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Aaum's - Presentation on Analytics for Matrimony Services

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<ul><li> 1. This document contains information and data that AAUM considers confidential. Any disclosure of Confidential Information to, or use of it by any other party, will be damaging to AAUM. Ownership of all Confidential Information, no matter in what media it resides, remains with AAUM. AAUM Confidential Analytics for Matrimonial </li></ul><p> 2. - 2 - 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 &amp; 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 &amp; Govt orgs Partnership Actuate, IIT Madras, TIE and 3 strategic partnerships Dedicated corporate office at IIT Madras Research park since 2009 Aaums office, IIT Madras Research Park 3. - 3 - 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 &amp; 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 4. - 4 - 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 &amp; feasibility studies. 5. - 5 - Competencies in Advanced analytics Build appropriate statistical models through which clients can measure and grow their business. Expertise in Digital Media Finance/Insurance Travel &amp; Logistics Retail Entertainment Human Capital Government organizations Research &amp; 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 6. - 6 - What Aaum can offer Performance analysis Recommendation engine Insightful dashboard Advanced Analytics Experience the magic by clicking our Genie 7. - 7 - Performance analysis at various levels are vital to the success Performance analysis Recommendation engine Insightful dashboard Advanced Analytics COLLECT Free registrants Expresses interest for paid Paid registrants Churn Churn Feedback GENERATE Registrants Right KPIs tied to right customer profiles at right time Partially completing the free profile 8. - 8 - 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 Performance analysis Recommendati on engine Insightful dashboard Advanced Analytics 9. - 9 - 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 Performance analysis Recommendatio n engine Insightful dashboard Advanced Analytics Strategic partnership with state of the art reporting solutions 10. - 10 - Advanced analytics to illuminate insights from the data Performance analysis Recommendation engine Insightful dashboard Advanced Analytics Predicting prospects willingness to continue Churn Acquisition Cohort analysis to know the effectiveness of the engagement Planning and Scheduling promotions/events Generic online Sunday meetings vs Caf 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 11. - 11 - 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), &gt;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 12. - 12 - Free period looking into the distribution(days) &amp; 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), &gt;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. 13. - 13 - 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. 14. - 14 - 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 15. - 15 - 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 16. - 16 - 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. 17. - 17 - Activity phases in the free period (15-30 days) various activities The free period is categorised as initial phase, mid phase, near payment phase, which are the 1/3rd, 2/3rd &amp; 1 of the free period. 18. - 18 - Activity phases in the free period (30-60 days) various activities The free period is categorised as initial phase, mid phase, near payment phase, which are the 1/3rd, 2/3rd &amp; 1 of the free period. 19. - 19 - Activity phases in the free period (&gt;365 days) various activities The free period is categorised as initial phase, mid phase, near payment phase, which are the 1/3rd, 2/3rd &amp; 1 of the free period. 20. - 20 - 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. 21. - 21 - 5 Phased activity period, free period (15-30 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. 22. - 22 - 5 Phased activity period, free period (30-60 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. 23. - 23 - 5 Phased activity period, free period (&gt;365 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. 24. - 24 - 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. 25. - 25 - 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. 26. - 26 - 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. 27. - 27 - 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. 28. - 28 - Basic profile modification, underlying activities (15-30 days) Basic profile modification, 15-30 days, the distribution of the underlying expected averages. 29. - 29 - Advance profile modification, underlying activities (15-30 days) Advance profile modification, 15-30 days, the distribution of the underlying expected averages. 30. - 30 - Match initiation, underlying activities (15-30 days) Match initiation, 15-30 days, the distribution of the underlying expected averages. 31. - 31 - Match communication, underlying activities (15-30 days) Match communication, 15-30 days, the distribution of the underlying expected averages. 32. - 32 - Basic profile modification, underlying activities (30-60 days) Basic profile modification, 30-60 days, the distribution of the underlying expected averages. 33. - 33 - Advance profile modification, underlying activities (30-60 days) Advance profile modification, 30-60 days, the distribution of the underlying expected averages. 34. - 34 - Match initiation, underlying activities (30-60 days) Match initiation, 30-60 days, the distribution of the underlying expected averages. 35. - 35 - Match communication, underlying activities (30-60 days) Match communicati...</p>