Matrimonial services

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AAUM Confidential

Analytics for

Matrimonial

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Corporate profile

Founded by IIT Madras alumnus having extensive global business experience with Fortune 100

companies in United States and India having three lines of business

Prof Prakash Sai

Dr. Prakash Sai is professor at the Department of Management Studies, Indian Institute of Technology Madras. He has wealth of international consulting experience in Strategy Formulation

Puneet Gupta

Puneet spearheads the IFMR Mezzanine Finance (Mezz Co.), is strengthening the delivery of financial services to rural households and urban poor by making investments in local financial institutions.

Padma Shri Dr. Ashok Jhunjhunwala

Dr. Ashok Jhunjhunwala is Professor at the Department of Electrical Engineering, Indian Institute of Technology Madras India. He holds a B.Tech degree from IIT, Kanpur, and M.S. and Ph.D degrees from the University of Maine, USA.

Analytics

• Appropriate statistical models

through which clients can measure

and grow their business.

Competitive Intelligence

• Actionable insights to clients for

their business excellence

Livelihood

•Services ranging from promotion of

livelihoods, implementation services,

livelihood & feasibility studies.

Key Focus Areas in Advanced analytics and Predictive analytics

Product – geniSIGHTS (Analytics/BI), Ordo-ab-Chao (Social Media)

More than 25 consulting assignments for Businesses & Govt orgs

Partnership – Actuate, IIT Madras, TIE and 3 strategic partnerships

Dedicated corporate office at IIT Madras Research park since 2009

Aaum’s office, IIT Madras Research Park

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Competencies in

Advanced analytics

Build appropriate statistical models through

which clients can measure and grow their

business.

Expertise in

• Digital Media

• Finance/Insurance

• Retail

• Entertainment

• Human Capital

• Government organizations

• Research & training

Competitive

assessment

Competitive intelligence

Provide actionable insights to clients for

their business excellence.

Expertise in

•Business Entry

•Business Expansion

•Market research

Livelihood

Perform livelihood services ranging from

promotion of livelihoods, implementation

services, livelihood and feasibility studies.

Expertise in

•Government organizations

•Non Government

organizations

•Corporate with livelihood

focus

•Research

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Corporate profile

Founded by IIT Madras alumnus having extensive global business experience with Fortune 100

companies in United States and India having three lines of business

Competitive Advantage –Advanced analytics, Predictive analytics

Executed more than 25 consulting assignments for Businesses and Govt organizations

Team of 20 members

Dedicated corporate office at IIT Madras Research park since 2009

Prof Prakash Sai

Dr. Prakash Sai is professor at the Department of Management Studies, Indian Institute of Technology Madras. He has wealth of international consulting experience in Strategy Formulation

Puneet Gupta

Puneet spearheads the IFMR Mezzanine Finance (Mezz Co.), is strengthening the delivery of financial services to rural households and urban poor by making investments in local financial institutions.

Padma Shri Dr. Ashok Jhunjhunwala

Dr. Ashok Jhunjhunwala is Professor at the Department of Electrical Engineering, Indian Institute of Technology Madras India. He holds a B.Tech degree from IIT, Kanpur, and M.S. and Ph.D degrees from the University of Maine, USA.

Analytics

• Appropriate statistical models

through which clients can measure

and grow their business.

Competitive Intelligence

• Actionable insights to clients for

their business excellence

Livelihood

•Services ranging from promotion of

livelihoods, implementation services,

livelihood & feasibility studies.

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Competencies in

Advanced analytics

Build appropriate statistical models through

which clients can measure and grow their

business.

Expertise in

• Digital Media

• Finance/Insurance

• Travel & Logistics

• Retail

• Entertainment

• Human Capital


• Research & training

Competitive

assessment

Competitive intelligence

Provide actionable insights to clients for

their business excellence.

Expertise in

• Business Entry

• Business Expansion

• Market research

Livelihood

Perform livelihood services ranging from

promotion of livelihoods, implementation

services, livelihood and feasibility studies.

