:05:55,210 Similarly, I I think again I've Susan's examples are always 99 00:05:55,220 --> 00:05:59,660 good. The the other one was a cluster 100 00:05:59,670 --> 00:06:04,000 of negative feedbacks simply because a member of staff was 101 00:06:04,010 --> 00:06:06,370 particularly slow on the checkout. 102 00:06:07,530 --> 00:06:11,090 Now that happens. It was a junior member of staff 103 00:06:11,880 --> 00:06:14,750 so she was able to respond straight away by flipping that 104 00:06:14,760 --> 00:06:17,290 person off the checkout, putting the more experienced staff 105 00:06:17,300 --> 00:06:20,930 member on the checkout and then applying some kind of training 106 00:06:20,940 --> 00:06:23,350 in the background to get that member of staff up to speed. 107 00:06:25,380 --> 00:06:27,070 You know it's it's many embarrassed. 108 00:06:27,080 --> 00:06:30,210 I think some organisation was a response actually over busy 109 00:06:30,220 --> 00:06:35,890 periods, very, very busy period and allocation of labour is 110 00:06:35,900 --> 00:06:38,330 often an issue, right? So whether we can get more folks 111 00:06:38,340 --> 00:06:40,820 onto the tools, onto the checkout process when we need 112 00:06:40,830 --> 00:06:42,130 to. But actually the other thing 113 00:06:42,140 --> 00:06:45,370 that she did, which is now quite commonplace, I noticed, but she 114 00:06:45,420 --> 00:06:48,070 she gave them headsets so they could communicate so they could 115 00:06:48,080 --> 00:06:51,000 discreetly notify each other when they were getting overrun 116 00:06:51,010 --> 00:06:54,070 and they needed help. How do people find this? 117 00:06:55,220 --> 00:06:56,650 So sorry, I missed the last point. 118 00:06:56,910 --> 00:06:58,670 Identifying pinch points? Really. 119 00:06:59,040 --> 00:07:03,030 Yeah, so how do you what's the gap between negative sentiment 120 00:07:03,040 --> 00:07:05,000 and attribute to negative sentiment? 121 00:07:05,630 --> 00:07:09,640 So if you're picking up negative sentiment like at the checkout, 122 00:07:10,350 --> 00:07:14,100 how were they able to what what what was the? 123 00:07:14,170 --> 00:07:16,690 How do they realise it was actually the person checking 124 00:07:16,700 --> 00:07:20,660 them out as opposed to, I don't know, the size of the queue or 125 00:07:20,670 --> 00:07:23,250 something? Yeah, sure. 126 00:07:23,260 --> 00:07:26,590 So it has a little bit to do with the variety of devices. 127 00:07:26,600 --> 00:07:29,180 So we have different devices in different form factors. 128 00:07:29,910 --> 00:07:33,880 So from the very lightest about devices which is a mini device 129 00:07:33,890 --> 00:07:36,980 which is purely touch buttons and they feedback, it's not a 130 00:07:36,990 --> 00:07:40,200 tablet based device that can be deployed individually on 131 00:07:40,210 --> 00:07:42,730 checkout lines. So we have an example with the 132 00:07:42,740 --> 00:07:46,940 supermarket in New York where they have 27 of their stores 133 00:07:47,050 --> 00:07:50,370 breaked out without your not equipment and actually they have 134 00:07:50,380 --> 00:07:52,340 a mini device on every single checkout lane. 135 00:07:52,610 --> 00:07:56,520 So they can find out if they're getting negative sentiment on, 136 00:07:56,570 --> 00:08:01,180 you know nine or eight or seven. They also have those mini 137 00:08:01,190 --> 00:08:05,460 devices or all of the specialty food stands, so the fish stand, 138 00:08:05,470 --> 00:08:07,320 you know, the cold meat stand etcetera. 