1.
Application of Decision Sciences
to Solve Business Problems
Customer Relationship Management

2.
Acquisition Usage & Loyalty
Value
Time
Retention
Activation
Make targeted decisions for effective relationship
management with the customer through every phase of
their lifecycle

3.
CRM
Analytics

4.
Acquisitions
Prospect Targeting
Prospect acquisition is specifically concerned with issues like acquiring the right prospect at an optimal cost,
acquiring as many prospects as possible, optimizing across channels, etc. The main objectives are ensuring
high profitability of new customers and acquiring them at a low cost. By analyzing prospect demographics,
predictive modeling techniques are employed to identify their propensity to respond. Profitability models are
then built for different segments.
It helps in answering business questions like:
 How do we proactively acquire new customers?
 Who will be the most profitable customers? And in which channels do we target them?
 Can the varied data sources be leveraged to expand prospect universe and implement efficient direct
marketing campaigns?
 How can direct marketing spend be lowered while maintaining results?
Response models to optimize Acquisition budgets
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6 7 8 9 10
Hot Leads Warm Leads
Random; 10.9% leads bought a new car
Predictive
Model
%Leadswhopurchasedacar
Predictive Model Deciles; Each decile has 10% of Leads
Cold Leads

5.
Who are my
Customers?
Customer Segmentation
In today’s competitive business scenario with customers having a multitude of options, their preferences and
buying patterns have been constantly evolving. For retaining the profitable and loyal customers, it is
therefore necessary to keep track of changing customer trends and accordingly tailor the offerings.
Segmentation is the practice of identifying homogenous groups of customers based on their needs, attitudes,
interests and purchase behavior. It enables identifying profitable customer segments and customizing
product and service offerings and marketing campaigns to target them effectively. It is typically done using a
combination of transaction data, demographic data and psychographic information.
It aids in answering critical business questions like:
 Which are the most profitable and loyal customer segments and how do we have tailored offerings for
these segments?
 How do we have special promotion campaigns, specifically to reach the high value customer segments?
 What are the revenue and profit contributions by different customer segments?
Platinum: Current investment > 50K
Gold: Current investment > 5K; < 50K
Silver: Current investment < 5K
Tenure<12mo
All Customers
1,889
1,637 MM EAD
87k AED/Customer
New Customers
4,568 (24%)
433 MM EAD (27%)
95k AED/Customer
Existing Customers
11,573 (76%)
1,203 MM EAD
(73%)
84k AED/Customer
Savers
2,944 (16%)
39 MM EAD (2%)
13k AED/Customer
Investors
7,316 (38%)
812 MM EAD (50%)
111k
AED/Customer
Redeemers
871 (5%)
60 MM EAD (4%)
69k AED/Customer
Revolvers
3,190 (17%)
292 MM EAD (18%)
92k AED/Customer
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver
Platinum
Gold
Silver
Segmenting customers based on their revenue contribution

6.
Who are my
Profitable
Customers?
Profitability & Loyalty analysis
For the sustainable growth of any enterprise, it is very important to identify the most profitable and loyal
customers. Having special schemes for these customers in form of offers and discounts, can help in realizing
the long term goals of increasing profits and expanding customer base.
Organizations use customer profitability and loyalty analysis to identify the most valuable customer segments
to prioritize marketing, sales and service investments. Transactional behaviour is analysed for creating a
Customer Value Score (C-score) for each customer, which explains their engagement levels. The C-Score can
be leveraged for proactive action to defend, retain and grow the customer base.
This can help answer key business questions like:
 Which are the most profitable and loyal customer segments and how much they contribute to the firm’s
profit?
 Which are the customer segments to be targeted for marketing programs and special offers?
 Which are the customer segments that can have a negative impact on the company’s profitability?
0%
10%
20%
30%
50%
ProfitContribution
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Watch Out:
Customers accounting
for losses.
Focus:
Customers who
have growth
potential
Sustain:
Highly profitable customers
Invest & Sustain:
Profitable customers
%Customers
Invest :
Highly profitable
customers
40%

