Delivering Next-gen Customer Experience: Leveraging technology for a new world order

Pari: Hello, everyone, and welcome to a brand new episode of the Zinnov Podcast. I’m Pari Natarajan, CEO of Zinnov and today we are sitting down with Shankar Arumugavelu, SVP and Chief Digital and Innovation Officer, Verizon Communications. A technology veteran, Shankar has been with Verizon right from when Bell Atlantic acquired it in 2000.

In his current role, he leads Verizon’s technology organization with global responsibility for information technology strategy, architecture, development, and management of the information system portfolio. Shankar helps drive Verizon’s adoption of emerging technology like AI and Machine Learning to improve customer experience.

Welcome to this episode of the Zinnov Podcast, Shankar.

Shankar: Pari, great to be with you.

Pari: It’s great to have you with us.

Pari: For today’s conversation, I want to discuss how you’re looking at delivering next generation customer experience and the technology innovation that are enabling it, especially from a Gen Z lens. Let’s just dive right in. Shankar, you are on the cutting edge of business building applications and tools for the next generation of Verizon’s customers. What are some of the key technology trends that you believe will shape the future of customer experience for Gen Z? And how do you stitch some of the new technologies such as Spatial Computing, AI into solutions that improves customer experience and engagement?

Shankar: Awesome. Great question, Pari. So, you qualified that with Gen Z. So I think a little bit of defining what Gen Z would be helpful as well. This is the first digital native generation as they have never known the world without the Internet. So they expect business to be available 24 hours a day, every day across all communication channels, offline, online, website, social media, you name it.

And there are a few trends that I see are absolutely critical that’s going to address and shape the customer experience for this population specifically. The first one, I would say, is around mobile-first. So the Gen Z-ers are constantly connected, and therefore they do everything on the go, whether it be shop, whether it be playing games, socializing, studying, et cetera, and their expectation of the website performance, the functionality is extremely high.

And when we talk about from a technology standpoint, the optimization efforts have to go beyond making the website just responsive and have it render on their smartphones, right? It’s got to be very flawless and natural and mobile-first for an engaging experience. So I would say mobile-first is the first one.

Then, it’s an omnichannel experience. What Gen-Zers also look for is consistency across all channels. And when I say all channels, it is the web, it is the mobile app, chatbots, voice assistance, social media, email, SMS, phone calls, physical stores, all of these channels, because they do interact in all these places.
And what they look for is that consistency across all these channels. Third would be that hyper personalization, that one-on-one personalization, And what the Gen-Zers also look for here is to really personalize. And they’re willing to even sacrifice privacy to a certain extent for the sake of that hyper personalized experience.

The fourth is all around self-service. This group, they want to be able to solve problems themselves. They are very much adopt a do-it-yourself mindset, and they prefer to use the self-serve and digital properties that a company may have to get that accomplished. The second part of your question on how in Verizon we’re leveraging emerging technologies. Clearly, when it comes to Artificial Intelligence, Machine Learning, and also Augmented Reality, Mixed Reality, all of them play a role.

I can give you a couple of examples, so if you talk about Augmented Reality in the case of Fios, this is a broadband solution that we offer to our customers in the northeast corridor of the United States.

And one of the scenarios where we apply Augmented Reality is when customers have trouble and they want to have that addressed, while we also provide all the self-serve and digital tools, in the event, the customer has to talk to an agent we now employ Augmented Reality. Essentially, the customer on one end can turn on… So when they call the customer service, the rep on the Verizon side will be able to send them a link. The customer taps on to that link. Now, the agent sitting in our call center essentially uses the smartphone camera on the customer’s hand as the eyes into what the network equipment, the network gear that is in the house, whether it be the setup box, whether it be the router, and they have capabilities to also annotate right on the screen to help and guide the customer through the troubleshooting and figuring out how to address those problems.

So this is a win-win situation. The way we see this. So the customer, we respect the customer’s time. There is a guide walking them through how to go about solving these problems, and it is also about instantly fixing the problem versus having to take a trouble ticket. And then roll out a truck at a later point in time until then the customer is out of service.

So it’s a win-win. And also from a company standpoint, it helps us from an efficiency perspective as well. So that’s one scenario how we put augmented reality to work. And the same thing on AI and Machine Learning use cases there are a plenty. So the entire next best action and the next best offer engine is powered by AI. And we make sure that that’s the common engine that’s used across all our systems of engagement as well to provide that hyperpersonalized experience for our customers.

Pari: Very interesting, Shankar. We talked about mobile-first, omnichannel, hyperpersonalization, and also it looks like Verizon is, be able to implement and adopt some of these technologies in real use cases of how customers are using your product. And one thing we didn’t touch upon in the emerging trends is, Gen AI. What are you experimenting?

Shankar: Absolutely. Clearly, that is a huge trend. AI is suddenly at a point of inflection starting from traditional Machine Learning into Deep Learning and now getting into generating new content. How do I personalize the emails that go out to customers at scale?

