Digital Media Analytics: Disrupting Media Business by Leveraging Big Data Analytics

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1 Digital Media Analytics: Disrupting Media Business by Leveraging Big Data Analytics October, 2018 QuantFarm 2 QuantFarm

2 Contents Digital Media Analytics State of the Art Who We Are What Solutions We Can Develop for You How to Get Started QuantFarm 2

3 Setting the Context: Trends in Media & Entertainment World Table: India Growth Story of Television vis-à-vis Digital Media CY2016 CY2017 CY2018E CY2020E CAGR Television % Digital Media % Data Source: Indians' appetite for binge-watching online videos is set to make the country among the top ten OTT (over-the-top) video markets in the world in four years* Recent report** on M& E industry reveals that there are around million "digital only consumers" in India today, who do not normally use traditional media This customer base (is expected) to grow to around 4 million by 2020 and generate significant digital subscription revenues for the media and entertainment (M&E) sector Tactical digital consumers who consume both paid television (cable and DTH) and have at least one OTT (over the top) subscription (like Netflix, Hotstar or Amazon Prime Video), on the back of digital and micro-payment systems being rolled out in the country, will reach as high as 20 million households by 2020, from 6 million now By 2020, the segment of consumers that consume traditional media (either pay or free) and free OTT content is expected to cross 500 million. Overall, the M&E industry is poised to touch $31 billion by 2020 *Source: PwC India **Source: 'Re-imagining India's M&E sector' published by EY & FICCI in March 2018 QuantFarm 3

4 Contents HR Analytics State of the Art Who We Are What Solutions We Can Develop for You How to Get Started QuantFarm 4

5 QuantFarm Innovative AI & Analytics Boutique Consulting Firm Based at Silicon Valley & Pune, India Team of Ph.Ds., Data Engineers, Data Scientists & AI Specialists Committed to Move the Needle for our clients with Disruptive Innovation & Build A Competitive Edge QuantFarm 5

6 Sushil Jha Founder & CEO Visionary Software Entrepreneur based in Silicon Valley for last 23 years Absolute Believer in the Power of ML/AI. Willing to Swing for the Fences Cofounded, OuterJoin, a Digital Marketing company and LeadFormix, Demand Generation and Marketing Automation SaaS Platform LeadFormix acquired by Callidus Software (now part of SAP) Brings in Technical & Business acumen Committed to make a Difference QuantFarm 6

7 TUHIN CHATTOPADHYAY, PH.D. CTO Artificial Intelligence Leader of the Year in 2018 by NASSCOM CoE IoT Best Analytics and Insight Leader of the Year in 2017 by KamiKaze B2B Media Featured in India s Top 10 Data Scientists in 2016 by Analytics India Magazine [ 15 years of experience in academia & industry delivers analytics solutions to organizations across USA, Europe, Africa and south-east Asia Keynote speaker at international conferences like Next Big Tech Asia 17 in 2017 at Kuala Lumpur and Sports Analytics Africa in 2018 at Johannesburg. Jury Member of data science competitions across Europe & USA Authored books and more than 30 publications in international journals and conference proceedings Editor-in-Chief of International Journal of Business Analytics and Intelligence (IJBAI) for last 6 years Interview to DZone, USA QuantFarm 7

8 Our Expertise 1. AI/ Data Science/ Machine Learning Deep Learning: Convolutional Neural Network Classification: Naïve Bayes, Decision Tree, Ensemble Methods Prediction: Regression, k-nearest neighbours, Time Series 2. Natural Language Processing (NLP) Topic Modeling: Latent Dirichlet Allocation (LDA), Density Based Spatial Clustering of Application with Noise (DBSCAN), Latent Semantic Indexing (LSI) Name Entity Recognition (NER): Spacy, Stanford NLP, Dbpedia Spotlight 3. Big Data Analytics/ Hadoop Distributed File System: HDFS, Gluster FS etc. Distributed Programming: MapReduce, Pig, Spark, Storm etc. NoSQL Database: Hbase & Cassandra (Column Database), MongoDB (Document Database), Neo4j (Graph Database) 4. Blockchain Technologies A decentralized database A peer-to-peer network An immutable (tamper-proof) distributed ledger A trust layer for the web without needing an intermediary QuantFarm 8

