Boost Retail Conversion: Gareth Johns on Data-Driven Strategies That Drive Sales 的完整文字稿

00:03:52.074 The topic is conversion. 70 00:03:53.004 --> 00:03:57.024 Tell us why conversion has been such a hot topic for retailers right now. 71 00:03:57.324 --> 00:04:01.344 What's driving this urgency and how have retailers traditionally 72 00:04:01.344 --> 00:04:03.864 approached some conversion challenges? 73 00:04:04.373 --> 00:04:07.093 So I think it's a hot topic because it's probably one of 74 00:04:07.093 --> 00:04:11.623 the biggest opportunities to grow revenue for many types of retail. 75 00:04:11.623 --> 00:04:14.863 Not all types, but you know, if you ignore the kind of the grocery stores 76 00:04:14.863 --> 00:04:16.673 where you're obviously once you walk in the door, you're gonna buy 77 00:04:16.673 --> 00:04:18.263 something or the distressed purchase. 78 00:04:19.073 --> 00:04:24.533 Definitely for the fashion footwear specialty type retailers that we work 79 00:04:24.533 --> 00:04:29.848 with conversion's a big opportunity because many of them are only seeing 80 00:04:29.848 --> 00:04:33.448 around 15 to 20% of the customers that may be coming into that store 81 00:04:33.448 --> 00:04:34.918 actually purchasing something. 82 00:04:34.918 --> 00:04:39.788 So there's a huge opportunity to grow revenue by cracking conversion 83 00:04:39.788 --> 00:04:42.538 and just put, even if you push that up a couple of percent you're 84 00:04:42.538 --> 00:04:43.588 really doing something there. 85 00:04:43.818 --> 00:04:47.298 Think that's why it's a hot topic in terms of, I guess what we're seeing from it. 86 00:04:47.455 --> 00:04:49.225 I think many retailers sort of ignore it. 87 00:04:49.375 --> 00:04:51.925 I think they, they think it's too complicated. 88 00:04:52.360 --> 00:04:54.400 You know, they kind of have a conversion is what it is. 89 00:04:54.400 --> 00:04:56.620 And there's nothing I can do that will change that kind of view, 90 00:04:56.620 --> 00:04:58.270 or it's just too hard to measure. 91 00:04:59.140 --> 00:05:02.650 And then I think maybe some others, you maybe get to the point where they 92 00:05:02.650 --> 00:05:05.900 say well, I can, I'm gonna do all this work to figure out how to measure it. 93 00:05:05.900 --> 00:05:08.600 I'm gonna put counters on the front of my store. 94 00:05:08.600 --> 00:05:12.900 I'm gonna use trackers on mobile phones to, to work out what's going on. 95 00:05:13.275 --> 00:05:15.965 But even if you measure it that's not much of our use unless you 96 00:05:15.965 --> 00:05:17.435 can figure out how to change it. 97 00:05:17.435 --> 00:05:21.165 So, yeah, I think that's sort of a starting landscape of really 98 00:05:21.165 --> 00:05:23.745 what's going on in conversion for a lot of our retailers. 99 00:05:23.745 --> 00:05:25.605 Well, if you can't measure it, you can't grow it. 100 00:05:25.800 --> 00:05:26.070 right. 101 00:05:26.250 --> 00:05:26.550 That's right. 102 00:05:26.550 --> 00:05:28.255 That's why the data is so important. 103 00:05:28.255 --> 00:05:31.225 One way we can

look at it, and I know this is how you talked 104 00:05:31.225 --> 00:05:33.375 about this in other places before, there's sort of a distinction 105 00:05:33.375 --> 00:05:35.415 between your non shoppers, right? 106 00:05:35.415 --> 00:05:39.135 And people who you want to convert obviously into someone who buys from you. 107 00:05:39.645 --> 00:05:42.465 But there, I guess there's some misconceptions around what to 108 00:05:42.465 --> 00:05:46.670 do and how to engage these non shoppers and converting them that 109 00:05:46.670 --> 00:05:48.200 retailers kind of struggle with. 110 00:05:48.200 --> 00:05:52.070 I mean, obviously some retailers will use things like focus groups. 111 00:05:52.070 --> 00:05:55.370 They'll try to get people to engage via a QR code or some 112 00:05:55.370 --> 00:05:57.150 exit questioning from the store. 