Realizing the Potential of Machine Learning

Transcription

1 Realizing the Potential of Machine Learning Hervé Bouvier - Pauline Maury - linepaul Andreas Polz - BearingPoint 2017 TM Forum 1

2 Agenda Who we are What is Machine Learning & its benefits for Telcos What is HyperCube and Why it can help Our experience Case study 2017 TM Forum 2

3 Agenda Who are we? A data science consultant team of 50 people in France and 80 in Europe with: PhDs in machine Learning, analysts Big data (Hadoop) architects & developers Full stack Web Developers Mastering a wide range of machine learning methods & developing A unique & proprietary Smart Analytics technology- HyperCube 2017 TM Forum 3

4 What is a Datascientist? What his friends think he is What his mother think he is What society think he is What his manager think he is What he think he is What he actually is 2017 TM Forum 4

5 Statisticians Developers «Machine Learner» Computer Science Machine Learning Math & Statistics Business Experts Traditional Software Data Science Subject Matter Expertise Traditional Research Architects Data Analysts 2017 TM Forum 5

6 Agenda Who we are What is Machine Learning & its benefits for Telcos What is HyperCube and Why it can help Our experience Case study 2017 TM Forum 6

7 What is Machine Learning? Computational learning using algorithms to learn from and make predictions on data TARGET Training data Test Data Data preparation Dataset Model Validation Prediction 2017 TM Forum 7

8 We address a wide range of business issues by unleashing value from operational data Customer Analytics Fraud & Risk Analytics Operational Analytics Which of my clients are likely to accept upsell offer planned in my next DM campaign? What are the intervention rules which would help to improve customer satisfaction? How to develop sales performance across my retail network? How do we identify, measure and mitigate fraud, especially ones that are hard-to-detect and low frequency/high impact? Which of my clients are likely to drop out of my loyalty program? How can we optimize costs to settle and costs to serve in claims handling? How to optimize my resource allocation for preventive maintenance effort? What are the reasons for journey delays and levers for better planning and accuracy? How to Support frictionless travel across multiple modes of transportation? ACQUIRE GROW RETAIN MONITOR DETECT CONTROL MANAGE MAINTAIN MAXIMIZE 2017 TM Forum 8

9 Operations Behavorial information Static information Machine Learning addressing TelCos stay awake issues Cust. profile Contracts Churn prediction Fraud detection Customer Targeting Payment incident risk Telco Usage > Identify risky cust., listen to concerns & push custom offers > Mitigate losses via fraudsters profiling > Boost enrollment / Upsell Program efficiency > Profile & assist unwealthy customers Cust. Interactions Network Recommendation & Personalization Network Experience PoS Performance Client Service Efficiency PoS > Build-up 360 view & enhance marketing effectiveness > Anticipate failures & Cust. felt experience > Leverage best practices to drive perf. across network > Maximize satisfaction & reduce AHT/CTO 2017 TM Forum 9

10 Agenda Who we are What is Machine Learning & its benefits for Telcos What is HyperCube and Why it can help Our experience Case study 2017 TM Forum 10

11 What is HyperCube? A cutting-edge analytics platform that derives operational insights and provides amazing accuracy and stability in predictive modeling CONNECT DATA VIZUALIZE DATA EXPLORE DATA MODEL DATA Connect easily to existing sql/nosql database interact with your data quickly and intuitively gain insights on key drivers and related relationships generate predictive models and measure performance in a few simple steps Along with its proprietary algorithm, it provides a selection of open source state-of-the-art algorithms and a framework to develop and deploy customized business apps tailored to clients needs 2017 TM Forum 11

12 Vac25 Logistique Regression Vac25 Log 50_var Vac25 Gd Boosting 50_var Gradient Boosting Random 50_var Random Vac25 HyperCube 50_var Vac25 Rand HyperCube Forest 50_var 50_var Vac25 Random Forest 50_var Var25 - Logistique Regression 50_var? Vac25 HyperCube 50_var Vac25 Gradient Boosting 50_var Vac25 Random Forest 50_var Visualisation Prédiction Data management Analyse Prescription 2017 TM Forum 12

13 Clients with Explain & Predicty at the heart of HyperCube value proposition Illustration with loyalty management Critical Business Issues Purpose Outcomes EXPLAIN Why my customers are willing to leave? Understand drop out rationales Features selection Critical threshold Business rules Data Vizualisation Analytics insights Age < 35 Owns Product A Contract Tenure [2;5] are 3.5x riskier PREDICT Who are the most likely to leave? Anticipate and target customers Client 123 Client 232 Client 133 Client 211 Client 121 Scoring & Local drivers 1 0,91 0,8 0,15 0,1 Billing / Age / Usage PdtA Tech Issue / HMoving / Age Usage Pdt B / Billing / Gender Usage PdtA / Tenure / Age Usage PdtA / Tenure / Billing 2017 TM Forum 13

