How To Analyze Data In A Database In A Microsoft Microsoft Computer System

Transcription

1 Big Data Technical Workshop Sept 24 Minneapolis, MN Data Discovery, Modern Architecture & Visualization Big Data Discovery Demo: Financial Services Customer Journey 1 9/25/2014

2 AGENDA Key Functionality Highlighted Business Scenario Demo Questions and Hands-On Time 2 9/25/2014

3 Key Highlights UDA in Action: Loading Data with SQL-H Multi-Genre analytics - SQL / SQL-MR / Statistical Analytic Functions > Attribution > npath > Naïve Bayes prediction Visualization in Tableau with custom SQL parameters Business Insights Iterative Discovery Ease of use for the Business User 3 9/25/2014

4 Retail banking customer journey covers important interactions over time Customer Journey 4 9/25/2014

5 Consumer Banking Interactions Customers interact through multiple channels generating multi-structured data Teller Withdrawal Teller ID, Event BRANCH Online Transfer ONLINE Page Type, Action Call Center Inquiry CALL CENTER Event ATM TELLER BRANCH ATM ID, Event, $$ ATM Deposit Teller ID, Event Teller Complaint Text Category (Intent) Complaint Branch ID, Mgr ID, Event Cancel account 5 9/25/2014

6 Data Data Filtering Data Preparation Weblogs in JSON on HDFS {"customer_skey":"7549","customer_identifier":" vmmhmxa "," customer_cookie":"sj-yclax- 1MzaWsOpOA45Rc","customer_online_id":"Y","customer_offline_id":" ","c ustomer_type":"lapsed Customer","customer_days_active":"1017","interaction_session_number":" ","interaction_timestamp":"[2012/02/24:11:57:26 AM]","interaction_source":"WEBSITE","interaction_type":"STARTS_APPLICATION","s ales_channel":"-1","conversion_id":"- 1","product_category":"CHECKING","product_type":"-1","conversion_sales":"- 1","conversion_cost":"-1","conversion_margin":"-1","conversion_units":"- 1","marketing_code":"-1","marketing_category":"- 1","marketing_description":"(null)","marketing_placement":"- 1","mobile_flag":"N","updt":"-1"}, Teradata: SQL-H HCatalog Hadoop MR Hive SQL-H Call: CREATE VIEW hcat_banking_web AS SELECT * FROM load_from_hcatalog( ON mr_driver SERVER(' ') USERNAME('horton') DBNAME('default') TABLENAME('ich_banking') ); Hadoop Layer: HDFS Pig 6 9/25/2014

7 Customer acquisition Credit Cards Business Objective > Identify which channels perform the best > Which promotions are the most effective in driving credit card signups? > Where should I increase my marketing spend to drive higher ROI? Analytics > Multi-channel Marketing Attribution Business Impact > Quantify marketing effectiveness to drive revenue > Optimize Marketing spend & placement > Run time-sensitive promotions 7 9/25/2014

8 Path to Adoption Wealth Management Business Objective > Adoption of additional high value products > Opening Wealth Management account > An important sign of customer loyalty Analytics > What are the common paths customers take? npath > Path/Pattern time series analysis that is hard to do in SQL > Affiliation with other accounts - What other accounts do wealth management customers hold? Business Impact > Fast growing business segment > Retail banks are looking to build their business in this area 8 9/25/2014

9 Churn Overview Business Objective > Detect and prevent churn Analytics > What are the common churn paths customers take? npath > Analyze the path to churn we just found to look for common steps > Identify customers at risk of churn - Naïve Bayes prediction algorithm Business Impact > Identify root causes of churn through novel visualizations and rapid, iterative analysis > Proactively retain high value customers at risk of churn > Preventing churn helps protect our brand as well as revenue 9 9/25/2014

10 Find the common paths to churn Basic SQL-MR npath function call Easy for SQL savvy business users to use Looking for the last 4 events before account closure 1.SELECT * FROM npath ( 2. ON (SELECT * FROM ich_banking) 3. PARTITION BY customer_identifier 4. ORDER BY interaction_timestamp 5. MODE (NONOVERLAPPING) Limit to a depth of 4 7. PATTERN ('(EVENT){4}.CLOSED') 8. SYMBOLS ( 9. interaction_type <> 'ACCOUNT_CLOSED' AS EVENT, 10. interaction_type = 'ACCOUNT_CLOSED' AS CLOSED 11. ) 12. RESULT ( 13. ACCUMULATE( interaction_type OF ANY(EVENT,CLOSED) ) AS interaction_type_list, 14. COUNT( interaction_type OF ANY(EVENT,CLOSED) ) AS click_depth 15. )) a; 10 9/25/2014

11 Iterate to clarify the path to churn Add in key events: > balance transfer, call complaint, fee reversal, link external accounts steps to the pattern Help reduce noise and see the main churner paths 1.PATTERN ('EVENT.(TRANSFER COMPLAINT LINK REVERSAL){1,3}.CLOSED') 2. SYMBOLS ( 3. interaction_type <> 'ACCOUNT_CLOSED' AS EVENT, 4. interaction_type = 'BALANCE_TRANSFER' AS TRANSFER, 5. interaction_type = 'FEE_REVERSAL' AS REVERSAL, 6. interaction_type = 'CALL_COMPLAINT' AS COMPLAINT, 7. interaction_type = 'LINK_EXTERNAL_ACCOUNT' AS LINK, 8. interaction_type = 'ACCOUNT_CLOSED' AS CLOSED 9. ) 11 9/25/2014

12 Predict future churners before it is too late Business Objective > Identify potential future churners Analytics > Aster s Naïve Bayes prediction algorithm > Analyze the path to churn we just found to look for common steps Business Impact > Allow for proactive customer retention actions > Protect our brand image 12 9/25/2014

13 Steps leading to Prediction Run npath for known churners & non-churners Generate training model (naivebayestext function) SELECT token, SUM(churn) AS churn, SUM(no_churn) AS no_churn FROM naivebayestext( ON churn_nb_training TEXT_COLUMN( 'interaction_type_list' ) CATEGORY_COLUMN( 'churned' ) CATEGORIES( 'churn', 'no_churn' ) DELIMITER('[,]') PUNCTUATION('[\\[\\] ]+')) GROUP BY token; Step Churn No Churn brows complete_appl balance_transf account_clos olb starts_appl account_booked_onlin account_booked_offlin loan_calc call_complaint referr 1 4 click 2 22 compar link_external_account fee_revers mortgage_calc /25/2014

14 Steps leading to Prediction Load the resulting training model into Aster (\install) Run npath for other customers Run prediction algorithm (naivebayestextpredict function) SELECT * FROM naivebayestextpredict( ON churn_nb_input DOCUMENT_ID ( 'customer_identifier' ) TEXT_COLUMN ( 'interaction_type_list' ) CATEGORIES ( 'churn', 'no_churn' ) MODEL_FILE ( 'churn_model.csv' )); 14 9/25/2014

15 Improving Customer Churn Models Higher Business Value with Multiple Analytics on All Data Customers that can be identified through Classical Statistics Current Statistical Model Churn Potential Common Prediction Aster Behavioral Pattern Model Space of all possible customers at risk of Churn Customers identified through Aster Behavioral Pattern Model The statistical model predicted it would happen, Aster is seeing it happen. Very likely customer will churn. 15 9/25/2014

16 Operationalize Insights from Aster Churn Prediction Model Campaign Management ( e.g. RM) Real Time Interaction Manager (e.g. RTIM) Customer Path Identified Churn Risk Predictions Retention Campaign for At Risk High Value Customers 16 9/25/2014

17 Additional Questions? 17 9/25/2014