113 00:05:57.150 --> 00:06:01.335 What are some of the, I wanna say stranger, weirder or ineffective ways 114 00:06:01.335 --> 00:06:05.725 that you've experienced retailers trying to engage with these non-shoppers, 115 00:06:05.745 --> 00:06:07.505 and why don't these methods work? 116 00:06:07.505 --> 00:06:09.995 What kind of blind spots do they leave and what have you seen 117 00:06:09.995 --> 00:06:11.885 with retailers you engaged with? 118 00:06:12.515 --> 00:06:15.905 definitely the perfect option would be if people that came into 119 00:06:15.905 --> 00:06:18.725 your store that didn't end up buying anything, were willing to sit down with 120 00:06:18.725 --> 00:06:20.370 you at the end and explain why they didn't 121 00:06:20.480 --> 00:06:21.110 Exactly. 122 00:06:22.190 --> 00:06:22.430 Yeah. 123 00:06:22.835 --> 00:06:23.075 Yeah. 124 00:06:23.375 --> 00:06:26.495 So I, I get where everyone's, if only that's, I get where 125 00:06:26.495 --> 00:06:28.825 everyone comes from and, and we get that a lot with our retailers. 126 00:06:28.825 --> 00:06:30.715 They're saying that, but these are the people I wanna talk to. 127 00:06:30.715 --> 00:06:34.461 And it's just, it's so difficult because we're all consumers, right? 128 00:06:34.461 --> 00:06:39.531 So we all go into stores, we all have a, either a slightly off-putting 129 00:06:39.531 --> 00:06:42.681 experience or a slightly frustrating experience, or we were just never 130 00:06:42.681 --> 00:06:44.061 in the market for buying anyway. 131 00:06:44.601 --> 00:06:47.331 And then we walk out the store, the last thing we wanna do is 132 00:06:47.331 --> 00:06:50.301 then sit down with somebody and explain why we didn't purchase. 133 00:06:50.301 --> 00:06:54.031 So, yeah, I think a lot of the things that you've talked about there the 134 00:06:54.286 --> 00:06:57.106 person kind of doorstepping you as you're trying to walk out the store. 135 00:06:57.526 --> 00:07:02.036 The, the QR codes asking you to leave feedback as you're leaving the store. 136 00:07:02.426 --> 00:07:04.456 Those all exist and we've seen them. 137 00:07:05.821 --> 00:07:09.141 One,

I think you're gonna be very unlikely to hear from 138 00:07:09.141 --> 00:07:10.641 many customers if you do that. 139 00:07:11.541 --> 00:07:14.601 And then the customers that you do hear from, I would have 140 00:07:14.601 --> 00:07:17.801 little confidence, are actually representative of your main customer. 141 00:07:17.801 --> 00:07:20.981 It's such a, it's such an unusual thing to give feedback. 142 00:07:21.251 --> 00:07:23.351 The people that will give you feedback are those that have a 143 00:07:23.351 --> 00:07:24.611 real reason to give you feedback. 144 00:07:24.611 --> 00:07:27.401 Like, that was the worst experience of my life. 145 00:07:27.761 --> 00:07:27.971 You know? 146 00:07:28.331 --> 00:07:29.231 There was no one to help me. 147 00:07:29.231 --> 00:07:31.301 I've been stood here for 20 minutes, whatever it might be. 148 00:07:31.301 --> 00:07:36.611 So you're just not gonna, I think if you go for that, if you go to that approach, 149 00:07:36.611 --> 00:07:39.251 you're gonna hear from the wrong people and you're gonna chase the wrong things. 150 00:07:39.251 --> 00:07:42.738 So I think, it would be a perfect world to tear from every single non 151 00:07:42.738 --> 00:07:45.678 shopper and get a representative sample and really uncover what's going on. 152 00:07:46.488 --> 00:07:51.108 But I think trying to tackle it in that kind of logical way of, 153 00:07:51.138 --> 00:07:53.568 well, I'm gonna go, I'm gonna find a way to talk to these people, 154 00:07:53.598 --> 00:07:54.018 Mm-hmm. 155 00:07:54.558 --> 00:07:56.658 I haven't seen a way that, that it works. 156 00:07:56.658 --> 00:07:56.668 So, 157 00:07:56.763 --> 00:07:57.303 Yeah. 158 00:07:57.468 --> 00:07:57.858 so, Yeah. 159 00:07:58.378 --> 00:07:58.668 Yeah, 160 00:07:58.698 --> 00:08:00.798 Yeah, the one-on-one isn't very scalable. 161 00:08:00.828 --> 00:08:05.478 My, the owner of my bodega would run after me and be like, wait, wait, wait. 162 00:08:05.958 --> 00:08:06.978 What did you want 163 00:08:09.448 --> 00:08:09.738 Yeah. 164 00:08:09.768 --> 00:08:10.518 not getting? 