WEBVTT 00:00:00.000 --> 00:00:03.000 Hello and welcome back to the Deep Dive. Today 00:00:03.000 --> 00:00:06.059 we are opening up the dashboard. And I don't 00:00:06.059 --> 00:00:08.000 mean the one in your car. I mean the one you 00:00:08.000 --> 00:00:10.779 stare at every, well, every Monday morning. If 00:00:10.779 --> 00:00:12.599 you're listening to this, you know exactly the 00:00:12.599 --> 00:00:15.210 ritual I am talking about. the Monday morning 00:00:15.210 --> 00:00:17.969 trade meeting, the heartbeat of the business, 00:00:18.710 --> 00:00:21.429 or sometimes the heart attack. Depending on the 00:00:21.429 --> 00:00:23.730 weekend, right? You've got your coffee, you've 00:00:23.730 --> 00:00:26.730 got your oversized Excel sheets, and you are 00:00:26.730 --> 00:00:30.109 just dissecting the holy trinity of retail metrics. 00:00:30.329 --> 00:00:32.289 You're looking at comp sales, you're looking 00:00:32.289 --> 00:00:35.649 at UPT units per transaction, and you're obsessing 00:00:35.649 --> 00:00:38.429 over conversion. And if you're really getting 00:00:38.429 --> 00:00:40.549 into the weeds, you're probably pulling apart 00:00:40.549 --> 00:00:44.020 GMROI and labor leverage. That's how we keep 00:00:44.020 --> 00:00:47.060 score. I mean, it's how every merchant, every 00:00:47.060 --> 00:00:50.039 planner, every VP of stores justifies their existence 00:00:50.039 --> 00:00:53.259 for the week. But here is the provocation for 00:00:53.259 --> 00:00:55.380 today's deep dive. And frankly, it's a bit of 00:00:55.380 --> 00:00:57.340 a challenge to that entire Monday morning ritual. 00:00:57.700 --> 00:00:59.320 We've been reading through a stack of research 00:00:59.320 --> 00:01:02.679 on retail operations and, well, next -gen training 00:01:02.679 --> 00:01:06.299 data. And the core argument is kind of unsettling. 00:01:06.840 --> 00:01:10.480 It suggests that all those numbers, comp, margin, 00:01:10.719 --> 00:01:14.000 conversion, are effectively an autoxy. That is 00:01:14.000 --> 00:01:16.260 a grim way to start a Monday, but it's accurate. 00:01:16.459 --> 00:01:18.780 It feels harsh, but think about it. Those metrics 00:01:18.780 --> 00:01:20.540 tell you what already happened. They tell you 00:01:20.540 --> 00:01:22.719 the patient, the margin is already dead. Or alive 00:01:22.719 --> 00:01:25.299 if you got lucky. Right. But today we're exploring 00:01:25.299 --> 00:01:27.900 a shift. We're looking at how to move from that 00:01:27.900 --> 00:01:31.730 reactive reporting. Why did we misplan? to proactive 00:01:31.730 --> 00:01:34.310 prediction. And we're doing it using what might 00:01:34.310 --> 00:01:37.689 be the most undervalued asset in your P &L. Real 00:01:37.689 --> 00:01:39.989 -time product training data. No, hold on. I have 00:01:39.989 --> 00:01:41.849 to stop you there. Because when I hear training 00:01:41.849 --> 00:01:43.469 data, and I think most of our listeners would 00:01:43.469 --> 00:01:46.590 agree, my eyes just glaze over. I think of an 00:01:46.590 --> 00:01:48.430 HR report. I think of a compliance checklist. 00:01:48.909 --> 00:01:51.689 Did Store 504 complete the safety module? Yes 00:01:51.689 --> 00:01:54.609 or no. How is that predictive? I mean, how is 00:01:54.609 --> 00:01:57.609 that business intelligence? That's the skepticism 00:01:57.609 --> 00:01:59.950 we need to address right up front. Because you're 00:01:59.950 --> 00:02:02.569 right, the old model, the LMS or learning management 00:02:02.569 --> 00:02:05.469 system model, is purely compliance. Right. It's 00:02:05.469 --> 00:02:07.849 binary. It just tells you if a body occupied 00:02:07.849 --> 00:02:10.710 a seat and clicked a button, that is useless 00:02:10.710 --> 00:02:13.650 for a VP of sales. Right. It's a CYA metric. 