Expertise in


• Non Government organizations

• Corporate with livelihood focus

• Research

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What Aaum can offer

Performance analysis

Recommendation engine

Insightful

dashboard

Advanced Analytics

Experience the magic by clicking our Genie

http://insights.aaumanalytics.com:8081/genisights/portfolio/home.jsp

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Performance analysis at various levels are vital to the success



Insightful

dashboard

Advanced Analytics

COLLECT

Free registrants

Expresses interest for paid

Paid registrants

Churn

Churn

Feedback

GENERATE

Reg

istr

ants

Right KPIs tied to right customer profiles at right time

Partially completing the free profile

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Recommendations based on the learnings from the

performance analysis

•What approach for what candidate profile

• Conversions (Likelihood to make the payment)

• Targeting right prospects

• Page access

• Time spent on tasks

• Submission of horoscopes, photos, references, etc

• Time spent on profiles

• Responses, acceptance, rejects, interests

•Advise/empower the “personal relationship manager” to adopt

appropriate preventive/corrective actions

• Kiosk/Center level recommendations

• What profiles to approach for quick conversions



Insightful

dashboard

Advanced Analytics

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Insightful dashboard to improve the business

• Monitoring the candidate specific score levels

•Make actions from it

• Categories

• forming elite communities IIT, IIM, etc

• IT software developer from top B schools with eng

Background

• Caste/Sub caste/Communities

• Category performance vs overall performance.

• Overall performance to median (Bellcurve)

• Trend analysis

• and many …



Insightful

dashboard

Advanced Analytics

Strategic partnership with

• state of the art reporting solutions

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Advanced analytics to illuminate insights from the data



Insightful

dashboard

Advanced Analytics

• Predicting prospect’s willingness to continue

• Churn

•Acquisition

• Cohort analysis to know the effectiveness of the engagement

• Planning and Scheduling promotions/events

• “Generic online Sunday meetings” vs Café Coffee Day meetings for a few”

• Building models that watch for thresholds

•Early alerts i.e. keeping track of attrition

• Remove fake profiles

• Based on advanced algorithms

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Free state activity - approach

Free period is computed, which is the difference in days between the next payment date and previous expiry date.

Free period is bucketed into the levels,

• 0-15 days(level 1), 15-30 days(level 2), 30-60 days(level 3), 60-365 days(level 4), >365 days(level 5).

Activities are classified into 6 broader categories,

Login

Basic profile modification

• Assured contact, Birthday offer, Family info modification, Hobby modification, Modification screening, Profile modification

Advance profile modification

• Add photos, Add reference, Horoscope generation, Ignore, Mobile alert registration, Profile delete

Match view

Match initiation

• Contact history, Filter, Match watch, Photo match watch

Match communication

• Block, Horoscope request receive, Horoscope request sent, Online payment failure, Personal message receive, personal

message sent,, Phone view request receive, Phone number request sent, Interest receive, Reference request receive, Reference

request sent, Sent interest, Voice message receive, Voice message sent, Phone number request receive

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Free period – looking into the distribution(days) & Customer segments

Free period is the difference in days of previous expiry

date and next payment start date.

The above plot gives the distribution of the free period, the

free period is ranging from a min of 2 days to max of 1563

days, with an average free period of around 130 days.

The second plot gives a better view of the distribution.

Free period of the customers been divided into

- 0-15 days (level1), 15-30 days (level2), 30-60 days

(level3),60-365 days(level4), >365 days (level5)

No free period in the level4, i.e, 60-365 days.

The plot gives us the proportion of the customers coming

under each level of the free period.

Around 80% of the customers became paid members

with in 2 months of free period.

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Activity in the free period levels – proportion of the customers

Login and Match view are umbrella activites, hence not considered while dwelling deep into the activities.

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Activities, sub activities in the free period

The above plot is the broad activity type and underlying activity view.

