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Data Driven Approach
Advanced Analytics & Machine Learning in the Non-Profit sector
DAVIDE CAMERA | MAY 18, 2018 | ROME
Davide Camera
Seventeen years of experience
in Data Science, in particular
on areas related to Business
Intelligence, CRM, Data
Mining, Customer Experience,
Campaign Management,
Customer Care, Loyalty
Program, Advanced Analytics
& Machine Learning.
Founder of Excelle
(www.excelle.it), consulting
company working in the Data
Science and Data Driven
Marketing field.
Currently CEO, teacher and
lecturer at Data Driven
Marketing conferences.
https://www.linkedin.com/in/davidecamera
Who we are
Excelle was founded in 2012. It is a consulting company working in the Data Intelligence
field. We are able to:
 structure and manage both “smart data” and “big data” integrating conventional and digital
channels
 understand data, identify strategies, develop and monitor marketing plans.
We implement strategies, action plans, algorithms, statistical and mathematical techniques to
increase «Profits» and «Marginal Revenues» for our Costumers
“Data Intelligence is about transforming data into information, information into
knowledge, and knowledge into value”
–quora.com
We develop Data Intelligence projects
The transformation of data into information, information into
knowledge and knowledge into value.
Data intelligence represent a facilitator of a culture and a
business strategy based on knowledge deriving from data
collection, reading, analysis and interpretation.
Data Intelligence becomes one of the main digital
transformation driver for companies.
The main company challenge is to gain competitive
advantage working on data.
Data Intelligence is...
 Data Intelligence is not a tool or a technology
 Big Data does not mean Data Intelligence
 Smart & Big Data and Technology are some of the
fundamental elements for a business approach based on
Data Intelligence.
 Analytics are not “data mixers” where solutions come out
magically
 Without goals, paths and methodology the results
obtained are bias
Data Intelligence is
NOT...
We develop Data Intelligence projects
ELEMENTS
When we talk about Data Intelligence we immediately
think about technological components or mathematical /
statistical skills.
Actually, Data Intelligence consists of numerous
elements, including:
 Strategic and operational objectives
 Methodologies
 Expertise
 Technology
 Data
 Mindset
 Creativity
 Actions
 Deploy and Delivery
 A / B test
 Measurement
 ...
BARRIERS AND
LEGENDS ABOUT
DATA
INTELLIGENCE
There are some barriers and legends about Data
Intelliugence:
 Lack of internal skills
 Data Accessibility
 Data Reliability
 Lack of technology
 Company not big enough
 Huge expenditure of time
 The results do not reveal anything new compared to
the one already known in the company
Analytics Assessment
A timely diagnosis of the current
analytical situation and a future
roadmap with the benefits that
can be obtained if methodologies
and processes guided by data
analysis are adopted.
Training
“Accademia del Dato” was
founded to disclose
knowledge towards actors
interested in the magical
world of Data Science, a
central element for a
business culture based on
Data.
Training courses for
Managers and Data
Scientists. Each course can
be customized according to
the needs and objectives of
the customer.
Descriptive
Analytics Tables, graphs
and main KPI's for an initial
description and knowledge of
customers.
Reporting & Monitoring
Understand and make sense of
business based on past events.
Ex .:
reporting by product / customer /
sales network
directional dashboards
Etc.
Predictive Analytics &
Machine Learning
Advanced predictive models that
span the whole life cycle, from
acquisition to win back.
Forecasting and what-if models
Marketing Mix
Model
Analysis to understand:
What is the ROI of
marketing and
communication activities?
How much do they
contribute to sales?
What is ROI per channel?
What is the ROI per
customer?
How long do I reach the
break even point?
Etc.
Marketing (Engagement Plan, Loyalty & CRM Program, Fundraising, etc.)
Sales
Digital & Innovation
Comunication
Data Science +
Market Research
Our partnerships with
research institutes allow us
to strengthen the Data
Intelligence offer by
integrating it with evidence
coming from outside the
company (eg finding
strategic targets even
when information on
corporate DBs is missing).