14 BearingPoint helped Telco operators to successfully optimize their operations Business Challenges What we did Our Cients Network preventive maintenance Find out patterns in core and access network to enhance customer experience & increase cost efficiency Prepare framework to establish preventive maintenance in a continuously learning organisation Fraud Profiles fraudsters and key drivers for non-payment behaviors Build-up predictive models at activation and after first 4 weeks of activity to assess level of fraud risk Churn prediction Build-up predictive models to anticipate level of churn risk across B2C customer database Customer satisfaction / Inbound call prediction Understand root causes of incoming calls from high value customers Predict customer base propensity to contact pro actively Client Service Employee Satisfaction Analyze employee level of usage of HR Dpt service portfolio Define employee segmentation (clustering) related to HR service usage Build-up predictive models to anticipate HR needs per employee and enhance relevance of HR push notification PoS Network performance Identify key drivers for Point of Sales performance defined as tnps, Opex intensity & Market share Build-up specific action plans for both existing store concepts 2017 TM Forum 14

15 Agenda Who we are What is Machine Learning & its benefits for Telcos What is HyperCube and Why it can help Our experience Case study 2017 TM Forum 15

16 Share of non-payers covered Use case 1 Use case 2 Use case 3 Reduce non-payment incidents for a Telecom Operator Context & Challenges Mobile handset subsidization at risk due do fraud rate level increase Need to revamp current targeting methods Willingness to understand & profile fraudsters vs good payers 400k+ clients 7%+ fraud rate 500+ variables Our results Est. ROI : 300k + /year/fraud rate point Actions plan & Quick wins identified Fraud predictive model ready for industrialization 100% 90% 80% 70% 60% 50% 40% Fraud Telecom Activation model 30% Activation + 20% Activity model Wizard 10% Client current scoring 0% 0% 20% 40% 60% 80% 100% share of customers covered 2017 TM Forum 16

17 static Use case 1 Use case 2 Use case 3 Reduce non-payment incidents for a Telecom Operator Variable Set Variables ranking Fraud Telecom Contract Usage COGS Price Offer type Subsidy level Onnet/offnet calls Ratio calls in/out Sms/mms in/out Data volume Customer Revenue Cust. Profile Age Gender Localization Correspondance vs billing Fraud behavioral Revenue Billing cycle Total revenues Revenues from roaming Revenues from data MFU Handset Nb of outbound calls Context Device Duration data usage Activation Channel Activation date Salesman code Agent MFU handset vs subsidized Smartphone Y/N Model brand Model price Nb of inbound calls Nb sms sent 2017 TM Forum 17

18 Use case 1 Use case 2 Use case 3 Reduce non-payment incidents for a Telecom Operator HyperCube helped to find out influencial factors and specific local profiles standing for a high level of risk Fraud Telecom Customers recruited via Telesales outbound are 2.6 times more likely to be non-payers Customers that match the following conditions Activation Channel Is TELESALES 2,6 OUTBOUND Offer Is ABC 5, Age Is between 17 and are 9 times more likely to stay loyal This rules concerns: 5% of Fraudsters 2% of total customers 2017 TM Forum 18

19 Use case 1 Use case 2 Use case 3 Alarms preventive maintenance: Increase customer experience Preventive maintenance Telecom Context & Challenges Enhance customer experience Enhance customer communication by better knowing the network and network event Increase cost efficiency Reduction of costs of operations Short term solution: Implement an Early Warning Dashboard (E.W.D. - Operations cockpit with daily reports) Improve response time and avoid blind spot outages: preventive maintenance Long term solution: the data structure and quality was not sufficient for most analyzed data sources Establish a structured and comprehensive data warehouse (DWH) Introduce Data Mining technologies and methodologies to improve the data quality and enable detailed analytics Raise network quality Higher network stability Upfront identification of incident root causes Faster reaction to incidents 6,000 4,000 2, Board type Board type Board type 3 Board type 4 5,000 4,000 3,000 2,000 1,000 0 Alarm_1 Alarm_2 Alarm_3 # alamrs 2017 TM Forum 19

20 Use case 1 Use case 2 Use case 3 Alarms preventive maintenance: Increase customer experience We analyzed different alarm types from January to May according to their impact on the network Preventive maintenance Telecom Alarm data Inventory data Device data Geographic al data N 0 = 43,634,389 Filter for German alarms N 1 = 2,912,658 Filter for relevant alarms N 2 = 1,761,884 ALARM_1 ALARM_2 ALARM_3 ALARM_4 ALARM_5 ALARM_6 ALARM_7 ALARM_8 ALARM_ TM Forum 20