Around 88% of the match initiation type is

due to the photo match activity

The activities – personal msg receive, receive

interest and sent interest account to almost 71.5%

of the match communication activity category

Add photo accounts to 60% of the advance

modification Profile modification accounts to 57%

of the Basic modification

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Free period levels – various activities

We have come up with a metric – Expected activity per day, to measure the activity of each customer

coming under various free time levels.

The metric is the ratio of the no.of times the activity happened to the free time days per each customers

each activity.

This metric is calculated at the broader activity type and at even each of the activities.

The following slides will help in understanding the expected underlying activity distribution over the

different free time levels.

Activity period in days, calculated as the difference from next payment date and activity date.

Further, the activity period is divided into three phases, namely “Initial phase”, “Mid phase”, “Near

payment phase”

Initial phase – first one-third of the activity period

Medium phase – second one-third of the activity period

Near payment phase – last one-third of the activity period

Further dwelled deep by looking into 5 parts of the activity period categorised as “Phase 1”, “Phase 2”,

“Phase 3”, “Phase 4”, “Phase5”

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Activity phases in the free period (0-15 days) – various activities

The free period is categorised as “initial

phase”, “mid phase”, “near payment

phase”,

There is sudden dip in the average

expected underlying activity from the

second phase to the third phase which is

the near payment phase and this tells us

that the underlying activity has been more

performed more in the start and mid phrase

of the free period.

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phase”, which are the 1/3rd, 2/3rd & 1 of

the free period.

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the free period.

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Activity phases in the free period (>365 days) – various activities




the free period.

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5 Phased activity period, free period (0-15 days) – various activities

The free period is categorised as “phase1”,

phase2”, “phase3”, “phase4”, “phase5”,

which are the 1/5th, 2/5th 3/5th, 4/5th,1 of

the free period.

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the free period.

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the free period.

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5 Phased activity period, free period (>365 days) – various activities




the free period.

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Basic modification, underlying activities – (0-15 days)

Further looking into the underlying activites of each broader activity classification. The above plot is the distribution of the various expected

activity distribution over the three phases of the free period of the activity type “Basic profile modification”.

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Advance modification, underlying activities – (0-15 days)

The above plot is the distribution of the expected activities distribution over the three phases of the free period of the activity type “Advance

profile modification”. Here we can see the average add photos, add referance, and ignore are high in the mid phase.

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Match initiation, underlying activities – (0-15 days)

The above plot is the distribution of the expected activities distribution over the three phases of the free period of the activity type “Match

initiation”. Here we can see the average of expected contact history and match watch are stable in the first two phases and dropping in the

third phase. Photo match watch average expected activity is dropping down over the phases.

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Match communication, underlying activities – (0-15 days)

Most of the activities remained stable over the three phases except for the activity interest receive

and sent interest where the average dropped in the near payment phase.

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Basic profile modification, underlying activities – (15-30 days)

Basic profile modification, 15-30 days, the distribution of the underlying expected averages.

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Advance profile modification, underlying activities – (15-30 days)

Advance profile modification, 15-30 days, the distribution of the underlying expected averages.

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Match initiation, 15-30 days, the distribution of the underlying expected averages.

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Match communication, 15-30 days, the distribution of the underlying expected averages.

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Basic profile modification, underlying activities – (30-60 days)

Basic profile modification, 30-60 days, the distribution of the underlying expected averages.

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Advance profile modification, underlying activities – (30-60 days)

Advance profile modification, 30-60 days, the distribution of the underlying expected averages.

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Match initiation, 30-60 days, the distribution of the underlying expected averages.

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Match communication, 30-60

days, the distribution of the

underlying expected averages.

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Basic profile modification, underlying activities – (>365 days)

Basic profile modification, >365 days, the distribution of the underlying expected averages.

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Advance profile modification, underlying activities – (>365 days)

Advance profile modification, >365 days, the distribution of the underlying expected averages.

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Match initiation, underlying activities – (>365 days)

Match initiation, >365 days, the distribution of the underlying expected averages.