Area & Service
We’ve developed and we are developing projects for:
AGENDA
• Introduction and goal
• Business case
• Q & A
Introduction and goal
Donors Journey
Acquisition Value Stimulation
Up sell. & Share of
Wallet
Churn
Prevention
Churn Retention
Dormienza
NEW Deactivation
Win Back
01 Donors
Analysis 02 Campaign
Analysis
04 Win Back
03 Churn
Prevention &
Retention
05 Media Mix
Model
No Profit Analytics
 Automated reporting & dashboard
 Donors clustering & profiling
 Machine Learning to prevent churn
 Machine Learning to identify donors journey
 Automated Forecasting based on past & future marketing campaign
 Machine Learning to optimize touch point & channels
 Machine Learning to identify donors to have interest to growth
donation value (up-selling)
 Machine Learning to identify donors to have interest to do another
donation (cross-selling)
 Media Mix Model to evaluate advertising investment
No Profit Analytics &
Machine Learning:
PROJECTS
Business Case
Machine Learning to prevent churn
INITIAL
CONSIDERATIONS
ON PREDICTIVE
MODELS
Each predictive model returns a result, but not all
results are useful. You can create models with 2
variables (es. gender, regions) but is this what you
need?
The Data Management & Data Preparation phases set
up the basis for a “winning” predictive model.
The context and trend analysis, the definition of the
Costumer Base (CB), the knowledge of the main
metrics about the Customer Base performance are
necessary conditions for developing a predictive
modelling.
The human component, the methodological paths, the
mathematical and statistical know-how, the sector and
business knowledge are indispensable and not yet
replaceable factors.
MISSION:
FUNDRAISING
INCREASE
1. …increasing the number of donors
2. …increasing the average donation value
3. …increasing the donation frequency
4.…LOSING LESS DONORS
STRATEGIC
OBJECTIVE:
INCREASE
DONORS
RETENTION
OPERATIONAL OBJECTIVES
1. How much is the churn risk?
2. What affects the churn?
3. How can I manage the churn abatement?
4. Is a timely communication important to establish a
loyal relationship with donors?
CHURN
PREDICTIVE
MODELS
The churn model’s aim is to determine, for each
Customers, the probability of abandonment/closure.
Churn analysis is strongly based on the definition of the
"abandonment / closure phenomenon" (in some cases it
may involve the analysis of total abandonment, in others
of the switch, of contractual downgrading, etc.)
In churn models, the variable to be predicted is
dichotomous (0: not abandonment 1: abandonment); the
Churn predictive model thus returns a probability
between 0 and 1 that the event takes place.
METHODOLOGY
CRISP-DM includes 6 not temporally-bounded
phases, which follow each other. It is always possible to
return to the previous phase or review the ideas on the
basis of analysis in view of the results at any point in the
process.
The process is not a linear process, rigidly
characterized by a beginning, a certain number of
predetermined steps and an end, but it is a cyclical
process, which becomes point of reference for possible
future projects.
DETERMINISTIC
ANALYSIS
Based on the project objective, preliminary context analysis
are performed.
The goal is to obtain useful insights for the development of a
predictive model and for improving the identification of CB
and target.
In the below dashboard the customers linked to the main regions in the «X» period.
% risk
Riskevolutiion
Livello rischio provincia in decrescita stabile in crescita % clienti
Minor 
rischio TN 2,4% 0,4%
VR 2,9% 1,6%
PD 3,2% 1,5%
BO 3,4% 2,6%
AN 3,5% 1,5%
TO 3,7% 2,8%
MI 3,8% 5,4%
RM 3,9% 10,1%
rischio  41,6% 49,4% 11,0% 40,0%
Medio 
rischio VA 4,0% 0,8%
CA 4,6% 1,5%
SA 4,8% 2,6%
VI 4,9% 1,5%
CE 5,0% 2,0%
NA 5,2% 4,6%
FI 5,3% 1,8%
rischio  37,7% 101,6% 33,8% 34,6%
Maggior 
rischio RC 5,6% 0,3%
PI 5,6% 2,5%
GE 6,1% 6,1%
LU 6,3% 3,1%
MS 7,4% 2,0%
VE 8,1% 1,8%
Maggior rischio Tota 17,2% 50,8% 56,8% 24,2%
% Rischio
Risk Trend
Digital C&D - All Rights Reserved
t0 t2 t3 t4t1tm1……tm12 tm2
T-1 (past) T0 (present)
Historical Datasets Start Forecast Target
T+1 (future)
Target and Costumer Base analysis
Each area has been analyzed
ALGORITHM
CHOICE
There are many factors contributing to the algorithms
choice: not only mathematical/statistical but also business
factors.
