 Hello everyone and welcome to this week's Product School webinar. Thanks for joining us today. Just in case you didn't know, Price School teaches product management, coding, data analytics, digital marketing, and blockchain courses online and at our 15 campuses worldwide. On top of that, every week, we offer some amazing local product management events and host online webinars, live streams, and ask me anything sessions. Head over to productschool.com after this webinar to check them out. Today, we have an awesome guest presenting. I'd like to introduce you to Gain Shwaran. Currently, Gain Shwaran is a product management leader at Wordplay in Boston. He has previously worked as a senior product manager at Salesforce. He is an accomplished product executive with a track record of successfully launching multiple new products. He also has extensive experience in bringing products to market from their inception. Feel free to leave any questions for Gain Shwaran in the Facebook comments and we'll be sure to address them at the end. Without further ado, let's welcome Gain Shwaran. Thank you so much for joining us today. Are you there Gain Shwaran? You're still muted and your video is muted as well. I'm going to unmute your audio right now, okay? Okay, I just unmuted your audio and let's see if I can, get your video started here too. There we go, hello. Hi Gain, thanks for having me. As product school, it's really a privilege. And hello everyone. Before I get into the presentation, let me make sure I reset over to see my screen. Are you able to see my screen? Well, we get our screen sharing going here guys. Is it not working? Dan, can you? We don't see your screen yet. Can you reconnect? I mean, I'll let me to... Yeah, let me see. Let me try sharing my screen here again and I'll try to bring it back to you. Let's share this and then I'll stop it. And then you should see down at the bottom a little share screen or at the top underneath the meeting ID. Yeah, I do not see that. Hmm. You remember how we did it before, yeah? It was different for your Mac, I think. If you want, you can also send me your presentation and I can share my screen and then click when you say next. Let me send my presentation. Cool. Sorry guys, just a couple of minutes while we get the presentation going here. Hi Dan, I just checked. Yeah, cool. Let me check my email quick, there we go. Perfect, opening slides. Now this will make it a little difficult for me guys, for me to ask you guys questions at the end so don't forget to post your questions and I will get to them. It'll just be a little different. Let's make this presents. Oops. Let me share my screen. Share. Share. And let's go present. Cool, now just give me a heads up when you want to go next slide. Thanks Dan. No problem. I'll let you go now. All right, so hope everyone is able to hear me. Okay, hello everyone. I'm Ganesh Varan, part of product management at WallPay. WallPay is the biggest payment processor in the entire world. I belong to e-commerce product organization. Previously I was a sales force, Nokia, and MapWorks serving with similar capacities. Today I'm going to share my thoughts on pricing data-centric products that you can go to next. So the topics of the day are just overview of data-centric product and the product categories and proving the value proposition. Based on that context, we'll be stepping into pricing metrics. And by no means, this is an exhaustive pricing discussion but I just take the pricing methods relevant for data-centric products that I have seen and I have observed. So with that, let's go to the next slide. Then we can go to the next slide. Thank you. Let's first set the context. Any product that takes decision based on set of data and actually put the previous slide? Is this the right slide? I've gone to the next one. I think that refreshes. Let me start with the overview. Any product that takes decision based on set of data that system has already gathered or learned over a period of time, this is a wide net to catch. However, for today's discussion, we are narrowing down to products that does whatever is being listed out here. The product that sits behind the scene and taking decisions based on set of data available to the system, products and services that have ability to learn in order to predict an event or suggest it to take an action. So this is a wide net to catch. These are data centered products that usually will not have a tangible or perceptible outlook, so to speak. They always sit behind the scene and that's a wonderful job. I think that's the right slide. Any product that takes decision based on set of data or decision based on set of data or decision based on set of data or decision based on set of data or decision based on set of data or decision based on set of data or decision based on set of data or decision based on detailed data those data products sit at the market and what you are going to get is a broad category just to build a sort of framework in order to... of a part process would help us to hone in on crucial details. So the broad categories outlined here are products that improve efficiency, products that improve effectiveness, expert systems, and products that drive innovation. Next slide. Use cases are just mentioned here. There are really exhaustive set of use cases, but just for our conversation, just to make sure of