9 Contents HR Analytics State of the Art Who We Are What Solutions We Can Develop for you How to Get Started QuantFarm 9

10 Advanced Digital Media Analytics Clickstream Clustering Churn Analytics Attribution Modelling Recommendation Engine QuantFarm 10

11 Solution # 1: Unsupervised Clickstream Clustering Segmentation & Developing Buyer Persona Traditional Value Seekers Leading Edgers Hierarchical Structure of Behavioral Clusters The Outcome Identify natural clusters of user behavior based on clickstreams Extract semantic meanings for captured behaviors Scalable for large online services QuantFarm 11

12 Solution # 2: Churn Analytics Churn Analytics Objectives Which customers are going to churn? When the customer is going to churn? Why are the customers leaving? What factors are critical for a customer to churn? How does a churner profile look like? What makes our churner different? QuantFarm 12

13 Proposed Solution Architecture Business Problem Research Problems Modelling Techniques Churner Profile in Non-Churn Look-alike Modelling Logistic Regression Churn Analytics Churn Scoring Decision Tree Support Vector Machine Predict Churn Time Survival Analysis QuantFarm 13 Note: One or more than one algorithms would be used depending on the nature of the problem/ data

14 Key Benefits of Churn Analytics 1. Prevention is better than cure: Customer churn reduces profitability through revenue loss 2. Churn results in greater marketing and re-acquisition costs: The customer acquisition cost is higher than the cost of retaining an existing customer sometimes by as much as 15 times more expensive 3. The probability of selling to an existing customer is a lot higher than to a new prospect 4. Decrease the likelihood that competitors will lure existing customers 5. Proactively detect customer value loss and take measures QuantFarm 14

15 Recommendation Engine Solution # 3: Recommendation Engine Analyzes available data to make suggestions for something that a website user might be interested in, such as a book, a video or a job Content Based Recommendations Collaborative Filtering Alternating Least Square Recommendations Hybrid Recommendation Engines QuantFarm 15

16 Content Based Recommendations Main Idea: Recommend items to customer X similar to previous items rated highly by X Example: Movie Recommendations Recommend movies with same actor(s) Websites, blogs, news Recommend other sites with similar content QuantFarm 16

17 Collaborative Filtering Finding users in a community that share appreciations If two users have same rated items in common, then they have similar taste Such users build a group or a so-called neighborhood. A user gets recommendations for those items that user hasn t rated before but was positively rated by users in his/her neighborhood. QuantFarm 17

18 Solution # 4: Attribution Model Determines how credit for sales and conversions is assigned to touchpoints in conversion paths Markov Chain for Attribution Modelling Markov chains is a process which maps the movement and gives a probability distribution, for moving from one state to another state. A Markov Chain is defined by three properties: 1. State space set of all the states in which process could potentially exist 2. Transition operator the probability of moving from one state to other state 3. Current state probability distribution probability distribution of being in any one of the states at the start of the process QuantFarm 18

19 Monte Carlo Markov Chain (MCMC) Attribution Modelling Probability of conversion for Path Channel_1>Channel_2>conversion is 0.27% (0.03*0.09) Probability of conversion for Path Channel_1>Channel_2>Channel_4>conversion is 0.024% (0.03*0.09*0.09) QuantFarm 19

20 Contents HR Analytics State of the Art Who We Are What Solutions We can Develop for You How to Get Started QuantFarm 20

21 Why QuantFarm? Dedicated Centre of Excellence (CoE) Non-Dependence on A Single Algorithm Contextualizing Off-the- Shelf Algorithms Real Time Solution Data scientists keep developing and fine tuning algorithms without any delivery pressure Multiple algorithms applied to solve each business problem to gain a holistic perspective Instead of directly applying any existing algorithm, necessary customization is done Automated models will keep generating real time solutions ROI Driven At least 5X-10X; Think Big & Start Small, Unmatched value QuantFarm 21

22 Business Problem Identification Use-Case & Relevant Data Proposal Submission Solution with technical & commercial details Commencement of the Project On acceptance of the proposal Realize the Benefits QuantFarm 22

23 Disrupt or Get Disrupted! Act Now Artificial Intelligence and Business Analytics fundamentally Disrupting Traditional Business Models Artificial Intelligence offers NEW Challenges & Opportunities Time to act is NOW due to imminent huge growth wave QuantFarm has that special X-Factor with the right expertise and experience to help you embark on AI Powered business QuantFarm 23

24 THANK YOU QuantFarm 24