00:02:13.729 --> 00:02:15.909 It doesn't put dollars in the register. Exactly. 00:02:16.069 --> 00:02:19.490 But the shift we are seeing in the source material, 00:02:19.810 --> 00:02:21.810 specifically looking at data from platforms like 00:02:21.810 --> 00:02:25.990 INCITE, is moving from completion to readiness. 00:02:26.150 --> 00:02:29.699 OK. It's shifting from did they do it to Do they 00:02:29.699 --> 00:02:31.819 get it? And more importantly, can they sell it? 00:02:32.219 --> 00:02:35.120 So we are not talking about a generic training 00:02:35.120 --> 00:02:37.979 score. No, not at all. We are talking about granular 00:02:37.979 --> 00:02:40.960 question level analytics that map the capability 00:02:40.960 --> 00:02:43.060 of your workforce against your inventory investor. 00:02:43.139 --> 00:02:45.819 It's about knowing, OK, we have $5 million of 00:02:45.819 --> 00:02:48.719 inventory landing on Thursday, but only 40 % 00:02:48.719 --> 00:02:51.259 of the fleet understands the key selling feature. 00:02:51.460 --> 00:02:53.919 OK, that is a completely different conversation. 00:02:54.039 --> 00:02:56.879 That links the human variable directly to the 00:02:56.879 --> 00:02:59.300 inventory risk. It does. But let's play devil's 00:02:59.300 --> 00:03:01.759 advocate here. Retail executives aren't flying 00:03:01.759 --> 00:03:04.780 blind. We have sophisticated forecasting tools. 00:03:05.000 --> 00:03:08.000 We plan inventory down to the SECCU level. We 00:03:08.000 --> 00:03:10.379 have open to buy. We know what should happen. 00:03:10.639 --> 00:03:12.560 You are flying blind on product. That's true. 00:03:12.800 --> 00:03:15.740 You know exactly how many units of that new technical 00:03:15.740 --> 00:03:17.719 parka are landing in the Chicago flagship on 00:03:17.719 --> 00:03:21.319 Tuesday. You know the AUR average unit retail 00:03:21.319 --> 00:03:24.300 you need to hit. Right. But here's the blind 00:03:24.300 --> 00:03:27.330 spot. Do you know if the human beings standing 00:03:27.330 --> 00:03:29.669 in that store on Tuesday actually know how to 00:03:29.669 --> 00:03:31.930 sell it? Well, we assume they do because we sent 00:03:31.930 --> 00:03:34.050 down a directive We send a lookbook and that 00:03:34.050 --> 00:03:36.550 assumption is the most expensive mistake in retail 00:03:36.550 --> 00:03:39.250 Because just because the store manager signed 00:03:39.250 --> 00:03:41.289 off on the directive doesn't mean the part -time 00:03:41.289 --> 00:03:44.210 associate who is actually facing the customer 00:03:44.210 --> 00:03:46.509 knows the difference between water resistant 00:03:46.509 --> 00:03:49.020 and waterproof and that's That's where the sale 00:03:49.020 --> 00:03:51.500 is won or lost. Like the whole game. So the traditional 00:03:51.500 --> 00:03:54.379 KPIs, they measure the transaction outcome, but 00:03:54.379 --> 00:03:57.139 they completely miss the interaction capability. 00:03:57.280 --> 00:03:59.780 Precisely. And this is critical because of a 00:03:59.780 --> 00:04:01.979 concept we found called the narrow window. The 00:04:01.979 --> 00:04:05.060 product drop. Right. In modern retail, especially 00:04:05.060 --> 00:04:07.960 fashion and lifestyle, the product lifecycle 00:04:07.960 --> 00:04:10.680 is just accelerating faster than ever. You have 00:04:10.680 --> 00:04:13.819 a honeymoon phase, maybe two weeks, maybe three, 00:04:14.120 --> 00:04:17.399 where a new item commands full price. It's fresh, 00:04:17.579 --> 00:04:19.360 it's exciting, it's front of store. That's the 00:04:19.360 --> 00:04:21.420 hype