139 00:08:07,330 --> 00:08:11,420 So they understand if they have a problem there and they then 140 00:08:11,430 --> 00:08:15,720 interestingly they complement that by using our tablet device 141 00:08:16,490 --> 00:08:20,650 back of house in the breakout areas supported by QR codes on 142 00:08:20,660 --> 00:08:24,780 the walls and on the lockers to get employees experience because 143 00:08:24,790 --> 00:08:28,940 they understand that sink between employee experience and 144 00:08:28,950 --> 00:08:32,120 customer experience and there's a very strong correlation slight 145 00:08:32,130 --> 00:08:34,679 statistically and not just intuitively. 146 00:08:35,809 --> 00:08:38,559 So it's a smart way for them to do that. 147 00:08:38,570 --> 00:08:40,820 It's a smart way to give their employees voice. 148 00:08:40,830 --> 00:08:46,050 And I think we probably all acknowledge that you know you if 149 00:08:46,060 --> 00:08:48,620 you've got a happy customer, if you've got a happy employee, 150 00:08:49,030 --> 00:08:52,120 that's no guarantee that you're going to deliver a really happy 151 00:08:52,130 --> 00:08:54,180 customer experience. But if you've got an unhappy 152 00:08:54,190 --> 00:08:57,370 employee, it's pretty much certain that it's not going to 153 00:08:57,380 --> 00:09:01,540 be a great customer experience. How does that so when when you 154 00:09:01,550 --> 00:09:05,780 look at experience and its relationship to conversion, 155 00:09:05,790 --> 00:09:11,140 upsell and you know the financial side of things, again 156 00:09:11,150 --> 00:09:15,860 how, what does that look like? Is there is there proven data to 157 00:09:15,870 --> 00:09:21,200 say X amount of happy customers led to higher conversion or 158 00:09:21,210 --> 00:09:23,860 repeat purchases? Yeah. 159 00:09:23,870 --> 00:09:27,620 Coming from a CR, a position and a sales background, I wish there 160 00:09:27,630 --> 00:09:30,050 was an absolutely simple formula to translate those to you 161 00:09:30,060 --> 00:09:32,840 because it would just make it so much easier to explain to people 162 00:09:34,310 --> 00:09:36,750 that genuinely campus and numbers behind it for you. 163 00:09:36,760 --> 00:09:41,540 So one of our customers are actually a large convenience 164 00:09:41,550 --> 00:09:44,870 store chain in the US Yeah, they're actually HQ in 165 00:09:44,880 --> 00:09:47,460 California. They've got 500 locations and 166 00:09:47,470 --> 00:09:50,960 then serving the across Washington, Oregon, Nevada, I 167 00:09:50,970 --> 00:09:55,690 think Colorado as well. They have been running a project 168 00:09:55,700 --> 00:09:58,820 with us for a period of time and multi phased rollout project 169 00:09:58,830 --> 00:10:04,350 across their fleet, across the brand and like most customers 170 00:10:04,360 --> 00:10:05,930 they wanna know what the impact is. 171 00:10:05,940 --> 00:10:07,230 What are you gonna do to top line? 172 00:10:07,240 --> 00:10:08,590 What are you gonna do to my bottom line? 173 00:10:09,560 --> 00:10:13,540 And they've run a comparison test between a control set of 174 00:10:13,550 --> 00:10:18,300 stores of exactly the same mix of configuration, square 175 00:10:18,310 --> 00:10:23,250 footage, etc. And what they noticed was that 176 00:10:23,260 --> 00:10:30,640 happy or not stores experienced between a 3% and 5% increase in 177 00:10:30,650 --> 00:10:34,360 sales versus the control group. Ohh wow. 178 00:10:34,710 --> 00:10:37,820 So obviously that would be specific to their business. 179 00:10:39,230 --> 00:10:41,490 It doesn't always that's why I'd love that to be a formula across 180 00:10:41,500 --> 00:10:42,810 the piece, but obviously it didn't. 181 00:10:43,360 --> 00:10:45,770 But it is specific to their business and and I would 182 00:10:45,780 --> 00:10:49,250 compliment them as there are there are company who look at 183 00:10:49,260 --> 00:10:52,250 the data and therefore take action on the data. 184 00:10:52,430 --> 00:10:54,510 So the data itself is worth nothing. 