How I'd automate an e-commerce business in 14 days with AI Automation (Full breakdown)

There are 4 main issues that I see holding e-commerce businesses back, and in this video I'm going to run through each of them and then exactly how I would solve all of them in just 14 days using AI automation. Just quickly, if you don't know who I am, my name is Rory Ridgers and I run an agency called Autonomously AI where we find and eliminate operational bottlenecks in just 14 days to keep business owners working on the business rather than in the business. So if that sounds like something you might be interested in, I'll leave a link to my website in the description. You can go ahead and book up a free call. But anyway, let's get into the video. And by the way, if you stick around to the end of the video, I promise you you'll know exactly to automate all of the key problems inside of any e-commerce business. Okay, so let's just jump straight into this. We have e-commerce businesses here, and the first thing that I'm going to do is I'm going to run you through the 4 main issues that I see, and then we're going to break each of them down. I'll tell you exactly how we'd solve each of them using AI. So the 4 main issues— the first one, customer support. This is going to be people asking questions about products, asking for recommendations, and then also saying things like, where is my order? I ordered it 3 days ago. Or potentially complaining about the quality of their product, that kind of thing. This is going to be customer support. And I know this is a massive bottleneck for a lot of e-commerce businesses because it just completely eats away at all of your time. The second thing is going to be fulfillment and logistics mistakes. This is going to be just very, very simply the wrong thing being sent to the wrong place, customers putting the wrong address on their orders, that kind of thing. It's going to be fulfillment and logistic mistakes. And then same as customer support, these things can just eat away at time. Usually it's just little mistakes that cause massive knock-on effects. And then number 3 is going to be market research for winning products. Or this could just be market research in general. It could be for new products, or it could also be for your existing products. And now this is something that either a lot of e-commerce businesses don't do very well or don't do at all. If they are currently doing it, a lot of the time it is very difficult to, you know, sit for hours on TikTok or Instagram and just look at the different product, look at what people are saying about them, look at the engagement on these posts. It's just, it's a very time-consuming task. And then we have reporting and forecasting, and this is just going to be looking at all the things you've sold, looking at the stock, this kind of thing, and just forecasting for the future and also reporting back. So getting the reports of saying like, this product's doing really, really well, we should push this more, this product isn't doing as well, uh, you know, customers that buy this have more lifetime value than customers that don't buy this or buy a different product. That kind of thing is a very time-consuming task, and a lot of the time this can be automated with AI because it is all just numbers, right? So AI, uh, it doesn't matter who you are, AI is always going to be able to look through numbers and analyze data quicker than a human. So let's take this first one, customer support. So with customer support, I already ran through the main issues. It's just replying to emails, replying to people on the website that are asking for recommendations, asking about a specific product like what size does this come in, what color, or also asking for updates on their order or to update their information, that kind of thing. So the solution for this is going to be customer support agent linked directly onto the website and also into email. It would also have access to customer and order information as well as a knowledge base on all of the products. It could easily answer any query, also do things like look up the status of customers' orders, or even change all the details or customer information as needed. So basically, we're looking at an AI agent that can integrate directly into your website and also into your email that is going to be able to answer any question on any product, and then also look at each individual person and answer questions about their particular orders or their personal information, and then even change that information. So for example, if they wanted to change the address on one of their orders then the agent would be able to go in and actually change the address. So here you can just see a very, very simple diagram. So we have two different inputs. We have email and website. It's all going to be linked up to the same agent because there's no point building two for the different channels. So we have email and website input. So a chatbot on the website, or just when someone emails in, this agent can reply. And it's going to be linked up to customer information, product information, and then order information. And it's going to be able to grab anything that it needs from these different data sources. So like I said, they want to update their address, it can look up the customer information and then it can actually have access to be able to change their address. Or if they said, where's my order? I ordered it 3 days ago, something like that, the agent will be able to ask for their order number and then just simply look up that order and say, oh, you know, it's been dispatched, it will be with you in 2 to 3 days. And then it can just simply reply through email or through the website, wherever it was they inquired