For example, neural networks can be a good prediction
model, but they do not provide details on their elements
(which variables estimate it, the weight they have, etc.).
The use of regressions or decision trees provides information
on the elements (with different level of detail) employed in
predicting processing.
The final predictive model is created by testing different
algorithms (logistic regression, neural networks, decision
trees, etc.) in order to choose the most "performing"
model.
Different algorithms can be developed for different objectives.
Digital C&D - All Rights Reserved
A
Main Algorithms
• Logistic regression
• Decision trees
• Neural Networks
• Principal Component
Analysis
• Association Analysis
• Hierarchical clustering
• Clustering K-Means
• Support Vector Machine
• ….
B
Type
• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
C
Evaluation
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1,00
2,00
3,00
4,00
5,00
6,00
7,00
8,00
9,00
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Performance of training vs test
TRAINING
Cumulative LIFT
TEST
Cumulative LIFT
Available algorithms
MODELS
2
TRAINING
3
TEST
5
Evaluation
4
VALIDATION
7
Predictive Scoring
1
DATA PREPARATION
6
Application to the entire
Customer Base
Predictive model flow
The information were studied and optimized first in a univariate logic and then in a multivariate logic in
order to derive the maximum information and the maximum predictive capacity.
Database :
 DB divided into Training &
Validation
 DB TEST
Models
 Logistic regression
 Decision trees
 Neural Networks
Model Choice
Logistics guaranteed:
 Best absolute performance
 Best performance in passing 
from Training to Validation
Confirm Results
The DB TEST confirmed
the results obtained
during the development
phase.
Predictive model flow and choices
MAIN OUTPUT
 Context and trend
 Focus on segments and relationship with the
abandonment phenomenon
 Report about the phenomenon with the main metrics
(eg seniority, customer value, CLTV, ...)
 Measurement of quality and potential risk on new
donors
 Risk factors
 Declination of risk factors on company processes (eg
caring)
 Probabilistic scoring
 Management probabilistic traffic light for simulations
and operational management
The scoring produced by the churn model is the probability
assigned to a given customer to deactivate itself in a given
time frame.
On this score, the «management probabilistic traffic
light» is subsequently constructed
Probabilistic Scoring
 % of Captured Phenomenon:
According to the score assigned to the client, it is now possible to identify among high-risk level clients those that guarantee
the best balance between true and false positive.
Once customers have been sorted by score, it is possible to divide the population into deciles and observe the % of actual
churner in order to assess at what level of risk to manage the client (trade off volumes - risk intercepted) 
Management probabilistic traffic light definition
Management Probabilistic scoring
TAG Innovation School - all right reserved
Identified risk factors (area Customer Care)
Contracts details last 12
month
Contracts details last 6
month
Increase/Decrease the churn
rate
Information about
procedures of
withdrawal last 2 month
Information about
procedures of
withdrawal last 3 month
Call complaints
last 6 month
Call complaints
last 12 month
Call for being in
arrears
last 3 month
High-risk level
contacts
last month
Medium-risk level
contacts last month
Call for lack of
communication
last 2 month
Contacts for «transfer»
last 3 month
Project timing
Activities
Month 1 Month 2 Month 3
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
Business Understanding
Data Understanding
Data Acquisition & Preparation
Analysis & Modeling
Evaluation
Deploy
ANSWERS
OPERATIONAL OBJECTIVES
1. How much is the churn risk? The churn risk is about XX%
2. What affects the churn? customer engagement, donation
frequency, donation methods, previous insolvency,
donations for multiple "products" and more
3. How can I manage the churn abatement? Thanks to the
deliverables of the predictive model (management score,
management probabilistic traffic light, actions on single
abandonment factor)
4. Is a timely communication important to establish a loyal
relationship with donors? YES
1 year after…
 “double digit” churn reduction
 Donation value increases several hundred thousand
euros
INCREASE FUNDRAISING
LOSING LESS DONORS
Data Driven Approach: Advanced Analytics & Machine Learning in the Non Profit sector. Davide Camera - Excelle Srl
Thanks for your attention! 
