Predictive Analytics. Noam Zeigerson, CTO

Transcription

1 Predictive Analytics Noam Zeigerson, CTO

2 Agenda The Predictive Analytics Need Innovative Technologies Business Solutions

3 The problem: Inconsistent stream of revenue Available Data Sources ERP data Web data calendar events seasonal data help desk data CTR data Campaign data more Objectives Available Technology DWH facilities Business intelligence platform Excel, Pivots, Dashboard Report generators Better understanding of the business behavior More insights (Peak/slowdown, association findings with: Calendar chronology, satisfaction level, stock availability And more ) Improve alerting, highlight on anomaly

4 The New Treasure of opportunities

5 Responding Strategy Investment in customer Acquire the right customer Customer s Orientation with Bank Increase usage & loyalty Retain the right customers Acquire the right customer Acquire Orientation Drifting Retention Win Back Investment in customer ARPU

6 Proactive Strategy Investment in customer Acquire the right customer Customer s Orientation with Bank Increase usage & loyalty Retain the right customers Acquire the right customer Acquire Orientation Drifting Retention Win Back Investment in customer ARPU

7 Predictive Analytics Solutions

8 Our Predictive Analitics Solution One Clear Visualization Marketing Intelligence Finance Intelligence Engineering Intelligence Customer Life-Time-Value Customer Profitability Service Availability

9 Ness Behavioral Prediction The Problem Customers are not the same. They vary in many aspects including their life style, life stage, needs and preferences from the bank etc. Nonetheless businesses approach their customers in the same way and sometimes fail to tailor the right suit for the right customer. To understand better the customers needs, Behavioral Prediction required. Our Solution Learn the deferent behavioral clusters for a comprehensive customer predictive analysis and for improved customer targeting. Examples of commonly used parameters: Customer Life Time Value (current, potential) Customer Life Stage Product preferences Price sensitivity Social behavior

10 Behavioral Data Model Customers (millions) Channels (multiple) Offers (hundreds) 1 Behaviour Parameter (day/time) Phone 1: Mon 01/3 2: Wed 10/3 www 3: Tue 16/3 Store Wireless 4: Mon 24/3 5: Fri 6/4 Lifestyle Needs Minimum Mail Run Budgets Contact Preferences Call Centre Capacity Product Targets Rules & Constraints

11 Ness Behavioral Prediction Business Benefits Improvement in marketing effectives Improvement in the services level Improvement in the relationship management Unique Ton Of Voice to each and every individual

12 Ness Customer Retention The Problem Some refers to the customers recruitment efforts as a Sliding Doors effort. Meaning that on one hand you invest great efforts in recruiting new customers but on the other hand customers are churning. Active Churn Prediction & Retention is essential in competitive markets Our Solution Ness developed A Churn Lab Concept that address and counteract all churn aspects. A Churn Lab will include the following: Define churn Measure churn Integrate with customer value Predictive Analysis Place churn triggers to the operational systems

13 Ness Customer Retention Business Benefits Decrees of 5% -15% the annual churn rates Increase in customer loyalty Increase in customer base Improvement in the effectiveness of marketing activities

14 Ness Customer Retention The Problem Networks Operation & Control centers are a huge leap towards business efficiency but it s not enough. The business needs to adapt proactive Engineering Approach. The Predictive NOC concept defines the key events for an engineered network so with prediction is can processes and support effective prevention maintenance planning, execution and analysis. Our Solution The Predictive NOC has been implemented by Ness in a large number of organizations, especially in the Telco sector allowing execute robust and scalable network by: Definition of fault events Maintenance planning, execution and analysis process KPIs and performance reports definition Predict and alert to all devices

15 Solutions: KXEN s InfiniteInsight Personalized product recommendations foundation of the company s website Using links analysis between products and creating weightings based on: visitor click paths items placed in shopping carts purchase transactions Propensity models for newsletters

16 Cluster of Devices Ness Predictive NOC

17 Ness Customer Retention Business Benefits Network inventory management Fault prediction Traffic optimization Impact of topology changes by predictive analytics

18 Customer Story - Cisco Leverage network data logs produced by physical network devices for: Network inventory management - automatic discovery of logical and physical inventory when devices are added or removed Optimize procurement patterns Fault prediction Increase quality of service Lower network administration Traffic optimization More efficient network with > less devices > lower costs Impact of topology changes by predictive Increase/kees quality of service

19 Technology & Business Partners We working in addition with IBM, Oracle, Microsoft, SAP, Teradata, SAS, Informatica, Microstrategy,Qlikview, Greenplum and more

20 So how we decide? STABLE DATA UN STABLE DATA

21 Data Science = Big Data Lab Data Warehouse Investigation Platform Big Data Lab Investigation Platform

22 Integration Internal Data DWH,CRM, Docs, s Data Warehouse External Data ETL Internet Websites, Social, Docs Analysis & Report Big Data Lab ETL

23 Before & After Innovative Technologies

24

25 The New Result Of the Business High Definition endless View

26 The New Result Of the Business High Definition endless View Available Data Sources Includes past resources Available Technology New Smart Engines

27 Available Data Sources Available Technology Includes past Tech. New Smart Engines Analytical DB s Hadoop Flavors No SQL More Analytical Data Bases Hadoop Flavors: No SQL More High performance, near real time inquiry Crunching Huge data sets, Multi structure, Parallel processing Online processing, Always On, Ease of use A. In memory data grids B. Middleware C. Dev. frameworks D. And more

28 Available Data Sources Available Technology New Smart Applicative Engine SNA Social Network Analytics Anomaly detection User Profiling More Social Network Analysis Anomaly detection: User Profiling More Identify sentiments 360 customer view Identify influencers Improve efficiency Identify Fraud Cyber treats Consuming patterns Machine data More Target by experience Churn patterns Crime and treats Recommendations Semantic analysis Smart Algorithms Scientific teams

29 Predictive Analytics HD view Impacts the button line Harnessing technology Business Solutions

30 Thank You