7.
Cross Selling & Upselling Strategies
It is not just enough to retain the profitable and loyal customers, but it has become a necessity to increase
the revenue contribution from the existing customer base. Cross Sell involves the sale of additional items in
order to increase the wallet share from the customers.
Market basket analysis is the technique used to evaluate customers’ purchasing behaviour and to identify the
different items bought together in the same shopping session. It uses transactional data and employs
predictive modelling techniques to identify customers’ preferences based on the associations between the
products recently purchased. It helps to determine which products are to be offered and which are the
customer segments most likely to be receptive to these cross selling propositions.
It aids in strengthening relations with customers by:
 Customizing layouts, product assortments and pricing so that it appeals to the customers
 Designing effective affinity promotions
• Stimulating trials and increase customer awareness during launch of new products and variants
• Handling excess stock by designing offers among associated products
Deepening
Engagement
CONFIDENCE Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product8
Product 1 100% 25% 9% 6% 18% 2% 28% 31%
Product 2 42% 100% 7% 8% 22% 6% 29% 22%
Product 3 31% 16% 100% 5% 10% 4% 18% 17%
Product 4 35% 29% 8% 100% 28% 7% 26% 12%
Product 5 47% 35% 8% 12% 100% 3% 37% 24%
Product 6 37% 66% 18% 19% 21% 100% 25% 21%
Product 7 45% 28% 8% 7% 23% 2% 100% 25%
Product 8 57% 24% 9% 3% 17% 2% 29% 100%
Probability that Product 8 is purchased given that Product 1 is bought is 31%
Probability that Product 1 is purchased given that Product 8
is bought is 57%
Increasing sales by creating cross-selling opportunities using MBA

8.
Campaign
Management
Promotion dashboard to track the effectiveness of different campaigns
Campaign Effectiveness
Campaigns include a variety of short term programs directed at consumers to stimulate product awareness,
trial or purchase. The most commonly implemented programs include sampling and free trials, free gifts,
couponing , loyalty reward programs, special pricing, promotional contests and so on. Advanced econometric
modeling techniques are used to help companies refine their promotion strategies, to understand the lift
generated by various campaigns and the associated ROI.
This information is then used by marketers to:
 Identify the impact of different campaigns and find out the most effective one
 Optimally allocate budget among different campaigns while increasing sales and maximizing ROI
 Design campaigns specific to a category instead of following “one-size fits all” approach
 Measure the campaign effectiveness for continuous improvement and track the profitability of retained
customers

9.
Customer
Lifetime Value
Customer Lifetime Value
Customer lifetime value (CLV) represents how much a customer is worth in monetary terms and is based on
customer’s expected retention and spending rate. It can be defined as the present value of the total profit
expected from the customers during the entire period they do business with the company. CLV analysis uses
customers’ past transaction data and employs predictive modelling techniques to forecast how much each
customer would contribute to the company’s revenues and profits till they remain with the company and do
not attrite. The analysis can also be extended to estimate the lifetime values of new customers. CLV analysis
takes into account estimated annual profits from customers, duration of business relation of the customer,
and the discount rate to assess the net present value of the customers.
CLV analysis is used for:
 Forecasting the expected revenue from new customers and weighing it against the acquisition and
retention cost for them
 Deciding how much to spend on marketing programs for different customers
 Identifying the high value customer segments that can contribute the maximum to company’s revenue
and have special offers for them
 Identify the prospects who can become profitable for the company
Computing CLV for a Cards Portfolio
Monthly
Expenses
Monthly
Net Revenues
Customer
Tenure
Net Margin
Accumulated
Margin
Acquisition
Costs
Customer Lifetime
Value
Predict monthly Revenues
Predict Customer Attrition
Predict Response RatesFrom existing P&L’s

10.
Customer
Retention
Churn Management
To retain customers, it is very essential to keep tracking customers’ activity regularly — their frequency of
shopping, evolution of their shopping patterns, how often do they shop and so on. Customers attrite on a
definite path to inactivity which can be identified and therefore managed. Also, acquiring new customers has
become far more expensive than retaining existing ones and hence customer retention has become a major
corporate priority. By employing attrition analysis, customers whose engagement levels have lowered and
who are likely to attrite can be identified and appropriate retention strategies can be formulated.
Churn analysis helps answer key business questions like:
 Which are the customer segments, to be targeted for retention programs?
 How do we identify the factors which are most likely to drive customers to remain:
• Creating segments based on preferences and buying patterns so that right offers can be made to the
right people
• Understand the variables that make high-value customers most likely to purchase and offer
incentives and personalized service
Attrition rate by customer tenure
AttritionRate%
0%
10%
20%
30%
40%
50%
60%
70%
-
1
2
3
4
5
0 - 0.5 year 0.5 - 1 year 1 - 3 years 3 - 5 years > 5 years
#Customers(MM)