165 00:08:10.608 --> 00:08:15.378 And I turned him into the bougiest bodega on the block. 166 00:08:15.768 --> 00:08:16.218 Yeah. 167 00:08:16.638 --> 00:08:16.818 Yeah. 168 00:08:16.818 --> 00:08:19.758 And I think it's also true, like just from a human nature point of view, 169 00:08:19.758 --> 00:08:23.688 you're, a consumer's more inclined to share that kind of feedback from 170 00:08:23.688 --> 00:08:27.618 a non purchase for negative reasons than they are for positive reasons. 171 00:08:27.618 --> 00:08:30.848 I'm sure most consumers mind, if it was positive, they would've bought something. 172 00:08:30.848 --> 00:08:34.148 So the fact that they didn't probably means there was something 173 00:08:34.148 --> 00:08:36.248 negative and offputting for them. 174 00:08:36.358 --> 00:08:40.048 So you all at TruRating, you, and, I think you have data around, how 175 00:08:40.048 --> 00:08:41.608 consumers feel about these things. 176 00:08:41.658 --> 00:08:45.568 You've probably collected, information around what are those key reasons that 177 00:08:45.568 --> 00:08:48.418 people decide not to convert in store. 178 00:08:48.418 --> 00:08:51.568 And can you walk us through some of the shopper feedback mechanisms 179 00:08:51.568 --> 00:08:55.043 that you do use and you know, what kind of questions do you ask and 180 00:08:55.043 --> 00:08:56.243 how do consumers respond to those? 181 00:08:56.706 --> 00:09:00.776 Yeah, so we ask questions predominantly not exclusively. 182 00:09:00.776 --> 00:09:04.316 We do have some of those other methods, but predominantly, I guess our USP is we 183 00:09:04.316 --> 00:09:10.136 ask questions on the pin pad when you go to pay, just before you tap your card. 184 00:09:10.616 --> 00:09:14.736 So it's in the moment of truth, it's a validated answer 185 00:09:14.766 --> 00:09:16.146 from a particular customer. 186 00:09:16.506 --> 00:09:20.106 And we have the ability to ask different questions, all of which are answered 187 00:09:20.106 --> 00:09:21.816 by the single tap of the button. 188 00:09:21.816 --> 00:09:25.446 So, we can ask lots of different questions and we can combine the 189 00:09:25.446 --> 00:09:28.626 results that we get with lots of other information about what's going on. 190 00:09:28.626 --> 00:09:32.316 Like, what did you buy, how busy was the store, which store was it? 191 00:09:32.626 --> 00:09:33.796 Which register were you on? 192 00:09:34.246 --> 00:09:36.586 How many other people were in the store at the same time? 193 00:09:37.023 --> 00:09:38.313 How often do you come to this store? 194 00:09:38.313 --> 00:09:41.753 There's lots of other kind of contextual information that we can add alongside 195 00:09:41.773 --> 00:09:45.873 the, whatever the question is that we ask and the answer that we get. 196 00:09:45.993 --> 00:09:51.123 And then I'd say our questions generally fall into probably three buckets. 197 00:09:51.633 --> 00:09:54.273 One's kind of about the staff experience that you had. 198 00:09:54.333 --> 00:09:57.213 Did staff do the things that maybe you were expecting them to do? 199 00:09:57.483 --> 00:10:01.143 One's there's another bucket, which is really the store was the store. 200 00:10:01.533 --> 00:10:04.223 Did the store experience play out the way you were expecting? 201 00:10:04.223 --> 00:10:07.043 And then the third is more, I guess more kind of customer 202 00:10:07.043 --> 00:10:08.603 intent or customer behavior. 203 00:10:08.703 --> 00:10:10.503 Did you know what you were gonna buy when you came in today? 204 00:10:11.073 --> 00:10:15.383 Which gets at the kind of browsing versus mission driven or is this 205 00:10:15.383 --> 00:10:17.063 store your first choice for clothing? 206 00:10:17.063 --> 00:10:21.093 Or first choice for shoes to really understand, is this a distressed purchase? 207 00:10:21.168 --> 00:10:23.608 Is this, have you,

