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Cross-Sell Opportunity Formulation for a reputed bank
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Cross-Sell Opportunity Formulation for a reputed bank

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Cequity has built a Tree Model to reach high propensity customers after identifying the variables which makes the difference through rigorous statistical modeling and analysis. ...

Cequity has built a Tree Model to reach high propensity customers after identifying the variables which makes the difference through rigorous statistical modeling and analysis.


To find out about Cequity's services visit this link http://www.cequitysolutions.com/analytical-marketing.php

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Cross-Sell Opportunity Formulation for a reputed bank Presentation Transcript

  • 1. Case Study Cross-Sell Opportunity Formulation for a reputed bank Building a Decision Tree Model to help identify customers susceptible to cross change initiatives Client: A new bank moving to target its liability customers for asset products
  • 2. Summary • Our client was trying to find out ways to gain wallet-share of it Business customers (savings & current a/c holder) Objective • It was looking for a Decision Tree model to give likely list of leads so as to focus the marketing campaigns towards them. • Cequity identified the variables which makes the difference Solution through rigorous statistical modeling and analysis • Once the variables were identified , the best possible path to reach high propensity customers through decision tree modeling • We built a quantifiable model for client to reach the best leads through decile treatment • Based on the behavior pattern, we could predict the right offerings Results for each segments. • There was a huge lift in conversion rate for our client using the Cequity model. Marketing & campaigning spends were also optimized.
  • 3. Business Objective Our client was facing low conversion rate in cross-selling the Assets products to its Liability customers. Although the Liability and Asset products have been on the market for quite some years, the overlaps for its customer into these Venns were very low. But it would have been imprudent to expend marketing resources on entire liability customer base with for cross-selling them asset product. It was desperately looking for a model to focus its resources better. We built a Cross-Sell model taking into consideration all factors like Demographics, Transactions, Psychographics and Response from previous campaigns. The result was evolution of a non-linear model for predicting the chances of buying its asset products within its liability customer base.
  • 4. Solution – Finding out micro segments Uni-Variate Analysis Multi-Variate Analysis Response Response Criteria Criteria Rate Rate Quantum leap in targeting Marital the right Marital Status = Status = Y1 % X1% XXX customers XXX Marital Status = Ledger Y2 % XXX & Ledger balance X2% balance < XXX < XXX Marital Status = XXX & Ledger Number of balance < XXX X3% Fixed Y3 % &Number of Fixed deposits < X deposits < X Marital Status = XXX & Ledger Amount balance < XXX & X4% Number of Fixed credited in Y4 % deposits < X & Amount last x months credited in last x > xxx months > xxx
  • 5. Solution – Analysis Uni-Variate Analysis Multi-Variate Analysis Response Response Criteria Criteria Rate Rate Marital StatusEmpower with Y1 % Power of Multi-Variate The Marital Status = = X1% XXX Analysis XXX Marital Status = Ledger Y2 % XXX & Ledger balance X2% balance < XXX < XXX Marital Status = XXX & Ledger Number of balance < XXX X3% Fixed Y3 % &Number of Fixed deposits < X deposits < X X4 is much much higher Marital Status = than Y4 XXX & Ledger Amount balance < XXX & X4% Number of Fixed credited in Y4 % deposits < X & Amount last x months credited in last x > xxx months > xxx
  • 6. Solution – Building the Decision Tree Supe rvised C lassification Criterion # 1 Increasing Gain – “Good” customer characteristics Gain Gain on 6,00,000 18% X 1 % Criterion # 2 INR xxx – INR xxx INR xxx – INR xxx < INR x x x INR x x x – xxx > INR x x x (montly avg balance) Gain Gain X 28% 2 % Criterion # 3 < x m onths x – y m onths y-z m onths z+ m onths (MOB) Gain Gain X3 35% % 0-a de bits a-b de bits b-c de bits c-d de bits d+ de bits Criterion # 4 (# of debits) Gain Gain X4 39% % Se lf e mployed Em ployed with Em ployed with Sm all scale Criterion # 5 PSU C orporate business Gain (occupation) se tup pe rson Gain X5 45% % p-q yrs q-r yrs r-s yrs > s yrs Criterion # 6 (age group) Gain Gain 49% X6 % 6
  • 7. Solution – Building the Decision Tree Supe rvised C lassification Criterion # 1 Increasing Gain – “Good” customer characteristics Gain Gain on 6,00,000 18% X 1 % Criterion # 2 INR xxx – INR xxx INR xxx – INR xxx < INR x x x INR x x x – xxx > INR x x x (montly avg balance) Gain Gain X 28% 2 % Criterion # 3 < x m onths x – y m onths y-z m onths z+ m onths (MOB) Gain Gain X3 35% % 0-a de bits a-b de bits b-c de bits c-d de bits d+ de bits Criterion # 4 (# of debits) Gain Gain X4 39% % Se lf e mployed Em ployed with Em ployed with Sm all scale Criterion # 5 PSU C orporate business Gain (occupation) se tup pe rson Gain X5 45% % p-q yrs q-r yrs r-s yrs > s yrs Criterion # 6 (age group) Gain Gain 49% X6 % Monthly A vg Bal INR XXX The customers belonging to the adjacent The Ideal Months on books x-y months segment would be the preferred target for our Profile # of debits b-c debits cross sell exercise (a given asset product) Occupation XXX A ge group q - ryrs 7
  • 8. Results Optimized marketing efforts and Increased spend Response rates and conversions Identify customers in Top Deciles who have propensity of Target right buying an customer Asset product with right product
  • 9. Thank you Customer Equity Solutions Pvt. Ltd. Worldwide Offices INDIA USA Mumbai Office: 105-106, 1st Floor, Chicago Office: 626, Anand Estate, 189-A, Grove Street, Evantson, IL Sane Guruji Marg, Mahalaxmi, 60201 Mumbai-400 011 Phone: +91 22 4345 3800 Fax: +91 22 4345 3840 www.CequitySolutions.com