11.
Business Situation:
The client, a leading retail chain offering various products across categories, wanted to understand its customers to better plan customized
campaigns and promotions with the objective of increasing customer engagement and overall revenues.
The Task:
Identify appropriate customer segments based on various factors such as purchase patterns, promotion response and demographics of the
customers.
Framework:
Customer Personas:
Analytics in Action
Increasing Revenues by better Understanding Customers
Client: A Leading Retail Chain
Define & Build
customer segments
Segment analysis Customer profile
Identified an appropriate
customer base based on the #
of visits and days on books
Built customer segments using
clustering algorithms after
treating the outliers
Analyzed the segments and
identified the customer
personas in each segment
Got a detailed profile of
customer in a segment to
target for promotion
Who?
What?
When?
70% sales
from FMCG &
Staples
Early morning
Weekend
Early Morning
Weekend Shoppers
Large family
High visits
60% sales
from FMCG &
Staples.
Multi-
category
shopping
Afternoon to
Evening
High sales, large
family shoppers
Salaried
staples
shoppers
70% sales
from Staples
Shops in rice,
oil, pulses and
flour
Morning to
Afternoon
1st 10 days
Salaried, Health
conscious,
staples shoppers
Salaried
Large family
50% sales-
staples, 30%-
FMCG.
Multi-
category
shopping
Morning to
Afternoon
Weekend 1st
10 days
Salaried, large family,
weekend shoppers
Low visits
Low sales and
high margin
45% sales
from Apparels
Shops in
Men’s casual
and formal,
ethnic wear
Morning to
Afternoon
Weekend
Weekend, apparel buying
shoppers
Single family
with kids
Health
conscious
70% sales-
FMCG
High
proportion of
baby care and
health SKUs
Morning to
Afternoon
single/small family
shoppers
Discount
seekers
50% sales
from Home
needs.
Shops in
utensils, bed
and luggage
Afternoon to
Evening
Weekend
Discount seekers
70% sales
from Staples
& FMCG
Evening
Evening shoppers
The Result:
• Developed relevant Customer personas like discount oriented, large family, weekend specific category shoppers,
impulsive buyers, high end buyers, etc
• Customer personas helped the business to appropriately target customers based on the day, time, affinity and category
of purchase with appropriate promotional offers, leading to incremental revenues
Identify an
appropriate
Customer Base
Small to
Medium size
families
Large families
shops mostly
in FMCG and
Staples

12.
Business Situation :
The insurance provider generated leads through cross-selling. Potential customers were targeted in a 4-stage process, and they generally
displayed 4 possible outcomes:
Resources were being wasted on pursuing unlikely Prospects classified in red boxes above. The insurance provider wanted to determine which
members were more likely to complete at each stage , and then fast track the application through the approval process. By reducing the
proportion of declined and incomplete applications, operating costs could be optimized.
The Task :
- Develop a framework of predictive models to calculate the probability of a prospect purchasing the insurance product
- Get a more targeted base of Prospects, and hence reduce costs by removing prospects with least probability of buying the Product
Analytical Framework :
- Historical data , which contained information from both, internal and external sources, was analyzed
- Logistic models were built for identifying separate probabilities for each stage of the approval process
- Testing was done if Oversampling or Undersampling would improve the performance of the predictive models
- All the models were then combined to identify the ‘best’ leads
- The models were validated and implemented as SAS Macros to enable real-time scoring
The Result :
• The framework of 3 models provided useful insights on probabilities associated with approvals and important factors affecting it
• Up to 25% of total applications were removed with a loss of just 5% of Paid customers
• With costs going down by 25%, we were able to achieve an increment 14% net profits.
Analytics in Action
Targeted Prospecting. Increasing Profits by 14%.
Client : An Insurance Provider in the US
Targeted
Prospects
Approved
Completed
Forms
Paid Premium
Didn’t PayDid notDeclined
Target : 100
Approved : 85
Completed: 68 Paid : 58
Predictive Models
Target : 75
Approved : 70
Completed: 62 Paid : 55

