 So, I'm going to talk about distribution hacking, and distribution hacking is data-driven paid advertising. And when I say data-driven, I mean we're going to be using analytics tools to measure everything and base our success on the data we collect. So paid advertising works. It actually works really well when it's done methodically. When you measure everything, and you're basing your decisions on the data that you collect. There are still plenty of underutilized distribution channels out there, and even within the really popular ones like Facebook ads and Google AdWords, there are plenty of underutilized tactics. Paid advertising is by far the easiest way to acquire new users. It's easier than search engine optimization. It's easier than email marketing. It's easier than trying to figure out virality. For example, search engine optimization. You may put in six to 12 months of work and potentially not see any results. But with paid advertising, you get results immediately. But easy does not mean free. The definition of paid advertising is you have to pay for it. But our goal is to acquire users cost effectively. Many startups don't actually do paid advertising, and there's a few reasons that I've heard. Some just don't have the expertise. Some think it's too expensive. Others don't know the potential. Something I've actually been talking about lately with my 500 colleagues is when a startup says something like, we've grown 5x without doing any paid advertising. And they actually look at that as a good thing. But it's not. Investors want to know that you've tried all the different channels out there, and paid advertising is definitely a big channel. So let's fix that. And this is where the distribution hacking framework comes in. This is a way of thinking about user acquisition that I've honed over my career. So the distribution hacking framework is a methodology for measuring, experimenting, and optimizing user acquisition. We're going to be as scientific as possible with our advertising. So there are two key metrics with distribution hacking. CPA, which is cost per acquisition. How much does it cost to acquire one new customer? And LTV, which is lifetime value. How much is that one new customer worth through our business? Now like I mentioned, our ultimate goal is to be cost effective when we're acquiring new users. So the goal is to have a CPA that is less than our LTV. So our acquisition cost is less than the value of that customer to our business. And we need to be able to do this at scale. Because acquiring a few users cost effectively is fine, but acquiring many users cost effectively. We can actually build a business upon that. So let's look at an example. Let's say we have a $20 LTV, and I should probably say 20 Cronus LTV, and an $8 Cronus cost per acquisition. That means for every 8 Cronus that we spend, we are actually earning $12, 12 Cronus, revenue per user. And if we're able to do that at scale, this example will be very happy. So let's dive in. Now there are six steps to the distribution hacking framework. Let's go through them. Step one, implement analytics and testing tools. Like I mentioned, we want to be as scientific as possible. We want to be as data driven as possible. So the way to do that is to have the tools in place to measure everything and test what we need to test. So let's look at this example. This is an e-commerce site with three steps in the conversion funnel. Step one, a user signs up. Step two, they add something to their cart, the product. And step three, they check out, they buy something. The check out is the conversion goal. Now I'll go into more detail about what conversion funnels and conversion goals exactly are later. But just know that we need to be able to track every single step in the funnel. We need to be able to see how many users enter the funnel, where they potentially drop off, and how many of them actually complete the funnel. And in this example, it is someone purchasing something. And we also need to be able to split test. For example, let's say we have a blue buy button on our checkout page. And we want to know what a red buy button get more people to convert. We need the tools in place to test that. Because when you have the data, and in this example, it's obvious where a weak point in the funnel is. Between step one and step two, nearly 90% of people, and this is just a generic example, nearly 90% of people don't continue past step one. So again, when you have the data, you can optimize. And again, I'm going to say optimize a lot, and what I mean by that is split test. So here are some example tools that you can use to actually do the measuring and testing. This panel, KISS Metrics, Google Analytics, all great tools for measurement. And optimizely and unbounce for split testing. So step two, define your target customer. We need to know who our advertisements are going to target. And we need to define that. Most startups, excuse me, all startups fall into two categories. Pre and post product market fit. Product market fit basically means you have a product and you have customers who need your product, and you know who those customers are. Now, if you're pre product market fit, you're in the customer discovery phase. And you can actually use distribution hacking to help you discover who your customer is. So for example, instead of having your advertising target one type of user, demographically speaking, you can actually spread out your advertising over multiple demographics and determine which demographic responds best to your product. Now, if you're post product market fit, you already know who your customer is. And your ultimate goal is to acquire as many of them as possible. You're into customer scaling. Now, for the rest of this talk, I'm going to be talking about post product market fit companies. So the question to ask is, who is your customer? Obvious question, right? And the obvious thing to think about are the demographic factors, age, gender, geography, income, so on. But let's think a little bit deeper about this. The critical question to ask is, what is your customer's persona when they are using your product? All right, so what does that actually mean? People have multiple personas. For example, you have work with your colleagues. You have your home life with your friends and family. You may have a sports team, so you have teammates. You might have a hobby, so you have people you do that with. All these different areas in your life are different personas. Let's look at