 Good morning, good evening, good afternoon to anyone who's there in whichever part of the world you are. Thank you so much for joining the session on a very quick talk on data driven product decisions. I hope you enjoy as much as I enjoyed making this presentation for you. So first up, some straight away disclaimers. Anything that you see over the next 20 to 30 minutes are purely my individual opinions and do not represent my present or past employees in any way. And so it's a little bit of hope me. I am a product manager at AmEx. I lead the small business segment when it comes to the customer relationship management platform that we're building. So I'm a platform product manager at AmEx based out of Bangalore, India right now. And of course I'm no expert. I would love to have your comments in the chat window of this session and I would be happy to take up those questions or comments as well. So let's jump right in. The why, right? So data has become so important for us as product managers now that you cannot do away with it at all. You know, so this one of my favorite quotes here that I've put out is without data you're just another person with an opinion. And there's not a lot of people, you know, who like opinions, you need data to prove your theory, your hypothesis. You know, your assumptions to make sure that you're going in the right direction, you're steering the team as a product manager in the right direction as well. Thanks to the great statistician who has, you know, put out that code. And this is why it's become increasingly important for product managers to rely on data to drive a lot of those key decisions at all. You know, opinions don't matter as much because data is going to tell you what you've been missing all along. And let's jump right into the what, right? So when you look at the other perspective of data, now that we've fairly established why data is really important as product managers in our everyday tasks, I'm going to stick to four key tenets on the what of data. Typically, you will find that, you know, this is happening across every single product that any product manager, you know, ever designs, develops with his or her team. There is usually a problem that we are after, you know, that we are offering a solution and that's something that we're going to start our assumptions with. There is a problem faced by a certain persona. It could be a certain user group, a target market, a customer segment that's usually facing that specific problem. There is a product that we're going to build that's going to address that problem for that very specific persona that we are after. Eventually, there is a business value that we generate out of it. There is revenue, there is profits that we generate, there is a business model that we can generate out of it, which the organization that's designing this product can benefit at the end of the day. So let's dive a little deeper into each of those key tenets that I'm talking about here. First up, the problem. I'm going to call it as opportunity instead of a problem. When you think about this, right, it could be a problem that we are solving for, you know, from scratch. This has never existed before. It could be an existing platform that you are redoing. You know, there is an experience out there that you will have to make take and make it better as product managers. So first up, if you are, you know, a product manager on an existing product, some ways where data can help you identify the problem, the opportunity rather in this case. My intention is to not to show you all the different data points and the KPIs that you're going to measure, but instead the aspects you could use to think about how you can utilize data in determining the actual opportunity at all. So typically, if it is an existing platform or a product that you are a product manager for, you could look at tickets where it's going to convey a whole lot of information about what's going on. It could be service now tickets, it could be cases that are being raised. It could be simply issues on whatever mechanism and tracking methods that you have in your organization. But without actually speaking to the customer, there is so much you could understand from these tickets on evolving patterns, commonly observed issues and rather pain points. It's going to directly tell you what the use of pain points are so that you could quickly put that into your prioritized backlog and start addressing that. But eventually what we are after is that could possibly be an opportunity for us to change the course of the product altogether based on that opportunity that's coming out of it. The next aspect is feedback. So if you are developing an Android app or an iOS app, you always end up looking at the Play Store or the Apple Store comments and feedback that you get. But it's such a powerful mechanism where feedback can directly tell you what the users are happy or unhappy about, what is working and not working for the user. And more so, again, what is missing and what could be your potential future feature on the product that is something that you are owning. Of course, discovery sessions. You could speak to the user directly. It could be the customer, a paying customer or it could be a user depending on the kind of product that you own. But discovery sessions are always going to tell you what your identified opportunities are, where you should actually go towards. There are several different methodologies on discovery sessions, but essentially a direct communication with your user is actually going to tell you what's missing out there and what's not missing, what's working well and what's not working well. It could more so be an unsolved or an unmet need as well. If it is a new product that you are developing from scratch, so far we've been looking at existing products that people are using out there. It's been out there for a while, but if it's a new product altogether, you could look at competitor analysis as well. If you have a competitor out there in the product segment that you are after, you could look at what the business model of your competitors are, what opportunities are your competitors solving and how are they providing an experience to which target segment that they are after to even drive your decision making on which opportunity you should focus on where you could monetize on and eventually build a winning product. Micro and macro economic factors come into play as well. Of course, if it's a new product that you're after, if it's B2C, for that matter B2B as well, you will have to look at all the economic factors that would eventually decide the course of your product as well. Data could be flowing in from several touch points, your social media, your simple news and research articles, the economic factors that are affecting that market, that region, that can help drive