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Match communication, underlying activities – (>365 days)

Near payment phase Near payment phase Near payment phase Near payment phase

Near payment phase Near payment phase Near payment phase Near payment phase

Near payment phase Near payment phase Near payment phase

Near payment phase Near payment phase Near payment phase

Match communication, >365 days, the distribution of the underlying expected averages.

Case studies

Analytical Advantage Using Mathematical modeling

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Project Context: Our client, a US based media and content platform firm for engaging the audience with the most money and influence online with more than 1,000 publishers as partners. The client approached Aaum to reap analytical insights from their humongous transaction data

AAUM’s Contribution: • Devised and tracked the Key proportions and Key Performance across the sites and publishers • Achieved “performance scores” to qualify the site/campaign performance characteristics (CTR, Margin, CPM and Delivery rate) and got validated. • Extended the scoring technique to derive segment specific indices and compared the peer campaigns • Root cause analysis: Inspection of site performance attributes of the critical campaigns

Case Study : Campaign and Publisher scoring for a major US based digital media &

content platform firm

Sample Deliverables

… Key proportions and Key Performance … qualifying the performance by scoring … scoring is extended further to derive segment specific indices and compared with their peers

… acid test on our scores

Root cause analysis of the critical campaigns

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Project Context: Our client, a national television network approached us to derive analytical insights by slicing and dicing multiple scenarios to help the top management to arrive at actionable results.

AAUM’s Contribution: • Understanding of target audience, frequent respondents and provision to reward the most loyal respondent (Integrated over a period of time) • Respondent behavioral characteristics - by age group, gender, voice/non voice • Media campaign/promotions efficacy - What time is better, Which channel is better?

Case Study : Analytical support for a major television network

Sample Deliverables

… extracted insights by dimensional analysis

… chennai’s female population are older than other vital circles

….analysis strengthened the media channel to reward the loyal customers.

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Project Context: Our client, a HR consulting firm, approached AAUM to develop comprehensive metrics which could be

standardized across its clients

AAUM’s Contribution: • Developed standard metrics that could be rolled out across to the clients of the consulting firm • Performed interaction analysis for ‘key metrics’ to derive a holistic understanding • Developed predictive models for some very useful parameters

Sample Deliverables

Case Study: Developed KPI’s and Predictive Analytics for a HR Consulting firm

Standardized metrics were defined and developed

Interaction analysis of key metrics was performed and

insights were derived

Predictive models were built using Random Forests for key

parameters

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Project Context: Our client, a major ticket booking company approached AAUM to provide comprehensive analytical insights to

help the top management to arrive at actionable results.

AAUM’s Contribution: • Developed RFM metrics to help the management to effectively segment customers and reward them. • Performed cohort analysis to qualify the churning, effective engagement with agents. • Qualified traveler’s behavioral patterns on routes, regularity, age, gender, per ticket value, lead days, etc. • Developed dynamic pricing strategy based on the unfilled inventory levels.

Sample Deliverables

Case Study: Analytical support for a major ticket booking company

Margin analysis … Cohort analysis to qualify churn & engagement disengagement levels …

RFM … acquisition …

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Project Context: Our client approached AAUM to offer effectively qualify their business operations with insights from data analysis.

AAUM’s Contribution: • Devised methodology to effectively qualify the payment cycle, ageing metrics, outstanding from customers. • Performed credit limit analysis, qualified credit consumption patterns and suggested efficient methodologies. • Performed fare analysis to optimize the travel offerings to customers.

Sample Deliverables

Case Study: Analytical support for the largest integrated travel and travel related

financial services company

lead analysis on ticket fare of airline travel …

promptness in payments … credit consumption and breaching patterns …

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Project Context: AAUM offered analytical support to a leading global lodging solutions company to leverage their business.

AAUM’s Contribution: • Slicing and dicing of data to understand customer’s hotel booking preferences qualified on savings, effective

rate and net paid.

