Hi everyone. Thanks for tuning into Con 42 Machine Learning 2025. My name is Mohan. I'm a data and AI expert with over 12 years of experience in this field. Today I'm super excited to talk about from insights to Impact, how can companies revolutionize customer experience using AI and data analytics. So without any further ado, let's get started. Here is a quick agenda for today. First, we'll start off with understanding what is the current state of customer experience. Then we'll spend some time understanding what are some of the key challenges in current CX strategies employed by companies. Then we'll touch upon what is the role of data and AI driven strategies in customer experience. Then we'll explore how companies can drive customer experience transformation using ai, and at the end, we'll conclude with. How can companies prepare for customer experience transformation using artificial intelligence? Before we dive into the specifics, let's first understand why transform customer experience. If we think about it, we are living in a world where customer expectations have evolved dramatically over time, and now they expect more personalized experiences across. All the touch points they have with a given product or s service and companies that deliver on this promise of hyper personalized experience, they tend to outperform its competitors, both in terms of customer growth as well as customer retention. In addition to that, there is a massive data opportunity as well. In the last decade or so, companies. Started to amass huge amounts of customer data, but lot of companies struggle to tap into this data to generate value and better experience for its customers. And now with recent advancements in AI and machine learning companies have even better ways to take advantage of this data and ultimately deliver superior customer experience to, to their customers. Now let's understand what is the current state of customer experience? As I said in the earlier slide in the last decade or so, there is this huge data explosion wherein companies started to collect huge amounts of data about its customers, about be it, be there. Product usage, their transactions, their behaviors within the website or app, or their engagement with the social media channels or the customer service interactions. The problem is a lot of companies struggle to turn this data into meaningful insights that ultimately improve customer experience and lead to improvements in customer engagement and satisfaction. There are a lot of reasons for that. And few challenges that I have noticed in my experience are oftentimes the data is fragmented, meaning they live in siloed systems, which limits the ability to comp for companies to have a single unified view of a customer. Also, companies tend to employ more reactive strategies than proactive. In terms of figuring out customer issues or anticipating customer needs because of this pro like these disconnected systems, data systems, it limits the ability to, for companies to personalize a customer experience and that ultimately lead to dissatisfaction among customers. Also, certain few companies are very hesitant to adapt to new technologies. And that causes serious damage in the long run in for the, for those companies. So if we were to take a more deeper look into what are some of the key challenges in currency CX strategies? As I said in the previous slide, the bigger challenge that I have noticed is siloed data systems. Yes, companies collect huge amounts of data that can be used to improve customer experience. But because the data is not so connected well and live in live in a siloed, disconnected manner, it becomes very difficult for companies to take advantage of this data and translate this into meaningful and actionable insights. So what happens is because of this. It leads to it leads to a, it leads to a place where companies cannot truly understand like what is the 360 degree view of a customer? What are the transactions or behaviors exhibited by customers? And that limits their ability to personalize their experience. So the root of all ultimately it leads to poor personalization, but the main thing that companies needs to solve. Is solving for these siloed systems and integrating all this data in a manner that generates meaningful insights to improve customer experience. And that is what now we are going to explore. So how can companies solve for these challenges and drive improvements to their customer experience? This is where the data driven CX strategy comes into play. So the very first step that companies needs to do is. They need to start off with data integration, meaning rather than having siloed, disconnected systems, they need to think about a, think about integrating all this data, consolidating all this data from different touch points, different systems into one single place so that they can have the true, that true holistic, unified view of a customer so that it leads to better understanding of their customers. And once the data is in data is integrated and they have and they have access to this data, then companies needs to think about how to translate this data into actionable and meaningful insights. Because without any insights coming out of data, it becomes really tricky and challenging for companies to act on or act on it and improve customer experience. So the second step is. How companies needs to understand how to translate this data into actionable insights. So it involves, looking at what are the different patterns or like different behaviors exhibited by customers like using this for more advanced predict to analytics and all that stuff, which we'll get into in later