How Customer Context and Smarter Algorithms will Power the Next Level of Experiences and Engagement

Principal, New York Office

 


Transcription of Audio Recording

Julio: We’re going to talk about customer context and smarter algorithms and how they’re going to power the next level of customer experiences and engagements.

Jon: Over the course of the presentation, we’ll cover kind of three generic topics. The first topic, the difficulty in understanding customer context. The second topic is what is being done to help understand customer context, and explaining that, we’ll throw out some buzz words like Machine Learning and Internet of Things. Then from there, we’ll talk a little bit about where we’re headed in the future, where our smart applications are bringing us, and how that’s ultimately leading to Skynet [SP].

Julio: Our focus is going to be on customer experience, and the reason for it is that customers or consumers nowadays have so many choices that increasingly we make decisions based entirely on the experience. There’s a lot of reasons for this. There’s a lot of focus being put into this, because there are numbers that basically dictate where you should be paying attention to. And you could see here, more and more consumers are choosing to even pay a higher price at this point just to have a better experience, be treated better, feel special, those kinds of things. You don’t have to think very hard, come up with a couple of examples that you’ve encountered yourselves about industries that have been completely disrupted just by customer experience alone. 

You can think about Uber, right? Who here has taken an Uber? Have you hopped on a regular cab since then? If you have, you probably did not enjoy it. That’s an industry that was resting on their laurels because they had a virtual monopoly on everything. Your cab driver begrudgingly asked you where you’re going, and you’re on your way and all that. But the reason we keep coming back to Uber is because they make it so easy. You ask Uber to pick you up, you don’t have to tell them where you live. They just come to your house.

You don’t even have to talk to the driver and he doesn’t get lost on the way there because he knows where you’re going. So it’s all about context. You might feel when it comes to customer experience, what makes a difference is being treated in a personalized manner, that’s as if someone knew you. We can see that the key metric that we’re trying to go for is really understanding the customer. But understanding the customer is really not that easy. There are a lot of interrelated conditions in which someone exists. When you ask, “Take me to Lincoln Center?” You need to understand where the person is and where they’re going to for example. There are a lot of facets of customer context at play when we’re talking about all these interrelated conditions.

There’s static context of things about your gender, or age or where you live. But then there are other more difficult things to try and figure out. What is your current location? That’s become easier with location or position devices in all of our phones and all that. But then there are difficult things like emotional state like how do you feel right now? What do you wanna eat? Like intent. There are a lot of driving factors that need to be figured out to really understand a customer. That’s not even beginning to scratch the surface. There’s this whole plethora of information that’s not even available for you to use.

Jon: So, traditional analysis of customer context has relied on these puristic or rules based metrics to determine what the business stakeholders want for their business and from their customers. Things like customer conversion rate, number of widgets sold, kind of the intuitive things that you might think about when you’re thinking about what’s important about a customer. In other words, as is the case and science in general, the typical sophisticated algorithm for understanding something is to first drum roll, take a guess, and then see how well that guess does. Right?

But guessing rules-based heuristic techniques might be ignoring things that aren’t so obvious but are still important. So maybe what matters about a customer in a given circumstance is whether they have brown hair and blue eyes and what they did on Tuesday at 4:12 p.m. and not their extensive billing history and things like that. That might seem ridiculous to a business person and a lot of cases, it’s the combination of unintuitive factors as well as intuitive factors that are influential in determining how customers act and the value that is provided by customers in general. 

Julio: The problem is even more complicated than what we just explained because as customers engage with organizations, they typically do so through various systems as they go through the customer journey. So you may have data that spread across multiple lines of business across partners, pretty much all over the place, and it makes it really difficult to have a complete picture of the customer as you’re engaging with them at these different points of interaction because you really never carry all that data along with you. 

Jon: Yes. Integration of all these systems still doesn’t provide complete view of the customers. Some enterprises have moved towards centralizing their data into data lakes and coalescing their data sets altogether. But still a lot of companies completely ignore their data, or like we said before, they use heuristic techniques or rules-based things that they’ve made guesses at in the past that might be important.

The question really is how do you make sense on all this interrelated data about your customers and what do you do about it? This case study is an example of a current project that’s going on at Pariveda with an unnamed energy provider. They were trying to unlock insights from existing customer context, their existing data that they had spread out through their various systems.

Julio: As is typical within organizations, their data was spread across multiple systems and in different backend databases. And if you wanted to ask a question for example on usage which is really important to the energy industry, you had to integrate all these different sources of data. If you wanted to ask a different question.. 

