Speaker 1: So how do we execute an analysis across customer data? What types of variables can we then apply to identify the customer groups that are active within our organization? So we can discuss the first four different ways how to identify customer groups. And identifying customer groups also has a lot of different variables that we can take into account. I will discuss four of them here. But if you do a quick search online, there are many, many, many different elements that we can use to score customer groups. Again, I would say, look at the industry where you are working, look at the organization how you're organized, and maybe already work with that data that you have internally, and then decide which data you can utilize best to make and define the customer groups. So the ones that I want to discuss for this presentation is, for instance, geographic data. So this can be, for instance, about the population, the country, but can also be a specific region, or maybe even environmental aspects that we want to take into account to understand how the customer group we are dealing with is set, right, so scoring the customer data based on that. The second one, what we can also apply is, for instance, more on the demographic level, right? So we are zooming in a little bit more on the customer itself, and we are going to score different things there. So that can be something like the age of a customer, but can also be the gender, maybe education level, or level of income. So we can really focus on the more personal characteristics around the customer that is buying products within your organization. The third one is already a little bit more complex, because we want to zoom in the behavioral side of the potential buyer of a product. So I would say one of the more easy things is what you can apply if you are using, for instance, Google Ads or something like that, that also measures how clients visit your online or your channels, how they visit your online channels. This can be done, for instance, via mobile phone, it can also be the case that they use a desktop to enter these environments. So this tells you something about their behavior towards the visitation of your organization, but can also be that you trace them more on the social media activities that we see coming across so that we learn about the behavior of the different customers that we serve within our organization. And the last one that we see in this model that we would like to discuss is, for instance, the psychological elements towards a client. And I think this one is also very, it's harder to collect the data, that's for sure. But it also tells you a lot about the state of your potential buyer. So this dives into the communication, so how this communication is coming across, how, for instance, customers feel about the current state of the economy, that can be something that can be measured in a sociological environment, but it can also be about beliefs, or even that they follow a certain trend in lifestyle settings. So these are different elements that can be applied within the psychological domain. Another model that we often see coming across is more financially oriented, as what we have seen by the products. And this is called the RFM model. The RFM model really focused on the monetary value that a customer is contributing to your organization. So in the RFM model, we measure three different things. The first one is the recency. So the recency dives or says something about freshness of a buyer within your organization. So how long has it been before that they have bought something within your organization? The second step that we then measure is the frequency. So what is the frequency that a client buys something within your organization and then comes back for a repurchase, or maybe buying different products, of course, there are multiple options there as well. So that is things that we measure. And then the final measurements that we can take is really on the monetary part. And the monetary part is all about what is the deal size that we make with the clients that we serve? Right? So can we even make strategies to increase the deal size that we close with our clients or are there any options there? But by measuring these three steps, we can zoom in more into the data that we have. I would say these types of models can already be used by organizations that exist for a certain amount of time, because probably they have the data set to work on these levels. Well, however, if you look back to the model that we discussed before, this sheet can also be applied if you don't have too much customer data yet, but you have some information. So out of that information, we can start making assumptions and build these customer groups. So also models depend on the maturity of your organization and in terms of how long you exist, of course, in the data collection that you have executed. So if we then take the RFM model in consideration, how do we use that data then to come to a certain separation in different customer groups, right? So that is where we want to work towards. So what we made here on the left side, we made the monetary value. So what is the revenue contribution of clients, of customers towards our organization? And then on the bottom side, we measure the kind of the clusters of the customers that they have bought the products with us. And as we can see, this line is going from the left top to the right bottom. And what we more or less can do is make them four different groups out of this one. So I would argue that the first group is between the first and the second dot, right? So they bring in lots of revenue, of course, it's the biggest contribution there. And the line is between one to two clusters, right? The second one is more to two to three. So here we see a little bit less contribution, but of course, still a very interesting group to bring some extra revenue within the organization. The third group is already bringing again lesser than the first and the second group. And the fourth one is the final part of the line where we see, of course, a bunch of transactions that we have made. But in terms of how much contribution they deliver revenue-wise to the organization is limited if you compare it to, of course, the first one, but nevertheless, the overall contribution can be still substantial, but that really depends, of course, on the number of transactions that are made.