13.
Business Situation :
The Automotive OEM, with dealerships across the US, was receiving almost 30,000 leads every month from various lead aggregator sites
across the internet. Individual leads came with limited information – name, address, email, time frame of purchase, vehicle of interest and
trade-in type. The auto retailer wanted to put in place a ranking system so as to classify each incoming lead into hot, warm or cold; depending
on the leads propensity to buy a new car in the next 30 days. This ranking system would enable the OEM to be the first to reach out to a Lead
and convert him into a Customer.
The Task :
- Develop a predictive model that will tag each incoming lead as hot; warm or cold depending on the leads propensity to buy a new car in the
next 30 days
- Implement the predictive model in a real-time system so that hot leads get scored and automatically routed to the appropriate dealership
depending on the location of the lead and the dealer
Analytical Framework :
A 4-step analytical process was used:
1. Lead information along with auto purchase status over the past 2 years was analyzed. It was found that on average, 10.9% of leads
converted and bought a new car within 30 days.
2. Lead information variables like name, address, email, time frame of purchase, vehicle of interest and trade-in type, etc were transformed
into derived variables. Text data entered online by leads as ‘comments’ was also considered.
3. A predictive model was built to classify each lead into hot, warm or cold.
4. The model was validated and implemented as a SQL Stored Procedure to enable real-time delivery of hot leads to the right dealerships.
The Result :
• The predictive model was able to segregate each incoming lead into hot, medium or cold.
• ‘Hot’ leads had an auto purchase rate of 19%; almost twice that of an average lead. These hot leads were instantly routed to the
appropriate dealership for immediate follow-up by their best salesmen. ‘Warm’ leads had a purchase rate of 11% and were actioned upon
in the usual manner. ‘Cold’ leads were not actioned upon.
• After 3 months of using the lead rating system, auto sales went up by 12% across dealerships.
Analytics in Action
Identifying Hot Auto Leads. Increasing Sales by 12%
Client : An Automotive OEM in the US
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6 7 8 9 10
%LeadswhopurchasedaCar
Predictive Model Deciles; Each decile has 10% of Leads
Hot Leads Warm Leads Cold Leads
Random; 10.9% leads bought a new car
Predictive
Model

14.
Business Situation:
The client, a US based technology corporation with a Global presence, has hundreds of partnerships across verticals and solutions. Recently
they noticed that some partners dilute their brand, are not strategically aligned, and are not being fully leveraged. Lacking a framework to
evaluate and prioritize partners, the client has witnessed a decline in brand equity which has stressed Marketing capabilities.
The Task:
To develop a framework that evaluates and prioritizes partnerships based on relevant criteria. This will score every partner on an index and
can be used to prioritize existing partnerships, identify future partnerships, or review risky partnerships.
Analytical Framework:
Developed a framework based on Multi Criteria Decision Analysis (MCDA) technique. Partnerships are evaluated on criteria like Brand equity,
financial health, strategic alignment, consumer perception, etc and scored on an index of 1-10.
The Result:
• After evaluating existing partnerships, 7 were identified as brand diluting and risky, and have been reviewed.
• The marketing team compared the scores for existing partners against the marketing funding received from partners for joint marketing
activities. When viewed from a strategic alignment perspective, some partners had very high synergy but were not being fully leveraged in
terms of marketing activities. This resulted in $20M increased partner funding towards marketing activities.
• This framework was further used to identify and prioritize new partnerships in Education, Healthcare and Technology segments.
Analytics in Action
Evaluate and Prioritize Business Partnerships
Client: Among the top PC manufacturers in the world
Decision scorecard for new partners in Education
Decision scorecard for existing partners
Define parameters
Gather data and
score the
parameters
Assign weights
based on user
preferences
Score partnerships
and derive insights
Identify all parameters that evaluate a
partnership. Can vary by vertical,
geography, etc.
Qualitative and quantitative data is scored
by ranking the outcomes in a hierarchy.
After a discussion with all the stakeholders,
assign weights to parameters based on the
decision makers preference.
Score the partnerships on an index of 1-10
based on the weighted average of selected
parameters.
MCDA framework process flow