what we are talking about. I just listed a few use cases so that we know what we are talking about, and also it's good to have these use cases in mind while we are talking about the KPIs and what are the metrics that we are able to ensure. The use cases, such as computing baseline, based on historical data, learning models to understand the trend, they are watched out, they are monitoring systems, and at least cost outing. At least cost outing is one of my current products in my portfolio. So with those use cases, let's go to the next slide. Next slide is value proposition. What type of products we are going to discuss? Let's spend some time on the value proposition which are essentially our pricing discussion. More often than not, senior management involves pricing decisions without paying much attention to what value these products bring to the market and also to the business. So without spending or without creating a pricing scheme based on the value proposition, this will be a difficult task if we start without understanding the value proposition. And hence, let's spend a few minutes on the value proposition. Next slide. So here really what we are talking about is proving the value proposition. Just sometimes the value proposition may be one or two lines are set up in bullet points, but that's not the case here. We are really talking about the data-centric products that we have to prove the value proposition. There exists a real value and make sure we track the value and be measured. For example, first and foremost is the market perception of the value. Let us say if we bring an intelligent engine to the market, there is a market ready to perceive the intelligence that comes out of the engine whether the market is ready to or otherwise we may have to educate the market about the value itself. So that is point number one. And similar to that particular attribute, there are other things such as commercial value for the business. A value proposition may or may not result in good a big commercial value. For example, recommendation engine in and out of itself may prove to be have some value, but how much of a commercial value can we measure it? Measuring is the next aspect. Can we measure and track the KPS? And that's it, attract and retain customers and can be monetized. End of the day, what is in it for customers? These are the six different perspectives. If you look at it, the value proposition is really what's going. And can we make a business of value proposition? And even so, whether the value proposition is really trackable and measurable so that we can quantify accurately and be able to monetize price. So that is the value proving the value proposition aspect of pricing. But with that, let's move on to the next slide. This is very light-based information. KPS for pricing. If you shift through all the data points on the value proposition, only a few would even demand for pricing. The others could be, yes, there is a value, but can we try to product based on that value portion? So we have to first identify the value elements. First, we list the value proposition. And in that, if you sort it through, we have to identify the value elements for both parties, for both customers and the business. And we should measure and make sure we have enough ROI can be obtained of those KPIs. So we go to the next slide. So with that in mind, having said that context, let's slowly step into pricing. By no means this presentation is about exhaustive pricing in models. It's not a discussion about extensive pricing techniques. However, it gives sort of a very high-level framework so that product managers do not miss the necessary attributes to be considered for pricing. Next slide. In this slide, what I have done is just broad buckets to identify. That's a framework. So the broad buckets are, this is nothing but the categorization we have seen just now in one of the previous slides. Product centered out of efficiency is one big bucket where efficiency could be cost-saving, high-performance, et cetera. And export products, the economic value is based on the judgment of recommendations. Products centered around effectiveness. Here, the key thing is how to measure the efficient workflow. But we should be able to measure that, that being the TPI out here, that in and of emphasis is the challenge. The next category is the innovation. How a product will drive innovation. So with that in mind, and as you can see in subsequent slides, every category bring challenges in terms of identifying the key value elements. Let's go to the next slide. See the efficient systems. What are they? The routine activities are optimized with well-defined rules. Then rules, configurations and processes to achieve a low cost performance. That's kind of a very high-level definition for efficient systems. And the value proposition could be how fast, what is the speed with which you can realize and things. And that's a reduced space. These are all the examples of the value proposition. And the KPI is being the measurable economic and strategic value of efficiency. Time and cost competition. Those are the key things to go after before price. So those key things will translate into how much value it's going to bring in terms of cost. And accordingly, we can factor that into pricing. For example, if an efficient system demonstrates reducing the waste, let us