Machine Learning in Customer Analytics


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Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.

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Machine Learning in Customer Analytics

  1. 1. Machine Learning in Customer Analytics January 23, 2014 | Proprietary and Confidential
  2. 2. Transformation Through Integration: Realizing the Full Potential of Your Information blueocean is a next-generation services organization with a deep focus on analytics, market intelligence and digital media, all uniquely delivered under one roof by 650 plus professionals. Our 360 Discovery TM process ensures the comprehensive utilization of all available structured and unstructured data sources, enabling us to bring the best to bear against each project. By combining the talent, speed and cost benefit of a flat world, along with our scalable delivery model, we are able to achieve a more nuanced and comprehensive understanding of the market at the delivery speed and price advantage that today’s business climate demands. 2
  3. 3. What is Machine Learning? Machine learns patterns in the training data using input features Patterns learned applied to unseen data to ensure generalization If generalization fails, input features modified; more training data fed to algorithm Regression or classification performed 3
  4. 4. Machine Learning Comes of Age The era of Analytics 3.0 combines structured transactional data and unstructured text data with complex machine learning algorithms to generate better and faster insights Analytics 1.0 Implementing business intelligence Reporting Descriptive Analytics Focus on internal, structured data • • • • Key Technology Enablers for Machine Learning • Better and inexpensive storage capacities • Increased processing power of machines • Large scale availability of data • Open source revolution • Advent of Hadoop ,NoSQL technologies Key Business Enablers for Machine Learning • • Analytics 3.0 • Combining structured and unstructured data formats • Analytics central to the business strategy • Faster technologies • Analytics model embedded into operational and decision processes 4 Applications in unconventional fields thus gaining wider acceptance Organizations have higher analytics maturity curve • Lower implementation cost
  5. 5. From Science to Enterprise – How Big Data is Assisting Machine Learning • • Big Data Analytics offers access to speech, text and social analytics tools and expertise on demand Machine Learning allows rapid processing of large amounts of customer centric data including customer conversations in the form of calls, email, chat Unstructured data comes from multiple sources: CCTV camera data CDR data (Telecom) Digital pictures and videos posted online Sensors used to gather information 5 Telephonic conversation Emails and feedbacks GPS data (from mobile devices) Transaction records Access Logs Posts to social media sites To churn big data to actionable insights brings in new practical and theoretical challenges: Data Acquisition l Storage l Processing l Data Transport and Dissemination l Data Management and Curation l Archiving l Security l Analyzing for Business Actions
  6. 6. What can Machine Learning Do for Business? Learn – Algorithms and computational models to learn and gain knowledge about users Cloud Computing Natural Language Processing – Sentiment Analysis Text Classification Knowledge Acquisition Multilingual language processing Predict – Predictive analytics to provide actionable information for organizations Big data Algorithms • Bayesian Classifier • Neural Networks • SVM With machine learning everybody wins Wide applications across industries: • Recommender Systems • Biotechnology • Supply chain optimization 6 • Product Marketing • Counter-Terrorism • Fraud Detection
  7. 7. Use-Case: Machine Learning in Customer Analytics (Telecom) Build single view of customer STRUCTURED Network data Analytics Engine Call Data Records Data Aggregation GPRS Data Records Next Best offer Churn prediction Campaign Mgmt Social Network Analytics Contact Centre logs UNSTRUCTURED 7
  8. 8. Categories of Machine Learning Algorithms Supervised Learning Algorithms: • • Training the machine on a training dataset with set of input features and a corresponding output Generalization: Machine learns a mathematical function which could be generalized and applied to unseen data Examples: • • • Classifying email as spam/not spam Predict loan default ( Yes/No) Forecast stock prices Unsupervised Learning Algorithms: • • • Training dataset does not require labeled outputs. Function mapping from inputs to output not done. Objective is to understand structure in the data. Examples: • • 8 Discovering different segments of telecom subscribers based on their call patterns and data usage. Social Network Analysis: Discovering communities within large groups of people.
  9. 9. Advantages of Machine Learning • • Large scale deployments of Machine Learning beneficial in terms of improved speed and accuracy • Understands non-linearity in the data and generates a function mapping input to output (Supervised Learning) • Recommended for solving classification and regression problems • Ensures better profiling of customers to understand their needs • 9 Useful where large scale data is available Helps serve customers better and reduce attrition