your decision as well. And lastly, of course, something that we are very fairly familiar with is trends, emerging trends, evolving trends that are coming over the course and of course that type cycle is going to show you a lot of that. You could basis your whole opportunity identification on these trends. So the first aspect that we have talked about is how you could use data for your opportunity identification. And in that, we have sliced it across an existing product that's being used there today or a new product that you are developing as a product manager. So going back to the second tenet, we started off with a problem which in this case is an opportunity. It is usually against... So this problem is usually faced by a certain set of people, in this case a persona. And how you could use data to decide which persona we are going to address this problem for or rather which persona we would want to design this whole new opportunity that we have identified is what we are going to discuss now. Starting with customer segment. Be it a new product or an existing product, you would always have to identify which customer segment you are going to target for that opportunity you are designing this product for. Of course, you will have to have your own identification of pilot users. But this will often be based on the product offering. It could be based on your business model, your strategy of your product and so on. But I'm just trying to highlight some key tenets on how you can identify the persona using data. So you will use it in your identification of pilot users. It could be demographics based on simply just the gender, their age, their usage category. And it could be the geography as well where they are located. And it could also be based on how you're going to scale that product over the course of time. If your product is specifically focused on mass segment, you would have to always consider your target segment. All the people you're going to address that problem for eventually to with the intention of scaling it, which is why scaling and how you identify the people with the intention of scaling for future is also important for you. Once again, I am not trying to highlight the different identifiers or the different metrics you could track. But I'm just simply highlighting what are all the different aspects you could consider when you would like to identify the people you are building those products for. So we talked about a certain opportunity against a certain persona and there is going to be a product which you will still have to track those metrics against. This is something we are fairly familiar with where when it's a product, you will have to look at several metrics against acquisition. What are all the different methods we have used for acquisition? Which one of those have worked? It could be email click-throughs, it could be campaigns, it could be outbound, inbound, cold calls, tele-calls, so on and so forth. But essentially, how has your acquisition strategy worked? Should we change it? And is this data going to drive my decision on the whole acquisition strategy at all? Consider the same thing about engagement. If you are a B2C, if you are a consumer of several of those mobile apps, there are so many engagement metrics that can be tracked that essentially tell you what is working in that product and what is not. Some of the common food delivery applications out there, you could always see the kind of recommendations that the users click on, how many of those recommendations did they actually convert it to a purchase and typically an engagement that they're going to look at. And the different data points that you could collect from that mobile app that drives your whole engagement strategy. Should we change the recommendation model? Should my ML model be changed altogether? Am I going to show a different user experience altogether which eventually drives engagement up or rather in the desired direction? Is how you could use data to drive your decision altogether from an engagement perspective? Of course, retention. No app is developed for the purpose of a one-time buy. At least there aren't a lot of apps out there that are meant for a purchase, for a one-time purchase position. Retention is key out there as well. You could look at several data points that eventually drove that customer to come back and make that purchase once again. Take the same food delivery application that we talked about. How could I drive those users to come back and make that repeat purchase? Could we throw in some different offerings that could be brought in over there? But essentially, how can I help them make that repeat purchase decision? And you are looking at different data points to eventually drive the course of your retention strategy growth. Every app is always having a total addressable market and you wouldn't stop until you reach that end or if you have acquired all of those addressable market as well. It could be 10 million, 100 million that you're after. What drives increased growth in your product is also going to be driven based on that data that you're looking for. And of course, something that we are usually familiar with is the AB or the multivariate test. This is a simple tool out there that helps you measure your product performance at all. Is the user going to rely on a certain variable that they have put out there? What worked for them? What didn't work for them? And should we drive deep into something that worked to build the course of our future product features at all? So this is essentially some perspectives on how you could drive your product decision. Essentially, you're going to measure your product performance putting these perspectives in mind. And another one, of course, I talked about which is the fourth tenant. After you identify your opportunity, your persona, and you've built a product around it, eventually all of them have to start monetizing out of it. So some of those decisions could change entirely based on the way your business model is driven around. Take the case of Zomato, which is recently a blockbuster IPO that we are all familiar with here in India. They started off with just a food restaurant review application, which eventually became a restaurant partnership food delivery program. So eventually they're even going to have their own payment spin-off that's going to come off from Zomato. What you need to do is look at data that can eventually drive those decisions as well. What is contributing to revenue? Which feature has eventually drove a revenue, a top-line revenue or a bottom-line revenue, okay? What has contributed to profit? Where is that future profit pool that we can actually bring in money from? What's your return on investment in terms of how much money you have put in to acquire customers and what's the lifetime value, which is typically