are you talking to a loyal customer here? 208 00:10:23.608 --> 00:10:25.528 Like tho those kinds of things as well. 209 00:10:25.528 --> 00:10:28.258 So we can ask lots and lots of questions. 210 00:10:28.258 --> 00:10:31.858 The only real limitation is the question needs to be relevant to the person that 211 00:10:31.858 --> 00:10:35.008 answers it and they need to be able to answer it with a single button press. 212 00:10:35.068 --> 00:10:36.688 So we do a lot of multiple choice. 213 00:10:37.018 --> 00:10:37.708 Binary choice. 214 00:10:37.738 --> 00:10:38.338 Binary, yes. 215 00:10:38.338 --> 00:10:39.088 No type stuff. 216 00:10:39.598 --> 00:10:44.268 And then some scaled things, I think in terms of what we learned from that data. 217 00:10:44.388 --> 00:10:48.078 And, you know, we're up to over a billion ratings now, so we've heard 218 00:10:48.078 --> 00:10:51.248 from lots of different people in lots of different retailers around the globe. 219 00:10:51.878 --> 00:10:55.808 From a conversion point of view, I don't think there's anything that's 220 00:10:55.808 --> 00:10:58.838 massively unusual or kind of mind blowing. 221 00:10:59.438 --> 00:11:02.905 You know, there's the customer wasn't even in the market to purchase. 222 00:11:02.905 --> 00:11:03.835 They were just browsing. 223 00:11:04.195 --> 00:11:07.225 There's a lot of that that you kind of learn and, and that's different 224 00:11:07.225 --> 00:11:11.065 depending on the business that you are and the store and, and where you are. 225 00:11:11.065 --> 00:11:14.515 So certain businesses just attract a more browser type customer who's 226 00:11:14.575 --> 00:11:18.565 only, some customers will only go to a certain store if they're pretty much 227 00:11:18.655 --> 00:11:23.270 ready to buy others, you're the type of, you attract a lot people, you know. 228 00:11:23.890 --> 00:11:26.440 You're a great place to go and just look, but people don't buy. 229 00:11:26.860 --> 00:11:30.040 And then I think where the store's located has a big impact as well. 230 00:11:30.040 --> 00:11:33.430 So you're in a shopping mall, you are gonna definitely get a lot more, 231 00:11:33.670 --> 00:11:36.130 movement of customers coming in and out. 232 00:11:36.430 --> 00:11:38.530 You're in a strip mall, that's a long way away. 233 00:11:38.530 --> 00:11:41.635 You've gotta drive to, you're likely to only go there if 234 00:11:41.635 --> 00:11:42.490 you're really in the market. 235 00:11:42.490 --> 00:11:44.075 So I think that's one bit. 236 00:11:44.315 --> 00:11:46.205 Then there's the, I couldn't get help quick enough. 237 00:11:47.045 --> 00:11:47.675 We've seen that. 238 00:11:48.275 --> 00:11:51.635 And how that plays out depending on what you buy is interesting. 239 00:11:52.115 --> 00:11:55.510 Or the product wasn't available or the, or if, if the product wasn't 240 00:11:55.510 --> 00:11:58.810 available, staff didn't do a good enough job of offering alternative products. 241 00:11:58.960 -->

00:12:02.050 And then finally, I think this whole kind of concept of closing the deal at the end 242 00:12:02.290 --> 00:12:07.540 staff maybe are more of a service mindset where they are quite good at helping you 243 00:12:07.540 --> 00:12:10.720 as a customer, but they're really not doing the sales approach of, I'm gonna try 244 00:12:10.720 --> 00:12:13.060 and actively close this deal at the end. 245 00:12:13.060 --> 00:12:17.230 So we see, I guess all those different things going on as, as 246 00:12:17.260 --> 00:12:18.640 reasons why people don't convert. 247 00:12:18.700 --> 00:12:21.250 And I, I don't think any of that to a retailer would be unusual. 248 00:12:21.250 --> 00:12:23.530 They're not gonna sit there and go, oh, you've, you know, you've, you've 249 00:12:23.530 --> 00:12:26.610 unlocked the silver bullet as to why my customers aren't converting. 250 00:12:26.640 --> 00:12:30.420 I think what our data allows us to do is say, how does 251 00:12:30.420 --> 00:12:31.950 each store do at those things? 