Sample Deliverables

Case Study: Lodging analytics for leading global lodging solutions company

Trend analysis to understand the effective rate pattern in a year …

effective rates over customer preference, location, position, etc …

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Project Context: Developing Business intelligence and analytical capability for a leading financial service company

AAUM’s Contribution: • Analyzed current BI maturity with comprehensive data analysis of the customer datum collected by our client • Evaluated and suggested comprehensive plan for BI maturity • Identified as strategic partner to perform predictive analytics in multiple phase spanning two years

Sample Deliverables

Case Study: Business intelligence for a leading financial services company

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Project Context: Research project funded by the company to devise a performance dashboard on various attributes for a movie to predict the success at the Box office.

AAUM’s Contribution:

• Team AAUM extracted and analyzed the information from the web sources like YouTube and twitter to achieve the specified Business Objectives

• Developed dashboards for various attributes and analyzed the trends in the sentiment scores by using clustering, classification techniques.

Sample Deliverables

Case Study :

4

-

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Case Study: Measuring the movie performance by mining social media sentiments

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Project Context: Our client approached us to develop a framework to analyze the transaction data of their customers and tie these insights with loyalty card program and reward their customers by their preference and loyalty factor.

AAUM’s Contribution <Pilot demo>: • Mined association rules to provide strategic insights for cross-sell/up-sell opportunities • Framed out loyalty management model for the client.

Sample Deliverables

Case Study: Market Basket Analysis and Loyalty program

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Project Context: Our client, police Department archived the crime data for the past ten years and approached AAUM to deliver insights by performing appropriate analytical techniques.

AAUM’s Contribution: • Discovered hotspots of crimes by cluster analysis of the historic data. • Trend analysis of different crimes • Mining insights from the police records by text mining • Derived KPIs for crime department to monitor the crimes.

Sample Deliverables

Case Study: Crime Analytics for Tamil Nadu State Crime Records Bureau

Hot Spots found out for crimes in Chennai city.

Trend movement for each type of Crime

Mining insights from the comments recorded

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Project Context: Developing Business intelligence and analytical capability for a leading rural bank

AAUM’s Contribution: • Analyzed current BI maturity with comprehensive data analysis of the customer datum collected by our client • Evaluated and suggested comprehensive plan for BI maturity • Identified as strategic partner to perform predictive analytics in multiple phase spanning two years

Sample Deliverables

Case Study: Predictive model to grade the performance of DCCBs

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Project Context: Our client has collected valuable data through their Kiosks and wanted to derive insights which can identify new potential business for rural India

AAUM’s Contribution: • AAUM profiled the villages and identified clusters • Thorough analysis done for seven villages in three districts based on income slabs adopted from NCAER & Mckinsey report • Identified new businesses which would flourish once the villagers migrate to higher income slabs

Sample Deliverables

Data Management: Seamless and secure data transfer using client infrastructure. More than 20 MB of raw data was cleansed and analyzed using tools available in excel.

44.79%52.56% 51.23% 48.72% 45.70% 42.59% 41.10% 36.37%

2.98%1.74% 2.83% 2.59% 2.81% 3.28% 3.26%

2.87%

12.56%

13.19% 10.63%11.64%

12.09%13.10% 14.22%

13.52%

7.00%

11.18%

7.04% 8.24%8.33%

6.45% 4.81%

1.21%

5.26%

8.13% 8.57%9.62%

12.12% 15.31%

18.65%

17.48% 16.77% 17.94% 18.92% 18.11%24.35%

3.38% 0.50% 2.66% 3.49% 3.50% 3.54% 3.18% 3.02%

6.35%

15.57%18.23%

Average Deprived

low

income

Deprived

- Lower

middleincome

Deprived

- middle

income

Deprived

- upper

middleincome

Aspirers Seekers Strivers

Healthcare

Education & Recreation

Communication

Travel

Housing and Utilities

Apparel

Food, Beverages and Tobacco

33.87% 35.77% 32.67% 32.43% 35.14%24.24%

0.00%

36.89% 39.33%36.67% 32.43% 27.03%

27.27%

20.00%

24.92%20.29%

26.67% 32.43%29.73% 48.48%

60.00%

3.13% 3.35% 3.00% 1.35%5.41%

0.00%

20.00%

0.42% 0.67% 0.00% 0.00%0.00% 0.00%0.33% 1.35% 2.70% 0.00% 0.00%0.43% 0.84% 0.00% 0.00% 0.00% 0.00% 0.00%6.35%