Jon: If they wanted to understand financial performance to see how that integrated with the rest of their data, they had to look at these disparate systems in order to figure out what was going on. And so this analysis was being performed ad hoc across multiple systems and in often cases in these magical and mysterious spreadsheets that did magical things, so asking simple questions, trying to solve a puzzle in general. What we did here was that we began consolidating the data into the cloud. With that data that was now centralized, the energy provider can do more advanced analytics to draw insights about their customers. Like we already said, some businesses have been smart enough to understand that data in important.

They collect it even though they don’t know how to make sense of it. They stand in front of a blackboard and they look at it, and they don’t know what it means. They’ve just stored it. Machine learning is this entire field that’s focused on using historical data to make predictions about future data. We’re saying that humans can’t perform this kind of analysis manually. There’s too much data; it’s too complicated. We know that people like to think that they’re very smart. That if they look at something for long enough that they’ll be able to figure it out. In general, simply not the case. There’s too much data. We need to start understanding the machines, the algorithms, and the math in order to gain insight to power these kinds of intelligent applications that we’re looking towards building in the future. This is extending the case study that we’ve already brought up. We’ve got data now centralized within the cloud.

Provides a view of the customer context, we’ve built a portal, a custom portal for the business to look at and to be able to see different views of their customers. Then from there, we’ve applied machine learning algorithms to analyze the data, and in doing that, we’ve started to see this immediate value in terms of being able to see the relationships between customers and how they interact. It’s like Netflix. When you go and login into Netflix, you get movies, and you can see movies that are recommended to you, and you can see how other people that are similar to you view movies that are similar. This whole customer context with machine learning is now available to the business in order for them to view it and view the relationships between them.

Julio: As organizations are starting to figure out how to manage and work with all these vast amounts of data, they’re doing it just in time to have to tackle yet another problem, and that’s the extension of basically of the customer journey. So what that means is that there are increasingly more and more points of interaction and more and more data being sent back that needs analysis, that needs to be made sense of. How many people here have a smart watch or some smart home appliance like an Alexa or something? You’re a customer now even when you’re not in the store, when you’re not online, you’re always a customer, and that means making sense of that data as the providers of services and goods. This isn’t a trend that’s going anywhere anytime soon.

Industry trends and investments are all going towards this concept of computing everywhere, ambient computing, and just like this omnipresent system that’s always listening and paying attention to everything around you. It also like I said provides a constant stream of data that can become overwhelming and needs to be made sense of but at the same time, provides these vast amounts of context so that you’re able to say something like, “Alexa, get me a pizza from the place I ordered yesterday.” How does it make sense of all that data?

Jon: So, to do that, the customer context needs to be analyzed and actioned on in real time, taking into account all these vast streams of customer data that are constantly flowing in. What information is meaningful and what information isn’t? How is the information important relative to the goods and services you can offer to the customer? Machine learning technology is like we said already, are used to do exactly this and learn from historical data and react appropriately according to that incoming data in real time and making smart decisions in real time that can affect the customer and the businesses as well. These algorithms, in general, are much faster and smarter than people are.

Julio: Going back to our use case or our case study about our energy provider, having centralized their data and drawn upon insights from their existing data, they’ve now decided to deploy smart sensors and smart meters in order to get even more context about their customers. Not only that, it’s not only just a give situation, they’re also able to provide value back to the customer in the form of energy savings or automatic thermostat regulations. There’s a value that goes both ways. 

Jon: Data is streaming in throughout customer houses and apartments and is connected to an app that we’ve built for their phones. The app then provides real-time information on energy consumption on different appliances and machines throughout their households. Also, the app can be programmed to automatically adjust and optimize consumption across various appliances and machines depending on different metrics that people find important such as desired costs, temperature, the time of day, relationships with other devices within the houses in general.

Julio: What we have here is a little map of where we see this all going, right? And really what we’re seeing is that this integration of IoT and Machine Learning into organizations, enterprise architectures is gonna lead them down a path of full-service automation and being able to provide and automate new personalized experiences to their customers. Right now, we’re in this current state of data collection, constant data collection, and we’re seeing a lot more of sensor integration. What this is doing is that they’re kind of iterating on these. We’re seeing a lot of companies just starting to experiment, and they’re drawing up on insights to develop new products and new service offerings that add value to their customers.

Jon: That’s the current state of affairs. So what happens in the future? Well, we’re kind of saying that in one to three years, we’ll come into this section that we’ll call AI integration. What does that mean? It means that centered deployment is pervasive, capturing all customer and operational context. It means that machine learning is being leveraged extensively to manage context and adopt the customer journey in real time, and algorithms are implemented to process and understand all of the customer interactions. An example of this is if you think about Google, their famous page rank algorithm for their searches is now being shifted towards a more AI focused on an algorithm called Brain Rank. 