185 00:10:55,350 --> 00:10:59,210 It's only when customers actually look at the data and 186 00:10:59,220 --> 00:11:02,300 decide actually, yeah, we need to do some more training here or 187 00:11:02,370 --> 00:11:05,200 we need to adjust our staff rostering here, etcetera. 188 00:11:05,210 --> 00:11:09,910 But 3 to 5% is hard to buy in today's markets. 189 00:11:09,920 --> 00:11:12,620 So obviously we're accelerating that project with them and look 190 00:11:12,630 --> 00:11:13,990 forward to that being a great success. 191 00:11:14,700 --> 00:11:17,270 Yeah, I mean, look, everyone's trying to do more with less, 192 00:11:17,280 --> 00:11:18,530 right? Budgets are constrained, 193 00:11:18,540 --> 00:11:22,990 inflations coming down but still high and and there's a, 194 00:11:23,040 --> 00:11:26,190 especially in the UK, there's a cost of living crisis and in 195 00:11:26,200 --> 00:11:30,110 some various other countries around the world. 196 00:11:30,200 --> 00:11:34,650 So do you find that people naturally drift by wanting to 197 00:11:34,660 --> 00:11:39,280 make the invisible visible? That's the primary sort of 198 00:11:39,290 --> 00:11:41,250 engagement there, right? Because they're not. 199 00:11:41,520 --> 00:11:43,570 What? What other metric or how else 200 00:11:43,580 --> 00:11:47,110 are they supposed to understand how well that store estate is 201 00:11:47,120 --> 00:11:50,420 performing? Yeah, 100%. 202 00:11:50,430 --> 00:11:55,120 It's trying to surface that magical bit of data that allows 203 00:11:55,130 --> 00:11:59,700 me to, I guess, make small incremental operational 204 00:11:59,710 --> 00:12:02,870 improvements. They're going to net out at an 205 00:12:02,880 --> 00:12:05,800 improvement on the bottom line. So that's definitely what 206 00:12:05,810 --> 00:12:07,800 they're after. And he used visible and 207 00:12:07,810 --> 00:12:09,880 invisible. You're right, it is invisible. 208 00:12:11,260 --> 00:12:14,890 This crazily invisible. But the kind of feedback you get 209 00:12:14,900 --> 00:12:19,450 from this stuff is really like having your customers as your 210 00:12:19,840 --> 00:12:23,750 McKinsey giving you advice about what you should do with your 211 00:12:23,760 --> 00:12:26,390 business. And they're not all right, For 212 00:12:26,400 --> 00:12:29,170 sure they're not all right. But if you look at that in the 213 00:12:29,180 --> 00:12:32,970 round and it's got statistical validity, I would definitely be 214 00:12:32,980 --> 00:12:36,140 listening to them all day long. And by the way, it surprises. 215 00:12:37,420 --> 00:12:40,110 There's a lot more positive feedback than there is negative 216 00:12:40,120 --> 00:12:41,730 feedback. And you asked earlier about what 217 00:12:41,740 --> 00:12:43,690 surprises do people get. That's actually probably the 218 00:12:43,700 --> 00:12:45,050 biggest one. No one. 219 00:12:45,060 --> 00:12:47,310 But they they they're desperately coming to the table 220 00:12:47,320 --> 00:12:50,020 looking to how we're going to tune the business and what can 221 00:12:50,030 --> 00:12:51,590 we do and what are we doing wrong. 222 00:12:51,860 --> 00:12:53,930 Yeah. Only enough they find out an 223 00:12:53,990 --> 00:12:56,630 awful lot about what they're doing, right because human 224 00:12:56,640 --> 00:12:58,330 beings are generous souls that are. 225 00:12:58,340 --> 00:13:01,890 I like to think so and and they share positive feedback probably 226 00:13:01,950 --> 00:13:04,930 more often than they do negative feedback and are really smart 227 00:13:04,940 --> 00:13:08,280 customers bake that in and make sure they read that back to 228 00:13:08,290 --> 00:13:11,340 frontline staff from regional management and store management 