https://www.linkedin.com/in/davidecamera

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Data Driven Approach: Advanced Analytics & Machine Learning in the Non Profit sector. Davide Camera - Excelle Srl

  • 1. Data Driven Approach Advanced Analytics & Machine Learning in the Non-Profit sector DAVIDE CAMERA | MAY 18, 2018 | ROME
  • 2. Davide Camera Seventeen years of experience in Data Science, in particular on areas related to Business Intelligence, CRM, Data Mining, Customer Experience, Campaign Management, Customer Care, Loyalty Program, Advanced Analytics & Machine Learning. Founder of Excelle (www.excelle.it), consulting company working in the Data Science and Data Driven Marketing field. Currently CEO, teacher and lecturer at Data Driven Marketing conferences. https://www.linkedin.com/in/davidecamera
  • 3. Who we are Excelle was founded in 2012. It is a consulting company working in the Data Intelligence field. We are able to:  structure and manage both “smart data” and “big data” integrating conventional and digital channels  understand data, identify strategies, develop and monitor marketing plans. We implement strategies, action plans, algorithms, statistical and mathematical techniques to increase «Profits» and «Marginal Revenues» for our Costumers
  • 4. “Data Intelligence is about transforming data into information, information into knowledge, and knowledge into value” –quora.com
  • 5. We develop Data Intelligence projects The transformation of data into information, information into knowledge and knowledge into value. Data intelligence represent a facilitator of a culture and a business strategy based on knowledge deriving from data collection, reading, analysis and interpretation. Data Intelligence becomes one of the main digital transformation driver for companies. The main company challenge is to gain competitive advantage working on data. Data Intelligence is...
  • 6.  Data Intelligence is not a tool or a technology  Big Data does not mean Data Intelligence  Smart & Big Data and Technology are some of the fundamental elements for a business approach based on Data Intelligence.  Analytics are not “data mixers” where solutions come out magically  Without goals, paths and methodology the results obtained are bias Data Intelligence is NOT... We develop Data Intelligence projects
  • 7. ELEMENTS When we talk about Data Intelligence we immediately think about technological components or mathematical / statistical skills. Actually, Data Intelligence consists of numerous elements, including:  Strategic and operational objectives  Methodologies  Expertise  Technology  Data  Mindset  Creativity  Actions  Deploy and Delivery  A / B test  Measurement  ...
  • 8. BARRIERS AND LEGENDS ABOUT DATA INTELLIGENCE There are some barriers and legends about Data Intelliugence:  Lack of internal skills  Data Accessibility  Data Reliability  Lack of technology  Company not big enough  Huge expenditure of time  The results do not reveal anything new compared to the one already known in the company
  • 9. Analytics Assessment A timely diagnosis of the current analytical situation and a future roadmap with the benefits that can be obtained if methodologies and processes guided by data analysis are adopted. Training “Accademia del Dato” was founded to disclose knowledge towards actors interested in the magical world of Data Science, a central element for a business culture based on Data. Training courses for Managers and Data Scientists. Each course can be customized according to the needs and objectives of the customer. Descriptive Analytics Tables, graphs and main KPI's for an initial description and knowledge of customers. Reporting & Monitoring Understand and make sense of business based on past events. Ex .: reporting by product / customer / sales network directional dashboards Etc. Predictive Analytics & Machine Learning Advanced predictive models that span the whole life cycle, from acquisition to win back. Forecasting and what-if models Marketing