252 00:12:32.820 --> 00:12:34.470 And when does the store do well at that? 253 00:12:34.470 --> 00:12:36.270 And when does the store not do well at that? 254 00:12:36.390 --> 00:12:39.480 And so if you're gonna try and change behavior so that you can change 255 00:12:39.480 --> 00:12:41.820 conversion, where do you need to focus? 256 00:12:42.210 --> 00:12:45.420 Because it's no good just going to a store and saying, you're at 15% conversion. 257 00:12:45.420 --> 00:12:46.920 I'd really like you to be at 20%. 258 00:12:47.280 --> 00:12:48.060 Please make it happen. 259 00:12:48.750 --> 00:12:51.740 You've gotta give them more of a, when customers are coming in buying 260 00:12:51.740 --> 00:12:54.590 women's footwear, they are not gonna stand around like a lemon and 261 00:12:54.590 --> 00:12:56.210 wait for somebody for 20 minutes. 262 00:12:56.810 --> 00:13:00.370 Whereas maybe for somebody coming in buying male footwear, they, 263 00:13:00.460 --> 00:13:04.160 they will just stand there for a while looking around, so, 264 00:13:04.550 --> 00:13:04.850 Right. 265 00:13:05.555 --> 00:13:06.305 What are you saying? 266 00:13:06.305 --> 00:13:07.325 We don't have patience. 267 00:13:10.460 --> 00:13:11.390 we've got more things to do. 268 00:13:11.555 --> 00:13:12.485 six months 269 00:13:12.510 --> 00:13:14.510 you may have higher expectations, 270 00:13:17.480 --> 00:13:17.840 Yes. 271 00:13:17.840 --> 00:13:19.340 Which I think is an important point, right? 272 00:13:19.340 --> 00:13:20.810 That expectation point is 273 00:13:20.810 --> 00:13:20.870 Yeah. 274 00:13:21.770 --> 00:13:23.660 we see a lot of our retailers say, but I, I offer 275 00:13:23.660 --> 00:13:25.370 everybody exactly the same service. 276 00:13:25.370 --> 00:13:26.780 You know, like, I don't discriminate. 277 00:13:26.810 --> 00:13:30.770 And it's like, well, yeah, but people have, this is all what everything we 278 00:13:30.770 --> 00:13:32.750 capture as a company is about perception. 279 00:13:32.900 --> 00:13:35.710 It's the customer's perception of what's going on and customers 280 00:13:35.710 --> 00:13:37.280 have different perceptions, so.... 281 00:13:37.520 --> 00:13:41.450 yeah, you can't meet expectations or exceed expectations if 282 00:13:41.450 --> 00:13:43.070 you don't know where the baseline is. 283 00:13:43.070 --> 00:13:49.790 And every consumer group has a different level of like basic expectations. 284 00:13:49.790 --> 00:13:50.360 I feel, 285 00:13:50.420 --> 00:13:51.140 you have to know, you 286 00:13:51.170 --> 00:13:52.905 gotta really figure out how to meet them where they are. 287 00:13:53.300 --> 00:13:53.630 Yeah. 288 00:13:53.900 --> 00:13:54.170 Yeah. 289 00:13:54.470 --> 00:13:57.627 So, Gareth, sticking with this footwear example. 290 00:13:57.787 --> 00:14:02.017 You've worked with a, a footwear brand where I think the data was that you saw 291 00:14:02.017 --> 00:14:08.347 an 8% increase in conversion that led to a, a 16.7 million in revenue opportunity. 292 00:14:08.347 --> 00:14:12.307 Can you tell us more about that story and what maybe some of the specific insights 293 00:14:12.307 --> 00:14:14.677 were that, that drove that change? 294 00:14:14.957 --> 00:14:18.717 Particularly, what you just mentioned about the role the store staff 295 00:14:18.717 --> 00:14:22.497 plays in that, and, how does that lead to the improving results? 296 00:14:22.883 --> 00:14:25.493 So what we did is we worked with a footwear retailer in the 297 00:14:25.493 --> 00:14:29.543 US that's got a number of different stores in, in lots of different states. 298 00:14:29.933 --> 00:14:32.543 Their stores are located to the point I was making earlier about 299 00:14:32.543 --> 00:14:34.013 this strip shopping mall thing. 300 00:14:34.103 --> 00:14:39.323 They have got shopping mall and strip mall and the main two types of store base. 301 00:14:39.323 --> 00:14:42.413 And they, what we see is that conversion is very different between 302 00:14:42.413 --> 00:14:44.123 those two different sets of stores. 303 00:14:44.489 --> 00:14:48.599 The other thing that we see is that the retailer was effectively 304 00:14:48.599 --> 00:14:51.299 managing their rotor based on revenue. 