There are several other use cases that we’re looking at as well. How do we provide a co-pilot experience for our customers when they are navigating our website? It’s all about providing that multimodal experience. So traditionally, it’s you get on digital, you interact and then you move out of that and just go straight into chat if you’re not able to find what you’re looking for. How about if we have a co-pilot that’s guiding the customer through the navigation, making it easier for them to find what they are looking for? So that is one of the multimodal use cases that we are looking at to have that co-pilot for our customers when they interact with our digital properties.

Another use case that we’re looking at is all around knowledge management. If you think about the key characteristics of Generative AI, one of the important characteristics is the seemingly infinite memory that these foundation models have. So if we ask the question, what is possible if we were to build a corpus with all the information that we have about the company, the products and services that we offer, and have that corpus, that knowledge base readily available to be a co-pilot for our frontline agents, what is possible? It shrinks the training time for our customer service advocates that we bring on board in a significant way.

And it fundamentally changes the whole notion of how long it takes to train our agents when they are interacting with customers, when they’re dealing with some difficult problems, what are the knowledge bases they’ll have to navigate to all of that is instantly available right at the fingertips, and that provides the superpower to every agent. And what we’re able to achieve with this is generally when you have a call center or inside sales kind of an organization or customer service, you have variability in performance of the different reps.

The question that we can ask ourselves with the power of Generative AI now that that’s available, we are well positioned to elevate the performance of all the reps and bridge the gap between the top performers and the bottom performers. Just think about the productivity that’s going to unleash and ultimately, the better experience that we will be able to provide to our customers as well.

Pari: Very interesting. It not just uplevels it, but also standardizes and makes your service very consistent and reliable. And one other question I had is we talked about how in the customer experience, Gen Z is really looking at self-service as a way to drive it, and we also talked about how this newer knowledge models allows your agents to provide better service.

But you see that the world is moving towards an autonomous enterprise where there’s a query coming from the customer and the resolution is automated. So we don’t have human in the loop, at least for certain kinds of solutions. You see that starting to happen or you’re seeing weak signals around those lines?

Shankar: Yeah, I think it’s a maturity curve that every organization goes through. So all the way from, Descriptive Analytics to Diagnostics, figuring out exactly why it happened to now being able to predict and what you’re now talking about is taking it to the next level to being prescriptive as well.

There are certain use cases where the decision-making can also be automated, but I subscribe to the approach where it’s important to still have human in the loop. We really are looking at several use cases where it’s really human plus machine, and we keep learning.

As we learn enough and we will get to a point where there are certain use cases where we have learned enough, we have figured out all the different scenarios where now we can say we can completely automate certain things.

So, for example, when it comes to certain things in our network, when we talk about a self-optimizing network, there are certain things where we learn, like, you know, under these circumstances, this is the deterministic action that we’ll have to take. Then we can go ahead and take the human out of the loop there and essentially take the action as well in an automated manner.

Pari: Got it. So the companies have to go through this maturity curve and certain use cases you’ll be able to automate and certain use cases, having human might provide the level of empathy with the customer and that improves engagement as well.

Shankar: Yeah, it is about like, you know, that augmentation that it provides, that superpower that it provides to agents or the human in the loop as well. So that way a lot of the heavy lifting is done by the machine and now human is involved in that decision making. But I’m sure, there are going to be many, many use cases where that action is also going to be done in an automated way, but it is going to take some time before we get to that.

One of the things that we’re working on as well is building out a customer digital twin. When we talk about hyperpersonalizing experiences, that one-on-one personalization, right? It’s important to know the entire customer journey. And for us, our customers, there is different telemetry that we get from different sources. There is information that we get about the device that the customer has.

There is information we get from all the touch points that the customer interacts with us. There are signals that we get from the interactions we have with the customers, because we send out a bill once a month, we send out marketing content on a regular basis, etc. and then, of course, the network experience as well. What is the experience the customer is having when they are on our network?

So if I look at these four clusters or domains, significant amount of data, telemetry that we get, that is crucial for us to really construct this customer digital twin that way we are able to figure out where the customer is in any given journey and be able to predict what is the reason that they are here when they show up in one of our channels, what is the next best action, being able to anticipate them, and personalize that experience.

And when we have that now, your customer personal voice assistance will be able to pretty much do the same thing in an automated manner working with our back ends as well on here is really what needs to go on, et cetera. And my belief is this will also fundamentally change the way we do customer experience measurements.

Today, when we do Net Promoter Score surveys, it’s all still very much survey based. But what if we are able to get the real-time sentiment of the customer at all times? And be able to know exactly what is the experience and that is only possible if we get all the signals, that 360 degree view that I talked about, the device, network, customer interactions, the interactions the company has with the customer, all put together we will truly be able to get to the point where we can get that real time sentiment of the customer and personalize the experience for the customer in the moment of truth.

