WEBVTT 00:00:00.000 --> 00:00:02.880 align:middle line:90% [MUSIC PLAYING] 00:00:02.880 --> 00:00:05.189 align:middle line:90% 00:00:05.189 --> 00:00:08.189 align:middle line:84% DANIEL TRIMMER: Merchandise planning, the role at Abercrombie and Fitch, 00:00:08.189 --> 00:00:12.780 align:middle line:84% is really kind of the art and science of buying the product, determining 00:00:12.780 --> 00:00:15.990 align:middle line:84% what product to buy, where it should be placed, 00:00:15.990 --> 00:00:18.510 align:middle line:84% what stores it should be received, as well as trying 00:00:18.510 --> 00:00:21.690 align:middle line:84% to understand the customer to determine what products go to what 00:00:21.690 --> 00:00:24.030 align:middle line:90% place for the right time of the year. 00:00:24.030 --> 00:00:28.810 align:middle line:84% And really when we took on this project within our denim teams 00:00:28.810 --> 00:00:31.500 align:middle line:84% across planning, merchandise, our inventory management 00:00:31.500 --> 00:00:36.090 align:middle line:84% team to execute it, it was really about using data information 00:00:36.090 --> 00:00:39.180 align:middle line:84% to quickly observe and really define who our customer is. 00:00:39.180 --> 00:00:42.570 align:middle line:84% The assortment that we're now sending to stores across our denim's business, 00:00:42.570 --> 00:00:45.120 align:middle line:84% which has been growing, and growing, and growing, 00:00:45.120 --> 00:00:47.880 align:middle line:84% and supported through a lot of information we're 00:00:47.880 --> 00:00:51.010 align:middle line:84% getting from Tableau to allow us to take it to the next level. 00:00:51.010 --> 00:00:52.740 align:middle line:84% We're making decisions at the store level 00:00:52.740 --> 00:00:55.650 align:middle line:84% to be able to impact each individual customer that's 00:00:55.650 --> 00:00:56.912 align:middle line:90% really coming into the stores. 00:00:56.912 --> 00:00:58.620 align:middle line:84% So it's really exciting, but I think it's 00:00:58.620 --> 00:01:01.260 align:middle line:84% creating a huge unlock across our business 00:01:01.260 --> 00:01:03.625 align:middle line:84% to continue to take that data model and start to fit it 00:01:03.625 --> 00:01:05.250 align:middle line:90% in across the rest of our organization. 00:01:05.250 --> 00:01:08.320 align:middle line:90% [MUSIC PLAYING] 00:01:08.320 --> 00:01:14.283 align:middle line:90%