229 00:13:11,350 --> 00:13:13,800 and some of them wrap incentive programmes around it. 230 00:13:14,030 --> 00:13:17,020 I would say the one thing not to do is make it part of a bonus 231 00:13:17,030 --> 00:13:19,960 plan. I don't feed somebody with this 232 00:13:19,970 --> 00:13:21,840 stick. It's not there to beat anybody 233 00:13:21,850 --> 00:13:23,540 with. It's far better to turn around 234 00:13:23,550 --> 00:13:26,140 and provide positive reinforcement than than any 235 00:13:26,150 --> 00:13:29,250 negative. And how do you see in terms of 236 00:13:29,260 --> 00:13:33,000 when you look at the evolution of how old is the company? 237 00:13:34,820 --> 00:13:38,220 I companies on over 10 years old, now I'm. 238 00:13:38,340 --> 00:13:40,890 Coming still young right in the in the context of things it's 239 00:13:40,900 --> 00:13:44,530 still a young concept but I mean just from experience I know 240 00:13:44,800 --> 00:13:48,250 building feedback loops has been that sort of magic Nirvana that 241 00:13:48,260 --> 00:13:50,410 reads out like the one that you said about the bathrooms and 242 00:13:50,420 --> 00:13:55,100 making sure you know bathrooms and grocers are you can walk in 243 00:13:55,110 --> 00:13:58,410 there and and and and it's an OK experience. 244 00:13:58,480 --> 00:14:02,070 I'm just curious what what you see the future, how you see the 245 00:14:02,080 --> 00:14:06,710 future then in terms of what, what's the type of of a I was 246 00:14:06,720 --> 00:14:10,480 the type of world we're moving towards in the feedback space? 247 00:14:10,910 --> 00:14:12,160 Yeah. I guess there's a couple of 248 00:14:12,170 --> 00:14:15,600 things in there. The first one I would pick out 249 00:14:15,610 --> 00:14:21,060 is of devices originally. If you go back to that inception 250 00:14:21,070 --> 00:14:25,640 point when Haiti walked into a particular store and repeatedly 251 00:14:25,650 --> 00:14:27,730 got bad service and suddenly decided he was going to go and 252 00:14:27,740 --> 00:14:31,650 build a company out of it, which I have to take it, it's pretty 253 00:14:31,660 --> 00:14:35,810 impressive response, right? Yeah, but we obviously started 254 00:14:35,820 --> 00:14:39,370 from purely button based feedback without having follow 255 00:14:39,380 --> 00:14:41,970 up questions and having open feedback or collection of 256 00:14:41,980 --> 00:14:44,420 contact details or all of that kind of capability, but that 257 00:14:44,430 --> 00:14:47,360 they're all similar kind of things, right? 258 00:14:47,370 --> 00:14:50,630 They've got us to the point where we understand what people 259 00:14:50,640 --> 00:14:53,350 are thinking, why they thinking where and when. 260 00:14:53,420 --> 00:14:57,020 So those four's are kind of covered were recently good 261 00:14:57,030 --> 00:15:01,480 example of AI is a demographics capability. 262 00:15:01,650 --> 00:15:05,860 So we lay it on that, at least a subtle nuance of The Who. 263 00:15:06,720 --> 00:15:13,280 So basically by using the camera and the tablet, not storing 264 00:15:13,290 --> 00:15:16,020 anything that we shouldn't be storing, but basically doing 265 00:15:16,170 --> 00:15:20,440 facial recognition and being able to establish a demographic 266 00:15:20,450 --> 00:15:26,110 fit, we were able to classify that data by age group ranges 267 00:15:27,410 --> 00:15:30,800 and gender as well. Which in retail, you know what 268 00:15:30,810 --> 00:15:33,240 it means, right? If I go into Abercrombie and 269 00:15:33,250 --> 00:15:36,140 Fitch, and I think it's a dark place with loud music, frankly, 270 00:15:36,150 --> 00:15:38,700 they're not that bothered because I'm not so much part of 271 00:15:38,710 --> 00:15:42,900 their target