through. So with this, if we look at the outcomes, we can say the time saved is usually going to be around 5 to 10 minutes per customer support inquiry because Each customer support inquiry, it really depends what you're doing. If it's just a quick one where they ask, you know, what size does this come in, you can just quickly send an email back if you know that off the top of your head, and that might save only, you know, 30 seconds. But if there is a long back and forth with a customer where you're sending sort of multiple emails back and forth trying to get to the bottom of whatever issue it is you're talking about, those ones are obviously going to be saving more time. So if we average it out per inquiry, you can say it's going to save about 5 to 10 minutes. So if you have 100 inquiries per month, that's 12 and a half hours that you're saving every single month. And I mean, there's a lot of e-commerce stores that probably have 100 inquiries a week or even 100 inquiries a day. So you can already see massive time savings for this because this is all completely automated. So if you're an e-commerce business owner and you're doing this all yourself, you can see this is kind of how much time you could be expecting to save. Or if you're an e-commerce business owner and you maybe have a customer support team, you can see again how much time this would be saving. Let's say everyone on your customer support team is, you know, answering 10 customer support queries a day and you have 5 people on the team, that's 50 a day. It would be able to handle pretty much all of that. So next we're going to look at fulfillment and logistics mistakes. So for this, the solution would be— there's 3 separate ones here— an inventory intelligence agent. So daily or weekly stock checks, low stock alerts, out of stock detection, and reorder alerts. So basically an agent that's directly linked with your inventory and it's able to do daily or weekly stock checks look for the low stock alerts, out of stock detection, reorder alert. So basically you can just go inside of your inventory and just anything that needs doing, it can just automatically alert you. So when stock is low, it can alert you, or when stock is completely out, it will alert you. Or if you need to order more stock in a certain place just because more people are buying that, it's going to predict that more people are buying that, then it'll just be able to, you know, send you a message and say get more of this stock. Order validation slash fulfillment checker. So when orders are placed, automatically validate key data like address, stock availability, payment confirmation. If that— if something's wrong, send initial alert plus auto notify customer for correction. So basically, if a customer puts their address in wrong before they actually order it, this agent could be able to pick that up and just send them a message and say, hey, your order's wrong. That way you don't have that big effect later down the line where you've actually shipped out this parcel or this package and it's going to the wrong address and it just causes much bigger issue. If you pick it up early, it's likely not going to be much of an issue. Late shipment and delivery tracking automation. So this will to shipping status. If delays or errors are detected, for example, a shipping provider shows, uh, delayed or lost, trigger a friendly apology plus update email plus internal ticket for customers. So basically, this is just going to be integrated directly with your fulfillment or your suppliers, and if they lose a parcel or something's going to be delayed or something like that, this agent will automatically be able to pick up on that and just send an email to all of the customers that it might concern, just saying, you know, the update or the status on their package and can tell them maybe when they can expect it. With all of this, you could expect an outcome that's going to be a reduction in fulfillment errors around 30 to 60%, faster shipping, happier customers, better inventory control, so fewer stockouts. And then also overall, the customer experience is going to be improved, so you can expect an increase in the lifetime value of each customer. Okay, so now we're going to look at market research for winning products, or just market research in general, because this is a massive part of owning an e-commerce store. So for this, the solution is going to be an automated system that can research products across social media. It can track the engagement, so things like comments, likes, video views. It can then validate any product suggestions and also automatically suggest new products to start selling. So what exactly I mean by that is basically you could have this product that maybe you're already selling or you're about to sell, you could feed it into the system, and then it can check all social medias for this product and then come back to you and say, hey, yeah, this is really, really good, everyone's loving this product, you should start selling it right away. Or if you're already selling it, you know, it might say that you should double down, order more stock, that kind of thing. That is one use case for it. But what it can also do is it can just be continuously running in the background. And you might have a certain industry. So let's say you're selling women's beauty products or something like that. You can have a system running in the background continuously, and maybe every week it can just ping you and say, hey, these are the top 5 products for this week in your industry, and then list them in order and say, this one got the most engagement, this one got the second most engagement, tell you exactly where to buy them, and then you don't even have to lift a finger. Additionally, it