what's something that we are familiar with, is essentially how much money you put in and how much money you made out of it after a preset period that we had initially agreed on. It could be three months, six months, one year. And have you actually made the intended returns on that? And most importantly, is your business model working or not? Are you going to acquire more customers or not? There is a direct feedback mechanism out there, which is just your promoter score or your customer satisfaction score out there. So over the course of this whole product lifecycle, rather it's just a concise version of the product lifecycle where you have an opportunity, which is identified against a certain persona, which you're going to address using your product, which eventually has a business model out of it. You have those key perspectives that you could consider when you measure your data. You could drive the course or steer the course of your decision for the product based on these data. And this is something that I as a product manager have realized, of course, late in my career that it is really important to look at data regularly as product managers. So we talked about the why and the importance of looking at data. We've talked about a brief explanation of the what. Of course, there are several different perspectives, several measuring aspects, several metrics out there, several KPIs that you would use out there if you just Google about these. This could be a starting point for you to start considering how and which data you could actually use, which brings me to the whole conversation about, we've talked about the why and the importance of looking at data, the what and the different perspectives of looking at data around the product, which brings us to the how. Nothing fancy out here if you look at it, but as product managers or aspiring product managers, you would first have to sharpen your analytical skills. Your product could pump out so much data on a regular basis and it would all go to waste if you do not have that acumen to analytically look at that data and find out trends that come out of it. So you would have to, there is no escaping this. You might hate numbers, but this has to be something that you look at from an analytical standpoint and this is something that all of us as product managers or aspiring product managers need to really sharpen our skills. That's the first part on how you could become better at utilizing data to drive decision making on your products. Next thing is baking it into your objectives and key resources. It's increasingly popular now for all of us as product managers to utilize OKRs to set the vision on the now, the next, the later, how you're going to track your success, where you're going to go next and so on, bring in data into your OKRs. A generic OKR is no longer interesting. You would have to be very specific. You would have to talk about how you're going to increase your NPS. You would have to talk about what that percentage increase is actually going to look like and you would have to specifically say when you're going to achieve that milestone. All of this is actually going to come in with different mechanisms of how you're going to measure those KPIs. When you bake it into your OKRs, you have no escaping from data. You backup your whole assumption based on data and steer the course of your product vision altogether. Lastly, something very, very simple is make it a habit to rely on data. It is important to analyze data and it is also equally important to know where and how you're going to design the data collection mechanism. Unless you make it a habit, it will be very difficult for you to rely on data and let data do the talking for the majority of that part. So spend a few minutes every day or perhaps a week, every week or at a regular cadence that you can agree on to look at data and figure out how your product is actually working out there. You don't have to speak to a ton of people. I know that's a stressful thing to do, especially if you have an unhappy customer that you're talking with, but unless you make it a habit, it will be very difficult for you to rely on data to drive your decision-making process. Don't go by assumptions. Don't go by generic methods of defining your success, but make it a habit to utilize data for the different tenets that I talked about, be it the opportunity, the problem, the persona or the product for that matter. Make it a habit to utilize data on an everyday basis and how you could rely on it increasingly to drive your product decision. So we talked about why data is important for us as product managers. We talked about the what and a very brief version of how you could utilize data or what you're going to look at from a data perspective. How? There is a simple way to look at how. Nothing fancy out here like I talked about. And now essentially, for us, the weight on our shoulders are increasingly high as product managers to rely on data as well, quantitative as well as qualitative, to look beneath that surface, which you often find that is majority of that information is sitting out there waiting to be unearthed, which is why you still see so many products being developed out there. A lot of it based on the search, a lot of it based on data. A lot of them are winning products and none of them became winning products without the reliance on data. There are several examples out there and one of the most popular ones that we are almost all of us are familiar with is Netflix. You could look at several of their case studies and HBS studies and how they have utilized data to steer the course of their entire product decision altogether, something with subscription service on DVDs to where it is known and what they are doing. They rely on so much data and you don't often see them speaking with you directly and asking, hey, what's working well, what's not working well. They simply sit there, collect a ton of data to utilize to steer the course of their product decision and their whole user experience altogether. All of us could be creating such winning products as well if we also relied on data other than just assumptions and hypothesis to go about. So I feel we could barely scratch the surface with data and we have so much more to uncover if we just looked at data. Lastly, it brings me to the end of a very brief presentation on how you could drive your data, how you could drive your product decisions based on data. Of course, I am no expert. I am learning every day on the job, off the job and I would be very happy to take in comments on the chat section or post the session if you would like to speak with me. My LinkedIn credentials are out here. Happy to respond to you and best wishes on relying on data, on data a little more from now on for all you product managers out there and aspiring product managers. Cheers.