demographic. Yeah, but they really do care, 272 00:15:42,950 --> 00:15:46,270 right? If they 18 year old is in there 273 00:15:46,280 --> 00:15:49,300 and turning around and saying this place is decidedly not, 274 00:15:49,640 --> 00:15:54,980 yeah, so you know demographics is everything, it's it's all the 275 00:15:54,990 --> 00:15:57,930 way through retail in terms of looking at football and video 276 00:15:57,940 --> 00:16:00,920 analytics etcetera. So that's really off side of 277 00:16:00,930 --> 00:16:04,150 your business in terms of what you're talking about there as in 278 00:16:04,440 --> 00:16:08,390 the software side as in there's some form of display you 279 00:16:08,400 --> 00:16:13,150 registered, you know the demographic details or is that 280 00:16:13,160 --> 00:16:16,700 computer vision assumption of demographic details? 281 00:16:16,760 --> 00:16:20,330 It's computer vision assumption of demographic details at the 282 00:16:20,340 --> 00:16:24,140 point of feedback collection from variety of devices retailer 283 00:16:24,150 --> 00:16:27,260 chooses to enable it etcetera. So it's another way, another 284 00:16:27,270 --> 00:16:30,120 rich dimension. It puts The Who, yeah, alongside 285 00:16:30,130 --> 00:16:36,820 the other 4 W And then the way I see it going is I see a whole 286 00:16:36,830 --> 00:16:43,980 load of AI generated insight rather than pointing and 287 00:16:43,990 --> 00:16:47,320 clicking around the charts and the analysis that's there. 288 00:16:47,770 --> 00:16:53,420 Yeah, so we're pretty much a notification based society, 289 00:16:53,430 --> 00:16:55,540 right? So what we really want to do is 290 00:16:55,550 --> 00:16:57,990 receive a notification. We do that right now with things 291 00:16:58,000 --> 00:17:02,750 like real time alerts, but I would like perhaps to subscribe 292 00:17:02,760 --> 00:17:09,339 to the interest over customer service or store presentation or 293 00:17:09,349 --> 00:17:12,180 anything about cleanliness. So I could subscribe to an 294 00:17:12,190 --> 00:17:17,190 interest area for a range of experience points across a 295 00:17:17,200 --> 00:17:20,869 number of stores, etc. And I could receive a 296 00:17:20,880 --> 00:17:26,410 consolidated set of insight from that set of subscribed interest. 297 00:17:26,880 --> 00:17:29,940 I can always dig back into the data afterwards, but I think 298 00:17:29,950 --> 00:17:32,830 that's the way it's going to get because our attention spans are 299 00:17:32,840 --> 00:17:37,770 so much shorter. Yeah, well now we're helping out 300 00:17:37,780 --> 00:17:40,310 loads of businesses by providing A managed service. 301 00:17:40,360 --> 00:17:43,090 So that's not unusual for SaaS companies. 302 00:17:43,910 --> 00:17:46,900 We're deeply familiar with the data that we produce and we also 303 00:17:46,910 --> 00:17:48,870 understand across different environments. 304 00:17:48,880 --> 00:17:51,620 So lots of people are turning around and saying actually, can 305 00:17:51,630 --> 00:17:55,010 you help us run service for us, Can you analyse the data, Can 306 00:17:55,020 --> 00:17:58,000 you help us manage some surveys? Actually, can you do specific 307 00:17:58,010 --> 00:18:01,420 education for our start as well. So we have a measure, analyse, 308 00:18:01,430 --> 00:18:04,900 educate programme as a managed service which is there to quick 309 00:18:04,910 --> 00:18:07,260 start, accelerate or do the happy little thing if you want 310 00:18:07,270 --> 00:18:09,630 to look at it that way. OK. 311 00:18:09,690 --> 00:18:14,210 And then so in in terms of so you you just see more AI LED 312 00:18:14,440 --> 00:18:18,650 analytics on on basically what's happening in your in your store. 313 00:18:18,660 --> 00:18:20,850 So rather than the the store manager. 