The document discusses creating a customer segmentation workflow using the Knime analytics platform, including introducing Knime, explaining what customer segmentation is and its benefits, demonstrating how to perform customer segmentation using k-means clustering in Knime, and providing a demo of customer segmentation for Domino's pizza. The workflow would involve data preparation, model training, clustering labels, sending clusters to marketing teams, and deploying the model as a web portal for collaboration.

1. Presented By:
Shubham Goyal Swantika Gupta
Data Scientist Software Consultant
Knoldus Inc. Knoldus Inc.
Creating a Customer
Segmentation Workflow
with Knime

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3. Segment 1 Segment 2

4. Agenda
Introduction to Knime Software
What is customer segmentation
Benefit and advantages
Customer segmentation with
Knime

5. ● Free and Open Source platform that
provides capability for:
■ Data Analytics
■ Reporting
■ Integration
● Allows to Create and Productionize
Data science using one easy and
intuitive environment.
● Enables every stakeholder to focus
on what he/she does best.
Introduction to Knime

7. Knime Analytics Platform
● Open-source software for creating Data science processes.
● Strong and Comprehensive platform for
■ Drag-and-Drop Analytics
■ Machine Learning, Deep Learning, Natural Language Processing
■ ETL
■ Statistics

8. Knime Workbench

9. Nodes and Workflows

10. Over 2000 Native and
Embedded Nodes are available
Nodes to Access Data from
➔ Databases [MySql, MongoDB]
➔ Files [CSV, txt, pdf, excel, PMML]
➔ Web, Cloud [REST, Web
Services]
Big Data Support
➔ Spark, HDFS
➔ Hive
➔ Impala
➔ In-database processing
Nodes to Transform Data
➔ Preprocessing data on the basis
of row, column, matrix
➔ Data Blending and Aggregation
➔ Feature Creation and Selection

11. Over 2000 Native and
Embedded Nodes are available
Nodes for Data Mining and Analysis
➔ Regression & Classification [Linear, Logistic, Decision
tree, SVM]
➔ Clustering [k-means, DBSCAN]
➔ Validation [cross-validation, scoring, ROC]
➔ Deep Learning [Keras, DL4J]
➔ External [R, Python, Weka]
Visualization
➔ Javascript based nodes [Scatter
plot, Box plot,ROC curve]
➔ Misc [Tag cloud, Open Street
Map]
➔ Script based visualizations [R,
Python
Deployment
➔ Writing results to Files [Excel,
CSV, Remote Storage]
➔ Writing results to Databases
➔ BIRT reporting

12. Knime Server
● Enterprise software for
○ Team-based collaboration
○ Automation of workflows
○ Management of workflows
○ Deployment of workflows as analytical
applications and services

13. Knime Server

14. Advantages of using knime as solution
Low Code Modular Scalable Plugin Based Inbuilt
Collaboration

15. Anticipate Customer
Behaviour

17. Can you imagine a writer, speaker or
film director who never anticipated
their audience?

18. What is customer
segmentation ?

19. Customer segmentation is the practice of dividing a
customer base into groups of individuals that are similar
in specific ways relevant to marketing, such as age,
gender, interests and spending habits.

20. Customer Segmentation Methods

21. Customer segmentation
procedures

22. Data Collection
Deciding what data
will be collected and
how it will be
gathered
Data Analyses
Developing methods
of data analysis for
segmentation
Segmentation
Establishing effective
communication
among relevant
business units (such
as marketing and
customer service)
about the
segmentation
Implement
Implementing
applications to
effectively deal with
the data and respond
to the information it
provides
01 02 03 04
Customer Segmentation
Procedure

23. K-means clustering
❏ K-means clustering is one of the simplest and popular
unsupervised machine learning algorithms.
❏ The mean in k-means refers to averaging of the data: that
is , finding the centroids in those data points.
A cluster refers to a collection of
data points aggregated together
because of certain similarities.

24. How the K-means algorithm works
To process the learning data, the K-means algorithm in data mining starts with a first
group of randomly selected centroids, which are used as the beginning points for every
cluster, and then performs iterative (repetitive) calculations to optimize the positions of
the centroids
It halts creating and optimizing clusters when either:
❏ The centroids have stabilized — there is no change in their values because the clustering
has been successful.
❏ The defined number of iterations has been achieved.

25. Customer segmentation
with Knime

26. Send those clusters to marketing
or sales team
Data preparation
In this workflow, we will prepare
data to feed into machine
learning model
Model deployment
In this step we will deploy the
model as a Web portal on knime
server
Collaboration
Clusters labeling
Model will give different clusters,
we have to tag those clusters
Model training
In this workflow, we will train a
machine learning model and
save it
Workflow
Customer segmentation workflow with knime
Knime Analytics Platform Knime server

27. Demo

28. Customer Segmentation for Domino’s
● Geographic
● Region – Domino’s outlet in different countries is a way of
segmenting their market
● Potential Markets of different Regions
● City – They also segment the cities as class i, class ii,
metros, small towns
● Demographic
● Age – under 13 years, 13 to 21 years, 21 to 35 years, 35
to 50 years, 50+ years.
● Family Income – Lower Middle Class, Middle Class,
Upper Middle Class, High Class

29. Benefits of customer
segmentation

30. ❏ Ability to Personalize Communication
❏ Upselling / Cross-selling Opportunities
❏ Higher ROI and CRO
Benefits of Customer Segmentation

31. Thank You !
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