the obvious example of personal versus business persona. Who I am at home is different than who I am at work. The products that I use at home are different than the products that I use at work. More importantly, the products that I could potentially purchase at home are different than the products that I would potentially purchase at work. So you really need to think about the persona of your users when you're thinking about who to target your advertising to. Okay, step three, define your conversion goal in funnel. We need a very specific goal because without a very specific goal, we have no way to determine the success of our advertising. So what is a conversion goal? Conversion goal is a single event, so when a user completes it, they are considered a customer. Let's look at some examples. So some examples are a sign up, a lead, an opt-in newsletter, a sale, a subscription. It all depends on what is valuable to your business. Now, a conversion funnel is the path your customers take to complete the conversion goal. Now, let's look back at that e-commerce example again. We have three steps, sign up, add to cart, add a product to the cart. And check out, buy something. Now, the three steps is considered our conversion funnel. The conversion goal is the purchase, the check out. And in this example, we have 10,000 users who start down the funnel, 40% of them sign up, 20% of them add a product to the cart, and only 5% of them check out. So we have 10,000 users at the beginning of the funnel and 500 customers at the end. So step four, hypothesize an acquisition channel. This is where we're going to decide where we should place our advertising. So what you need to think about is demographic factors and the persona of your customers. And you need to relate this to where they could potentially be hanging out online, where they go online. So here are some examples. These are the four big boys of the web right now. And, you know, let's look at an example. Facebook is a personal product. You use it mostly to connect with friends and family. So advertising a business product on Facebook most of the time doesn't really make sense. On the other hand, LinkedIn is a business product. So advertising business related products on LinkedIn makes a lot of sense. Twitter, again, more personal. Google, maybe a hybrid of both. Now, all four of these have self-serve advertising platforms. You can go in there, you can start today. They have zero to minimal budget requirements and they all have different ad formats and they are all very good ad platforms to use. So step five, set up your ads and execute the experiment. So you need to learn each ad platform that you want to use. I listed Facebook and Google and the others. They're all very different in terms of what ad formats they require. Image sizes, for example, what they offer you, targeting criteria. So you just need to go in there and just learn them. They are pretty straightforward. Now, a question I get quite frequently is how much budget should I spend on my experiment? And the answer is you need enough data, you need enough budget spent to acquire statistically significant data. And that basically means that you need enough data for that if you got a few more clicks or a few more conversions here and there, it's not going to change the result significantly. Now, here are some general tips. For creating ads, these are not specific to any platform, but use simple images. Computer monitors are pretty small. The browser is within them, even smaller, and the ad within that, very, very small. So you need to have your images very clear and very simple so that when a user glances at it, they know exactly what they're looking at. Also, have a clear value proposition. Explain to your potential customers why they should be using your product. What is the value of your product? Last, have a single call to action. Tell your users exactly what to do. For example, click here to buy or get started today, sign up today, make it very clear what you want your users to do. That's called pre-qualification. So last step, optimize. The question to ask after you run your advertising experiment is, do my ads have potential for success? And what does that actually mean? That means you have a cost per acquisition goal in mind, a target goal. Let's say it's 20 cronies. Now, after the experiment, if you found that you acquired users for 80 cronies, even with optimization, it's going to be very difficult to get that 80 number down to 20. On the other hand, if you're at 25 and your goal is 20, with some optimization, split testing, you can actually, most likely, hit your goal. So after the question is asked, do my ads have potential for success? If the answer is no, go back to step four, hypothesize another acquisition channel, and start over. You could do this very rapidly. Now, if the answer is yes, we optimize. Now, what can you actually optimize? There's two sides. External, the advertisement, and internal, your landing page. So for the advertisement, you're going to mostly be optimizing for click-through rate, how many people you can get to click on the ad and come to your page, and cost. How much does it cost to get each of those clicks to your page? Now, on the landing page side, you're optimizing for conversion rate. Out of all those users you're bringing to your site, how many of them can you get to convert to your conversion goal? Now, let's look at some generic examples of ad optimization. Again, this isn't specific to any platform, this happens just to be Facebook ads. So in this example, you can optimize your call to action, your copy, and your image. Now, internally, for the landing page, you can optimize the design, the call to action, value proposition, and the number of fields you require for a conversion. It all depends on what makes sense for your business. So, let's recap one more time. So, let's recap one more time. Step one, implement analytics and testing tools, because we want to measure everything, we want to be scientific, so that's the first thing we have to do. Step two, define your target customer. Who is your advertising going to target? Step three, define your conversion goal and funnel. We need a specific goal, because otherwise we have no way to determine success. Step four, hypothesize an acquisition channel, choose where your ads are going to go. Step five, set up your ads, execute the experiment. And step six, optimize or return to step four and hypothesize another acquisition channel. Thank you, talk.