 Hi everyone, my name is Natasha Harpalani and I am a senior technical product manager at EWS. Thank you so much for joining this webinar. I will be speaking about how to use data to drive customer outcomes. I'll speak about how you can use quantitative and qualitative data as a PM in every step of the process of product development. Before diving into the content of this webinar, I wanted to give some quick background on myself. I started out my product management career at a startup called AppNexus, which focused on ad tech products. AppNexus was later acquired by AT&T, so I continued to work on machine learning products, but as a part of a much larger company. I then shifted back into the startup world and into the clean tech space by joining mainspring, which is a clean tech company that builds a hardware product. I focused on developing software that enabled their product. And finally, I currently work at AWS as I mentioned as a senior technical product manager. I focus on building cloud services for the clean energy space. Needless to say, I've worked at a lot of different companies, different size companies and worked on different products at various stages of maturity. And a lot of what I'm going to share today drives from my experience as a product manager, as well as a lot of mistakes that I've made where I maybe haven't leveraged all the data that I have access to, or simply could have made decisions in a more data driven manner. Just to give you a sense of what you can expect over the next 30 minutes or so, I'm going to start by speaking about data and how it really ties in with the product development process in the various stages. I'll specifically dive into using market data, user analytics, and sort of tie things out by speaking to how you as a product manager can improve your ability to storytell by using data. And finally, I'll hit on a few key takeaways from this webinar that I hope will be useful. So with that, why don't we get started. I want to kind of set the stage around where data comes into play with the product development process, and specifically what I will be focusing on in terms of different data sets that you can really use to be a great PM. So I'll start by stating the obvious. Data is important. I think we all know this, and I think we can all find multiple articles, webinars, publications, speaking about being data oriented, data obsessed, working in a data driven culture. I think I've hit on a few of the buzzwords there, but I'm sure there's several that I'm missing all that to say I don't think you all need convincing on the importance of data. My assumption is that we all know this. But what I would like to do during the session is really speak to how specifically you can use data to really help the product development process and be a better product manager. And what I'll focus on in terms of how specific tactics that you can use is I'll speak a bit about how you can use market data to better identify the most important challenges in a specific industry or market and really focus on what customer challenges exist. I'll also speak about really leveraging user analytics to delight your customers. And finally, I'll spend some time talking about how to embed data into your ability to be a great storyteller. And with that, I also want to set the stage for, I'll kind of hit on through this presentation, what parts of the product development process data is most useful. There's several ways to kind of describe this, but I think very broadly about product development in six different stages, that's ideation or coming up with and determining what challenge you want to focus on requirements where you define the product itself, design and testing where you actually build the solution development where you execute on building that solution within engineering and design team commercialization where you sell the product to new customers and then maintaining and growing the product with hopefully new customers. So, in terms of these different stages, I think that data can really be used across every single one of these to make each component of the product development process better, as well as to be a better p.m. And continue to sort of build a team, motivate a team and develop products that really solve important customer challenges. So, first, I'll speak a bit about market data. Personally, I think this is one of the most underutilized data sets by product managers and teams in general. I think one of the reasons might be because sometimes market data has great quantitative data, but a lot of times market data can also contain really great, great qualitative data. So, sometimes it takes a little bit of searching, it might not be as obvious and in your face but if you use this data, I think it can really supplement your ability and decision making criteria when determining what challenges to focus on. So, with that, I think the four areas of product development where market data can really, really serve as useful is first ideation and maybe this is, I would argue this is the most important part, which is really figuring out where do you want to start. What market do you want to enter? What is the potential revenue and opportunities with that market? What is even the market segmentation? What part of the market do you want to focus on? Second, market data can be used in defining the product itself when you're building out requirements. This may seem unintuitive and it's certainly not the only data set that should be used. But I think market data can really lend a lot of feedback on what user personas you might want to focus on as well as understanding whether they're potentially adjacent systems that your users use that should be addressed and considered in product requirements. Finally, or I would say in addition to requirements, I think the commercialization phase has a lot of opportunity to use market data as well. In being able to really communicate the value proposition of the product to customers in the future, it's important that you sales customer success can really show that you understand the market problems that are driving some of your customer challenges and really being able to shape and communicate how your product fits into solving those