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MK99 – Big Data 1
Big data & cross-platform analytics
MOOC lectures Pr. Clement Levallois

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MK99 – Big Data 2
What you will learn today
1.
The role of segmentation in the marketing plan
2.
Techniques for segmenting
3.
Segmentation: beyond the marketing plan

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MK99 – Big Data 3
How to market the right product to the right people?
We have different tastes!
Your fancy product or service
I have a limited set of features!

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MK99 – Big Data 4
STP: Segmentation → Targeting → Positioning
1.
Segmentation
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First, cut the crowd into segments of customers with similar characteristics
2.
Targeting
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Evaluate the attractiveness of each segment, select most interesting ones.
3.
Positioning
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Creating a value proposition for each of the targeted segments

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How to segment in practice?
1.
Get market data
2.
Develop measures of association
3.
Identify and remove outliers
4.
Form segments
5.
Profile segments and interpret results
Source: Principles of Marketing Engineering 2nd ed by Lilien et al., 2013, p. 83.

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Example: segmenting in the car industry
1.
Get market data
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For example, data on car drivers from consumer panel providing info on their demographics, tastes and needs
2.
Develop measures of association
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This means creating a measure of “which customer is similar to which” in terms of their demog., tastes, etc.
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For example, families with young children will be roughly similar in terms of demographics, needs and budget.
3.
Remove outliers
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Remove car drivers that have extreme values (the one car driver that needs a race car, etc.)
4.
Form segments
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Use analytical techniques to create groups of car drivers based on their associations. Also called “clusters” or “communities”.
5.
Profile segments and interpret results
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Groups have now been found automatically. Identify what these groups mean and how they show a path for action.

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MK99 – Big Data 7
Example of a nice segmentation
- Each ball represents a car driver.
- Segments of car drivers are shown in different colors.
- A link between two car drivers indicates that they are “strongly similar”, in terms of their characteristics
 Individuals with strong associations landed in the same groups
 Groups are neatly differentiated
 We identified a clear meaning for each group
Families with young children
Single men with sports tastes
Professionals who need pick-ups

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MK99 – Big Data 8
Tools for segmentation
1.
The qualitative approach
Relying on experts from the industry. Qualitative insights are always key!
2.
Quantitative approaches
a) Supervised or non supervised?
Supervised: the number of clusters is pre-chosen by the analyst.
Non-supervised: the number of clusters is left to determine by the analytical method itself.
b) Hierarchical or partitioning?
Hierarchical (see next slide)
Partitioning (see in two slides)

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Hierarchical clustering (here, with a bottom-up approach)
1. All car drivers Their colors represent their different characteristics
2. First level of clusterization
Drivers showing the strongest similarities are grouped together
3. Second level of clusterization
Clusters with the strongest similarities are grouped
4. Third level of clusterization
Clusters of level 3 with the strongest similarities are grouped
Note 1: this figure is called a dendogram
Note 2: this technique does not scale up well.

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Partitioning (here, k-means clustering)
1
2
All car drivers
Same car drivers, mapped on a chart according to their characteristics (“revenue” and “ecologically conscious”)
Note: here we simplify with just 2 characteristics. Of course, k-means can include all the relevant features of your data)
3
“Show me 3 clusters”
“Ok now show me just 2 clusters”
ecologically conscious
revenue
revenue
revenue
ecologically conscious
ecologically conscious
How does it work?
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The algo chooses the clusters that make the smallest distance between the car drivers of the cluster and the cross.
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Can be implemented in Excel.

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Partitioning (here, community detection in networks)
1
2
All car drivers
Same car drivers, represented as a network:
A connection between 2 car drivers means they share common characteristics
Communities detected by an algorithm and represented visually with different colors: The algorithm identifies groups of individuals which share many connections, and which have relatively fewer connections with the rest.
Can be implemented in Gephi

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Before leaving: Segmentation is useful beyond the marketing plan
Segmentation / Clustering / Community detection:
-> Useful to understand large datasets in general:
- Reveals groups, relations between groups
- With the network approach, can even point to the position of single individuals in each group (are they central? Do they bridge to other segments?)
-> Useful for operational marketing (ex: email campaigns), not just strategic product launch!

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Summary
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Segmentation transforms a crowd of data into sub- groups, enabling targeting and positioning.
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Traditional and recent approaches to segmentation are available in click-and-point software (Excel, Gephi)
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Segmentation is useful for operational decisions as well – not just the big strategic product development

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MK99 – Big Data 14
This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com)
Contact Clement Levallois (levallois [at] em-lyon.com) for more information.