say $50,000 for 12 months period of time, but if we price it about $100,000 for a product as an annual fee, it won't affect. So we have to make sure it demonstrates the right value proposition and currently translates to value. The next is the expert systems. What are they? Expert systems are designed to solve complex problems by reasoning through the bodies of knowledge. They make decision-making ability of human experts. So it's actually all elements. And the expert, in this case, an expert could be a surgeon, or a mining person, or anyone who is already expert in his job. And the expert systems would augment, support his decision-making ability. So such systems, what are the KPIs look like? So the knowledge base itself is a value there. And the impact and suggestions that the system could provide so that a human who is already expert in that field now takes advantage of such a recommendation by the system, by the algorithm system. The KPIs are the economic and strategic value of suggestions made by recommendation and inference engine. As you can see, how to measure that, how to measure an economic value of a recommendation is an exercise in and of itself. Let's move on to the next slide, the effectiveness of the system. Some data-centric products are centered around providing an effectiveness. It improves overall ability to a group or set of machines to produce certain not-decided results. It enhances coordination, communication, and collaboration. So in that case, one has to measure these value proposition and translate and translate the economic value and the economic value out of its underlying proposition. Because these products, it orchestrates the complex workflow. Then it orchestrates a complex workflow. So having this product, how the workflow is going to look like and what the result is going to look like. And having this product, how the workflow improves. So here in this case, we are kind of talking about an AB testing so that we can clearly measure having a system and not having a system, what the effect and that effect has to measure it and translate it to economic or strategic value. Next is the system that drives innovation. This is another level of complexity. Here in this case, it again helps humans to really go to extreme conditions. For example, simulating volcanic eruptions so that the duct simulation can be used to test certain things, to verify certain influences. These systems would help really creative folks like scientists and designers, architects, so on and so forth. These are crucial systems. However, the KPS, if you see, being able to emulate extreme conditions and new design suggestions are general reasons of these systems. But how to measure their value proposition is for our pricing. It's the key thing out here. With that, let's go to the next slide. So this next slide is a very basic slide. The reason I have brought this slide into our conversation is more often than not, when it comes to pricing, the basic things are just not even considered or discussed. So what you see here, what I have just illustrated here is all the data-centric products I have just alluded to takes a lot of time to hit the market. You have to first build a data center, build a data mark, and the mining could have happened and the learning could have happened in order to realize certain research. All would have consumed a lot of money. So the total cost, the total cost of development, success cost, and variable cost, everything should be included so that when we hit the market and when we price the product, we get the bang for the buck. So that's basically what this slide is for. This slide is to just remind ourselves that, hey, we have spent a lot of money in order to reach thus far, and let's consider this. And there's nothing new if the product manager would know this underlying cost, but I'll just put that in place so that we don't miss it. It often gets more complex. This is helpful for the breakeven. So once we have the breakeven, once I have found the breakeven number, let's go to the next slide. So here we are starting out with the premium pricing model. Generally, because of the intelligence involved in data center products, organizations tend to go with the premium pricing model. But for a premium pricing model, we have to make sure the breakeven pricing has been calculated somehow, like in previous slide, and apply the premium push. Here, the point is the unique value proposition in the market is the key. And another key aspect is the high quality. If an organization is going after a premium pricing model, it has to make sure the product is of high quality. And it has a really unique value proposition that there are no competition at least at the moment when the product hits the market. And that's what would give us the premium cushion. And the premium cushion, whatever that may be, accordingly you can price it. That's going to be the market price. So that's a premium pricing model. With that, let's go to the next one. The penetration pricing model we all know. This is done to increase the adoption. Here, the pre-work is necessary. Meaning, what is the cushion that the market actually represents? That price cushion is what will be layered on top of the breakeven price. So if we put them together, then that's going to be the market price. Here, unlike the premium cushion, this price cushion in