314 00:18:20,900 --> 00:18:23,360 But there's one thing I always think poor store manager 315 00:18:23,370 --> 00:18:26,190 especially in grocery they get so many reports, right. 316 00:18:26,440 --> 00:18:29,900 You get like a report on every single aspect of the store and 317 00:18:29,910 --> 00:18:31,930 it's like what do I act on, right. 318 00:18:31,940 --> 00:18:37,230 What's priority for me or for, yeah, for them in that in that 319 00:18:37,240 --> 00:18:40,220 moment. And and I I guess that at some 320 00:18:40,230 --> 00:18:43,840 point the AI will say, hey this is real priority because it has 321 00:18:43,850 --> 00:18:47,410 an impact on bottom line which is what the in my mind that's 322 00:18:47,420 --> 00:18:51,610 what feedback really should be able to, to help you accelerate. 323 00:18:51,620 --> 00:18:54,610 So you're getting to that. Is this having an impact on 324 00:18:54,620 --> 00:18:57,010 people checking out as much, for example? 325 00:18:57,590 --> 00:19:00,680 100% and and the data that we produce. 326 00:19:00,730 --> 00:19:03,800 Incredibly powerful in its own way. 327 00:19:03,810 --> 00:19:06,020 More powerful when it's mashed together with a few other 328 00:19:06,030 --> 00:19:09,000 things. So smart environment. 329 00:19:09,010 --> 00:19:12,030 We look at football data as a data source. 330 00:19:12,860 --> 00:19:17,190 We look at the labour management package to look at exactly how 331 00:19:17,200 --> 00:19:20,110 many employee hours are deployed on the floor at any one point in 332 00:19:20,120 --> 00:19:23,170 time. And we also take the sales data 333 00:19:24,060 --> 00:19:28,810 and we then doing that we can generate kind of four subsidiary 334 00:19:28,820 --> 00:19:33,550 metrics that are very, very insightful because kind of 335 00:19:33,560 --> 00:19:36,410 counterintuitive actually in a lot of situations you look at 336 00:19:36,420 --> 00:19:39,740 the data and you think this store is understaffed or this 337 00:19:39,750 --> 00:19:42,970 store they actually really one being understaffed. 338 00:19:43,100 --> 00:19:46,200 And in some situations you then compare it with another store 339 00:19:46,210 --> 00:19:50,530 within the control group and you realise that actually another 340 00:19:50,540 --> 00:19:56,230 store with less staff is serving a higher degree of football and 341 00:19:56,240 --> 00:19:58,690 getting a higher degree of customer experience. 342 00:19:59,060 --> 00:20:02,050 Interesting and. Actually speaking to somebody 343 00:20:02,060 --> 00:20:07,390 very insightful comment from one of the guys at at the IGA and 344 00:20:07,400 --> 00:20:10,520 the way he looks at it and he deals with this all the time, he 345 00:20:10,530 --> 00:20:13,300 said actually management teams probably know that that 346 00:20:13,310 --> 00:20:16,870 particular store is a problem. They probably know that it's low 347 00:20:16,880 --> 00:20:19,980 engagement, they know it's underperforming, but they don't 348 00:20:19,990 --> 00:20:22,860 have the data to point to and turn around and say why and what 349 00:20:22,870 --> 00:20:26,220 should we do over it. So it's it's really about 350 00:20:27,070 --> 00:20:31,320 providing the data that allows somebody to maybe confirm some 351 00:20:31,330 --> 00:20:33,960 of the stuff that's in there. Intuitively, we're making it a 352 00:20:33,970 --> 00:20:35,800 little bit more of a data-driven exercise. 