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Applications of Analytics
Ahmet Kocamaz
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open data sources
3
A Brief Introduction
Crede Consulting is an information based strategy consulting company providing services in marketing, risk and process
management. Crede’s core competency lies in turning predictive analytics to value-increasing actions
Capabilities
Analytical approach
Customer segmentation
Marketing and sales action planning
Next-best-product analytics
KPI dashboard
Channel optimization
Customer lifetime value maximization
Process optimization
Activity-Based-Cost management
KPI development & reporting system
implementation
Operations
Customer credibility scoring
Fraud detection
Payment behavior & projection analysis
Collections management
Financials & Risk
ManagementMarketing & Sales
4
Crede Differentiators: Our Culture
1 Value focus There is always a focus on the value and value generation, and not on the trivial
2Quick project execution due to
focused approachDue to focus of all capabilities and resources, projects can be carried out quite quickly
3Integrated business, statistics and
IT skill sets
When it comes to analytical modelling, three vital capabilities of business, statistics, and
IT have to be integrated to get the best and most effective results
4 Software vendor neutralAnalytical consultancy services can be provided regardless of the software vendor and
work effectively with any vendor’s infrastructure
5Confidential and security
conscious
Any data either provided directly by the customer and learned in due process while
serving the customer is to be treated with utmost confidentiality and certain security
measures to be applied
6End-to-end project management:
design and ımplementation
The projects are solutions with several components to be implemented at different
phases. While designing and implementing these projects, end-to-end perspective is
always being taken into consideration
7 Long term supportResources are allocated for long term support just in case if certain projects take longer
to implement or more support is needed after the implementation of the project
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
6
Lead Generation
Lead generation is identification of potential customers among a large set of prospects in order to improve sales efficiency
Crede collects potential customer data from various resources; finds out behaviors and hidden patterns in data; uses this insight to develop propensity
modelling or scorecards; and finally delivers high propensity customer leads.
While traditional model results some improvement in sales performance, a recommendation engine via analytical approach leads to best sales
performance.
Because it is learning system using Machine Learning Algorithms, a typical implementation takes 2 to 4 months to reach its maximum performance.
The sole purpose of this model development process is improving sales conversion rate and reducing operational costs.
In Brief
Traditional model
Potential customers
Classic targeting Filtering
Industry
Location
Company size
Branch like
Selection Became customers
New customer
New model
Similar behaviour
Targeting
Recommendation
Engine
Target audience
High propensity
customers
LO
W
MID
HIG
H
Customers
Classic targetingSource
Customer pool
Database
Up to X10 Return
7
Product Propensity Modeling
What is Upsell & X-Sell?
X-Sell is all about identifying the right existing customers for the
right and relevant additional products
at the right time and channel
Upselling is the analytical approach for maximizing share of wallet
for the right and strategic products
The lifetime value of a customer with 4 and more active products
is 8-10 times more than single
product customers
X-Sell and Upsell efforts are not one-time and ad-hoc activities. It
is a long-term journey which needs
continuous improvement to achieve sustainable value
Deliverables
An action based decision
tree to be implemented
with the proper sales
product offer, to the right
customer at the right
time
Recomm
endation
engine
Map
A penetration map
pointing out the regions
with potential
Process
Data correction
requests
Data
provision
Clean data
provision
Select
variables
Recommendation engine
Business
logic
K-Nearest
neighbours
SVM Regression
Neural
networkDecision tree
Test model
results
Potential
customer list
Implement
modelRevise model
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
9
Not-on-us Turnover Leakage
Revenue leakage detection project
Project
definition
Income loss due to not-on-us
credit card transactions
became a major leakage in
banks P&L. Crede identified
the not-on-us heavy
merchants, identified their full
names via fuzzy matching
and identified found out their
contact information; resulting
an easy sell operation
Process Delivery
Masked
company
name
Full company
nameTelephone Address
İlgün Orman***İlgün Orman
Ürünleri902161234567
Kozyatağı Mah.
Gülbahar Sok.
No:5
Vatan Gıda***
Vatan Gıda İnşaat
Ve Turizm Sanayi
Ticaret Limited
Şirketi
902121234567
Selimiye Mah.
Kavak iskele
cad. No:5
… … … …
ExampleGet masked company name
from Interbank Card Center
Find full name of company in
Crede DB via Fuzzy match
Check fuzzy match result1
2
3
Complete contact information
of company4
Business card/POS lead generation
Project
definition
Centralized lead generation
and performance
management in business
card and POS sales is not
easy due to its relationship
based nature. Crede
identifies the companies to
address for these products at
the moment of need.
Process Delivery
Company Website Telephone Reason
Yılmaz Kardeşler www.yilmazlar.com 902161234567New
establisment
Kyani www.kyani.om 902121213434Expansion in
sales team
… … … …
ExampleDefine the rule set for
business card propensity
Apply the rule set on Crede
Systems
Validate results1
2
3
Complete contact information
of prospect customer4
2
If X>Y
If Z=M
Then .....................................