Pari: It’s super exciting, you’re creating a digital twin of the customer, also able to predict almost like the patch people have now to track insulin levels. We talked about these digital twins, but also creates this important issue around biases around the customer. What are the steps you are taking to ensure your customer experience offering are inclusive and accessible to customers of all backgrounds and abilities and it’s free of any bias?

Shankar: I think it’s very important, right? So, when we talk about AI in our practice ethical and responsible AI is a big area of focus for us as well. How do we make sure we respect the customer’s privacy and be upfront and allow customers to opt-in with some of that information, so we will be able to provide that personalized experience, be very transparent with the customers on what is that data that we use to personalize the experience.

We are constantly striving to move our world forward through exactly the points you talked about, Pari. Diversity, Equity, Inclusion. And what that means is improving the customer experience for everyone. The accessibility is one important part of that as well. We look at accessibility by design. We have a dedicated accessibility testing team. We make sure all our pages on our digital properties are designed with accessibility at the core, whether it be through high contrast, large display, large fonts, zoom, magnification, you name it. And strive to meet the online accessibility standards recommended by the World Wide Web Consortium, in its WCAG, the Web Content Accessibility Guidelines. And the same is true for our frontline employees as well.

We want to create an inclusive environment for employees with different abilities to be able to still perform at their best. So both from a customer standpoint and a frontline employee standpoint, we make sure accessibility is something that we look at as an important requirement. So when we do things, when we roll out new capabilities, et cetera, just like how we talk about security by design, it is accessible design as well.

Pari: And trying to step back a bit. We talked about customer experience, but I also want to talk about more broader CIO priorities, right? And given the current macroeconomic scenario and the rapid pace of technology evolution, how are your priorities evolving? How do you balance the need to look at short term needs versus long term goals?

Shankar: You’re absolutely right. When we talk about the technological changes that’s happening, things are moving so fast. So we talk about AI, like, you know, not too long ago, it was all just traditional Machine Learning then came around Deep Learning. And here we are talking about a point of inflection in the AI, with Generative AI as well, and how democratized that has become and demonstrated to the whole world, what is the art of the possible?

So as the CIO as well, clearly it gives us more tools in the toolbox. And, I think the technology leaders are very well positioned to meet the unarticulated needs of the business, to truly demonstrate how technology can drive a competitive advantage for the business and how can we truly reimagine the way we do things.

The kind of examples that we talked about today, whether it be fundamentally reimagining how we can equip our frontline agents with the kind of knowledge management solutions. Fundamentally reimagining how you can have a co-pilot that provides the superpower to the agent to handle these customer queries. Gen AI is one such. And in fact, for all the buzz around Gen AI as well, there is still a whole lot more that can be done even with traditional Machine Learning that I think this is suddenly put a light on.

Now to your second question on prioritization. So this is where this is a challenge that from a technology standpoint, it’s been there forever, continues to be the case as well. What I like to follow is the Eisenhower matrix. It talks about this 2 by 2 matrix of what’s important and what’s urgent. So that is a good framework that I have found to really figure out how do I balance the priorities for the short term here and now, and at the same time designing for the long term.

So when I think about urgent, not urgent, important, not important. Obviously, if it’s urgent and important, absolutely do it now, right? So that is like, you know, highest priority. We absolutely make sure that is being done now. If it is not urgent, still important, that is the area whereas technology leaders, we always have a challenge of prioritizing. So how do I prioritize my tech debt reduction plans versus some major modernization programs? What are some of the programs. Because again, this has to be balanced with what the business needs are as well, because IT strategy is very much linked to the business strategy as well. So that’s at a high level how we look at prioritizing.

Pari: And as we come to the close of this episode, I would like to do a rapid fire round with you if you’re up to it.

Shankar: Sure. Let’s go. Okay.

Pari: Who is a leader from your peer group who you look up to and admire?

Shankar: From my peer group, there are so many leaders. I can’t name one, because I’ll also tell you one thing that I pick up from leaders is as much as I learn what I like to follow, there are some leaders also where I’ve learned what not to do as well.

Pari: Great. And finally, what is the one trait you look for when you hire people?

Shankar: Integrity. Hands down, integrity.

Pari: Thank you, Shankar. It was great discussing with you what are the expectation of Gen Z from a company like Verizon and how you are prioritizing and architecting some of the technology like AI, Machine Learning, Spatial Computing, Augmented Reality and provide the level of service that Gen Z are looking at in terms of being mobile-first, omni channel, a hyper personalized service, as well as enabling them to do self-service both in terms of engaging with Verizon as they buy your products, as well as in engaging with you in resolving the challenges they might have in using your products. I really enjoyed the discussion. It was super insightful very practical. You’re solving for hundreds of millions of customer. Very few people in the world will have that level of insight into customers. Great to hear from you, and our audience will thoroughly enjoy this session.

Shankar: I enjoyed the conversation, Pari. Thank you so much for having me. Thank you.