  10. 10. Disadvantages of Machine Learning • Limited understanding of the machinery of classifiers (Black Box) • Requires significant amount of data • May not work in cases where data collection is difficult or expensive • Problem of over-fitting if model fitted on small dataset 10
  11. 11. Challenges in Machine Learning Implementation • Integration of data from different sources within the organization • Good business understanding required to build better input features • Thorough understanding of algorithms required before it can be deployed • Appropriate selection of machine learning algorithm essential • Implementing algorithms interpretability and insights 11 which can give more business
  12. 12. Statistics in the Age of Machine Learning • Statistics: Mainly deals with probabilistic or deterministic approach • Popular in fields where data collection can be difficult or expensive in nature • Provides good understanding of population where only sample data can be collected e.g. Brand survey, quality control checks, clinical trials • Intuitively provides more understanding about drivers of the objective function 12
  13. 13. Case Studies 13
  14. 14. Case Study: Gender Prediction Using Supervised Learning Algorithms Challenge Machine Learning • • • • The client is a pioneer in measurement of mobile subscriber behavior The metering application installed on smart devices captures behavior of the device accurately The client wanted to predict gender of the subscribers based on installed mobile Applications This information was to be used by advertisers in order to ensure focused and targeted marketing. Approach • • • • • Initial data provided by the client was a set of user IDs along with the application names Data cleansing and transformations were performed in order to ensure data can be fed to a supervised learning algorithm The data provided was highly imbalanced and skewed towards males as it was the dominant class to be predicted Applied weighted measures to give more importance to the minority class Support Vector Machines Learning Algorithm was applied to predict gender of the subscribers Result • • 14 Achieved accuracy close to 80% for both classes of interest Developed an integrated solution with a GUI to enable real time results to be obtained based on real time data feeds to the learning algorithm
  15. 15. Case Study: Incentivizing existing policies for a leading Insurance Company Challenge Machine Learning & Predictive Analytics • • Approach • • • The two policies Traditional and ULIP were in two states – In-force and Lapsed. Data cleansing was done using a proprietary statistical tool A binary logistic regression algorithm was applied on each of the policies with lapsed and in-force data Result • • 15 Access lapsed insurance policies having a potential of repayment (and hence reactivation) within a specific time frame Identify criteria to incentivize existing in-force policies Predictors that influenced the predictive model were: o Premium to be paid o Income of the policy holder o Occupation and total sum assured at the end of maturity It was important to target lapsed policies within a specific time frame beyond which customers would be difficult to be re-activated
  16. 16. Case Study : Applying face recognition to enable multiple applications Challenge • Design a face detection and recognition algorithm for applications across multiple domains Approach • • Create a databases of faces and performed face detection using Haar cascades algorithm Matched captured face images in the existing database of facial images of people. - We used face recognition algorithms using Principle component analysis Result • • 16 Achieved accuracy close to 60% for face recognition and 70% for face detection Can be applied to strengthening security measures in organizations, identifying and providing offers to repeat customers in retail stores
  17. 17. In Summary • With big data a reality machine learning is finding wider acceptance across various industries • Machine learning is paving the way to solve complex business challenges in an efficient and effective manner • To reap the benefits of machine learning it is essential to identify the areas where it can be applied effectively • Good business understanding is required to build smarter solutions 17
  18. 18. Blueocean Analytics Service Areas Customer Analytics Marketing Analytics Focus on better customer experience through enhanced engagement • • • • • • • Customer Acquisition Portfolio Management Attrition/Churn Analysis Loyalty Management Customer Contact Analytics Customer Risk Analytics Others … Special Focus Areas Develop and optimize marketing strategies through smart evaluation of programs • • • • • • ROMI Market Mix Modelling Simulated Pricing Models Promotion Analytics Product Analysis Others … Specialized intelligent solutions that keep pace with socioeconomic trends • • • • • • • • Collections Analytics Real Time Analytics Social Network Analytics Telemetry Visual Analytics Speech and Text Analytics Social Media Analytics Others… Data Management, Big Data and Smart Business Intelligence Focus on creating a single source of “truth” and providing insightful analysis rather than plethora of reports Datamart Solution 18 Reporting and Smart BI Services Big Data Services
  19. 19. Thank you For more information: Durjoy Patranabish Senior Vice President Eron Kar Analytics Delivery Lead 19