Gmid Associates provides analytics services including predictive modeling, descriptive analytics, data mining, and dashboard solutions. They have experience across industries including banking, insurance, and retail. Case studies highlighted include developing churn prediction models for a telecom company, sales forecasting for an apparel retailer, and implementing collection scorecards for a bank. Gmid aims to help clients make better data-driven decisions through analytics.

2. Clarity on constantly
evolving Business Dynamics
Data backed decisions
are more contemplative
and thus wiser
Data Disabled Decisions
Data Powered Decisions
The Power of Analytics…
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3. Agenda
About the Company
Capabilities and Services
 Predictive Analytics Solutions
 Other Services
 Descriptive Analytics Solutions
 Data Mining & Cleansing
 MIS / Executive Dashboard/ Simulation Tools
Relevant Case Studies
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4. About the Company
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Experienced Team –
Professionals with decades of
International Experience
Delivery Across the
Globe – Analytics Partner
Best Talent –
Graduates from IITs, IIMs, ISI
Industry Knowhow –
Complete Lifecycle of Industry

5. Gmid Associates has a global footprint
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HQ
Gmid Rep office
Tie ups/networks
California
London
Australia
Delhi
Mumbai
Bangalore
New Jersey

6. Services
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7. Analytics is at the core of banking
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Identification
Validation
Authentication

8. Predictive Analytics Solutions
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Using statistical techniques –Regression, Time Series, Neural Models etc. on historical information, one can
accurately predict the outcomes of future events and use this information to plan preemptively
Model Development Framework
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Model Validation Chart
Maximize the divergence between the distributions of
target / non target (good / bad) accounts
Estimate the unknown population characteristics based on
sample information
Use propensity scores to calculate probabilities of default,
expected loss, intentional fraud etc.
Maximize revenues, rationalize expenses.

9. Descriptive Analytics Solutions
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Use statistical clustering schemes, econometric techniques and overlay with business inputs to group
‘identical accounts’ from a pool of customer population and create targeted segments for focused
treatment
Use segmentation solutions to optimally allocate marketing budgets, increase customer service levels and
loyalty, manage bad debts and maximize collections

10. •We help organizations transform and combine disparate data, remove inaccuracies,
standardize on common values, parse values and cleanse dirty data to create consistent,
reliable information
Data Cleansing and Enrichment
•Your customer contact numbers are buried in a dataset that has all sorts of text entries.
This makes contactability on those datasets very difficult
•We have tools that dig valid phone numbers from deep into the text data, intelligent
enough to complete incomplete numbers (i.e. adding STD codes)
•Tools can be customized to suit your business requirements
Contactability Improvement Tools
•We have proven tools and expertise to run customer de-duplication algorithms on data,
identifying unique customers/ households/ relationships and establishing mappings
amongst them
•This helps you understand your customer data, draw critical conclusions and make
meaningful business decisions
Data De-duplication Solutions
Data Mining & Data Cleansing
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11. MIS/ Dashboards/ Simulation Tools
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 Measure efficiencies/inefficiencies
 Ability to identify and correct negative trends
 Ability to generate new business opportunities
 Align strategies and organizational goals
 Save time over running multiple reports
 Gain total visibility of all systems instantly
Enables better decisions
making by building
monitoring systems that are :
• Real time,
• Correct, and
• Efficient
We use advanced Analytical techniques to make sure that the
techniques which best captures the business problem is used. The
outcome algorithm is built into scenario analyzer tools.
Ability to make more informed decisions based on
collected business intelligence

