WEBVTT 1 00:00:00.825 --> 00:00:03.765 If I told you that retail data is rapidly becoming one 2 00:00:03.765 --> 00:00:04.845 of the most valuable ways 3 00:00:04.845 --> 00:00:06.445 to reach shoppers, would you believe me? 4 00:00:06.785 --> 00:00:09.325 You should, because retail data is about 5 00:00:09.325 --> 00:00:12.445 to be your new secret weapon today on the resource desk. 6 00:00:12.655 --> 00:00:15.965 Three ways retail data can help you reach a higher quality 7 00:00:16.285 --> 00:00:18.445 audience at scale and ways other data can. 8 00:00:27.645 --> 00:00:30.855 Welcome to Part one and our retail video series. 9 00:00:31.465 --> 00:00:33.295 Today I'll be discussing a few 10 00:00:33.295 --> 00:00:36.695 of the reasons why verified deterministic retail data 11 00:00:37.005 --> 00:00:40.255 creates the perfect balance between quality and scale 12 00:00:40.675 --> 00:00:43.575 and how it helps advertisers reach the right audience. 13 00:00:43.995 --> 00:00:45.415 To fully understand this dynamic, 14 00:00:45.665 --> 00:00:48.535 we'll look at three key use cases, past 15 00:00:48.555 --> 00:00:52.175 and predictive segments, lifestyle and life stage segments, 16 00:00:52.275 --> 00:00:55.095 and holistic cross channel frequency management. 17 00:00:55.385 --> 00:00:58.055 First up is past and predictive segments. 18 00:00:59.315 --> 00:01:00.655 So the deeper your record 19 00:01:00.875 --> 00:01:02.895 of a customer's past purchase behavior, 20 00:01:03.355 --> 00:01:04.895 the better your predictions of 21 00:01:04.895 --> 00:01:06.055 how they'll buy in the future. 22 00:01:06.285 --> 00:01:08.775 Without a robust verified purchase history, 23 00:01:09.045 --> 00:01:10.695 like the one you're getting from retail data, 24 00:01:10.795 --> 00:01:12.055 you're kind of flying blind. 25 00:01:12.635 --> 00:01:13.935 By using these historical 26 00:01:14.115 --> 00:01:16.855 and predictive data segments, you can reach people 27 00:01:17.155 --> 00:01:20.455 who are statistically most likely to purchase your product, 28 00:01:20.845 --> 00:01:23.295 even if they never have in the past all 29 00:01:23.295 --> 00:01:25.415 before they even set foot in a store. 30 00:01:25.705 --> 00:01:29.415 Everything from diapers, dog food, fitness equipment, 31 00:01:29.515 --> 00:01:31.495 health snacks, beauty products. 32 00:01:31.875 --> 00:01:33.415 If they're buying it in store 33 00:01:33.635 --> 00:01:37.055 or online, you can use this verified purchase data 34 00:01:37.395 --> 00:01:39.215 to accurately predict shopping habits. 35 00:01:39.795 --> 00:01:41.455 That's the power of retail data. 36 00:01:41.875 --> 00:01:43.375 But what if you don't market a product 37 00:01:43.435 --> 00:01:44.455 that's sold in stores? 38 00:01:45.035 --> 00:01:47.215 Can retail data still be valuable to you? 39 00:01:47.465 --> 00:01:50.255 Let's take a look at our second feature, lifestyle 40 00:01:50.435 --> 00:01:52.135 and life stage segments. 41 00:01:53.335 --> 00:01:55.695 Retailers have been trusted with years, even decades 42 00:01:55.955 --> 00:01:57.135 of their customer's data. 43 00:01:57.525 --> 00:01:59.255 Keeping that trust is valuable 44 00:01:59.395 --> 00:02:02.455 and that's why many retailers only make this data available 45 00:02:02.715 --> 00:02:04.215 in a privacy conscious way 46 00:02:04.515 --> 00:02:05.855 by partnering with the Trade Desk. 47 00:02:06.165 --> 00:02:08.335 This enables them to build high fidelity segments 48 00:02:08.355 --> 00:02:10.935 for any brand to reach the right audience. 49 00:02:11.005 --> 00:02:13.015 Whether you sell products at retailers 50 00:02:13.035 --> 00:02:14.815 or not, this takes purchase behavior 51 00:02:14.915 --> 00:02:16.295 to an entirely new level. 52 00:02:16.805 --> 00:02:18.095 Here you can identify 53 00:02:18.235 --> 00:02:20.815 and reach your audience within different life stages 54 00:02:21.275 --> 00:02:22.655 or specific lifestyles. 