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 focusing on the
customer part of customers analytics. Why? Because even if you know how to do the technical
analyses 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! We’d like to mention something else we’ve
put together – a very comprehensive data science training. The 365 Data Science program
contains the full set of data science courses you need to develop the entire skillset 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 skillset, 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 ‘S T P framework’. The ‘S T P 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 B-2-C 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 ‘F M C G’ company. A typical example of
an ‘F M C G’ 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 ‘F M C G’ a perfect example for our customer analytics course.
Great! Now that we’ve clarified that, let’s take
a closer look at the ‘S T P’ framework. It lays out the classical process of exploring
potential customers and understanding them. The ‘S T P framework’ consists of 3 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.
Marketers make the case that taste or spending habits are not the only behavioral features
that could be generalized for a segment. In fact, people within 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 customers’ 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 will look precisely
into that! Great!
Once we have our segments it’s time for the second stage from the ‘S T P‘ 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.
This 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 consumers’ perceptions. They involve psychology and usually budget constraints. Therefore,
targeting goes out of the scope of this customer analytics course and into the ‘advertising’
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 product 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 and P parts of the ‘S T P 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.
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