And so usually, these large corporations and companies are able to adopt those kinds of technologies first, and smaller corporations will follow suit in that. And so from there, in the next three to ten years, we see it coming to a point where we have this end to end automation. What does that mean? We’re looking at advanced artificial intelligence capable of understanding operational contexts and acting completely autonomously. 

Julio: The way we see it, these advancements aren’t happening kind of sequentially. Rather, both technologies are being integrated and evolved in parallel, but we see it culminating in the next five to 10 years really where things really are mature enough to deliver this kind of experience.

So far, we’ve talked a lot about data, a lot about sensors and a lot about machine learning and how these things are shaping the experiences, our experiences with organizations. All these components are really making up the smart systems that will power the next generation of services and products. It’s almost difficult to imagine where we’re headed from here but there are plenty of examples of…there are plenty of hints in the horizon about where we are headed.

Jon: Imagine a big-time movie studio in the future. They’re making hundreds, if not, thousands of movies across multiple departments and they wanna make the best movies they can for their customers so that their customers are satisfied, and then also so that the businesses are making the highest revenue. We’ve got different departments like Finance, Marketing, Casting, Research and Merchandise where previously we have said they are all disparate systems. Now, they are working together, and so we’re asking questions like, “Which actors get cast in which movie so that the customers overall like the movies the best and they get the best critical response? How are we marketing things to customers so that they’re most interesting to people who are interested in what we’re marketing? How do create merchandise such that the customers are the most satisfied?”

To answer those questions, you could think about networks of machine learning systems. We’ve talked about these kinds of one-off machine learning systems, but in the future, we’re envisioning these kinds of networks of machine learning systems that are acting together. These systems are connected and configured and driven by this artificially intelligent software at the center. Perhaps, even robots are involved as the AI or as components of the AI. And then we’ve got IoT systems, people’s phones and their interactions with social media and things like that, feeding in data and driving this whole system around our central intelligence. 

We’ve kind of completely moved away from having dozens of disparate systems to this central consciousness, and this AI makes decisions, controls the flow of information throughout the systems within the organization. Finally, it is completely self-aware and has human emotions as well. No, that’s not right. That’s completely ridiculous of course, but what we’re saying is that all of this again is geared towards the mutually beneficial relationship between the businesses and their customers and staying ahead of the curve will always be key for competitive advantage.

Julio: Lastly, we just want to leave you with this. Just a couple of more examples and the reality is that context-aware services and applications are going to be the key to delivering these personalized and high touch experiences. We only need to look at the pioneers and typical pioneers in the IT industry to see what they’re doing now and hint at and take cues from them as to what we really should be thinking about going forward. We have, for example, Facebook has started to integrate AI heavily into all of its algorithms, reading your comments and all that. You no longer have a timeline. It surfaces information not based on what it thinks you want to read about, but they’re going even a step further. In their messaging platform, they’re going to integrate AI. They’re going to have a little contact in your Messenger that you can include in group chats and is always listening to you and your friends talk. Let’s say you’re trying to coordinate dinner plans, you’re having a back and forth with your friends, and you’re all trying to figure out where to eat. “Hey, Facebook Messenger Bot, where should we eat tonight?” It’s followed the whole conversation through and knows your friend's' preferences, and it will find a place that you all like to eat that’s close to people, to everyone’s house.

That’s the app value-add that these artificial intelligent applications are going to be providing. You know, as Jon said, Google is also investing very heavily in machine learning and AI. Like he said, they’ve replaced without us even knowing their traditional page rank algorithm that made them as famous as they are with neural networks that they call Brain Rank now. So it’s a machine learning algorithm picking your search results now. Then there are a couple of other providers as well. I don’t know if anyone’s heard of Amy, which is a calendar application that you just cc on your email and handles your scheduling for you. It’s like an executive assistant for free like I can have one. I’m not even a VP yet. Then you have, of course, the successor to Siri, Viv which promises to take it to the next level. Siri, we thought was smart, but it was really just based on rules and things that Apple thought we might ask it. This Viv is supposed to take third party services and make things happen on your behalf, like schedule me or make a reservation for me at this restaurant or my favorite restaurant, and it will just do it for you, so you don’t have to basically do more work. 

Presented by Julio Santos and Jon Landers at a Pariveda Perspectives® event

About the Author

Mr. Jon Landers is a data scientist and application architect with many years of relevant experience.  He has applied machine learning and data science techniques in a variety of fields including biology, sports, and retail energy and spent several years conducting and presenting original scientific research.  Also, he has managed many teams and implemented software solutions using a variety of technologies and languages. 

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