If Z=M
Then
.....................................
1
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
11
Collections management
Collections process
Collections management aims raise in collected amount, more structured and cost effective operations, best channel-script-time combination
Define business
objectivesAnalyze data
• Faster collections
• More collections
• Customer
satisfaction
• Cost optimization
Pre-analysis Analytics
Develop collections
segmentation
Develop collections
scorecards
Run collections
modelMonitor results
• Investigate
availability of data
fields
• Analyze historical
depth
• Check consistency
• Define self and non
payers
• Identify segments in
grey area
• Develop
segmentation
parameters
• Build collections
scorecard
• Get collections segments
• Calculate collections
score
• Fine-tune segments
• Define optimum
collections action tree
• Define KPIs and
targets
• Establish monthly
reporting for
collections
performance
Payment behavior
segmentsCollections actions decision tree
1 2 3 4 5 6
DeliverablesName Score
Ahmet Yılmaz 430
Mehmet Öztürk 545
Ayşe Aksoy 600
Fatma Korkmaz 590
Mustafa Can 450
… …
Collection score card
Company
GİB/Mernis
Crede
Payment behavior
score
Example
Applications of
Analytics
The Motivation
Product Propensity modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics for procurement department prediction
Open data sources
13
Turnover Prediction
What will be the new size of the market after the entrance of the new customers forced by law
In practice
Theory
Value of network grows exponentially
after the entrance of each node
)1( nn
Pazardaki entegratörler dışı e-Fatura (gelen+giden) ve e-Fatura mükellefi adedinin projeksiyonu
e-Fatura (gelen+giden) adedi e-Fatura mükellefi sayısı
6.654
16.23322.005
30.373 30.830 31.294 31.767 32.247 33.234 33.741 34.256 34.780
18
41
41 41 41 41 41 41 41 41 41 41
0
10
20
30
40
50
60
70
80
0
5.000
10.000
15.000
20.000
25.000
30.000
35.000
40.000
Bin
ler
Bin
ler
41
32.466
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
15
Customer retention
Retention program is predicting the customers who have a high propensity to churn, who have already churned
and taking pro-active actions so that these customers stay with the company
Retention process
Data cleansing
Logical
analysis
Inactivity
analysis
Consistency
analysis
Distribution
analysis
Outlier
Analysis
Data
provision
Clean data
provision
Demographical, behavioral &
derived variables
Demographical
Behavioral
Derived
Pre-churn model development
(Classification algorithms )
SVMNeural
Network
Decision
Tree
Post-churn segmentation
(Clustering algorithms)Post-churn business flow
Pre-churn model
output tree
Pre-churn customer
list
Potential Churn customers list
is delivered on monthly basis
Performance of the list is to
be evaluated after 2 months
Deliverables Benefits
• Customer life time extension by taking preemptive actions
• Improvement on internal costs by targeting only customers likely to
come back
• Decrease in average customer acquisition cost
• Better investor relations by improvement in major KPIs
Post-churn customer
list
Churn customers are identified
to measure the performance of
pre-churn model
Winback actions
Active customer
definition
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
17
Analytics for Procurement Department Prediction
The model predicts whether a offer will address procurement department or the business owner
Applications of
Analytics
The Motivation
Product Propensity Modelling
Not-on-us Turnover Leakage
Collections
Turnover Prediction
Retention: Saudi Investment Bank
Analytics For Procurement Department Prediction
Open Data Sources
19
Open Data Sources
Open data sources are more than what they used to be
20
Customer detection
Event based marketing
Event based marketing, is also called trigger marketing. This is a form of targeted marketing that identifies key events in the customer activities. When
an event occurs, customer specific marketing activity is proposed
Final customer list
Company
name
Telephone
number.. Source
Company
ABC+90 212 0123456 … ISP
Company
KLM+90 312 1234567 … ISP
Company
DEF+90 242 2345678 … Job Posting
Company
GHI+90 212 0123459 … Job Posting
Company
OÖP+90 236 4567890 … Web site
Company
RSŞ+90 212 0125479 … Web site
Company
TUV+90 236 4327890 … Web site
EXAMPLE
Customer Pool
Set alarm
Change in ISP
Job Posting
Doubled job posts in the last 6 months
Looking for “a sales representative” in the last
3 months
Change in web site
Mention “side benefit” on web site
Mention “export to Africa” on web site
Renew web site with the latest technology
Some
examples
Change of ISP frequently
Change of ISP into expensive one
21
Problem that we are dealing with
Algorithm for extracting the company name from the website
Predicting e-commerce likeliness of company by its website
Predicting the main field of operation by analyzing the landing and «about us» pages of a company
http://www.crede.com.tr
90 212 988 19 18
Kozyatağı Mah. Gülbahar Sok.Perdemsaç Plaza No:17/45Kadıköy İSTANBUL