slides. But they need to figure out a way to translate this wealth of data into more meaningful and actionable insights and. Once they have a good understanding in terms of their customer, the next step is how can they incorporate AI and machine learning to basically automate the experience the customer experience for its customers. So this is where, adopting technologies like ai, machine learning comes into picture, which will lead to, not only personalizing customer experience, but improvements in engagement, improvements in customer satisfaction, and. The most important thing is this has to be a continuous loop. It's not like companies do it once and just be done with it. They have to continuously refine this process, figure out if there are any gaps or issues, and figure out ways to solve for these things. And that is only possible, like once they start measuring what are the outcomes from this, like from this system, from this framework where data is integrated, insights are being generated. Machine learning, ai, ML solutions are deployed. And what are the outcomes of it? What are like, what are the areas where they're still falling short of customer expectations and go there and start to improve those process. So I think employing this data driven framework really helps companies to not just personalize experiences, but also do achieve much more things rather than just personalization. Now let's try to understand these things in a more detailed manner. So let's start off with data integration. So when I say data integration, what I mean by that is, as I said earlier, companies collect data about customers through various systems. In my experience, I have seen companies tracking behavioral data. Like what? Clicks or like what, I mean what buttons or like what features have been used by customers within the website or within the app? So they do capture this data through systems like Adobe Analytics or like Google Analytics. They do track this information. They also collect customer feedback and sentiment data. This is where they talk about, reviews or support interactions or social media mentions. They also collect profile data that is with. It, they have information on demographics of a customer, their preferences, their segment, whatnot, and they also collect like contextual data, such as like location, device. But overall, most importantly, they track the transactional data. That is very important because the end of the day customers are coming to your website or your app and ultimately buying something from you, tracking the transaction data is also important because that tells you what are, like, what do customers like, doing with your website or with your app so that gives you a lot of, lot more information. So once we have all this data collected, this data needs to be integrated at a customer grain level. Meaning for every customer company like once integration is completed for every customer's. For every customer company should be able to help understand, okay, like how many transactions this customer had made in the last 30 days or in the last 30, 60 days. What are the frequently bought products or services by this customer? What do they do when they come to a website or like when they come, when they use app? So by having this single unified 360 degree view of a customer, it becomes very easy to truly understand. Who what is that individual customer do and like what their preferences and needs are. And that leads to better outcomes, better experience for customers at the end of the day. So that the very first step is how do they integrate all these data sources together so that it gives the foundation to do the next things in this framework. So moving on to the next one once a robust. In data, like robust data integration is completed. The next step is extracting actionable and meaningful insights. Sort of it, and it's typically a four step process where it starts with data refinement. Typically raw data, is will, may not give you better insights. So there needs to be sometimes spent on cleaning that data, structuring that data in a way that. It can be used for insights generation. So once the data refinement step is completed, the next step is doing some data analytics on top of that to understand and do some pattern recognition to see what do customers love to do, like what sort of patterns or behaviors they exhibit when it comes to a specific product or a service. And from there, the next step is insight generation. This is where all these raw patterns are translated into. Business relevant customer specific insights. And once these insights are produced, the very important step is actually acting on these insights. Generating insights is one thing, but acting on these insights is where we actually see better improvements to customer experience. We see improvements to metrics like csat, NPS, so I think acting on these insights. Is far more important than just producing insights. So that insights to action is a very important step that companies needs to act on. So once you have a robust data foundation built, and once you have these insights started to come in, like that's when you start to see the benefits. Of like improvements in your CX metrics or benefits in terms of better retention or like better engagement with products and services that a company offers? The next step in this process is incorporating ai. So using AI or machine learning can be done in numerous ways. I'm just highlighting few examples here. Through ai, ml, companies can have. More intelligent virtual assistants, like nowadays, chatbots have become a huge trend in terms of using incorporating genea and everything to build