Speaker 1: Hi, everyone. This is a quick crash course video where we'll talk about customers analytics, data science, and how the two work together. Alright, here's something we all know. Leading companies are always on the lookout for savvy data scientists to join their fast-growing customers analytics teams. In that sense, considering a career as a data scientist in customer analytics is a super smart choice. But here's why exactly. First, companies need people who know how to use data to understand their customers' needs. Once they understand their needs, they can provide the products customers want to buy. Second, and that's a bit more technical, companies need people who have the skills to build the analytics capabilities that will help them provide these innovative customer experiences. In these videos, we'll be discussing on the customer part of customers analytics. Why? Because even if you know how to do the technical analysis well, unless you understand the customer, you won't be able to meaningfully help your company. So let's build those foundations, shall we? Just one more thing before we get started. I'd like to mention something else we've put together – a very comprehensive data science training. The 365 Data Science Program contains a full set of data science courses you need to develop the entire skill set for the job. It's completely beginner-friendly. For example, if you don't have any maths or statistics knowledge, we'll teach you that first. And if you'd like to build a more specialized skill set, you can do that with courses on time series analysis, credit risk modeling, and more. If you'd like to explore this further, or enroll using a 20% discount, there's a link in the description you can check out. Perfect. Now, let's get into customers analytics. A good understanding of customers is extremely important for running a successful business. KYC, or Know Your Customer, is what actually makes all the difference for many companies. KYC helps them do their best in creating, communicating, and delivering their offerings by tailoring them to their customers' needs. And that makes customer analytics the most important part of both marketing analytics and the marketing function of a company in general. But understanding and meeting customers' needs is easier said than done. In fact, customer analytics is a very broad area. It may include a wide range of characteristics of customers and their behavior and numerous different outcomes and performance indicators that the business might be interested in. That's why, in this course, we've decided to focus on one of the most fundamental marketing frameworks – that of segmentation, targeting, and positioning – known as the STP framework. The STP framework is the most logical choice as it applies to all areas of business and marketing activities. The datasets that we'll work with come from a B2C business model. This means that the clients of our business are individual people rather than firms or institutions. And that's much better from a data science point of view, as we usually have more data points. Okay, the data we'll use throughout the course come from a fast-moving consumer goods or FMCG company. A typical example of an FMCG marketplace is a supermarket. People visit supermarkets every day and most types of goods in store are purchased daily too. Therefore, we have lots and lots of data available, making FMCG a perfect example for our customer analytics course. Great. Now that we've clarified that, let's take a closer look at the STP framework. It lays out the classical process of exploring potential customers and understanding them. The STP framework consists of three consecutive steps. S for Segmentation, T for Targeting, and P for Positioning. Let's start with Segmentation. Segmentation is the process of dividing a population of potential or existing customers into groups that share similar characteristics. The underlying idea is that, most likely, these groups will have comparable purchase behaviors. Furthermore, these segments would probably respond similarly to different marketing activities. For example, not everyone likes the same brand of chocolate candy bars. Moreover, not everyone can afford the same brand of chocolate. However, based on certain characteristics like income, age, and gender, we could divide our customers into segments where each segment prefers a certain type of chocolate. Others make the case that taste or spending habits are not the only behavioral features that could be generalized for a segment. In fact, people with the same segment may also respond in the same way to different marketing activities. Examples are TV advertising, online advertising, and promotions. Moreover, individuals from different segments may respond differently to each of these marketing activities. So far, so good. But what characteristics exactly do marketers use to perform segmentation? In general, the types of characteristics used for segmentation may be separated into two broad groups based on whether marketers are using consumer behavior data or not. Most often, especially in the process of new product development, consumer behavior data are not available. Therefore, marketers rely mainly on demographic and geographic customer data – age, income, education level, and others. In other cases, marketers can use psychographic characteristics. For example, some customers have a better planned buying behavior while others more impulsive. Alright. The second type of segmentation characteristics is much preferable. It's used when we have existing data for the customer's consumer behavior. For instance, historical data from purchases, how often customers buy, at what time they buy, what quantities they buy, product ratings, and many more. Usually, based on these specific criteria, we can divide the customers into much more representative segments. And in this course, we'll look precisely into that. Great. Once we have our segments, it's time for the second stage from the STP framework – targeting. Targeting involves evaluating the potential profits from each segment and deciding which segments to focus on. Marketers may decide to offer products to one segment, to all segments, or just to a selected few. They take into consideration factors such as segment sizes, expected growth, and competitors' offerings. The third stage of the framework is also the point at which we decide on the different ways to promote our products. We can target one segment on TV and another online. Unfortunately, targeting activities are often focused on the qualitative examination of the consumer's perceptions. They involve psychology and usually budget constraints. Therefore, targeting goes out of the scope of this customer analytics course and into the advertising territory. Alright. Finally, once marketers have decided which segments to target, we come to positioning. In positioning, the important question to answer is what product characteristics do the customers from a certain segment need, or more like, what products can be offered to them that would have the characteristics closest to the ones they need. So we can say that positioning consists of implementing the targeting actions for the product. But positioning concerns not only the characteristics a product should have but also how it should be presented to the customers and through what channel. In fact, this process is so important that it has a framework of its own, called Marketing Mix. In this course, we'll learn how to perform the S&P parts of the STP framework. The first, big part of the course will be devoted to segmentation of customers, while the second to positioning and more precisely, to the Marketing Mix. And Marketing Mix is what we'll explore in more detail in one of our next videos. We hope you found this video helpful, and if you enjoyed it, please take a second to hit the like button, share the video with your friends, and subscribe to our channel. Thanks for watching.