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’ 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 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. We hope you found this video helpful. And
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