305 00:14:51.359 --> 00:14:55.109 So you do this much in sales, you get these many people in the store, 306 00:14:55.439 --> 00:14:58.839 you do this much in sales, you get these many people in the store not. 307 00:14:59.229 --> 00:15:02.679 You have these many people walk in the door to service that many people 308 00:15:02.679 --> 00:15:05.069 and to close that opportunity, you need these many people. 309 00:15:05.069 --> 00:15:10.919 So I think if you are the first point of, if you're just looking at revenue 310 00:15:10.919 --> 00:15:14.909 as a driver of how many people, how many staff members should I have? 311 00:15:15.449 --> 00:15:16.679 You see huge disparities. 312 00:15:16.679 --> 00:15:20.430 So what they

were seeing was like some stores, a member of staff for every 313 00:15:20.435 --> 00:15:23.755 staff hour that there was, that they were basically putting in that store, 314 00:15:24.475 --> 00:15:28.375 they might have 35 customers come in, or 35 potential customers come in. 315 00:15:28.825 --> 00:15:30.685 Another store might only have five come in. 316 00:15:30.685 --> 00:15:33.475 So it's a very different kind of customer experience that was going on. 317 00:15:33.475 --> 00:15:36.895 So just understanding that and being able to kind of get below an average of, 318 00:15:37.135 --> 00:15:40.615 you're at 20% conversion or whatever, and actually say your behavior. 319 00:15:40.805 --> 00:15:43.595 The setup in stores is very different, even within the same 320 00:15:43.595 --> 00:15:45.085 retailer was our first point. 321 00:15:45.645 --> 00:15:48.105 And then the second thing we looked at was of all the things 322 00:15:48.105 --> 00:15:51.465 that we were asking, which one had the biggest impact on conversion? 323 00:15:51.465 --> 00:15:54.915 So when a store had a high conversion number, what was it 324 00:15:54.915 --> 00:15:57.525 doing well, but another store that didn't have a high conversion 325 00:15:57.525 --> 00:15:59.195 number maybe wasn't doing as well. 326 00:15:59.483 --> 00:16:01.733 And that really came down to staff availability was a 327 00:16:01.733 --> 00:16:02.983 really big issue for this one. 328 00:16:03.033 --> 00:16:04.713 And that's from a customer's perception point of view. 329 00:16:04.713 --> 00:16:08.383 That's not a, I had these many customers come in the door and I had 330 00:16:08.383 --> 00:16:11.623 these many staff on the floor, so my availability was definitely this. 331 00:16:11.833 --> 00:16:15.403 It's as a customer, did you feel that staff were attentive and 332 00:16:15.403 --> 00:16:16.778 available and that point of view? 333 00:16:17.228 --> 00:16:20.708 And so we could see that basically what we did with them 334 00:16:20.718 --> 00:16:22.218 is we did a test where we said. 335 00:16:22.442 --> 00:16:27.702 If you took some of the hours that you were investing in staff and you 336 00:16:27.702 --> 00:16:32.112 moved it around to try and change the, I guess, the profile, could 337 00:16:32.112 --> 00:16:33.882 you make staff availability better 338 00:16:33.882 --> 00:16:34.302 Mm-hmm. 339 00:16:34.357 --> 00:16:35.797 and what the test showed is Yes. 340 00:16:35.797 --> 00:16:39.037 You, you could, you could definitely get it to payback. 341 00:16:39.307 --> 00:16:42.907 It would have a positive ROI, it wouldn't be the most amazing ROI. 342 00:16:43.237 --> 00:16:45.457 There are probably other places you could invest that money. 343 00:16:45.997 --> 00:16:49.082 That in itself wasn't really the key point of what we learned. 344 00:16:49.082 --> 00:16:53.222 What we actually learned was two stores that had the same number 345 00:16:53.222 --> 00:16:56.642 of staff per number of customers coming in, had very different 346 00:16:56.642 --> 00:16:58.382 perceptions of staff availability. 