cycle. That's when you get your AUR lift. 00:04:21.759 --> 00:04:23.720 If you miss that cycle because your team spends 00:04:23.720 --> 00:04:26.540 the first 10 days figuring it out on the floor, 00:04:26.839 --> 00:04:28.980 stumbling through customer questions, you never 00:04:28.980 --> 00:04:32.120 get that margin back. The sales report two weeks 00:04:32.120 --> 00:04:34.649 later will tell you low sell -through. but it 00:04:34.649 --> 00:04:37.009 won't tell you why. It'll probably just say traffic 00:04:37.009 --> 00:04:39.949 was soft or price resistance. Exactly. It masks 00:04:39.949 --> 00:04:42.889 the real issue. The issue wasn't the price. The 00:04:42.889 --> 00:04:44.709 issue is that the team was scared to talk about 00:04:44.709 --> 00:04:46.769 the product because they didn't understand the 00:04:46.769 --> 00:04:49.860 tech specs. They avoided the item. So the premise 00:04:49.860 --> 00:04:52.600 here is capturing this readiness data before 00:04:52.600 --> 00:04:54.980 the inventory even hits the dock. But let's get 00:04:54.980 --> 00:04:57.000 into the weeds of what this data actually looks 00:04:57.000 --> 00:04:58.980 like. You mentioned question level analytics. 00:04:59.639 --> 00:05:02.220 Give me a specific example. What should a VT 00:05:02.220 --> 00:05:04.699 of stores be looking for on a dashboard? OK, 00:05:04.720 --> 00:05:07.779 perfect. So imagine you have a new denim launch, 00:05:08.339 --> 00:05:11.300 high stakes. You've got a new fit. Let's call 00:05:11.300 --> 00:05:14.279 it a high rise wide leg. You send out the training 00:05:14.279 --> 00:05:16.579 bite. A sophisticated platform won't just tell 00:05:16.579 --> 00:05:18.420 you who watched the video. It will tell you. 00:05:18.490 --> 00:05:22.050 85 % of the fleet in the Northeast region failed 00:05:22.050 --> 00:05:24.689 the specific question about how to size this 00:05:24.689 --> 00:05:27.689 fit for a curvy customer. Okay, now that is a 00:05:27.689 --> 00:05:30.350 diagnostic. That is a red alert. It's a leading 00:05:30.350 --> 00:05:32.230 indicator. You haven't sold a single pair of 00:05:32.230 --> 00:05:35.110 jeans yet, but you now know... mathematically, 00:05:35.569 --> 00:05:38.189 that the Northeast is going to struggle to convert 00:05:38.189 --> 00:05:40.629 on that fit. You can predict the failure before 00:05:40.629 --> 00:05:42.990 it happens. Exactly. And there's another layer 00:05:42.990 --> 00:05:45.009 here that I found really interesting in the research, 00:05:45.430 --> 00:05:47.550 the difference between competence and confidence. 00:05:47.910 --> 00:05:49.930 Because I can guess on a multiple choice quiz 00:05:49.930 --> 00:05:51.850 and get it right. That doesn't mean I can sell 00:05:51.850 --> 00:05:54.970 it. This is huge. The unconsciously incompetent 00:05:54.970 --> 00:05:57.750 associate is the most dangerous person on your 00:05:57.750 --> 00:05:59.790 sales floor. The one who doesn't know but thinks 00:05:59.790 --> 00:06:02.769 they know. Exactly. The associate who confidently 00:06:02.769 --> 00:06:05.990 tells a customer, yes, you can machine wash this 00:06:05.990 --> 00:06:09.990 raw silk blouse. Oh, disaster. Returns, bad reviews, 00:06:10.250 --> 00:06:13.509 brand damage. Exactly. So modern platforms are 00:06:13.509 --> 00:06:17.310 measuring confidence, asking the associate, how 00:06:17.310 --> 00:06:20.910 sure are you? Or even measuring the speed of 00:06:20.910 --> 00:06:23.930 response. If they take 45 seconds to answer a 00:06:23.930 --> 00:06:26.050 simple feature question, they don't know it. 00:06:26.110 --> 00:06:27.949 They're guessing. They're hunting for the answer. 