12. Case Studies
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13. Outline BenefitsProject Description
Monthly default
prediction models
for active Auto Loan
portfolio
 After implementing the
model, the monthly
default rates are down by
16%
 A 100 year old leading vehicle finance company from US with a sub prime
portfolio wanted to make scientific and optimal collection strategies.
 We developed an early warning delinquency predictor scorecard on the
portfolio and implemented the same on the client’s system. The scorecard
runs on the last day of every month and gives scores to all the accounts
based on their propensity to default on the payments in the next month. It
also categories the accounts into risk segments, using which the company
can make effective, targeted collection strategies
‘Bad’ Application
Prediction system for
US Auto Finance
company
 Early Write off losses
have gone down by 12%
within 4 months of
model implementation
 A leading auto finance company from Texas wanted to devise effective
strategies to separate good applications from bad ones to improve the
portfolio quality and minimize future credit losses
 We developed two predictive scorecards- write off prediction and early pay
off prediction. Since both of these were loss making scenarios for the
business, we clubbed them together and devised “high/ Medium/Low” risk
bands. Every application is given scores and risk segment and business
makes effective acquisition strategies
Case Studies: Predictive Analytics
13
Cross-sell Strategies on
a diversified financial
portfolio
 Cross-sell penetration
increased from ~1% to
~4% on a base of
2,50,000 accounts in 8
months time after
implementation
 A global financial services firm needed to develop effective and easily
implementable cross-sell strategies on a diversified portfolio containing
mutual funds, life insurance, general insurance and mortgage loans
 Developed multiple statistical models and used clustering techniques,
overlaid with business rules to design efficient cross-sell strategies
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14. Outline BenefitsProject Description
Collection Scorecards
and Strategy for Retail
Bank
 Write-off losses down
by 4% within six
months. Impact in the
range of $5million
 South India’s leading bank wanted to put in place a centralized collections
system powered by predictive analytics.
 Two statistical models were developed- first on the current portfolio to
predict potential payment default cases and second, a payment prediction
model on the 90+ DPD portfolio.
 Segmentation and collection strategy put in place to run preventive
campaigns in order to minimize delinquencies at lowest expenses
Case Studies: Predictive Analytics
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Churn Prediction
Scorecards for US
Telecom Major
 85% potential churns
captured in top 20%
‘risky’ population 15
days in advance
 Churn rates down by 7%
 One of the world’s largest Telcos wanted to replace their existing churn
prediction system with a better quality product to arrest increasing churn in
their consumer and business portfolio
 Two fold problem description- early identification of subscribers most likely
to churn, identification of important factors driving the churn.
 Developed predictive models to aid in churn prevention campaigns. Ran
statistical tests to identify most important variables that affect churn
behaviour.
Sales Forecast Model
for world’s biggest
fancy dress e-retailer
 80% accurate forecast
figures within 3 months
of model deployment
 Increased service level by
about 41%
 The client, a large textile manufacturer in UK, wanted to build a reliable
model that complements or takes over their in house traditional approach to
forecasting primarily driven by gut instinct.
 Developed a multi variable time series forecasting model for all of the
product categories. The model uncovered patterns caused by seasonal
variations, lead times, minimum quantities, re-order quantities,
extraordinary usage (special orders) etc.

15. Outline BenefitsProject Description
De-duplication of
Records on a Multi
Product Financial
Portfolio
 The client was able to
know exactly how many
customers it had
 As a result of customer-to-
account mapping, they
are able to make better
informed business plans
 A leading private sector Insurer was having troubles managing their data
warehouse (DW) because there was no unique identifier at client level. Every
new acquisition was a new relationship at DW level.
 Developed a de-dup algorithm by using matching algorithms like levenshtein
distance, soundex, metaphone etc. Created a new client level UID.
Intensity
Optimization for a
Cards Collections
Campaign
• The collections unit was
able to reduce their
costs by 15% for the
same collection
revenues.
 A multinational bank’s cards collection business needed help to identify
optimum reach out intensity/ channel at bucket and segment levels.
 Using statistical and econometrics techniques on historical data, we devised
optimum channels and intensities at bucket and segment levels.
Data Cleansing on a
Life Insurance
portfolio database
• As a result of improved
contactability, cross-sells
went up by 11% in a
year’s time
 Our client, a leading Insurer needed to increase phone number
contactability on their database. The phone numbers were not in machine
dialable format s hence dialer machines could not be used directly for
customer reach out/ cross-sell/ renewal campaigns.
 Developed an algorithm which picks correct phone numbers from text
strings and modifies them into machine dialable format (adds std code on
landlines, if required). Automated it in an excel tool to be used on
incremental data.
Case Studies: Data Mining & Cleansing
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16. Thank You
Mudit Chandra
Sales and Marketing Head
Gmid Associates
Mobile Number
+91-98107-96148
Email
mudit.chandra@thegmid.com
Web
www.thegmid.com
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