Mix Model Analysis to understand: What is the ROI of marketing and communication activities? How much do they contribute to sales? What is ROI per channel? What is the ROI per customer? How long do I reach the break even point? Etc. Marketing (Engagement Plan, Loyalty & CRM Program, Fundraising, etc.) Sales Digital & Innovation Comunication Data Science + Market Research Our partnerships with research institutes allow us to strengthen the Data Intelligence offer by integrating it with evidence coming from outside the company (eg finding strategic targets even when information on corporate DBs is missing). Area & Service
  • 10. We’ve developed and we are developing projects for:
  • 11. AGENDA • Introduction and goal • Business case • Q & A
  • 13. Donors Journey Acquisition Value Stimulation Up sell. & Share of Wallet Churn Prevention Churn Retention Dormienza NEW Deactivation Win Back
  • 14. 01 Donors Analysis 02 Campaign Analysis 04 Win Back 03 Churn Prevention & Retention 05 Media Mix Model No Profit Analytics
  • 15.  Automated reporting & dashboard  Donors clustering & profiling  Machine Learning to prevent churn  Machine Learning to identify donors journey  Automated Forecasting based on past & future marketing campaign  Machine Learning to optimize touch point & channels  Machine Learning to identify donors to have interest to growth donation value (up-selling)  Machine Learning to identify donors to have interest to do another donation (cross-selling)  Media Mix Model to evaluate advertising investment No Profit Analytics & Machine Learning: PROJECTS
  • 16. Business Case Machine Learning to prevent churn
  • 17. INITIAL CONSIDERATIONS ON PREDICTIVE MODELS Each predictive model returns a result, but not all results are useful. You can create models with 2 variables (es. gender, regions) but is this what you need? The Data Management & Data Preparation phases set up the basis for a “winning” predictive model. The context and trend analysis, the definition of the Costumer Base (CB), the knowledge of the main metrics about the Customer Base performance are necessary conditions for developing a predictive modelling. The human component, the methodological paths, the mathematical and statistical know-how, the sector and business knowledge are indispensable and not yet replaceable factors.
  • 18. MISSION: FUNDRAISING INCREASE 1. …increasing the number of donors 2. …increasing the average donation value 3. …increasing the donation frequency 4.…LOSING LESS DONORS
  • 19. STRATEGIC OBJECTIVE: INCREASE DONORS RETENTION OPERATIONAL OBJECTIVES 1. How much is the churn risk? 2. What affects the churn? 3. How can I manage the churn abatement? 4. Is a timely communication important to establish a loyal relationship with donors?
  • 20. CHURN PREDICTIVE MODELS The churn model’s aim is to determine, for each Customers, the probability of abandonment/closure. Churn analysis is strongly based on the definition of the "abandonment / closure phenomenon" (in some cases it may involve the analysis of total abandonment, in others of the switch, of contractual downgrading, etc.) In churn models, the variable to be predicted is dichotomous (0: not abandonment 1: abandonment); the Churn predictive model thus returns a probability between 0 and 1 that the event takes place.
  • 21. METHODOLOGY CRISP-DM includes 6 not temporally-bounded phases, which follow each other. It is always possible to return to the previous phase or review the ideas on the basis of analysis in view of the results at any point in the process. The process is not a linear process, rigidly characterized by a beginning, a certain number of predetermined steps and an end, but it is a cyclical process, which becomes point of reference for possible future projects.
  • 22. DETERMINISTIC ANALYSIS Based on the project objective, preliminary context analysis are performed. The goal is to obtain useful insights for the development of a predictive model and for improving the identification of CB and target.