00:06:28.089 --> 00:06:30.829 And you can't hunt for an answer when a customer 00:06:30.829 --> 00:06:33.110 is standing in front of you waiting. Correct. 00:06:33.790 --> 00:06:37.170 So when we treat training data as business intelligence, 00:06:37.689 --> 00:06:39.970 we're looking for those gaps. We are mapping 00:06:39.970 --> 00:06:43.560 the knowledge health of the organization. And 00:06:43.560 --> 00:06:45.800 that allows you to intervene before the damage 00:06:45.800 --> 00:06:47.600 is done. Okay, let's talk about that intervention. 00:06:47.959 --> 00:06:50.060 Because data is great, but let's be real, retail 00:06:50.060 --> 00:06:52.600 executives are drowning in data. Absolutely. 00:06:52.779 --> 00:06:55.300 If I'm a district manager or store manager, I 00:06:55.300 --> 00:06:57.160 don't need another report telling me I have a 00:06:57.160 --> 00:06:59.180 problem. I need to know what to do. Right. What's 00:06:59.180 --> 00:07:01.040 the action? The source material breaks us down 00:07:01.040 --> 00:07:03.019 into actionable levels, and I want to start at 00:07:03.019 --> 00:07:04.839 the store level. This is where the rubber meets 00:07:04.839 --> 00:07:07.430 the road. There was a concept here about scheduling 00:07:07.430 --> 00:07:09.829 that honestly disrupts the traditional labor 00:07:09.829 --> 00:07:12.730 model. Because usually, how does a manager write 00:07:12.730 --> 00:07:16.250 a schedule? Availability and seniority. Or maybe 00:07:16.250 --> 00:07:18.350 sales per man hour, if they're being tactical. 00:07:19.129 --> 00:07:21.029 Sarah can work Saturdays, and she's been here 00:07:21.029 --> 00:07:23.149 three years, so she gets the peak shift. Right. 00:07:23.290 --> 00:07:26.769 It's logistical. But the sources suggest scheduling 00:07:26.769 --> 00:07:28.949 based on readiness. Walk through the scenario. 00:07:29.629 --> 00:07:31.389 You have that major denim launch this weekend. 00:07:31.569 --> 00:07:33.569 You have two associates available. Associate 00:07:33.569 --> 00:07:37.009 A has a 98 % retention score on the training 00:07:37.009 --> 00:07:39.649 for this specific fit. They know the fabric, 00:07:39.850 --> 00:07:43.350 the sizing, the story. OK. Associate B, who might 00:07:43.350 --> 00:07:46.120 be a senior key holder. Watch the video, but 00:07:46.120 --> 00:07:48.439 failed the quiz twice. Usually the key holder 00:07:48.439 --> 00:07:50.540 gets the hours. And that's the friction point. 00:07:50.579 --> 00:07:53.040 Yeah. But the data argues that if you put the 00:07:53.040 --> 00:07:55.740 unprepared person on the floor during peak traffic, 00:07:56.480 --> 00:07:59.139 you are statistically guaranteeing lost sales. 00:07:59.639 --> 00:08:01.699 You are burning traffic. So it gives the store 00:08:01.699 --> 00:08:03.759 manager the ammunition to make a hard decision. 00:08:03.879 --> 00:08:05.759 I'm putting the rookie on the floor because she 00:08:05.759 --> 00:08:07.639 nailed the specs and I'm putting the veteran 00:08:07.639 --> 00:08:09.800 in the back of house until they get up to speed. 00:08:10.139 --> 00:08:13.899 Or you use the data for peer pairing. This is 00:08:13.899 --> 00:08:17.379 a smart low conflict tactical takeaway. You don't 00:08:17.379 --> 00:08:20.720 just bench the unprepared person, you pair them. 00:08:21.420 --> 00:08:23.800 Hey, Shadow Sarah today. She nailed the training 00:08:23.800 --> 00:08:26.319 on the new fit. So it turns the floor into a 00:08:26.319 --> 00:08:28.300 live training environment. It's micro coaching 00:08:28.300 --> 00:08:31.100 based on facts, not just a manager nagging their 00:08:31.100 --> 00:08:33.600 team. Exactly. It removes the emotion from the 00:08:33.600 --> 00:08:35.940 coaching. It's not, I think you don't know this. 00:08:36.240 --> 00:08:38.879 It's the data shows we missed this question. 