challenges. And then finally to really continue to grow a new product, I think market data can be used to really understand what new features to build, what is available in the market, what isn't, and help make a case for what new features and functionality should be developed. There's a lot of different ways to get your hands on really valuable market data. The first sort of category of insights and data that you can find come under the category that I would call sort of desk research. This is data that you can find just by using the internet and doing some research yourself. There's a ton of industry reports out there for, you know, that span many different industries. You might notice that one of the images I have on this slide, sort of mimics or showcases the Gartner Magic Quadrant. Gartner puts out a number of industry reports, and it will sort of lay out and share insights around different industries, what were trends from the past year, emerging trends, what were trends forecast for coming years, who competitors are, and will even lay out what different players look like in terms of vision and ability to execute. Some of these reports are require payment, but several of them are free and can easily be found through internet research. There's also venture capital reports. I love using this data source, finding the C firms that potentially focus on the market that you're in can be really helpful because they frequently publish articles and reports that cover that industry, and that sometimes even focus and highlight the problems that that industry is facing. So that can be really great data to understanding what those challenges are. There's always news articles, depending on the industry that you're in going to sort of industry specific news mediums can be really useful, but there's also more general news articles depending on the industry and customers that you're working with that can be a really great source of data. And then finally, there's always company websites. I think it's sometimes easy to forget just how much information companies might share about their products, new product launches, and there's a lot that can be learned there to really understand the market, as well as what types of problems other players are trying to to solve. Next is really leaning on industry subject matter experts or Smith. These are people that have a unique background or understanding and can give you insights around the industry around the types of customers you're looking into and researching based off of their experience. There's a few different ways in which one might be able to find these industry sneeze. Sometimes depending on the company you're at, you might be able to find sneeze just within your own company. These folks might sit in sales. And this could be based off of their previous experience or previous work experiences. There's always online forums like webinars, panels that are held and podcasts and publications that can be found that can be really useful. I personally love reading through white papers that tend to contain information around problems as well as available solutions in a certain industry. And finally, there's a lot of in person things you can do as well such as attending panels or conferences and meetups. And then last but not least, I think another really valuable set of that means of finding great market data is looking at case studies from other industries, or other businesses. A lot can be learned just by looking at the trends and products that have potentially emerged in adjacent industries. There's also a lot to be learned just by reviewing and better understanding mechanisms that have been successful across other companies. For example, Netflix is one of the first streaming services. There was a lot to be learned from their business model, what they did in the industry, and that could be used across different businesses. Just to put this into perspective, I want to kind of hit on an example of how market data can really make you a better PM and sort of tactically help you in something you're trying to do. So let's look at an example. Jane is a product manager, and she's working within a company and she's trying to convince her leadership company to fund developing a new commerce platform, which she's calling secondhand fashion. This platform will help customers purchase clothes that have been purchased and used by previous customers, aka secondhand clothes. She encounters the following challenges. Her company and leadership team don't necessarily have experience in secondhand marketplaces. This is because her current company focuses and builds out a logistics platform. And she can't necessarily gather insights quickly or easily from existing customers, because once again her current company builds a logistics platform, and her customers don't necessarily focus on secondhand marketplaces. So in terms of being able to convince her leadership, Jane uses a few different market resources to help educate and convince her leadership to fund this new product. She finds from a commerce report that shares that the new generation of people and younger folks are more and more interested in secondhand purchasing. In fact, they're driving a lot of this purchasing, and it's anticipated they'll continue to drive this type of market. There's also a lot to be learned from similar trends in the past. So for example, similar platforms for used cars surge 52% as online reselling became popular. She was also able to complete some analysis on her end by using market research to develop statistics such as the resale market for used clothes today is estimated to be more than $10 billion. She's also able to review online forums where she finds feedback suggesting that alternative solutions in the market are simply slow and require a lot of work on the sellers part. And then finally she develops her own PNL based off of market insights and estimations of the market opportunity that shows and estimates a $1.3 billion potential revenue over the next five years. The result of being able to share these statistics with her leadership team results in Jane being given a one 6% engineering team to develop and test a prototype with customers over the next year. Now of course this isn't a