Customer segmentation has undoubtedly been one of the most implemented applications in data analytics since the birth of customer intelligence and CRM. Data scientists and modern business analysts work closely together to achieve and automize a comprehensive description of the company’s group of customers. However, they usually came across these two challenges: ~ Need to implement a customer segmentation frame that can accommodate a self-adjusting procedure. ~ Need an interactive way to inject their knowledge into the customer segmentation frame without ever opening the underlying data processing workflow. Learn how to generate different customer groups using clustering and how to provide insights into the performance of sales activities.

1. Customer segmentation for
business success with
Knime
Presented By: Shubham Goyal
Data Scientist
Knoldus Inc. & MachineX Intelligence

2. Agenda
Knoldus Introduction
Whats is customer segmentation
Benefit and advantages
Customer segmentation with
Knime

3. Our Global Presence
9+ Years
Years of Profitable Growth
175+ Engineers
Reactive products, Fast Data strategy, AI
05 Offices
Toronto, Chicago, Singapore, New Delhi,
Pune
17+ Customers
Multi-year Global Customers

4. Our Partners
Through our strategic partnerships, we have an unwavering commitment to equip your
organization with the knowledge, skills, expertise, resources and tools to succeed.

5. REACTIVE
PRODUCTS
Microservices & API
● Scala
● Functional Java
● Rust
● Elixir
● Haskell
ENTERPRISE
DATA PROGRAM
Data Lake
● Delta
ARTIFICIAL
INTELLIGENCE
Machine Learning
Data Science
Deep Learning
● Knime
● Tensorflow
● PyTorch
● Scikit
●
BLOCKCHAIN
● Hyperledger
● DAML
● ScoreX
Knoldus Practice Areas
Fast Data
● Spark
● FLink
● Hadoop
Agile
Transformation
Reactive UI/UX
Test Automation
Practice
Reactive DevOps
Product
Engineering

6. 6
About Knoldus MachineX
MachineX is a group of data wizards.
We are a team of Data Scientist and engineers with a product mindset who deliver
competitive business advantage.

7. Knoldus MachineX Offerings
Natural Language Processing
Computer Vision Solutions
Data mining
Chatbot Development
Artificial intelligence research and solutions

8. 8
An Intelligent Meeting
Assistant Application
Record Videos
View DashBoardAn Intelligent Meeting
Assistant Application

9. 9

10. 10
Machine learning
library in scala
KSAI
FishEye

11. 11
Enable organizations to
capture new value
and business capabilities
Innovation Labs
Consistently blogging, to
share our knowledge,
research
Blogs
Knime, Deeplearning,
Coursera, Stanford certified
professionals
Certifications
Insight & perspective to help
you to make right business
decisions
TOK Sessions
It’s great to contribute back
to the community. We
continuously advance open
source technologies to meet
demanding business
requirements.
Open Source
Contribution

12. Anticipate Customer Behaviour

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

15. What is customer segmentation ?

16. 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.

17. Customer Segmentation methods

18. Customer segmentation procedures

19. Customer segmentation procedure
Data Collection
Deciding what data will be
collected and how it will be
gathered
Data Analyses
Collecting data and
integrating data from
various sources and
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

20. Benefits of customer segmentation

21. ● Ability to Personalize Communication
● Upselling / Cross-selling Opportunities
● Higher ROI and CRO
Benefits of Customer segmentation

23. Knime Analytics Platform

24. Knime Server

25. 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

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

27. Demo

28. Feedbacks !!!!
shubham.goyal@knoldus.com

29. Thank You
www.knoldus.com
+(91) 1204287693
hello@knoldus.com
@Knolspeak
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