0.00%0.32%

Average Deprivedlow

income

Deprived -Lower

middle

income

Deprived -middleincome

Deprived -upper

middle

income

Aspirers Seekers

Never visited doctor

Ayurveda siddha doctor

VHN

Private Clinic

Private hospital

Government hospital

PHC

22.32%

51.82%

32.63%

32.73%15.70%

8.41%

7.14%

2.73%3.77%

17.89%

3.95% 0.45%

Income Slab (value in %)-

Allivilagam

Number of house holds in

Allivilagam

Seekers (Rs 200000 - 500000)

Aspirers (Rs 90000 - 200000)

Deprived - upper middle income (Rs62000 - 90000)

Deprived - middle income (Rs 45,000 -62000)

Deprived - lower middle Income ( Rs22500 - 45000 )

Deprived - Low Income (<Rs22500)

More than 50% of villagers in Allivilagam

earn less than Rs. 22,500 per annum

As the villagers start earning more they will spend on

communication and health care ….

….and they prefer private hospitals.

Case Study : Analytics on data collected by a rural research firm

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Project Context: Our client approached AAUM to build and implement analytical models for their Healthcare Claims Intelligence

and Insights solution that was targeted at US Healthcare providers

AAUM’s Contribution: • A workflow and architecture for the claims engine was designed after interactions with the client • Comprehensive collection of data from the client was undertaken to frame the input for the model • Based on the design and data, the model was built with flexibility to be customized across client

requirements

Sample Deliverables

Case Study: Building Claims Analytics solutions for Healthcare

Workflow and architecture of the claims engine were

designed

Claims, Provider, Billing team and call data were used as input variables to build the model

The outcome of the approach was a model which could be customized

as per client requirements

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Sample Deliverables

Case Study: Credit scoring framework for Micro Finance Institutes (MFI)

Project Context: Our client wanted Aaum to evaluate and identify a suitable credit scoring framework for scoring MFIs

AAUM’s Contribution: • Aaum presented a white paper evaluating various credit scoring methodologies • Plan in progress for solution implementation.

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Project Context: Our client approached Aaum to build cost-effective analytical dashboards to reap the benefits of analytical

insights for their business activities.

AAUM’s Contribution: • Customized analytical dashboards specific to client business • Dashboard and reports developed and customized specific to roles. • Appropriate KPIs and metrics generated specific to the client business. • Ability to store reports in multiple formats

Sample Deliverables

Case Study: Business intelligence dashboard

Dashboard and reports were developed/deployed specific to roles …

Enriched with reports, KPIs and information as per user privileges …

Multiple possibilities to store

content in the format desired

by the business …

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Project Context: Aaum is developing the individual credit scoring model in partnership with NABARD, National banks, Government, NGOs to grade individuals/SHGs.

AAUM’s Contribution: • AAUM team simplified the process with no physical requirement to travel physically to SHG/NGO locations. • The team is devising credit score algorithms and configured in the server to perform automatic scoring. • The entire model is getting deployed in the mobile phone to facilitate quick reach in the remote places.

Sample Deliverables

The system would captures all receipts (for the money collected from individual members)

Case Study: Design & delivery of innovative financial products for empowering SHG

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Project Context: Our client wanted AAUM to build geographical dispersion of business risks in India

AAUM’s Contribution: • AAUM identified the variables needed to build risk at a location and created demographic risk framework • AAUM used demographic risk frame work and identified risks for 60 districts in 18 states in India

Sample Deliverables

Case Study: Geographic Dispersion of Business Risks for a financial institute