 Hi there. If you are looking for tips and tricks to create a data-driven strategy, stay right there. You've come to the right place. In this presentation, we will be going over ways to create a strategy based on consumer and market data to maximize benefits for our products and more importantly, our end users or customers. With that, welcome to how to create a data-driven strategy. In case you're wondering, I definitely could practice that. So, let's move forward. Before jumping in, I should introduce myself and show you two pictures of me. My name is Nicole Munson. I'm a Senior Product Manager at American Express, currently based in Salt Lake City, Utah and overall, I'm a big fan of product management. I personally don't believe there's better career path for me and I'm very excited to be able to talk about it with you today. As a way of disclaimer, all content discussed in this presentation are representative of my thoughts and opinions and not that of my employer. With all that out of the way, let's get started. It is my goal that by the end of today's webinar, anyone listening will be able to better understand the relationship between product management and data analytics, and have some answer to the following questions as their main takeaways from today's discussion. With this information, I hope that we can build upon your current knowledge and better understand together why data analytics matters, what is data, when to use it, and how can we use it. If I do not answer any of these questions, please contact me for a full refund after this presentation. Just kidding. This presentation is completely free. You can only ask me for questions after. I haven't been asked before, why does data analytics matter? I'm not a stats expert, I'm a PM. I really believe a strong and smart PM knows how to use data, create it, and establish our star metrics for their product delivery. Let me illustrate this in three lessons I learned that prove understanding the stats and metrics behind your product is what creates a strong PM. I started in product management by dabbling in business intelligence tools. I learned early on that I could drive efficiencies and a strong product strategy through understanding the importance of clean data and performance behaviors. I personally hated doing things that I knew computers could do better than me and really faster than me. That was my first lesson in education and data analytics. Confirmed to me that success in business was self-service, automation, and the manual process was no longer in fashion and no one wanted to do it. The next thing I learned was that data was powerful. It could identify gaps, guide prioritization, pinpoint customer needs. I've never been in a high-stakes debugging call or trade-off analysis where key metrics were not incorporated. This shows me that if you gave someone clean data, a couple of tests in some time, a person could identify their target audiences and really begin to understand them. That if we can harness this power, a great product manager can do everything, including what I believe predict the future. Lastly, the data analytics can make communications significantly easier for product managers. If you are a product manager, you've likely had to get something called buy-in. Whether it was convincing a leader of your idea or communicating the importance of a feature, we all know it is significantly easier to convince the room of stakeholders if you have data that supports your argument. I remember being a junior member of my team and bringing up on my bright and shiny new ideas, and the most common response I would receive is, where is the data on that? For a while, I was in the cycle of going to these meetings sharing ideas, leaving with takeaways and then coming back with evidence. Eventually, I did learn that if I prepped before, I would make this entire process more efficient. I could present my idea, my vision, and the supporting data analytics in a way that created an argument and drove strategy for my products overall. Now, I want to clarify that these were my data lessons. Each PM I know has different perspectives and experiences with data and their products, and they know how to best utilize it for their specific needs. It's key to remember that every product's goal is to fulfill its organizations and its mission as a product. The key here is that data has a purpose in our products, and the strategies are important to remember that we can use data and implement it into our daily functions that drive our product strategies overall. I think specifically, we can use data to measure, analyze, and adapt our roadmaps if we utilize it correctly. Now, like any good product manager, I hope that set the foundation to our webinar today, and I hope that you feel like you have a clear understanding of what we're going to talk about. Next, we will dig into what data really means. As a product manager, the word data or one of its synonyms will be used over and over again and again in the many, many meetings we have or hope to have every day. I can almost guarantee that anyone with an interest in product management has had some exposure to the terminology. Whether it was a word in the job posting for PAM role, something like looking for a data-driven customer-centric PM, or maybe you've heard the term used by your engineering partners during prioritization. Where's the data on that? Can you tell me why this matters? Or perhaps a manager has asked you for the data on one of your features, trying to better understand what the ROI is going to look like, and better get an idea of the feature and end results for their product. Whatever your experience is, we all know that data rules the day in the product management discipline. As a PM, it is imperative that our product strategies take a data-first approach in order to ensure that we are letting the voice of the customer come through for our products. Let's dig in to better understand what the definition of data really means. My personal favorite definition of data actually just comes from Merriam and Webster. It states that as factual information, such as measurement statistics used as a basis for reasoning, discussion, and calculation. I like the terminology basis because that means it's our baseline. This is where we start our deliberations to understand where our strategy should go. If we break this down even more, we can really learn three things about data. First, in that first line, it says that it's factual information. The data needs to be true. That one is pretty self-explanatory. As a product manager, do not lie or deceive using your data or your metrics. It will not end well, so be an honest PM in all our measurements. Pretty self-explanatory, hope we all do that. Second, as a PM, you will