55 00:02:23.275 --> 00:02:25.975 Say for example, you have a customer who bought a lot 56 00:02:25.975 --> 00:02:28.135 of college essential items a few years ago. 57 00:02:28.895 --> 00:02:30.135 Recently you noticed 58 00:02:30.135 --> 00:02:32.095 that they've started stocking up on diapers. 59 00:02:32.095 --> 00:02:35.375 All of a sudden, by using this data, you can be confident 60 00:02:35.375 --> 00:02:37.535 that your customer is a young new parent 61 00:02:37.915 --> 00:02:39.615 and is likely in market for all 62 00:02:39.615 --> 00:02:41.015 of the products that go along with it. 63 00:02:41.805 --> 00:02:44.215 Baby clothes and stroller brands are obvious here, 64 00:02:44.435 --> 00:02:46.855 but you'd be surprised at other non-retail brands 65 00:02:46.885 --> 00:02:48.455 that can benefit at this stage. 66 00:02:48.725 --> 00:02:51.735 Insurance and finance, for example, gotta put that kid 67 00:02:51.735 --> 00:02:52.855 through college after all. 68 00:02:53.365 --> 00:02:56.375 Finally, there's the third use case for retail data. 69 00:02:57.055 --> 00:02:59.135 Holistic cross channel frequency management. 70 00:03:00.465 --> 00:03:02.165 By taking all of this information 71 00:03:02.345 --> 00:03:04.165 and activating it in our platform, 72 00:03:04.465 --> 00:03:06.325 you can manage frequency across your 73 00:03:06.325 --> 00:03:07.845 entire omnichannel media. 74 00:03:07.985 --> 00:03:10.605 Buy using insights only found in retail data 75 00:03:10.945 --> 00:03:12.645 to deliver optimal efficiency 76 00:03:12.905 --> 00:03:16.045 and the best customer experience at every stage on their 77 00:03:16.115 --> 00:03:19.845 path to purchase before, during, and after. 78 00:03:20.465 --> 00:03:23.605 So an average customer who streams bad reality TV at home, 79 00:03:23.675 --> 00:03:25.965 listens to true crime podcasts on their way to work, 80 00:03:26.405 --> 00:03:28.725 researches products on their laptop at the coffee shop, 81 00:03:29.105 --> 00:03:31.365 and finally walks into the store to make a purchase. 82 00:03:31.865 --> 00:03:33.845 Not only can you reach them at the right moment, 83 00:03:34.225 --> 00:03:36.845 you can reach them the right number of times finding 84 00:03:36.845 --> 00:03:40.085 that sweet spot that delivers the best possible return on ad 85 00:03:40.085 --> 00:03:42.845 spend for you and the best experience for them. 86 00:03:43.235 --> 00:03:45.445 It's a win-win by using retail data 87 00:03:45.625 --> 00:03:47.725 to get a better gauge on purchase behavior. 88 00:03:48.095 --> 00:03:50.365 Understand more about your customer's lifestyle 89 00:03:50.625 --> 00:03:51.725 and deliver your ads 90 00:03:51.795 --> 00:03:54.245 with more thoughtful placements and frequency. 91 00:03:54.825 --> 00:03:56.365 You can reach your audience in new 92 00:03:56.365 --> 00:03:58.165 and exciting ways without any 93 00:03:58.205 --> 00:03:59.725 reliance on third party cookies. 94 00:04:00.195 --> 00:04:02.485 Part of the magic of retail data is 95 00:04:02.485 --> 00:04:04.085 that it will only get more valuable 96 00:04:04.225 --> 00:04:05.565 as we move forward into the future. 97 00:04:05.985 --> 00:04:08.805 And the best part, you can do everything we've talked about 98 00:04:08.815 --> 00:04:10.205 today all in one place. 99 00:04:10.705 --> 00:04:12.125 And that's on the Trade Desk. 100 00:04:12.535 --> 00:04:15.285 We've partnered with the world's largest retailers 101 00:04:15.465 --> 00:04:17.885 to help marketers use data in ways 102 00:04:17.885 --> 00:04:19.085 they never thought possible. 103 00:04:19.265 --> 00:04:21.725 Thanks for joining me today on the Resource Desk. 104 00:04:22.265 --> 00:04:23.325 Be sure to stay tuned 105 00:04:23.465 --> 00:04:25.645 for even more on the future of retail data. 106 00:04:26.265 --> 00:04:26.965 See you next time.