Hello and welcome back to this last module. At this point in our course, we have covered all the key dimensions you will go through while launching an entrepreneurial or intrapreneurial project: getting to know yourself better, finding an idea, gathering a founding team, raising money, and structuring your sales strategy. Now comes the last phase of our entrepreneurial journey: the "ignition" of your project, the launch of your product, the scale-up of your team, and later your exit. The first step of this last phase is launching your product or service and monitoring what happens in a very specific way. Indeed, as an entrepreneur, as soon as you launch a product you want to get through the maximum amount of information in a minimum amount of time. To do this, you need to step away from the typical business metrics, like average number of visits, or average basket, etc. The best metrics to use are those which will maximize your chances to catch an issue as early as possible. To do this, you need to use what Eric Ries calls a "cohort analysis". Let's take a quick poll. Have you ever used a cohort analysis for a project? 1) Yes 2) No To analyze your product launch with cohorts, you will follow your first client's behavior on a daily or weekly basis, cohorts after cohorts. What you're looking for is not how they behave on average, but on the contrary, you're looking for progress and exponentiality. You want to know if you're going in the right direction as soon as possible. You also want to modify any aspect of your product or service immediately if you catch something that's not going in the right direction. Remember, these measurements are a key to help you demonstrate market traction to future investors. To set up your cohort analysis before the launch of your product or service, you should follow this five-step methodology: 1) Define your strategy goals for the launch. An example could be to reach a hundred thousand paying customers for an app. 2) Identify the metrics related to those key goals. If you want to reach a hundred thousand downloads for your app that means you should follow, on a daily basis, all the metrics related to the process of acquiring a new paying customer. For instance, this could be the number of downloads of the free version and the conversion rate between the free version and the paying version. 3) Define and measure the root causes of each metric, again, on a daily basis, the root causes of the metric, or all the potential factors that could influence it. For instance, regarding the number of downloads for your app, you could think of: a) the number of visits on the Facebook page of the app, b) the number of publications on the page, c) the number of likes you get, d) the number of fans you have, e) the average rating of your app on the app store, f) the number of visits you get on the website presenting the app, g) or the data from Google Adwords you bought. 4) Now that you have set up your metrics and the root causes for each metric, you can begin to follow each of them and their potential root causes, on a daily basis. This would give you two kinds of output: 1) You will see very quickly how each piece of your puzzle behaves and how fast it goes up or down. You will also see if you begin to have some exponentiality. If you double the number of paying customers each day, even if you began with just one customer, this is just what you are looking for. On the contrary, if you begin to hit a plateau, say, 20 paying customers per day, this is a very bad sign. 2) You will be able to connect the evolution of each of your key metrics with its potential root causes. For instance, you may find a linear relationship between the number of paying customers and the average star rating of your app. Lastly, as soon as you have acquired a significant trend on each of your metrics and root causes, and you have begun to understand the potential interactions between the root causes of each metric, you can begin to act on each root cause. Every time you act on a root cause, you know you will be able to measure the impact of what you change within a few days. For instance, maybe you could try for a few days to change the way you describe your product on your website and see if it increases the number of downloads. To act on your root causes, you can also do some "A/B testing". A/B testing means that you try, at the same time, several configurations, measure the impact of each one, and generalize the one with the best results. For instance, you can have two versions of your website and you will soon see which one maximizes app downloads.