be asked to create metrics of success for your features and products. These can be found in research results, key performance indicators, and in objectives and key results for OKRs. Ultimately, these are quantifiable numbers that guide everything from our discovery to the results after launch. These are the confirmation numbers you'll see on PowerPoint presentations and reports. They guide our conversations with management and others, which then guide the future of our roadmaps and our products. Lastly, we must use the data and statistics we find when determining the what, why, when, where, who, and how our products come to be. The 5Ws and 1H are common principle in product management. They get asked as drivers for every deliverable. They can help explain the vision and projections that we see for our products and better understand our performance indicators. They're very helpful and I consider them a key part of our business strategy. Moving forward, we're going to go over the basic structure of creating a metric analysis. It is not a complex process. Most of us have likely learned something like this similar a long time ago. The key here is learning to utilize it in a way that we're using it to better understand our products and track their success. So any product can have many variations of intermediate metrics, but most important for a product manager is what is known as a North Star metric. A North Star metric is a terminology that can be interchangeable, but in the basics, what it is doing is identifying a certain metric that is defining our product as successful or not successful. That is key because it's what we talked about the most. A basic metric is comprised of a basic fraction, a numerator and a denominator. The numerator represents the total number of the target actions and the denominator represents the total. These make sense because they are driving what they're driving our projects. So a common metric, for example, is monthly active users for tech companies. Often a percentage is calculated to measure success. The formula is pretty simple. We find the number for unique users to visit in a month and divide it by total users. That will give us the monthly average users for the site and get an idea of how many people are actually engaging with the product. This can be tracked month over month and gives a quantifiable measure to track engagement across that customer base so that we know where we need to improve, what is going well, and how we can better serve our customers. As a product manager, you will need to create many versions of this to better measure success of your deliverables. Keep this in mind as you consider creating new stats for your project. Now that we've gone over some basic elements that describe data as a product manager, let's go over some tactical examples for product managers to incorporate data analytics to their product cycles and define success in using data to create reports to define user problems, and get an idea of what the solution is going to be. Let's start with the value cycle. Product managers know about the value cycle because it's typically the process we take when we are trying to create value. The first thing a PM needs to understand is the value their product has. During this stage, we should be able to understand the gap in the market and what the customer need actually is. This can be utilized through customer research methods, like user experience surveys, research labs, customer utilization reports, customer interviews, and so on and so on. These reports can dissect gaps in customer needs. The data in combination with insight and intuition can be used to understand pain points and market opportunities for our products, which is a key to being a successful product manager. Next, we need to be able to create value. This is where we take the opportunities we find and create a product or feature to fill that need. Data at this stage can take form of capacity models, t-shirt sizing, and effectiveness. Ultimately, we want to understand how long will it take to create value for the product. Next, we move on through capturing the value. This is where we test. We create KPIs and OKRs, implement reporting mechanisms, and capture the value that is going to be added to the market. This is the key step because it is how we measure and communicate the value of any product. Next is communicating value. A key job of a product manager is to communicate. This is where we communicate the feature and the value it's adding to the user, the product organization through feature modeling, understanding the ROI, communicating the value to stakeholders, and clearly articulating the value that we plan to deliver. The last step is actually delivering the value to the market and our customers, which is pretty self-explanatory. As a product manager, we remain in lockstep to this cycle and often are working on different features at the same time, in different phases. We balance the competing priorities and processes to continue this cycle, to continue to add the value to our customers through our products and through our deliveries. We do this by having empathy for our customers, understanding them through personas and archetypes, and incorporating measurements, and yes, by that I mean data analytics. So we are consistently delivering value to our customers and identifying their needs. On the next slide, I'd like to go through a hypothetical example on how someone might use the data and the value cycle to deliver a real-life product. Let's take company X, for example. Let's say they are a growing company. They've delivered multiple successful social media products and have caught the interest of the market, though still mid-sized. Everyone is asking, what are they going to do next? And this example, the goal of the product team is to find a new product opportunity for their business. The company is looking to diversify in a way that still meets their company mission while also entering into a new untapped market. The question is, how do they find this next opportunity? Let's find out through the next stage, which is designing through data. So, company X has been given an idea. Let's say they want to create a social media app specifically for the health market. The business stakeholders have presented their use case for creating value and have identified a market gap in the health sector. So they've given the idea to the product management team to really vet through it and decide, what are we going to build? So, on the next part, the first step the PM will do is identify who the customer is for this idea. They will do analysis of the competitive market, they will conduct customer research, analyze personas and archetypes and find any holes in the market for their build. The