Data Mining for Campaign Management and Customer Insight at Swisscom Fixnet

Transcription

1 Data Mining for Campaign Management and Customer Insight at Swisscom Fixnet Methods and Processes Dr. Miltiadis Sarakinos Swisscom Fixnet AG, Switzerland

2 The Swiss Fixnet telephony Market (roughly) 3 Million Residential customers 0.3 Million SME Access, Voice, Internet (Narrowband, Broadband) Until 1997: Swisscom Monopoly 2

3 The Swiss Fixnet telephony Market: Monopolies fall 1997: Voice monopoly falls. Alternative providers (PTS) have a Voice market share of ~40% 2000: Cable provider starts with Broadband Internet. Currently: 0.3 Million Customers 2001: ADSL (provided by Swisscom and other PTS providers). Currently: 0.9 Million connections, more than half by Swisscom 2003: Cable provider offers full telephony service (Access and Voice) in the German speaking part of Switzerland. Currently: ~ Customers 2006: Swisscom offers TV through telephone connection 2007: ULL? Last Mile Monopoly falls. PTS providers offer access previously owned by Swisscom 3

4 Further challenges to all providers Price decay Call-through providers VoIP Fix-to-Mobile Substitution 4

5 Business Modell Evolution: In 10 years: for 100 EURO/month flat: Fixnet, Mobile, Internet, TV (CEO at Sunday Press) For Swisscom: Incumbent in traditional areas (voice, access). Keep customers and revenue ADSL: still growing Challenger in TV: obtain market share from current monopole (acquisition) Reduce costs 5

6 A Miner s Paradise? Support the business Model Customer Retention (Winback) Customer Acquisition, Growth Marketing: Campaigns Offers 6

7 Customer Data Customer leaves traces everywhere: Callcenter Surfing Revenues, Usage SAS Creditwor thiness Portfolio Sociodem TV 7

8 Data Swisscom Fixnet Business Intelligence Center Customer Insight: Mining Reporting Market Research 8

9 Data Mining Predictive Modelling: Churn (Winback) Modelling, Product Push Customer Clustering Identify customer groups with similar needs Design appropriate offers 9

10 Predictive Modelling 10

11 Models meaningful/useful when trend saturates 100% 100% Modelling makes sense Market Share Swisscom High Churn Rates Market Share Swisscom Low Churn Rates, Winbacks Other Carriers Other Carriers Monopoly falls Today 11

12 Typical Predictive Modelling Scheme Model Calibration Nov Apr Model May Apply model to predict future Deliver: customer score for churn/winback and product affinity 12

13 Data Mining for CRM Data Mining Inbound Call centers. The best offer for particular customers Outbound Campaigns: the best customers for a particular offer 13

14 Customer Selection from Predictive Modelling Combine Probability (Risk, Opportunity) Consequences (Benefit, Risk usw) Customer Profitability available v 1 v p 2 12 p v 13 p 34 4 v 3 p 35 v 5 Modell customer evolution using a Markov Chain Modell (p ij is the transition probability from state of value v i to state of value v j ). Flexible and expandable modell t 1 t 2 t 3 14

15 Modelling: Data Preparation is the key to success About 900 Variables: Aggregate raw data according to business rules in order to obtain the right predictive/descriptive variables (e.g. Usage Voice National, International etc.). Treat seasonalities Automation: One flat file as source for all models (training and scoring) 15

16 Model Training Decision Tree(s) for Variable Selection and Modell understanding Neural Network for obtaining the highest lift and stability Churn Risk 16

17 Clustering 17

18 Customer Clustering Delicate Excercise: which variables best portray customer needs and can be used as input to clustering algorithms? Behaviour: e.g. 80% of the customer voice traffic is international Value: Customer makes 65 CHF monthly voice traffic But: What about customers that have defected? Use historic data to extract profile 18

19 Pattern recognition algorithm (SAS- Base) Last available traffic Last month not used Length of interval beh_start muster_string Monate Traffic _ t -50 Monate t 0 Valid interval should be of length 3. 19

20 Dara preparation for Clustering Over 200 SAS Data sets merged and processed in a single data step Use macro language programming to produce readable, debuggable code Algorithm is highly robust to: Data quality issues (e.g. missing data) Customer whits and whims (vacations etc.) Seasonality Track cluster migrations over several months SAS Base is the only tool that can stand up to the challenge 20

21 Next Challenges Price evolution: lower prices, flat rates Revenue attributes become categorical and are less usable for e.g. clustering. Usage attributes must take the lead Capture new attributes relevant to new products (e.g. TV) Automate processes and reduce interfaces, costs, cycle duration (e.g. by means of SAS/Oracle Interface) 21

22 Thank you 22