347 00:16:58.382 --> 00:17:02.552 So it wasn't so much throw money at the problem and this will go away. 348 00:17:02.612 --> 00:17:06.392 It was more you have to crack the behaviors first before 349 00:17:06.392 --> 00:17:07.772 you put more money into this. 350 00:17:07.922 --> 00:17:11.042 And actually what they did is they just plotted every single store and they 351 00:17:11.072 --> 00:17:14.792 would have whole set of stores that were roughly similar in terms of they're 352 00:17:14.792 --> 00:17:16.562 all in a similar type of environment. 353 00:17:16.922 --> 00:17:18.392 Strip mall versus shopping center. 354 00:17:19.082 --> 00:17:23.192 They all have a similar number of potential customers walking in the store 355 00:17:23.192 --> 00:17:24.962 each hour for a number of staff hours. 356 00:17:25.502 --> 00:17:30.212 And yet perceptions of availability of staff were 14, 15% different 357 00:17:30.212 --> 00:17:31.292 between best and worst. 358 00:17:31.442 --> 00:17:34.232 So they could just then focus on those at the bottom and say, you 359 00:17:34.232 --> 00:17:36.692 guys are doing something incorrect. 360 00:17:36.872 --> 00:17:41.312 You're either, you're either giving people tasks to do when they should 361 00:17:41.312 --> 00:17:45.032 be out serving customers, or you've just employed people that just really 362 00:17:45.032 --> 00:17:48.302 don't wanna deal with customers or, you know, whatever it might be. 363 00:17:48.932 --> 00:17:52.112 And really just by focusing in on the stores that were at the bottom, over 364 00:17:52.112 --> 00:17:56.582 the course of a kind of a three month period, they were able to reduce that 365 00:17:56.582 --> 00:17:59.132 gap between best and worst stores over. 366 00:17:59.577 --> 00:18:03.943 That leads to better overall availability of staff leads to better conversion. 367 00:18:03.973 --> 00:18:06.733 And I think for a lot of what we do, that's kind of where we, where we 368 00:18:06.733 --> 00:18:12.133 end up is comparable results between stores that can be compared and it 369 00:18:12.133 --> 00:18:13.963 just pinpoints where you need to focus. 370 00:18:14.212 --> 00:18:16.657 Well, I always love conversion conversations. 371 00:18:16.657 --> 00:18:18.907 Coming back to people and staff training, 372 00:18:19.482 --> 00:18:19.722 Exactly. 373 00:18:21.157 --> 00:18:21.907 So important. 374 00:18:22.897 --> 00:18:25.387 what's the one thing you wish more retailers 375 00:18:25.387 --> 00:18:26.917 understood about conversion? 376 00:18:27.247 --> 00:18:30.697 And how can someone get started with a more effective conversion 377 00:18:30.697 --> 00:18:33.413 strategy, let's say next week. 378 00:18:33.443 --> 00:18:35.370 Well I think the first thing, I mean whether it's 379 00:18:35.370 --> 00:18:38.640 retailers or whether it's the staff, well, what I would really. 380 00:18:38.745 -->

00:18:42.345 Is for staff in stores to realize how big an impact they can have 381 00:18:42.405 --> 00:18:42.485 Hmm. 382 00:18:42.815 --> 00:18:43.165 Right. 383 00:18:43.575 --> 00:18:46.535 And I think that is underplayed and they don't 384 00:18:46.985 --> 00:18:50.615 give themselves the credit of how much of an impact they can have. 385 00:18:50.871 --> 00:18:53.781 And maybe that's 'cause they, the management in that retailer don't give 386 00:18:53.781 --> 00:18:56.451 them enough credit or don't tell them enough, or they just don't have the 387 00:18:56.451 --> 00:18:58.611 data to prove it to them and back it up. 388 00:18:58.611 --> 00:19:01.851 But I, yeah, what I'd really want staff to know is that they are the front 389 00:19:01.851 --> 00:19:05.751 face of that brand and for everything else that goes on, they're the thing 390 00:19:05.751 --> 00:19:10.606 that have they have a bigger, a big opportunity to really impact on revenue. 391 00:19:10.746 --> 00:19:14.615 In terms of what retailers could do more, based on knowing that, I'd I'd 392 00:19:14.615 --> 00:19:19.745 say probably spend less time worrying about what is the reason my conversion 393 00:19:19.745 --> 00:19:23.356 is low because everything that we've done there's not been anything where 394 00:19:23.776 --> 00:19:26.976 I think any of us have been surprised as to what customers are after. 