00:08:38.899 --> 00:08:40.940 So let's review it. Now let's zoom out to the 00:08:40.940 --> 00:08:43.879 district manager level. The DM ride. I've been 00:08:43.879 --> 00:08:46.179 on those. You have 12 stores. You have eight 00:08:46.179 --> 00:08:48.700 hours in a day. You usually go to the store that 00:08:48.700 --> 00:08:51.960 is burning down the one missing plan by 20 percent. 00:08:52.080 --> 00:08:53.899 The firefighter approach. You go where the heat 00:08:53.899 --> 00:08:57.179 is. Or you go to the flagship because the RVP 00:08:57.179 --> 00:08:59.200 is coming next week and the visuals need to be 00:08:59.200 --> 00:09:03.580 perfect. The source suggests a pivot. Prioritize 00:09:03.580 --> 00:09:06.879 visits based on readiness gaps. It's pre -firefighting. 00:09:07.000 --> 00:09:09.019 So I'm looking at my iPad before I start the 00:09:09.019 --> 00:09:11.379 car. And you see that Store 104 is hitting its 00:09:11.379 --> 00:09:13.750 sales plan today. They look fine on the sales 00:09:13.750 --> 00:09:16.190 report, but their readiness score for next week's 00:09:16.190 --> 00:09:18.990 promo is sitting at 30 percent. That's a ticking 00:09:18.990 --> 00:09:21.269 time bomb. That store is about to drive off a 00:09:21.269 --> 00:09:24.330 cliff. If the DM visits now, they can fix the 00:09:24.330 --> 00:09:27.289 knowledge gap before the inventory lands. They 00:09:27.289 --> 00:09:29.470 shift from being an auditor of past failures 00:09:29.470 --> 00:09:33.529 to a strategist ensuring future success. It effectively 00:09:33.529 --> 00:09:36.389 changes the conversation from why did you misplan 00:09:36.389 --> 00:09:39.269 last week? to how are we going to crush plan 00:09:39.269 --> 00:09:41.490 next week? Which is a much more empowering conversation 00:09:41.490 --> 00:09:44.190 for everyone involved. It builds culture instead 00:09:44.190 --> 00:09:46.610 of destroying it. And then at the HQ level, the 00:09:46.610 --> 00:09:48.950 merchant and planning level, this feels like 00:09:48.950 --> 00:09:51.809 it solves a massive disconnect. Oh, a huge one. 00:09:51.929 --> 00:09:53.610 I've sat in those Monday trade meetings where 00:09:53.610 --> 00:09:56.250 the buyers are frustrated because the stores 00:09:56.250 --> 00:09:58.029 just didn't get the product and the stores are 00:09:58.029 --> 00:10:00.470 frustrated because the buyers bought stuff nobody 00:10:00.470 --> 00:10:04.029 wants. The eternal war. Bad product versus bad 00:10:04.029 --> 00:10:06.850 execution. How does this data broker a peace 00:10:06.850 --> 00:10:09.970 treaty? It removes the anecdote. It removes the 00:10:09.970 --> 00:10:12.809 opinion. If a product fails, you can look at 00:10:12.809 --> 00:10:16.210 the data. If the readiness score was 95%, meaning 00:10:16.210 --> 00:10:18.370 the team knew exactly how to sell it, they were 00:10:18.370 --> 00:10:20.590 confident, they were ready, and it still didn't 00:10:20.590 --> 00:10:22.610 sell. Then it's a product problem. The buyer's 00:10:22.610 --> 00:10:24.850 got it wrong. The fit was off. The price was 00:10:24.850 --> 00:10:27.509 too high. The trend is dead. But if the readiness 00:10:27.509 --> 00:10:31.210 score was 40%, Then it's an execution problem. 00:10:31.490 --> 00:10:33.529 The product might have been a winner, but we 00:10:33.529 --> 00:10:36.370 failed to equip the team to sell it. This clarity 00:10:36.370 --> 00:10:39.909 allows HQ to actually fix the right problem. 