can check for reviews of what people are saying about the products you're currently selling or thinking of selling, making it easier to market them on your site. So again, you can also check all the reviews. They're not just looking at social media, but it can just check all the reviews of all the products in the same way— the ones that you're thinking of selling, you're already selling, all the ones that it automatically suggests. This way you know exactly kind of what pain points to call out on your site. It's going to be much, much easier to market these products when you know exactly why people are buying them or the issues with other products. This is something that is massively, massively beneficial for e-commerce stores, and it's not something I see them doing that often. It's just having some kind of automated system or some kind of system that you can feed data into where you can just validate all of your ideas and all of the products that you're thinking of selling, and also just suggest you products to sell. Because this is probably stuff that you do manually anyway. If you're running an e-commerce store and you want to start selling a new product you probably go on to TikTok, Instagram, see if anyone else is selling it and see what kind of engagement they're getting, and then what the reaction is from people that are buying it. And AI can literally do all of this in minutes. What will take you hours or even days to do something like this, AI can do it in sort of 2-3 minutes. So for this, the outcome that you could expect would be countless hours wasted selling the wrong products, as well as countless hours saved researching products. So you're basically going to cut all of that time that you have selling a product that just basically isn't going to sell because you don't actually know if people want this product. You haven't looked it up, you haven't seen what people are saying about it, and if you know other people are selling it. So you can just eliminate that entire process— selling a product for weeks or months wondering why it's not moving, and then you go over to TikTok and Instagram, realize no one's selling it, or people are trying to sell it and everyone's saying it's rubbish. And then also just the countless hours saved researching these products as well. And the reason I've written countless and countless instead of an actual number tied to this is because I don't just want to throw around random numbers and say you're going to save X amount of hours a week or X amount of hours a day doing this stuff, because obviously it massively depends. With the customer support system, we can say sort of 5 to 10 minutes per query because that's reasonable and that makes sense. But with this, it's very hard to say because it drastically depends on how many products you're selling, how much research you do, what kind of product it is. So we can just say all of the time that you would be spending doing market research, we can cut all of that. There's no reason for a human to really be doing any of that market research. And then we're going to look at reporting and forecasting. So the solution for this would be a single automation that pulls daily data from things like Shopify or WooCommerce, whatever you're selling on, your ads that you're running, the inventory, the fulfillment, and the support. And it's going to generate a daily business health summary, produce SKU-level inventory forecasts and stockout predictions build a weekly performance dashboard for your sales, marketing, fulfillment, and retention, and then create a monthly AI-written insights report with recommendations. So essentially what this system is doing is just looking at all of the data across your business— so the ads that you're running, the sales that you're making, your inventory, your fulfillment, everything like that— and just coming up with reports to tell you exactly what you're doing right and what you're doing wrong. Because a lot of the time with e-commerce, a lot of it is just numbers. So for example, If you're running an ad, you can say, okay, this ad's costing me $30 per day, my cost per click is $2, and my conversion rate is 20%. So from that, you can work out if that ad's worth running or not, because it is all just numbers. And if you're running like multiple different ads, sometimes it can be hard to keep up and know exactly what's working and what isn't. So with a system like this, you'll be able to automate all of that, and it can tell you to cut this ad, put more budget into that ad, and it can do that across your whole business. So it can say the exact same thing with— it can go into your Shopify, see what you're selling, see what isn't selling, see the conversion rate of the different pages, of the copy of the landing pages, tell you what to change, what to keep, what to double down on. And the outcome for this, you can expect around 5 to 10 hours per week saved from reporting, just because the average that I've seen is about 5 to 10 hours spent doing this kind of reporting stuff that this simple automation would be able to solve. 20 to 40% fewer stockouts, 10 to 25% less overstock, much more predictable cash flow, 3 to 7% higher profit margin just because of the ads optimization and also the conversion rate optimization on the actual site. And then faster and more confident decision-making because everything is going to be data-backed. You're not really going to be shooting in the dark. You're going to know exactly why you're making each decision based on all of these reports. And like the title of this video says, everything like this can be built and up and running within 14 days. That is really all it takes for you to be able to automate this kind of stuff in your business.