395 00:19:27.096 --> 00:19:31.196 I think if you can focus on those five or six things that can impact on 396 00:19:31.196 --> 00:19:35.846 conversion and you know, anybody that's a consumer can think of opportunities 397 00:19:35.846 --> 00:19:39.046 where that's what are the reasons why I sometimes go into a store and end up 398 00:19:39.046 --> 00:19:40.216 getting annoyed and not buying stuff. 399 00:19:40.846 --> 00:19:44.066 It's can you measure effectively how you're doing on those things? 400 00:19:44.126 --> 00:19:45.836 And can you start to improve those things? 401 00:19:45.986 --> 00:19:51.086 And I think focusing much more on the action and less on the complicated 402 00:19:51.086 --> 00:19:53.576 insight or clever technical methods 403 00:19:53.691 --> 00:19:54.111 Mm-hmm. 404 00:19:54.146 --> 00:19:56.156 measuring conversion would be my 405 00:19:56.531 --> 00:19:56.951 Mm-hmm. 406 00:19:57.446 --> 00:19:59.256 actually where I'd say if you're gonna do one 407 00:19:59.256 --> 00:20:00.516 thing, focus on the action. 408 00:20:00.516 --> 00:20:03.596 Not so much the, I've done a really clever regression in our 409 00:20:03.596 --> 00:20:06.166 syetem and I've got lots data. 410 00:20:06.321 --> 00:20:06.611 Yeah. 411 00:20:06.756 --> 00:20:07.746 It's all about the execution. 412 00:20:08.800 --> 00:20:09.280 Exactly. 413 00:20:09.450 --> 00:20:10.100 Exactly. 414 00:20:10.160 --> 00:20:10.650 Exactly. 415 00:20:11.330 --> 00:20:14.706 Well, Gareth, this has been a great discussion on conversion in so many 416 00:20:14.736 --> 00:20:16.356 impactful insights you've shared. 417 00:20:16.746 --> 00:20:20.226 You know, based on all the data TruRating's captured over these years 418 00:20:20.226 --> 00:20:23.646 from consumers across so many brands, how can listeners and viewers learn 419 00:20:23.646 --> 00:20:25.266 more about TruRating's approach? 420 00:20:25.266 --> 00:20:27.901 Well, there's hopefully lots of webinars out there where I've 421 00:20:27.901 --> 00:20:33.451 been talking now and, and TruRating obviously at our website is always ready 422 00:20:33.451 --> 00:20:37.151 to chat to any retailers about what we do and we've got lots of clients 423 00:20:37.171 --> 00:20:40.521 who are also willing to talk about how they use us as well, so defintiely 424 00:20:40.521 --> 00:20:42.131 reach out through TruRating.com. 425 00:20:42.450 --> 00:20:43.760 Thank you Gareth. 426 00:20:44.030 --> 00:20:46.490 Again, thank you so much for joining us today, 427 00:20:46.730 --> 00:20:47.330 Ricardo. 428 00:20:47.330 --> 00:20:52.210 I'd say this is a wrap for episode one of our new Season Two of Data Blades. 429 00:20:52.235 --> 00:20:53.225 Yes, it is. 430 00:20:59.832 --> 00:21:02.922 If you enjoyed today's episode, please give us a five 431 00:21:02.922 --> 00:21:06.872 star rating and review on Apple Podcasts, Spotify, or Goodpods. 432 00:21:07.242 --> 00:21:11.742 And don't forget to hit subscribe on your favorite podcast app or on 433 00:21:11.742 --> 00:21:13.722 YouTube so you never miss an episode. 434 00:21:14.202 --> 00:21:15.342 I'm Casey Golden. 435 00:21:15.477 --> 00:21:16.647 We'd love to hear from you. 436 00:21:16.677 --> 00:21:20.127 Follow us and share your feedback at Retail Razor on LinkedIn, 437 00:21:20.127 --> 00:21:22.017 Bluesky, Threads, and Instagram. 438 00:21:22.437 --> 00:21:25.497 You can also subscribe to our Substack newsletter for highlights 439 00:21:25.527 --> 00:21:29.727 from every episode, and visit retailrazor.com for transcripts 440 00:21:29.727 --> 00:21:31.587 and more details about our guests. 441 00:21:31.901 --> 00:21:35.026 Data Blades is part of the Retail Razor Podcast Network. 442 00:21:35.446 --> 00:21:36.226 I'm Ricardo Belmar. 443 00:21:36.551 --> 00:21:37.671 Thanks for joining us. 444 00:21:38.061 --> 00:21:41.221 Until next time, Stay sharp, Be data-driven, and Harness AI. 445 00:21:41.221 --> 00:21:43.301 This is the Retail Razor Data Blades.