00:10:40.450 --> 00:10:42.850 You aren't adjusting the buy if the problem is 00:10:42.850 --> 00:10:45.509 training, and you aren't retraining the staff 00:10:45.509 --> 00:10:47.850 if the problem is the fit. That brings us to 00:10:47.850 --> 00:10:51.250 the bottom line impact. We teased the 15 % markdown 00:10:51.250 --> 00:10:53.230 reduction earlier. I want to really drill into 00:10:53.230 --> 00:10:56.049 this because in retail, where net margins can 00:10:56.049 --> 00:10:59.210 be single digits, saving 15 % on markdowns is 00:10:59.210 --> 00:11:01.490 not just a nice -to -have, it's transformative. 00:11:01.769 --> 00:11:04.070 It's the difference between a bonus and a layoff 00:11:04.070 --> 00:11:06.909 in some years. It's that significant. But I have 00:11:06.909 --> 00:11:10.330 to be the skeptic again. Retail is noisy. You 00:11:10.330 --> 00:11:12.070 have weather. You have competitor promos. You 00:11:12.070 --> 00:11:15.250 have macroeconomic factors. How can we confidently 00:11:15.250 --> 00:11:18.269 attribute that saving to training? How do we 00:11:18.269 --> 00:11:20.149 know it wasn't just a cold winter that sold the 00:11:20.149 --> 00:11:22.909 coats? That's a fair push. Correlation is not 00:11:22.909 --> 00:11:25.850 always causation. But the mechanism here comes 00:11:25.850 --> 00:11:29.750 down to Time on floor and weeks of supply. We 00:11:29.750 --> 00:11:32.409 know that the longer an item sits, the less valuable 00:11:32.409 --> 00:11:34.470 it becomes. Inventory is like a banana. It goes 00:11:34.470 --> 00:11:37.549 bad quickly. Perfect analogy. The goal is to 00:11:37.549 --> 00:11:40.110 sell the banana while it's yellow. When an associate 00:11:40.110 --> 00:11:42.370 is fully prepared, when they have high readiness, 00:11:42.769 --> 00:11:44.830 they can position the value immediately. They 00:11:44.830 --> 00:11:47.190 can handle the objection to the price point on 00:11:47.190 --> 00:11:50.529 day one. Why is this jacket $400? Because it 00:11:50.529 --> 00:11:53.169 uses a three -layer Gore -Tex membrane that breathes 00:11:53.169 --> 00:11:55.490 while keeping you dry, and the seams are fully 00:11:55.490 --> 00:11:58.950 taped. Boom, sale made, full price. And if they 00:11:58.950 --> 00:12:01.070 don't know that answer, if they hesitate. The 00:12:01.070 --> 00:12:03.629 customer walks. The jacket stays on the hanger. 00:12:03.909 --> 00:12:06.529 It sits there for four weeks. The weeks of supply 00:12:06.529 --> 00:12:09.730 creeps up. The allocation algorithm notices the 00:12:09.730 --> 00:12:13.330 stagnation. The planner panics because they need 00:12:13.330 --> 00:12:15.649 to free up open to buy for next month. And the 00:12:15.649 --> 00:12:18.529 red sticker goes on the tag. Exactly. So the 00:12:18.529 --> 00:12:21.590 markdown. is essentially the penalty we pay for 00:12:21.590 --> 00:12:24.029 the associate's silence. That is a powerful way 00:12:24.029 --> 00:12:26.450 to frame it. Markdowns are the cost of ignorance. 00:12:26.850 --> 00:12:29.690 By increasing readiness, you are increasing the 00:12:29.690 --> 00:12:32.730 velocity of full -price sell -through. You are 00:12:32.730 --> 00:12:35.350 clearing the inventory while the margin is intact. 00:12:35.590 --> 00:12:37.509 You're keeping your weeks of supply healthy. 00:12:37.769 --> 00:12:40.110 And that changes the conversation in the boardroom. 