  • 23. In the below dashboard the customers linked to the main regions in the «X» period. % risk Riskevolutiion Livello rischio provincia in decrescita stabile in crescita % clienti Minor  rischio TN 2,4% 0,4% VR 2,9% 1,6% PD 3,2% 1,5% BO 3,4% 2,6% AN 3,5% 1,5% TO 3,7% 2,8% MI 3,8% 5,4% RM 3,9% 10,1% rischio  41,6% 49,4% 11,0% 40,0% Medio  rischio VA 4,0% 0,8% CA 4,6% 1,5% SA 4,8% 2,6% VI 4,9% 1,5% CE 5,0% 2,0% NA 5,2% 4,6% FI 5,3% 1,8% rischio  37,7% 101,6% 33,8% 34,6% Maggior  rischio RC 5,6% 0,3% PI 5,6% 2,5% GE 6,1% 6,1% LU 6,3% 3,1% MS 7,4% 2,0% VE 8,1% 1,8% Maggior rischio Tota 17,2% 50,8% 56,8% 24,2% % Rischio Risk Trend
  • 24. Digital C&D - All Rights Reserved t0 t2 t3 t4t1tm1……tm12 tm2 T-1 (past) T0 (present) Historical Datasets Start Forecast Target T+1 (future) Target and Costumer Base analysis
  • 25. Each area has been analyzed
  • 26. ALGORITHM CHOICE There are many factors contributing to the algorithms choice: not only mathematical/statistical but also business factors. For example, neural networks can be a good prediction model, but they do not provide details on their elements (which variables estimate it, the weight they have, etc.). The use of regressions or decision trees provides information on the elements (with different level of detail) employed in predicting processing. The final predictive model is created by testing different algorithms (logistic regression, neural networks, decision trees, etc.) in order to choose the most "performing" model. Different algorithms can be developed for different objectives.
  • 27. Digital C&D - All Rights Reserved A Main Algorithms • Logistic regression • Decision trees • Neural Networks • Principal Component Analysis • Association Analysis • Hierarchical clustering • Clustering K-Means • Support Vector Machine • …. B Type • Supervised Learning • Unsupervised Learning • Semi-supervised Learning C Evaluation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Performance of training vs test TRAINING Cumulative LIFT TEST Cumulative LIFT Available algorithms
  • 29. The information were studied and optimized first in a univariate logic and then in a multivariate logic in order to derive the maximum information and the maximum predictive capacity. Database :  DB divided into Training & Validation  DB TEST Models  Logistic regression  Decision trees  Neural Networks Model Choice Logistics guaranteed:  Best absolute performance  Best performance in passing  from Training to Validation Confirm Results The DB TEST confirmed the results obtained during the development phase. Predictive model flow and choices
  • 30. MAIN OUTPUT  Context and trend  Focus on segments and relationship with the abandonment phenomenon  Report about the phenomenon with the main metrics (eg seniority, customer value, CLTV, ...)  Measurement of quality and potential risk on new donors  Risk factors  Declination of risk factors on company processes (eg caring)  Probabilistic scoring  Management probabilistic traffic light for simulations and operational management
  • 31. The scoring produced by the churn model is the probability assigned to a given customer to deactivate itself in a given time frame. On this score, the «management probabilistic traffic light» is subsequently constructed Probabilistic Scoring
  • 32.  % of Captured Phenomenon: According to the score assigned to the client, it is now possible to identify among high-risk level clients those that guarantee the best balance between true and false positive. Once customers have been sorted by score, it is possible to divide the population into deciles and observe the % of actual churner in order to assess at what level of risk to manage the client (trade off volumes - risk intercepted)  Management probabilistic traffic light definition Management Probabilistic scoring TAG Innovation School - all right reserved
  • 33. Identified risk factors (area Customer Care) Contracts details last 12 month Contracts details last 6 month Increase/Decrease the churn rate Information about procedures of withdrawal last 2 month Information about procedures of withdrawal last 3 month Call complaints last 6 month Call complaints last 12 month Call for being in arrears last 3 month High-risk level contacts last month Medium-risk level contacts last month Call for lack of communication last 2 month Contacts for «transfer» last 3 month
  • 34. Project timing Activities Month 1 Month 2 Month 3 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Business Understanding Data Understanding Data Acquisition & Preparation Analysis & Modeling Evaluation Deploy
  • 35. ANSWERS OPERATIONAL OBJECTIVES 1. How much is the churn risk? The churn risk is about XX% 2. What affects the churn? customer engagement, donation frequency, donation methods, previous insolvency, donations for multiple "products" and more 3. How can I manage the churn abatement? Thanks to the deliverables of the predictive model (management score, management probabilistic traffic light, actions on single abandonment factor) 4. Is a timely communication important to establish a loyal relationship with donors? YES
  • 36. 1 year after…  “double digit” churn reduction  Donation value increases several hundred thousand euros INCREASE FUNDRAISING LOSING LESS DONORS