the penetration model is, we need to do the market analysis to understand how much of a price cushion the market can withstand for a product that we are trying to bring it to the market. So it's a breakeven price and we identify, this is at this point, it's all of estimated values. The estimated value, we can bring the price cushion on top of it to hit the market. This is based on market affordability. So at this gesture, I would like to introduce one pricing model, which is a premium pricing model. So generally, premium is, I have not introduced a slide for premium model, but we all know. The commodity price would be either free or very basic price. And with that, now there's a high premium to be paid for the value, for whatever value the premium product comes with it. So the premium is just a blend of the premium and the penetration model. And that's all, that's all just to it. Next is the bundle pricing model. And again, the bundle pricing is done, let's say usually the data product comes with a lot of other peripheral things such as reporting, user interface, so on and so forth. And that brings a big package. So generally, the pricing would be based on the entire package. However, the key to the package is the data center product. And the entire package, all the components of the package are built around this data center product. So you sell it as a package. In this day, other peripherals do not need separate marketing and other efforts. So you can sell it as a package. The key, the heart of the product is the data center product that drives your product. And here, again, the base price, which is the breaking price on picture and then whatever we measure on top of the discount total component price and the package. Package question is what actually determines the final micro price. Another pricing model is a revenue share model. This is an interesting model. One of my products actually go by this philosophy. We have a, we talk about the KPIs and the value elements of our product. Once we clearly have identified how we are proving the value and how we are measuring the value. And that value can be translated to dollar amount. So that dollar amount is for consumers or our customers of the product to take. But that value could be shared as well. And once we prove that value to our customers, and what we can do is we can ask for a split. Hey, this is the value that you are getting. Here you go. We have reports and other to prove the value. Once the value is proven and customer realizes the value, having split that value is the revenue share. So what is the revenue, the total revenue realized? And here we have a service provider and the customer who is the beneficiary of the service can split the value. That split could be 50-50. More often than not, that's not the case. And for a lot of procedures, we agree to be shared with them. So this is the revenue share model. Let's move on to the next. Next is the value event value model. Usually you will see this trend in fraud alerts and things of that nature. For every event that's got triggered, that is the key value proposition of the product. For example, every fraud alert. For every fraud alert, we can charge. That's the impact event model. And otherwise, the product actually really be kicking in the background, but it doesn't generate money. And it will generate money. The revenue is proportional to the events generated. So that's how the product, that's how we can monetize that event model. So those are the high level pricing models that can be really useful for data-centric products. There are other models that I explore that may or may not work out very well. But again, maybe this is not an exhaustive pricing exercise as I mentioned. And these are the successful models that are not available just to keep in mind. With that, I'm going to end this presentation. And I'm here to field any questions. Hi. Hey, thank you so much for that presentation. You're on. That was great. So one question I always like to ask our speakers before we go is, do you have any advice for aspiring product managers? If there was one thing you could tell someone who wants to become a product manager, what would it be? Yeah, one suggestion is consider yourself a problem solver. And try to solve a problem for the market. And that's going to be where it is. Great. Thank you so much. So before we go, I just wanted to give you guys also more information about our upcoming courses and events. So you have the resources to become a product manager. We offer part-time courses for anyone ready to take their career to the next level. Our product management, coding, digital, digital marketing, data analytics, and blockchain courses are taught by industry experts working at companies like Google and Facebook. In addition to that, we offer weekly online events like this one and on-site events at our 15 campuses across the US and UK and Canada. And if you're located near a campus, head over to productschool.com and you can do a product, you can do a campus tour. And you can also find us on social media at product school and be sure to keep up with the latest product management content at the product blog at productschool.com. Thank you all for joining. Enjoy the rest of your day. And I hope to see you next week. Thank you so much. Have a great day again tomorrow. Thank you, Ben. Thanks for having me.