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Segmentation, Targeting and Positioning - Learn Customer Analytics - YouTube
Channel: 365 Data Science
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Hi everyone! This is a quick crash course
video where we’ll talk about customers analytics,
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data science, and how the two work together!
Alright, here’s something we all know – leading
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companies are always on the lookout for savvy
data scientists to join their fast-growing
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Customers Analytics teams. In that sense,
considering a career as a data scientist in
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customer analytics is a super smart choice.
But here’s why exactly:
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First, companies need people who know how
to use data to understand their customers'
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needs. Once they understand their needs, they
can provide the products customers want to
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buy.
Second – and that’s a bit more technical
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– companies need people who have the skills
to build the analytics capabilities that will
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help them provide these innovative customer
experiences.
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In these videos, we’ll be focusing on the
customer part of customers analytics. Why?
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Because even if you know how to do the technical
analyses well, unless you understand the customer,
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you won’t be able to meaningfully help your
company. So let’s build those foundations,
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shall we?
Just one more thing before we get started!
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We’d like to mention something else we’ve
put together – a very comprehensive data
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science training. The 365 Data Science program
contains the full set of data science courses
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you need to develop the entire skillset for
the job. It’s completely beginner-friendly.
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For example, if you don’t have any maths
or statistics knowledge, we’ll teach you
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that first. And if you’d like to build a
more specialized skillset, you can do that
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with courses on Time Series Analysis, Credit
Risk Modeling and more.
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If you’d like to explore this further or
enroll using a 20% discount, there’s a link
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in the description you can check out.
Perfect! Now, let’s get into customers analytics.
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A good understanding of customers is extremely
important for running a successful business.
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KYC or ‘know your customer’ is what actually
makes all the difference for many companies.
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KYC helps them do their best in creating,
communicating, and delivering their offerings
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by tailoring them to their customers’ needs.
And that makes customer analytics the most
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important part of both marketing analytics
and the marketing function of a company in
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general.
But understanding and meeting customers’
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needs is easier said than done. In fact, Customer
Analytics is a very broad area. It may include
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a wide range of characteristics of customers
and their behavior and numerous different
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outcomes and performance indicators that the
business might be interested in. That’s
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why in this course we’ve decided to focus
on one of the most fundamental marketing frameworks
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– that of segmentation, targeting, and positioning,
known as the ‘S T P framework’.
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The ‘S T P framework’ is the most logical
choice, as it applies to all areas of business
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and marketing activities. The datasets that
we’ll work with come from a B-2-C business
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model. This means that the clients of our
business are individual people rather than
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firms or institutions. And that’s much better
from a data science point of view, as we usually
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have more data points.
Okay.
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The data we’ll use throughout the course
come from a fast-moving consumer goods, or
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‘F M C G’ company. A typical example of
an ‘F M C G’ marketplace is a supermarket.
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People visit supermarkets every day and most
types of goods in-store are purchased daily,
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too. Therefore, we have lots and lots of data
available, making ‘F M C G’ a perfect
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example for our customer analytics course.
Great!
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Now that we’ve clarified that, let’s take
a closer look at the ‘S T P’ framework.
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It lays out the classical process of exploring
potential customers and understanding them.
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The ‘S T P framework’ consists of 3 consecutive
steps:
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• S for Segmentation
• T for targeting,
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• and P for positioning.
Let’s start with segmentation.
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Segmentation is the process of dividing a
population of potential or existing customers
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into groups that share similar characteristics.
The underlying idea is that, most likely,
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these groups will have comparable purchase
behaviors. Furthermore, these segments would
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probably respond similarly to different marketing
activities.
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For example, not everyone likes the same brand
of chocolate candy bars. Moreover, not everyone
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can afford the same brand of chocolate. However,
based on certain characteristics like income,
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age, and gender we could divide our customers
into segments where each segment prefers a
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certain type of chocolate.
Marketers make the case that taste or spending
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habits are not the only behavioral features
that could be generalized for a segment. In
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fact, people within the same segment may also
respond in the same way to different marketing
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activities. Examples are TV advertising, online
advertising, and promotions. Moreover, individuals
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from different segments may respond differently
to each of these marketing activities.
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So far, so good.
But what characteristics exactly do marketers
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use to perform segmentation?
In general, the types of characteristics used
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for segmentation may be separated into two
broad groups, based on whether marketers are
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using consumer behavior data or not.
Most often, especially in the process of new
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product development, consumer behavior data
are not available. Therefore, marketers rely
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mainly on demographic and geographic customer
data: age, income, education level, and others.
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In other cases, marketers can use psychographic
characteristics. For example, some customers
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have a better-planned buying behavior, while
others - more impulsive.
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Alright.
The second type of segmentation characteristics
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is much preferable. It’s used when we have
existing data for the customers’ consumer
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behavior. For instance: historical data from
purchases, how often customers buy, at what
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time they buy, what quantities they buy, product
ratings and many more. Usually, based on these
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specific criteria we can divide the customers
into much more representative segments.
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And in this course, we will look precisely
into that!
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Great!
Once we have our segments it’s time for
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the second stage from the ‘S T P‘ framework:
Targeting.
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Targeting involves evaluating the potential
profits from each segment and deciding which
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segments to focus on. Marketers may decide
to offer products to one segment, to all segments,
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or just to a selected few. They take into
consideration factors such as segment sizes,
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expected growth, and competitors’ offerings.
This stage of the framework is also the point
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at which we decide on the different ways to
promote our products. We can target one segment
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on TV and another online.
Unfortunately, targeting activities are often
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focused on the qualitative examination of
the consumers' perceptions. They involve psychology
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and usually budget constraints. Therefore,
targeting goes out of the scope of this customer
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analytics course and into the ‘advertising’
territory.
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Alright.
Finally, once marketers have decided which
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segments to target, we come to Positioning.
In Positioning, the important question to
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answer is what product characteristics do
the customers from a certain segment need?
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Or more like, what product can be offered
to them that would have the characteristics
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closest to the ones they need? So, we can
say that positioning consists of implementing
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the targeting actions for the product.
But positioning concerns not only the characteristics
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a product should have but also how it should
be presented to the customers and through
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what channel. In fact, this process is so
important, that it has a framework of its
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own called: Marketing Mix.
In this course, we’ll learn how to perform
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the S and P parts of the ‘S T P framework’.
The first big part of the course will be devoted
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to segmentation of customers, while the second
- to positioning, and more precisely to the
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Marketing Mix.
And Marketing Mix is what we’ll explore
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in more detail in one of our next videos.
We hope you found this video helpful. And
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if you enjoyed it, please take a second to
subscribe to our channel, hit the like button,
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and share the video with your friends!
Thanks for watching!
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