00:12:40.629 --> 00:12:42.669 You aren't asking for a learning and development 00:12:42.669 --> 00:12:44.690 budget anymore. That's usually the first thing 00:12:44.690 --> 00:12:47.190 cut. Right. You are asking for a margin protection 00:12:47.190 --> 00:12:49.080 strategy. I want to circle back to something 00:12:49.080 --> 00:12:51.200 you mentioned earlier about the confidence metric 00:12:51.200 --> 00:12:53.840 because I feel like this applies to more than 00:12:53.840 --> 00:12:56.259 just the product features, it applies to the 00:12:56.259 --> 00:12:58.360 brand experience. Oh, an absolute. We talk a 00:12:58.360 --> 00:13:00.539 lot about experiential retail. We are seeing 00:13:00.539 --> 00:13:03.759 a massive shift where the transaction is moving 00:13:03.759 --> 00:13:06.220 online. If I just want to buy the thing, I can 00:13:06.220 --> 00:13:08.720 do it for my couch. If a customer drives to a 00:13:08.720 --> 00:13:11.299 store, parks, and walks in, they are looking 00:13:11.299 --> 00:13:12.919 for something the website can't give them. They 00:13:12.919 --> 00:13:15.500 want expertise. They want validation. They want 00:13:15.500 --> 00:13:17.500 a human to look them in the eye and say, that 00:13:17.500 --> 00:13:19.919 fits you perfectly, or this is the right tool 00:13:19.919 --> 00:13:23.320 for the job. You cannot automate that confidence, 00:13:24.159 --> 00:13:26.639 but you can train for it. and you can measure 00:13:26.639 --> 00:13:28.879 it. And if your data shows that your team lacks 00:13:28.879 --> 00:13:31.080 that confidence, you are effectively paying rent 00:13:31.080 --> 00:13:33.539 on a warehouse, not running a store. Exactly. 00:13:33.580 --> 00:13:35.299 You're just a fulfillment center with better 00:13:35.299 --> 00:13:38.340 lighting. So let's recap the journey here. We've 00:13:38.340 --> 00:13:40.500 moved from the rear view mirror of comp sales 00:13:40.500 --> 00:13:44.039 and UPT to the forward facing windshield of readiness 00:13:44.039 --> 00:13:46.919 and capability. We've looked at how this granular 00:13:46.919 --> 00:13:49.779 data changes scheduling, moving away from seniority 00:13:49.779 --> 00:13:52.139 to readiness. We discussed how it changes the 00:13:52.139 --> 00:13:54.379 district manager's route, moving from firefighting 00:13:54.379 --> 00:13:56.860 to fire prevention. And we landed on the financial 00:13:56.860 --> 00:13:59.679 reality that prepared teams protect margin by 00:13:59.679 --> 00:14:02.580 increasing inventory velocity. It's a comprehensive 00:14:02.580 --> 00:14:05.700 shift in how we view the people metric. It really 00:14:05.700 --> 00:14:08.059 is. Which brings me to the final thought for 00:14:08.059 --> 00:14:11.799 our listeners, the VPs, the directors, the managers 00:14:11.799 --> 00:14:14.399 finishing that second cup of coffee and getting 00:14:14.399 --> 00:14:16.279 ready to walk into that trade meeting. What's 00:14:16.279 --> 00:14:18.320 the challenge for them? Next time you open that 00:14:18.320 --> 00:14:20.940 dashboard, look at your wins and your losses 00:14:20.940 --> 00:14:23.899 from last week and ask yourself a hard question. 00:14:24.159 --> 00:14:27.440 Did we win because we were lucky? Because the 00:14:27.440 --> 00:14:31.139 weather brode and traffic spiked? Or did we win 00:14:31.139 --> 00:14:34.679 because we were ready? And conversely, do we 00:14:34.679 --> 00:14:37.740 lose because the market was tough? Or did we 00:14:37.740 --> 00:14:39.840 lose because we sent our team onto the field 00:14:39.840 --> 00:14:41.879 without a playbook? Because if you aren't measuring 00:14:41.879 --> 00:14:44.080 readiness, you're just guessing. And in this 00:14:44.080 --> 00:14:46.899 economy, with these margins, guessing is a luxury 00:14:46.899 --> 00:14:49.850 nobody can afford. Amen to that. That's it for 00:14:49.850 --> 00:14:52.370 this deep dive into the world of retail intelligence. 00:14:52.529 --> 00:14:54.309 We'll catch on the next one. Go check those readiness 00:14:54.309 --> 00:14:55.330 scores. See you next time.