chatbots so that they can help assist customers to solve their issues. Rather than driving all these all these customers to a customer service center or a contact center these chart boards can actually help to solve a lot of problems upfront. So that. You can companies can reduce the traffic or they reduce the cost to in terms of customer service as well. And then the other thing is personalized recommendations as we discussed in the earlier slides, companies, customers want more personalized interactions, more personalized experiences, and AI is enabler of these personalized experiences. Using ai ML companies can take advantage of, the data that they have and understand like what are the historical behaviors that were exhibited by these customers. And use that data to predict what I mean what can be done for these customers based on their past past behaviors, past transactions, everything. And then practice service alerts. I mean by, by, by integrating ai. Companies can try to predict, what could be the potential issues or customer issues that may arise in future so that we are having the proactive strategy to mitigate these issues rather than being very reactive in nature. And also, through an lp through machine learning, through NLP companies can actually take advantage of all the feedback data that they have received from customers and try to understand what is the sentiment expressed. By these customers? Is it positive, is it negative? And if it is negative, like what is the best way to route that particular customer to his appropriate agent or appropriate our team so that it helps to resolve those problems early on rather than just leaving customers out in the dust. At the end of the day, the whole purpose of incorporating AI into translating into like a. Making improvements for customer experience is we want to see better outcomes because of this. We want to see higher customer satisfaction scores. We want to see improved loyalty from customers for brand, and we want to see improved retention rates for these customers because those will really translate into meaningful bottom improvements in bottom line metrics for a given company. So let's spend some time understanding how can companies drive CX transformation using ai. If we were to think about it AI is completely reshaping the way that that a lot of con industries operate nowadays. And see a customer experience is also is also a no stranger to this transformation by incorporating AI into customer experience. Companies can completely reshape the customer interactions by blending advanced technology with human-centric design. These are like few examples as in how companies can leverage AI to drive CX transformation. Like one is predictive and cognitive experiences. These AI systems can anticipate customer needs before they even before they even arise. So by having systems in place that can actually understand what customer wants, the companies are being very proactive in figuring out the needs of the customer and deliver those to customers beforehand itself. So that will lead to better customer experience as a whole for customers. And then the second one is omnichannel journey orchestration. We talked about how personalization is becoming very critical for customers, and having this personal personalization embedded into every aspect of a customer journey is going to be a truly game changer for a lot of companies. So companies can take advantage of integrating AI in that whole. Customer journey lifecycle so that delivers more superior and more personalized experience for customers, which will ultimately lead to better enhancements in terms of customer metrics, customer satisfaction metrics or other CX metrics. Last but not least is augmented human service. This is where I mean by having EA systems in place, customer service representatives or associates can have, better insights about a customer for more effective interaction with the customer. Like by having systems in place that can tell what meaning, like who that customer is, what are their preferences and needs and having these insights, making it available to service representatives can make it easy for them to deliver. That, that superior experience to these customers as part of their interactions when they reach out to a customer service representative. So these are some of the ways in which AI is going to reshape the customer experience as a whole. The end goal is, again, as I discussed in the previous slide, the end goal is the same like it is to deliver that more personalized. Experienced experiences to customers so that will ultimately improve all the metrics related to customers, related to the company. And if we were to understand like what is the role of AI and ML and CX transformation here, we are going much more deeper into what are some of the ways companies can actually employ AI ML in their transformation journey. Like predictive analytics still is one good example. This is where like machine learning models can actually look at the data that you have, that companies have about customers and use the data to predict or forecast customer behaviors so that companies can proactively plan and meet the evolving needs of customers. And there is the, there is this real time decision aspect also now with AI powered systems. They can actually process data in real time. Pro data related to customer interactions in related real time across different channels. And that will lead to personalized responses. Personalized experiences ultimately drives improvement in customer satisfaction. And a IB is being employed heavily when it comes to automation. Or by auto, by using AI for automation. A lot of