21 Use case 1 Use case 2 Use case 3 Alarms preventive maintenance: Increase customer experience We identified regional alarm concentrations and local problem spots which covered the majority of alarms in the areas in different cities in Germany Preventive maintenance Telecom Regional alarm concentrations and rules were identified where under specific hardware and software setups alarm concentrations occurred Local spots in large and small cities could be due to single incidents or single problem boards which caused a majority of the identified alarms The geographical location and the socio-economic factors did not have a significant influence on the number of alarms on the network elements Berlin* Pattern Rule on the average number of alarms Under the following conditions Board Type is Board A Hardware Version is B Software NE Version is NEv2.02 Software Board Version is v1.01 Ort is Berlin* The average number of alarms per board is 36,2 times higher than the average of all locations! This rules concerns: 87,2% of all occuring alarms in Berlin* 30,3% of the total number of alarms matching: «Board type» «Hardware» and «Software» *Anonymised data 2017 TM Forum 21

22 Use case 1 Use case 2 Use case 3 Understand & Predict Customer service inbound calls CRM Telecom 1 Build classifier to predict future Client Service caller Targeting enhanced generating short term business impacts Ex : ~500k contact cost savings / mktg campaign > Telecom Robustness over the time that limit models updating effort Ex : <4% loss of prediction accuracy after 3 months > Telecom Potential synergies with others tools & methods Ex : Up to 40% of additional targets list with standard tools 2 Determine root cause of Client Services inbound calls Compare qualitatively analysis outcomes to already existing analysis performed by marketing teams Ability to map and confirm proven facts & figures Capacity to increase current understanding with new insights 2017 TM Forum 22

23 Use case 1 Use case 2 Use case 3 Understand & Predict Customer service inbound calls HyperCube has ingested and analyzed a large volume of information to ensure results completeness and accuracy CRM Telecom 1M Customers 10 k Variables Client Age Sexe Localisation CSP #déménagements Ancienneté Orange Contrats # migrations Data Usage Ligne de marché browsing sms mms Offre TV Options activées Internet Actuelle Précedente Nature Dates Ancienneté offre Actuelle Moy. Hist. Pay. Grat. Pay. Grat. Voix > fixe > mobile Internet Roaming 56 k Callers 1,7 Call/caller Appels en CC volume motifs M-x N-x # campagnes MD reçues #interventions tech terrain M1 M2 Contacts Alertes service # total interactions client (+hist.) selfcare ARPU Forf.Hors forf. # visites # clicks #modifs contrat voix # visites magasins data # sms sortants Evolution ARPU roaming Revenus Surappels #incidents impayés # & durée suspensions abo 2017 TM Forum 23

24 Share of callers Use case 1 Use case 2 Use case 3 Understand & Predict Customer service inbound calls Among full set of customer data, few are correlated to significant rate of Client Service inbound calls CRM Telecom Segment Value 5+ Feb At least 1 bill unpaied Feb Extra Call Pack > 11 euros Jan Migration Feb Claims Dunning Sales Technical after-sales Payments Segment Billing Selfcare At least 2 bills unpaied Feb Dunning Process Feb OS Change Feb Acq/Ter paying option Feb Mobile Change Program Feb Unlocking Feb Gesture of Goodwill Mar At least 1 connection to coordinates webpage Feb Extra Call Pack > 88 euros Jan Acq/Ter insurance option Feb Handset renewal Feb Caller rate 2017 TM Forum 24

25 Use case 1 Use case 2 Use case 3 Understand & Predict Customer service inbound calls Migration is the most significant reason for contacting Customer Service but combined with handset renewal, value segment or invoice issues. Customers willing to change their mobile Offer are 5,2x more likely to get in touch with Customer Service CRM Telecom Customers matching the following conditions: Has migrated Belongs to 5+ value segment Has already had a non-paying incident Has migrated Has renewed handset Has migrated Has consulted recently online billing Has reduced invoice amount by > 11 (1month later) Are 9x more likely to contact CS Are 15x more likely to contact CS Are 9x more likely to contact CS Value segment & non-paying incident historic effects Handset renewal effect Invoice issue effect Union of those customers stands for 7% of incoming managed by CS (i.e. 76k calls) > very low segments intersection 2017 TM Forum 25

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27 HyperCube key features Geospatial Analyze Text Mining Ad. Univariate Analysis Rules set mining Ad. Vizualisations Unique ML Algorithms Open source ML Algorithms Embedded notebook 2017 TM Forum 27