0:00 Hey, wanted to just create a quick loom on kind of, you know, what we’re hoping to build in the forecast space, uhm, so. 0:08 Essentially, you know, here’s a bunch of different sheets that we have. And then I built some prototypes and chat GPT that, you know, just to kind of walk through. 0:16 Think kind of how we’re visualizing it. So essentially this is like a forecast builder that, um, we use. You know, everything in blue is kind of an input here, uhm, so, you know, by month, kind of across the board, then it matters up to quarter. 0:32 Quarterly and then annually, kind of here, uhm, and essentially, you know, each of these inputs can kind of be changed and, you know, all the rest are kind of formulas. 0:41 Uhm, the thing that we need, you know, uh, probably more than more help on is this section, making that, you know, a cohort predictor based on previous customer trends that I can show you a few examples there. 0:54 But, you know, for brands that have been acquiring customers or brands on heavy subscription, like, you know, what does this forecast? 1:00 Look like we want to make sure that this is built out with, you know, some sort of Python script or something along those lines. 1:07 Uhm, but regardless, yeah. You know, kind of build the base model based on this. Uhm, and then you can, you know, play around with different spend levels and CAC levels and it all kind of. 1:17 Ladders up to a quarterly and annual plan. Uhm, then each of these months would have kind of like an internal. 1:24 All right. So, you know, tracker for daily basis. So once we have kind of our forecast for January, it would then get spit out into kind of a, kind of like, you know, a day-by-day plan of what we’re expecting. 1:36 Um, and then over here, it kind of pastes that accordingly. So, you know, you know, how are we, you know, pacing towards goal for a bunch of key different metrics, and what does that look like for the month? 1:47 So, I built out, two, uh, examples here. Umm, just to kind of show you what I think we’re thinking. Okay. 1:57 So, you know, essentially you could come in here and kind of build your forecast for the year, the rest of the year, next year. 2:05 Or whatever the case may be. Uhm, so you could say, hey, a brand wants to do, you know, $4 million or whatever the case may be. 2:12 Uhm. You know, a few different inputs. I think these are just kind of dummy. And examples. Seasonality, you know, you can either do equal split, you know. 2:21 Based on your previous year of data, what percentage of revenue came in each month. Or you can adjust it however you see fit. 2:28 And then it would build out. So, kind of a target here in terms of, okay, here’s what your monthly. Basically just replicating what’s in this sheet. 2:37 Into this plan. Uhm, so then this plan would become kind of your forecast for the year, or future months, and then after after. 2:45 Or month is completed, it would fill it in with, like, you know, actual data. Uhm, so like, once January is completed, this would be, you know, finished data. 2:51 Thank for listening. We a good. Okay. And then what we’re looking a second tab here, uhm, which would be your daily. 3:01 You plan. So it would take in, you know, from the previous slide, uh, you know, what the forecast is for, you know. 3:09 Umm, November, umm, what the revenue target is, and this would all pull in from that previous slide. And then it just shows, It shows you how you’re tracking, okay? 3:19 So through yesterday, you know, we did 21K in revenue, we were supposed to be at 26K, you know, we’re 21K. 3:25 We sent behind, ad spend, new customer revenue, M-E-R, et cetera. How are we pacing against those? Some graphs to show you. 3:33 Thank you. How are we pacing against those? And then here’s where it gets, you know, uh, probably more in-depth is, Umm, a daily breakdown. 3:44 So this is across the business. On this tab, it’s just like, you know, what’s our- It will add spend. How is that pacing? 3:51 What’s our new customer revenue, existing customer revenue, total revenue, and M.E.R. How are those pacing? On a day-by-day basis based on the plan that we have, you know, how is this kind of pacing, you know, like, hey, yesterday we spent 42 I’m going to, dollars more with 6% ahead and, you know, yesterday 4:09 our existing customer revenue was 18% ahead and then down at the- the bottom, it would give you, you know, how it’s pacing. 4:18 I actually think we’d move this up to the top, but, you know, how are we pacing against- against. These, what’s the variance against each of these. 4:25 And then when a day is still yet to come, you know, we can adjust this. So if we actually think- Hey, you know, moving forward, we want to be spending, you know, 800 per day, right? 4:34 And how does that adjust the forecast and so on and so forth? And then also a channel by channel breakdown. 4:40 So, you know, switching over to meta, have a spend row as purchase and cack target, switching over to Google. Same thing. 4:47 Umm, so we can kind of keep an eye on, like, hey, which channels are pacing ahead, behind, so on and so forth as well. 4:53 So, uhh, uhh, uhh, that’s what we’re thinking. You know, I think the other component here is, is we do have, uhh, with another agency, uhh, uhh, uhh, some, like, python code that can pull in kind of all of the, customer data from Shopify, or at least, you know, uhh, we would need to figure out a way 5:12 to pull this in, but essentially. It pulls in all of the data from Shopify, a repeat customer, and then it has this Python code here. 5:20 I’ll you in the To use this to build out, uhm, you know, based- on the spreadsheet of data, you know, how many new customers are we expecting? 5:30 Then Umm, you know, for each year, kind of, or each month rather, which would then pull into these tabs. 8 8 8 9 8 8 8 8