made up example, but I do want to share that I have personally used very similar tactics and really tried to use similar types of market findings to be able to a help make a decision on what market to enter and what type of product to focus on but also use that to communicate to my leadership. Next I'll speak a bit about user analytics. As a product manager, we want to be obsessed with analytics when we have existing users that are consuming and making use of a product that we own. User analytics, I think are the most helpful and can be leveraged during the design and testing phase of product development commercialization and maintaining and growing a product. In the design and testing phase analytics are absolutely crucial to understanding how do users like the product, how are they onboarding onto the product, are users actually getting business value out of using the product that you've developed. In the commercialization phase, user analytics can really be used to help sell and convince other users to purchase your product. So being able to answer questions to prospective customers such as how many customers have successfully used the product, how they benefited from it can really go a long way in helping someone understand how a certain product might be a value to them. And then finally, when you're trying to not just maintain the product but acquire new users. It's really important to constantly monitor what do customers like about the product, what don't they like as a PM and as a team focusing on a product you always want to do more of what customers like and less of the things that they have challenges with. So, being able to decide and develop a strategy around how to get more users and how to get existing users to use the product more are all things that really can be fed by looking at user analytics. There are a ton of different ways to a gather and be make sense of this type of data. UX interviews are great way to really get great solid qualitative feedback. There's nothing like getting direct feedback from individual customers sharing what their experience looks like. Similarly, you can leverage surveys that help you get feedback from potentially a larger number of users, since you can create a survey and send it to multiple people and gather feedback that way. Those surveys can then be used to gain broader insights such as the types of things customers really enjoy, or whether they would use the product in the future. There are a number of tools and software solutions out there that also really help a product manager and a team gain insight into analytics around user workflows. So for example, how is a user moving through the various pages of a website or different parts of a mobile application. The types of analytics can be really useful in understanding is the user taking the journey that was designed and expected. Are there problems that users encounter as they're going through the desired workflow, and that can really help iron out and help a team focus on what parts of the product to focus on. The usage usage and adoption metrics are very useful in understanding how many customers do you have how regularly are they using them. What is your rate of adoption look like. And then finally, there's always feedback coming through customers through meetings they might be having with account management teams with you as the PM with customer success teams and with very various points of contact that they might have with your company. So I kind of want to put this into perspective again, and go back to the example I shared of Jane who was building a new marketplace. So, hypothetically, Jane launches her product. She sees great adoption, but she also sees a high volume of support tickets. She's getting negative feedback that are shared by customers, and there's a really significant amount of work coming from the customer success team. Jane needs to figure out how to solve this problem. So she uses analytics to help evaluate the root cause of customer complaints and also to help prioritize engineering work to help focus on areas of the product that could be improved. She does this by running UX interviews. These UX interviews highlight the fun that the functionality of secondhand fashion is great. That's why the product is seeing really great adoption. However, customers give feedback that they need increased notifications. For example, they aren't aware of when a delivery has taken place or when a delivery is on its way, which makes it hard to understand and track the progress of purchases. She uses analytics from a platform called App Cues to see that buyers aren't necessarily going through the workflow in the most efficient manner. They're going through three different pages before getting to the purchase page, and they're not supposed to. So it seems like there's potentially an issue in that workflow. And then finally, taking a look and digging into support tickets, Jane is able to see that customers just need a better onboarding experience to learn how to best use the platform. There's a lot of questions that come up as they're using it or as they're trying to make purchases that could actually be taught to the user upfront as they're first engaging with the product. So with this, Jane takes this feedback, and she works with the engineering team and the UX team to implement workflow improvements. Jane also works with her technical training team and customer success teams to create additional onboarding and documentation. The result of this is after two quarters support tickets decreased by 66%, which is a great result. Hopefully this helps highlight how user analytics really can be used to better the product and to meaningfully drive down the cost of support for a product. Once again, this is a very much an experience I've had on some of the products that I've managed. Finally, I want to spend a little bit of time speaking about storytelling and how you can become a great and better storyteller by really embedding data in how you present a story and how you convince someone of an idea. So as product managers, or really as someone working together on building software, you're constantly working with a number of stakeholders. I think PM specifically are managing up to