Understanding Customer Behaviour Using Analytics. Frankie Chan 8 th October, 2014

Transcription

1 Understanding Customer Behaviour Using Analytics Frankie Chan 8 th October, 2014

2 About Me 2013 Ageas

3 Agenda 1. Adding Value Using Analytics 2. Overview Of Customer Analytics 3. Case Study: Direct Marketing 3

4 Adding Value Using Analytics 4

5 The 2 Analytic Questions Question 1: What is the business problem we are trying to solve, & how does modelling/analytics form part of the equation? Be specific Question 2: What am I going to do with the model/analytics to help me make (more) money? Be specific 5

6 The Four Pillars to Analytic Success Clear and Specific Goals Analytics/Modelling Tools System IT and Admin system to execute analytics and capture information People Skilled modelling/analytic resource together with commercial people to understand, interpret, communicate and commercialise the output Data Quality data that is readily available to allow timely rollout of Analytic initiatives, also requires feedback loop Engagement Organisation must be engaged and aligned starting from the top 6

7 Overview Of Customer Analytics

8 The Need To Treat Our Customer RIGHT 8

9 But What is RIGHT? 9

10 Customer Analytics Process Personalised actions 10

11 Data Data Data

12 Where does data come from? 12

13 Single Customer View - CAR 13

14 Customer Analytics Segmentation 1 Purpose: XSell and Contact Strategy Example: Life Stage Segment - INTERNAL Mass 14

15 Customer Analytics Segmentation 2 Source: Mosaic Hong Kong 15

16 Triggers and Event Based Marketing Working on the clues your customers leave you with Some needs trigger such as: Buying a car / Applying for a car loan Applying for a credit card/ personal loan Currency exchange Buying or refinancing a home Getting married/have kids Spending time browsing on a specific webpage Downloading product information Forum posts 16

17 Customer Analytics Propensity Modelling Purpose is to Learn and Predict Identifies the drivers and quantify likelihood of a binary outcome (e.g. Take up/not take up, Renew/ not renew, fraud/no fraud etc) A range of Statistical and Data Mining techniques are available Logistic regression Inputs: CAR (x1, x2, x3.. Xp) eg Age, Balance, # of products Output: Probability score between 0 and1 17

18 Questions It Helps Us Answer Who to target? Which product to sell? How best to contact my customers? Who is at risk of leaving? 18

19 Customer Analytics Next Best Offers Purpose: Determine the best product to offer the customer, and target proactively or reactively 1. Association/Sequence Analysis 19

20 Customer Analytics Next Best Offers 2 2. Basket of Propensity Models NBO = Product with the highest Propensity Scores Propensity Score Ranks Customer A B C D NBO B A B D 20

21 Case Study Direct Marketing Etiqa, Malaysia 21

22 Modelling the Behaviour of Personal Accident Customers The Problem Response rate The Solution Two models were built: 1. Propensity to Take up, 2. Propensity to Lapse upon take up Banking data was used 22

23 Examples of Model Inputs Customer - Age, Gender, Income, Maritial Status, Tenure, Occupation, Geography etc Product Holding & information - Type & Number of Products Banking - Deposit and Lending Balance - Credit score - Internal segments Claims - Number and total claim size Activity/ Feedback loop - Historical marketing activities and response - Have they done this (eg accepted DM?) before? External - Bankruptcy - Insurance claims 23

24 Example Model Lift Curve Best 40% of customers will give 75% of the sales Gini = 45% Best 20% of customers will give 50% of the sales 24

25 Profile of customers likely to take up a new PA Has more than 3 products with the Bank Low or unstable income, likely to be Blue collar Low deposit balance and insurance holdings 25

26 Profile of PA holders likely to lapse Policies purchased through Telemarketing Small deposit balance < $250, no lending Low/no income /Blue collars 26

27 What Do I Do With The Models? Exclude Customers with low scores Target Customers with high scores

28 Target Only The Best Likely to Buy Low High Likely to Lapse Low High 28

29 Results Response rate more than doubled! Embedded as part of BAU Expanded into Telemarketing with 50% uplift to date 29

30 Challenges VS Opportunities For Actuaries

31 Thank you for your participation 31