In this module, we'll start talking about customer journey in AI together. Especially from the point of view of what can AI do for a, either predicting the customer journey or making it shorter. Let's take some examples and better understand how technology, machine learning, AI, all the things that we talked about in the previous modules can help us in understanding the customer journey. Let's start talking about predicting the customer journey. These are very common examples. For example, the Amazon 1, when you go on your Amazon page, if let's say you're an Amazon Prime customer, you'd probably see, for example, lots of things that are popular purchases or based on what you have purchased in the past, Amazon might make a recommendation of what you should purchase next. That's an example of if somebody has purchased something's in the past, Amazon and all the algorithm behind Amazon are trying to predict what would their customer journey look like in the future? The same example, perhaps it's from Netflix. Of course, if you have a Netflix subscription, you'd have gone to your page and your page might be different from my page. Why? Because based on what you're viewing, Netflix using its algorithm, is making a recommendation of what might be some things that you might be interested in seeing in the future. Of course, that algorithm is personalized, so to speak. Based on what somebody is watching, the algorithm might decide what might be some things that they might be interested in looking at in the future. The same example might be a company called Stitch Fix. Now here it's a subscription company which basically offers different kinds of clothing, that they would like to know, for instance, if let's say Roku is a subscriber of Stitch Fix, what would they like to know given the clothes that Roku has had, what would he like to purchase next? In all of these example, what's the idea here? In all of these example the way algorithms, the machine learning, all of that stuff behind the scenes what is it trying to do? Looking at what you have purchased so far, where would you go next? This is all about predicting the customer journey. Let's take another example of what machine learning and AI can do in terms of making the journey shorter. An example might be all the different kinds of apps that you have these days where you can take a picture and the apps tells you what the product is. There are many examples of this. For example, there was an app called Snap Find Shop. Now what does the idea of that app? The idea of the app was this was a company that was releasing it. You could perhaps take picture someone shoes, let's say you found a very nice, you want to find out what those shoes are. The app will open up and it'll tell you if the shoes were obviously from that company. If not, they'll recommend something else. What's the idea here? Think back to the customer journey that we were talking about in a previous module, where we talked about the fact that once you have awareness, then you start thinking about consideration. You start thinking about choice and perhaps start thinking about where to purchase it. What is this app trying to do using Vision AI? What it's trying to do basically is making that journey shorter. As soon as you take a picture, the vision algorithms so to speak, the Vision AI is trying to figure out what is that picture. In this case, it happens to be a shoe. It will start recommending where you can buy it. What is it trying to do? What it's trying to do in some sense, is making that journey between awareness and final purchase as short as it can be. This way, of course, the company can then try to in some sense, world of competition. Because you might be then looking at that particular app, looking at those shoes that the company is recommending and not look elsewhere. There are many other examples. For instance, the virtual fitting room that I was talking about is very similar to this. You can imagine that when you're thinking about buying certain products on the Internet, pants, dresses, so on anything that's more experiential. Now you might end up going to a fitting room looking at those things but if you're at home, you still want to purchase, but you want to reduce, for example, the fact that you get it, it doesn't fit very well. You're to return it, if you want to reduce the parcel cost. That's where some of these companies are coming in, Style.me, [inaudible], MagicMirror, there are many examples of these companies. What they're trying to do is find the best fit. Again, using Vision AI, what are they trying to do? They try to figure out what is a way in which they can reduce the time that you take by going into an actual fitting room, perhaps trying out different types of clothes this way there, again, reducing that journey. The whole idea here by looking at some of these applications which are broadly around Vision AI. The idea is, how can I, as a company, by offering good technology, be able to make that journey shorter. Examples also come from B2B companies. Now there are many chatbots, for example, language AI in this case, which are allowing businesses to talk to customers from other businesses as well. This way, in some sense, if you're a business, you want to quickly transact from other businesses. Lots of examples of where language AI chatbots, in this case are helping out businesses. There are many other examples where Google, for instance, the Google Glass as an example, or other visual software, is helping out in different factory floors where for example, a person does not need to carry big manuals. What they can do in some sense is have Google Glass or any other software of that kind and be able to see exactly what needs to be done. So here's another example of how a journey can be made shorter from a business point of view. Of course, a classic example of how journey can be made shorter from a voice AI perspective. Again, if for example, Amazon Echo and Google Home. Think about what they do. For instance, some of us who may have Amazon Alexa at home. You can ask Alexa, for example, to play different songs and of course what you can also do is to start making your grocery list. You can do all of that. Similarly, Google Home. You can ask google home, for example, to search for restaurants nearby and perhaps even try to make a reservation. What are they trying to do these applications? What they're trying to do is as soon as you have awareness of a particular need, they are trying to in some sense use voice AI in this case, to then be able to say, how can make the journey shorter from the point of view of awareness to find purchase? Again, just to sum up, when you start thinking about different applications, as we saw here, we saw voice AI application, the language AI application, and a Vision AI application. But of course, what I wanted to focus on is not just the application itself, is exactly what is it doing when you start thinking about the customer journey. Two things that you've seen this far. One is making sure that they can understand what the customer journey is like in be able to predict it. That's predicting the next steps in the customer journey and Machine Learning algorithms helping that. The other set of examples that we just saw was making the journey shorter. Which is as soon as a company understands that there's an awareness of need, how can we make sure that they can satisfy that particular need? Now going forward, we'll also look at other customer journey and what AI can do.