15.
Business Situation:
The client, a US based database publisher, has 12-15 specific products designed for Enterprise Markets. Products vary from repository of
research documents, tools that aid research processes; research documentation; databases that identify new technologies, and technology
partners; engineering, Oil and Gas, pharmaceutical and other domain specific databases. Higher versions of a product package are also
available. Corporate clients that are research oriented subscribe to some products, and not to others due to lack of need and/or awareness.
The Task:
To build a cross-sell strategy that identifies customers with a propensity to buy a specific product in addition to their current portfolio. To build
an up-sell strategy that identifies customers with a propensity to upgrade current products to higher versions. The exercise consisted of
scoring both the customer and each product with respect to the customer’s profile and life-cycle.
Analytical Framework:
The analytical framework was built using Market Basket principles. A scoring model was then developed to evaluate each Customer, followed
by evaluation of each product with respect to the Customer.
The Result:
• Cross-sell and up-sell recommendations implemented by business in a targeted fashion
• Revenues from current customers increased by 18% in Q1 and Q2 2013 as compared to the same period last year
• The Marketing team now uses the Cross-sell framework as an enabler in setting new Account Expansion strategies
Analytics in Action
Increasing B2B Customer Engagement via Targeted Cross-sell
Client: Leading Business Database Publishing House

16.
Business Situation:
The client, a South-east Asia based oil and gas Retailer encountered a significant increase in customer churn at their gas filling stations despite
having a tried and tested loyalty program in place. This resulted in a 4.6 % drop in sales during Q2 2012. The business wanted to monitor and
control Customer churn at regular intervals.
The Task:
To develop and implement a program that monitors Customer engagement levels and attrition risk, measure business impact from Customer
churn, and develop actionable strategies to manage Customer Attrition.
Analytical Framework:
High value customers that left the business impacted sales significantly. Segments were developed to slot each high-value Customer on the
basis of recent purchase patterns. Movement of Customers across segments and over time was used to identify the level of ‘engagement’ the
Customer had with the Business. Segment-specific offers and campaigns were implemented to manage customer attrition. Results from the
campaigns were used to continuously refine targeting and messaging.
The Result:
• Based on the analysis, the Business was able to identify high value Customers at risk of attrition. Suitable Retention programs were
designed and implemented for these Customers.
• Business was able to more efficiently utilize its Retention budget as targeted customers consisted of only 15% of the overall customer base
• Sales in Q4 2012 were up by an average of 2.1 % as compared to the previous two quarters.
Analytics in Action
Proactively Retaining your most Valuable Customers
Client: A Leading Petroleum Retailer
10%
39%21%
32%
39%
24%30%
6%
# of customers Revenue contribution
0%
25%
50%
75%
100%
High Medium-High Medium-Low Low

17.
MANAGEMENT TEAM
GLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. Cherian
CEO
Roy has over 20 years of rich experience in marketing, advertising and media
in organizations like Nestle India, United Breweries, FCB and Feedback
Ventures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhD
COO & Head of Analytics
Anunay has over 15 years of experience, with a significant portion focused
on Analytics in Consumer Finance. In his last assignment at Citigroup, he was
responsible for all Decision Management functions for the US Cards
portfolio of Citigroup, covering approx $150B in assets. Anunay holds an
MBA in Finance from NYU Stern School of Business.
Kakul Paul
Business Head, CPG & Retail
Kakul has over 8 years of experience within the CPG industry. She was
previously part of the Analytics practice as WNS, leading analytic initiatives
for top Fortune 50 clients globally. She has extensive experience in what
drives Consumer purchase behavior, market mix modeling, pricing &
promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
ADVANCED ANALYTICAL SOLUTIONS
MARKETELLIGENT, INC.
80 Broad Street, 5th Floor, New York, NY 10004
1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com
CONTACT www.marketelligent.com
Industry Business Focus Tools and Techniques
Consumer Finance Investment Optimization SAS, SPSS, R, VBA
Credit Cards Revenue Maximization Cluster analysis
Loans and Mortgages Cost and Process Efficiencies Factor analysis
Retail Banking & Insurance Forecasting Structural Equation Modeling
Wealth Management Predictive Modeling Conjoint analysis
Consumer Goods and Retail Risk Management Perceptual maps
CPG & Retail Pricing Optimization Neural Networks
Consumer Durables Customer Segmentation Chaid / CART
Manufacturing and Supply Chain Drivers Analysis Genetic Algorithms
High Tech OEM’s Supply Chain Management Support Vector Machines
Automotive Sentiment Analysis
Logistics & Distribution
YOUR PARTNER FOR
DATA ANALYTICS SERVICES
Greg Ferdinand
EVP, Business Development
Greg has over 20 years of experience in global marketing, strategic planning,
business development and analytics at Dell, Capital One and AT&T. He has
successfully developed and embedded analytic-driven programs into a
variety of go-to-market, customer and operational functions. Greg holds an
MBA from NYU Stern School of Business