rapid two tasks, particularly in the context of customer service, can be automated. So that way it gives it frees up the time for human agents and they can focus on complex issues and providing more empathetic support and solving bigger problems or like bigger issues for customers. So automation is gonna play a very key role in terms of driving CX transformation in future. And last, but not least, is personalization. This is the, this is a theme that we've been touching from the beginning, that personalization is very crucial nowadays to elevate customer experience. So by having more advanced AI platforms and systems that can analyze customer data to deeply understand what customer wants. And delivering those personalized experiences is a real game changer for the company when it comes to differentiating, like when it comes to differentiating itself from its competitors. Now let's spend some time to understand how can companies prepare for this transformation, because this is not just a one night or a one. Like one month thing. It is a continuous process. It takes time, it takes effort. More importantly, it means changing things. Cha changing things or systems that were done certain way. So the very first thing that I would, that I want to touch upon is it requires mindset shift. Which means like, it requires that culture shift in within the company because. Unless, until there is the true change in culture, it becomes really tricky for companies to adapt onto like to get onto this journey. So having that customer centric, data driven mindset is really crucial for companies to have the CX transformation. Also, companies need to invest in developing their employees to work with these new tools, new AI tools, new AI systems, because otherwise. They'll be left behind and become it, and it becomes really tricky for them to take advantage of these tools. Also, there needs to be a strong data governance strategy. There are teams at various companies like for the, for the companies that have done really well in this space. They have strong data governance teams that look at what, like what is the data that they're collecting from customers? What sort of data quality they're maintaining and they have a very strong, robust framework when it comes to maintaining privacy of the data also. So it is very important for companies to have this strong data governance frameworks in place to take care of this data. And ultimately companies need to measure this, right? You can't simply go on a. Go on and then say, I'm gonna do CX transformation. You need to quantitatively measure like how, what is the success criteria to say we achieved CX transformation. So companies need to establish certain KPAs and metrics to measure this whole process. It can be a. Taking a look at customer experience metrics like csat, NPS, effort score, whatnot. Or it can be looking at like revenue led metric. Like what's the gross sales, like what's the do sale volume? Or like what look what is, what are the bottom line metrics, top line metrics, how are they trending after CX transformation, prior to CX transformation? So by having that robust framework to measure this is going to be really crucial. For leaders to invest in this whole thing. In addition to that as I was saying, right? In addition to that, it requires cultural and organizational change because that mindset, mind mindsets shift are like the change is very important. It is very important for executors to really believe in this data driven cx otherwise. If there is no leadership support or executive support, it becomes really tricky and challenging for companies to act on this like to execute on this thing. Also, it's not a one team effort. It is a cross-functional teams collaboration where like teams from IT, marketing, customer support or product. They all need to come together and then they all need to collectively think about customer experience as an end goal. And form teams accordingly, set set objectives and strategies accordingly and try to deliver on the CX transformation. Last but not least is companies need to empower the frontline employees. I. Meaning they need to educate them, give access to these customer insights because they're the ones, at the end of the day, they are the ones who talk to a customer who interact with customers face-to-face at times, if it is a physical store or something. So these frontline staff staff also need to have access to these insights so that they can serve customers in a better way. Give that the, give them that sense of, having a better experience when they visit the store or when they interact with this frontline staff. So I think encouraging the frontline staff to actually look into these insights and figure out a way to have an empathetic view towards the towards these customers and like delivering better experience at the end of the day becomes really important for a company to succeed in the CX transformation journey. So that's why like it starts with that cultural and organizational change. And it needs to have that strong support from leadership. It needs to have that collaboration with cross-functional teams and ultimately everyone in the company should be empowered to enhance customer experience. So with that, I would like to conclude the presentation. I hope this presentation helps you to gain some insights into how can companies drive CX transformation. What are the like what's the, what are the frameworks they can employ to, to get on this journey and ultimately achieve the CX transformation journey? I hope you enjoyed the talk and it's a great pleasure to. Speak at this con 40 to machine learning 2025. So thanks for the opportunity.