executives working really closely with engineering, marketing, design, customer success and sales teams. And of course, constantly getting feedback from customers as well as talking to customers and sharing insights around a new product or working sales and customer success teams to communicate what's coming with a new product. In order to manage these stakeholders across really every part of the product development process, it's important to pull in data to make your story convincing and objective. So for example, when you're trying to get funding for a new product idea as I sort of touched on earlier, it's really valuable to be able to share actual data that showcases why a market has opportunity, why a customer challenge could reasonably be solved with a software solution and what that would do for customers. Data really also helps as you are developing requirements and being able to set up the stage and drive the use cases and the customer need for engineering and design teams. During the design and testing phase data is again really important to be able to constantly showcase how is testing going, what are early customers and testers saying, and really how to improve the product itself. During development, there's a lot of things going on but I think one very common task or work that a PM does is communicating how development is going and how progress is looking to other stakeholders. In commercialization, customers love to hear data, they love to be able to understand what, you know, percents and statistics. So being able to embed data and how a product is being sold to a customer is extremely valuable. And finally, as a product is being used by, you know, a number of customers and is continuing to scale. It's really important to be able to share stories and insights around adoption and hopefully around success both internally to teams that are working on that product but also externally to customers. So once again, I will go back to the example of Jane, who is developing secondhand fashion. Her manager is asked by the CEO to provide an update on how the product is doing. Jane has two challenges. Her CEO is a harsh critic of new products and secondhand fashion is still a fairly new product. And second, Jane's manager isn't necessarily in the day to day and in the deep weeds of secondhand fashion. So Jane really wants to develop a storyline that paints the picture not only for her manager, but for eventually the CEO of how the product is doing the success that it's seeing and why investment in the product should continue. So, as a part of developing the storyline, Jane sort of speaks to the number of active buyers, which in this case is 2 bit 2 million, the number of sellers which is 55,000. She speaks to the percentage of users that have shared that they would prefer secondhand fashion overall alternative solutions in the market. And she also shares a bit more qualitative feedback of being highlighted in a news or industry report as being one of the top reselling products that is poised to topple the giants in the industry, like the reseller marketplace. Finally, Jane herself performs some calculations and puts together a forecast that estimates an increase of sales of 48% in 2024. After sharing the storyline with her manager, her manager, Jane's manager shares this with the CEO, CEO, the CEO provides positive feedback to Jane's manager and shares that this new product is helping expand the company into new markets, which is always a great thing. The CEO also shares that she will allocate increased engineering and design support for secondhand fashion. The outcome here is really great. Positive morale across your manager, as well as the CEO. And then really meaning a really meaningful outcome of continued investment in this new product and continued support from the leader of the company. Once again, the stems from experiences that I have seen and been a part of. After going through a few different ways that I think data can really tactically be used as a product manager. I really want to end with just a few key takeaways. I'll hit again on why should we be data driven. I think this is obvious, but at every step of the way of developing new products, data can always help drive customer outcomes, which is always the end goal. It helps deliver better products that delight customers and make them happy and are easy to use, and that most importantly help customers achieve the outcomes that they care about. Outside of driving customer outcomes, being data driven as a product manager and constantly using data can help in so many other ways. It helps motivate and excite engineering and cross functional teams. It allows product managers to make a case for getting more resources. It helps product managers and teams really themselves make unbiased decisions, earn trust with stakeholders and customers, increase revenue. And finally, being data driven can really actually come back to helping get you promoted. So if you have wandered at all, I would ask that if there's anything you take away from this presentation today, it's that you take a step back and evaluate how you use data today, and where you can use data more to improve developing products and making better decisions. I myself am constantly evaluating where in my processes and methodologies, I could be using more data. I'm constantly evaluating and trying to find new data sources that could be useful for my industry and what I'm focusing on. And I see this as a muscle that I'm constantly trying to build. And I think that's something that everyone can do, whether you're a product manager, work with product managers, or are really, frankly, in any role building software or hardware. So with that, I want to say thank you for joining this webinar. I also want to thank Product School for helping host this webinar. If you have any questions or are interested in connecting, please reach out to me over LinkedIn. I always love connecting with people and building out my community. And once again, I just want to say thank you so much for spending time today and hearing a bit about how data can drive better product management processes and customer outcomes. I really hope that you were able to take away some useful insights from this webinar.