353 00:20:36,130 --> 00:20:41,280 So Tim, having worked with a lot of store managers and retailers 354 00:20:41,290 --> 00:20:44,740 in general and obviously head office workers have a 355 00:20:44,750 --> 00:20:48,020 perspective of the reports that they produce and you speak to 356 00:20:48,030 --> 00:20:50,220 the store manager. But there's this thing about 357 00:20:50,290 --> 00:20:54,680 data and action and the discrepancy between the two. 358 00:20:54,830 --> 00:20:59,580 And obviously that's why I was really interested in how live 359 00:20:59,670 --> 00:21:05,370 sentiment analysis potentially upscales your data. 360 00:21:06,330 --> 00:21:07,560 I don't know if you have any thoughts. 361 00:21:07,570 --> 00:21:10,230 Is that true or is that just or or not? 362 00:21:11,290 --> 00:21:15,380 Are people able to get actionable on feedback? 363 00:21:15,390 --> 00:21:19,900 Specifically, actionable insights that are data-driven 364 00:21:20,660 --> 00:21:25,600 the act in the now. Yes, totally. 365 00:21:25,610 --> 00:21:29,860 I think it's a really key point. Actions, the word that they 366 00:21:29,870 --> 00:21:32,340 excite people, right, That's that's the best, that's what 367 00:21:32,350 --> 00:21:36,530 it's all built on. And I think the fact that the 368 00:21:36,540 --> 00:21:40,690 feedback is captured in the moment is the key to this one. 369 00:21:40,750 --> 00:21:45,050 So I guess my example would be, we're all customers, we all go 370 00:21:45,060 --> 00:21:46,400 into retail outlets all the time. 371 00:21:47,170 --> 00:21:49,550 We all know what it's like when something's not working out and 372 00:21:49,560 --> 00:21:52,700 we can see a resolution for it, but there's no action being 373 00:21:52,710 --> 00:21:55,880 taken and that's up frustration. I can kind of measure it. 374 00:21:55,890 --> 00:21:58,920 My blood pressure probably goes up a couple of pips and and my 375 00:21:58,930 --> 00:22:01,200 pulse rate increases as well. So I could be looked up to 376 00:22:01,210 --> 00:22:04,360 something that would detect it. But if I'm given the option at 377 00:22:04,370 --> 00:22:07,880 that point in time to share some feedback, not irrational 378 00:22:07,890 --> 00:22:10,860 feedback, but actually provide to answer a question, I think 379 00:22:10,870 --> 00:22:13,360 this is the problem and this is what I would do about it. 380 00:22:13,650 --> 00:22:17,020 That is a really valuable piece of business information in real 381 00:22:17,030 --> 00:22:20,030 time. Now reality is I get through 382 00:22:20,040 --> 00:22:23,010 that problem area, I get through that friction point, and I 383 00:22:23,020 --> 00:22:27,800 guarantee that I'm not even halfway across the parking lot 384 00:22:28,170 --> 00:22:30,460 and that valuable piece of business information is 385 00:22:30,470 --> 00:22:31,940 evaporated. It's into the ether. 386 00:22:31,950 --> 00:22:36,320 We will never ever get it back because the negative experience 387 00:22:36,330 --> 00:22:38,460 has kind of launched somewhere in my head and I don't know 388 00:22:38,470 --> 00:22:41,680 where that place is. Like, I'm not neuroscientist, 389 00:22:41,690 --> 00:22:45,160 but it persists. It affects my behaviour in the 390 00:22:45,170 --> 00:22:47,560 future, it affects my probability of coming back and 391 00:22:47,570 --> 00:22:50,160 using the same store again. But I want the next in my life, 392 00:22:50,170 --> 00:22:51,540 right? Whatever the heck that is. 393 00:22:52,320 --> 00:22:55,350 But that piece of business information has effectively been 394 00:22:55,360 --> 00:22:57,520 lost. Unless you captured it in the 395 00:22:57,530 --> 00:22:59,570 moment. Unless you got it back to the 396 00:22:59,580 --> 00:23:01,470 core. Unless you gave the operational 397 00:23:01,480 --> 00:23:04,730 teams chance to react to it and do something good. 398 00:23:04,740 --> 00:23:08,450 So 100% concur, It's all about the action. 