product manager is going to gather data, key metrics, everything they can to find and identify why we need this product and who specifically needs it. This is important because that is where we add value. Let's just say for this case, the product team learns through consumer research that Americans on average go to the doctor four times a year. 50% of consumers research their medications and new neck diagnoses after their visits and 65% feel they do not have a space where they can find community about their health. For the record, all of those numbers are completely made up. So, keep that in mind for legal purposes. Now, keep in mind that these fake numbers, the product manager has not only helped gather the data to create an argument, but they then analyzed it, identified it, found a market opportunity and identified a customer need that they can now use in their build. So, in this example, the PMs decide to build a segment of social media dedicated to healthy communities for the consumer that we're trying to affect with company acts. Now, next, product manager may gather demographic information of their current consumers, get an idea of who they're designing for and identify their specific customers. When they move to designing and building and prioritizing their product, the product manager will continue to need to gather data to get an idea of how that's going to work. So, company acts as product managers are now designing what is called the MVP scope or minimal viable product. They could be looking at business models and return on investment projections or ROI to get an idea of what is going to deliver the most value for their identified customers on the best timeline. And this is key because during the stage, the PM is balancing the voice of the customer and business outcome and technology capacity to create the best product for the customer need. In this scenario, they may run AP tests for controlled groups to better understand their designs. They may obtain customer feedback during testing periods. Overall, the PM is really creating a space where they can create value for that customer and really create a product that drives customer need, which then will affect all of the other areas. After company acts has completed and tested their new product in the market, during delivery, there may be some research on timing and when to launch. This is often done by marketing and product and teams with data. They will look at reports to target based on when they think the product will do the best. They will continue to work together to make sure that they can continue to track what they're delivering. The PM team will monitor KPIs and OKRs to measure the success of the product overall. OKRs represent objectives and key results. This is often a target for their North Star metric. One goal of our product, an example, is that we're trying to increase customer engagement by 50% through this product. So for an OKR, company X is going to be tracking what does customer engagement look like and maybe it is monthly active users, but they will have that one line item which will show if they're being successful or not. Now a key performance indicator, or KPI, is an ongoing metric that identifies specific target areas for the product that show whether the product is being successful or not. So for example, if they enable a feature for sharing, they're going to track how many sharing events happen for users in that month period and that would be considered a KPI. For this use case to tie it all up, let's say company X releases this health social media product and they were able to track the success and increase engagement by 60% for their customers and increase their bottom line by 10%. Again, these numbers are made up, but I like to have a happy ending to my use cases. Moving forward, I hope that this case study was intuitive and provides a conceptual framework of the relationship of product creation and how data can be incorporated at each stage of the product manager and each stage of the development cycles that they're following. Moving forward, I wanna talk about how we can utilize data. This can be seen in the different data analysis that used in the previous example, but here let's focus on what we do once we have the data. Obviously, there are a lot of ways to ingest data as a PM. It is paramount that we use it when making judgments. It's significantly easier to just prioritize items based on our own biases. As humans, we're very comfortable with what we already know. It's familiar and it's comfortable. I like comfortable, but as product managers, we have to be willing to guide their products and to be unknown. We should compile research at every stage and make decisions based on the customer needs and the data they gather that are showing what the customers want. This includes using data to prioritize the roadmap and ensure we aren't delivering what we want for what our customers want. The data we gather should be able to measure performance metrics for the customer and clearly identify when someone has been successful or not. This way, once we have that information, we can then clearly communicate what went well, what could be improved, and what should be reevaluated in the future. Data can be used to predict our product's future and guide us as we advance technologies, experiences, and additional products. On the next slide, I really think data should be the driver of our decisions. It can help us understand, identify, and reimagine our products. If we can do these three things, we can create data-driven product strategies that add value for our customers and help them with their data. Help them with their daily lives and really identify key areas that we can help. Lastly, I would like to close with the definition on product management that I found. A product manager wears many hats and many roles. They are, one, many CEOs responsible for every aspect of their product. Two, the defender of the customer, making sure the company is building what the customer wants and not just what we want. Three, they are product spearheads, having full responsibility and ownership over their product deliverables. And four, the jack of all trades. At the intersection between business, technology, and user experience, they must be passionate about and familiar with all three. That is a lot. There is a lot of work that product managers have to do every day. And I hope as we spoke about the power of data that you'll be able to enhance and grow your skills as the pilot and future of our products. I hope we can continue to do this together. I'm so grateful for your time and hope that you were able to learn a little bit more about data and how we can use it in our products. And I hope you have a great rest of your day. Thank you.