In this module, we'll talk about customer journey. In the previous one, we talked a lot about AI applications. I think the best way to think about AI applications is to think about customer needs, and what better way to think about customer needs than thinking about what the journey is like. If you think about a typical customer journey, it starts with awareness of a need. You start thinking about perhaps you want to buy a new sweater, let's say. Then you start looking at the consideration set, which is what are the companies out there that will help me get a good sweater? Then you start carefully evaluating all the different alternatives. Perhaps make a purchase after that. Then you go ahead and say, well, how was that purchase? If it's positive, you obviously have positive feedback. If it was negative, perhaps you might have to start all over again. But as you can imagine in this day and age, when you start thinking about all the different AI applications out there, so for instance, when a customer is thinking about a sweater, let's say, let's take that example, he or she might be looking at Instagram. One of their friends perhaps has a nice sweater on Instagram. Or they might go on a website or they might go and look at customer reviews. They might go through some of this journey and then decide whether they want to start all over again because the product that they were interested in is not available. What's the big takeaway? The big takeaway is that the customer journey that I painted earlier, which is very linear in nature, you have the awareness, then you have in some sense consideration set, then you have your choice, and then you think about how good the choice was, nowadays you find that that journey is quite non-linear in nature. This is in some sense a limitation sometimes you can think about it. But I think from the point of view of AI applications, it's quite an opportunity because consumers first, they need help to think about how to go about in their journey and B, for companies, you can then start thinking about where your AI applications, be it voice, be it vision, be it language, whatever the case might be, how can they help in the customer journey? That's what we want to talk about. With this, just stepping out of the technology for a second and start thinking about the customer journey and customer needs and then start thinking about where exactly would needs be best served. Now the other thing that I want to bring up, as the slide suggests, is that each person's customer journey might be quite different. This is about customer journey and segmentation. The idea that no two customers are the same. Now again, you can think of this as a limitation sometimes, you can think of it as a challenge. But I want you to think of this as an opportunity again. Because this is where AI and all the possibilities that it has in terms of being able to customize, being able to understand which customer is going around their journey and where they are in that journey, that's where the opportunity lies. Let's take some examples to understand how you think about these questions. Let's take a very popular example, Disney. Many of us, of course, may have been to Disney Resorts or of course, must have heard of Disney. Now Disney has made a lot of investment and as you see here, there are lots of buzzwords around, for example, the billions of dollars Disney has spent in the Internet of Things, there's big data, machine learning, all of that. What's the idea here? The idea is basically on something called the MagicBand. This person on this slide is wearing a MagicBand. The idea is the MagicBand basically can give you in some sense a lot of information as a customer, but also gives Disney a lot of information. Let me give you an example of the information it gives you. If you buy a MagicBand before you go onto a Disney Resort, you can put a lot of information in that MagicBand, for example, where you're staying. If you're staying in the resort, you can use the MagicBand to open your hotel room and you can add money to it so they're cashless transactions, you can decide, for example, which rides you want to go to. Disney used to have something called the FastPass before. They've tried to replace that with the MagicBand. Now from a customer perspective, of course, lots and lots of opportunity, lots of interesting things going on. But let's start thinking a little bit from Disney's perspective. Now from a technology perspective, clearly this is very interesting lot of investments in big technology, big data, machine learning. But let's start from Disney's perspective to think about why would they want to invest in this technology and why would they care about this data? There lots and lots of things going on here. Let's take it one at a time. Now you can imagine from an operations perspective, this is a big help to Disney. You can imagine traffic flow is a big issue within a Disney Resort. You can imagine if you can go to a resort, you are going with your family, but there so many other families out there. Disney's trying to think about what different families need, where do they want to be, what rides are they taking, what is the traffic in one particular part of the resort. How would they get this data? Well, earlier they had to in some sense somehow try to get this data from the FastPass. But now it's all digitized. From an operations perspective, they can get very quick information on the fly as to what the traffic flow is like within the resort. Clearly it helps the operations people. Now think from a marketing perspective. From the perspective of Disney, they also want to understand what are the kinds of rides you're interested in, what are things that you were excited about when you plan this Disney trip. Now clearly understanding what customers are going through, understanding which rides they want, understanding what are their expectations is a huge plus for Disney as they can personalize all of this. Finally, of course, from a new product development, this is exciting as well, because you can then start thinking about what are some things perhaps people are looking for. Obviously, Disney has Disney Plus now, their streaming platform. You can imagine that there is a lot of interesting interactions between going into Disney Resort, Disney Plus, and being using all that information for new product development. More broadly, what I want you to think about, and I'll give you another example as we go forward, is to think less about technology per se at this stage, but more about the customer journey. What are customers needs? What are some things that will make their experience seamless? Then we can start thinking about how to go ahead and implement a seamlessness using new technology. Let's take another example, this time from a business to business perspective. Just like we talked about tracking of customers within a Disney Resort, Unilever, the big CPG company, consumer packaged goods company, they also implemented real-time tracking of trucks. Now you can imagine how these two applications are quite different. Indeed they are. But let's start from the basic questions, the why. Now why would they want to do this they being Unilever? Now who are their customers? Their customers typically are not you and me as individual customers, their customers are other businesses. Now you can imagine what are the other business needs. Think about retailers. Retailers want to know where is the inventory, when are they going to get the next shipment. Again, from their perspective, they would like to have much better idea of when the Unilever truck is coming in. That's one example. Another thing that Unilever had found was that many of the retailers, that is their customers, wanted to buy more frequently but less things each time. Before they started tracking the trucks, it was quite burdensome for Unilever to keep sending trucks back and again. That is the question I want you to start with every time you start thinking about using new technology, the why. What customer needs are you solving? Of course, after that, the how is equally important. How would you go about it? This is where machine learning, all of that stuff came in. What did Unilever end up doing? They started thinking about in some sense like the Uber of trucks, which is they started tracking where the trucks were going, how the weather patterns are like, how the traffic patterns are like. What did they want to do? They basically wanted to in some sense route trucks in the most efficient manner such that they can A, get as many deliveries on time as possible and B, also make sure they didn't have to make redundant trips. That's what they ended up doing. They basically ended up tracking those trucks really well. They reduced their own carbon emission. They actually increased the amount of stuff that was there in the trucks, so a win-win solution all the way through. But notice the order of questions that we asked. The why should be the very 1st question. Then of course, the how is very important. That's where new technology comes in. But I want you to think about the why from your customer's point of view. Just to summarize this discussion that we had before we move on to other aspects of the customer journey, these are the kinds of managerial questions I want you to ask. Start with the why. What customer needs are you solving? Whether it's the B2C example that we took in Disney or whether it's the B2B example that we took in Unilever. I think customer needs are something that are quite general, so put yourself in your customer shoes. Think about the hurdles that they are facing. Think about where you can help them solve their hurdles. Then start thinking about the data assets, all the other technology that are necessary to solve that problem. Finally, always keep the ROI, return on investment, insight. Many times we as managers perhaps get very excited with what I call the latest shiny object. I think it's good to be excited. It's good to know what's coming, what's next. But once you start thinking about what's the return on investment, that starts making you think carefully about where I should be putting the money and where perhaps I should do a little bit of testing before I decide to invest.