399 00:23:08,560 --> 00:23:10,980 It's not about the pretty charts and bits and pieces, it's all 400 00:23:10,990 --> 00:23:14,080 about the action and I guess. That's where you would consider, 401 00:23:14,150 --> 00:23:16,600 that's where you differentiate yourselves I would say, right, 402 00:23:17,480 --> 00:23:18,350 yes. Totally. 403 00:23:18,530 --> 00:23:21,090 I've worked. I I think I mentioned at the 404 00:23:21,100 --> 00:23:23,670 beginning this I'm a data guy, but over data now time it's been 405 00:23:23,680 --> 00:23:26,770 my background. Yeah, so work with huge 406 00:23:26,780 --> 00:23:29,070 warehouses, marks, lakes, whichever ones you want to call 407 00:23:29,080 --> 00:23:31,300 them these days because whichever turns to become more 408 00:23:31,310 --> 00:23:34,290 fashionable. But you know a huge terabytes of 409 00:23:34,300 --> 00:23:37,310 data sliced every which way from Sunday, millions of charts of 410 00:23:37,320 --> 00:23:41,710 different formats, etc. The reality is sometimes people 411 00:23:41,720 --> 00:23:45,550 ask the question, why is the title in green, that they're 412 00:23:45,560 --> 00:23:48,390 really not focused on it. We're we've got far too much of 413 00:23:48,400 --> 00:23:49,460 that. Yeah. 414 00:23:49,470 --> 00:23:53,460 So yeah, platforms that just serve up a huge amount of charts 415 00:23:53,470 --> 00:23:56,270 I think could possibly less interesting to retailers like 416 00:23:56,280 --> 00:23:58,080 you said, they they get a lot of reports. 417 00:23:58,090 --> 00:23:59,740 A heck of a lot of reports, yeah. 418 00:23:59,790 --> 00:24:01,340 Absolutely got a lot. On their shoulders. 419 00:24:01,350 --> 00:24:02,950 So yeah, it's actionable insight. 420 00:24:02,960 --> 00:24:08,440 That's the key. OK, come to the end of the 421 00:24:08,550 --> 00:24:11,940 podcast where can people learn more about, you know, feedback 422 00:24:11,950 --> 00:24:14,100 loops and and what you guys do and help you. 423 00:24:14,170 --> 00:24:16,360 Are you an RF? Are you at any of the major 424 00:24:16,370 --> 00:24:18,160 events coming up? We have. 425 00:24:18,170 --> 00:24:23,680 Been to NR. If we tend to not do a whole 426 00:24:23,690 --> 00:24:27,360 bunch of events, we get a lot of people coming to us, actually. 427 00:24:28,750 --> 00:24:30,340 OK. And I think if you've noticed 428 00:24:30,350 --> 00:24:33,910 this, particularly in the retail space as well, references 429 00:24:33,920 --> 00:24:36,810 everything. Yeah, People trust what they 430 00:24:36,820 --> 00:24:41,150 hear from other folks. And we get a huge amount of our 431 00:24:41,160 --> 00:24:44,270 inbound business from people having positive experience with 432 00:24:44,280 --> 00:24:45,650 us and sharing that with other people. 433 00:24:45,660 --> 00:24:50,110 So I would encourage anybody to literally hit the website 434 00:24:50,170 --> 00:24:54,990 havefearnot.com, take a look at it, tell us what we can tell you 435 00:24:55,040 --> 00:24:59,430 more about what we do and have a conversation with us because 436 00:24:59,690 --> 00:25:04,290 we're actually about trying to help and prescribe the right 437 00:25:04,300 --> 00:25:07,950 solution for people. We're not here to sell you 438 00:25:07,960 --> 00:25:09,890 something that doesn't fit within the business. 439 00:25:10,020 --> 00:25:11,880 If we're not going to make an impact, if we're not going to 440 00:25:11,890 --> 00:25:15,490 move the needle, then we're, we're not going to do business. 441 00:25:15,500 --> 00:25:18,870 But if we can, we've got Tim. As you can see, I can carry on 442 00:25:18,880 --> 00:25:21,970 asking questions forever, but I really appreciate you taking 443 00:25:21,980 --> 00:25:24,320 some time out of your day to come and have the conversation. 444 00:25:25,190 --> 00:25:27,260 Absolute pleasure, Alex. Really enjoyed it.