Industrialisation of Analytics in India


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Industrialisation of Analytics in India

  1. 1. 1
  2. 2. Copyright ©2014 NASSCOM® International Youth Center, Teen Murti Marg, Chanakyapuri, New Delhi – 110 021, India Phone: 91-11-23010199, Fax: 91-11-23015452 E-mail: First Print: July 2014 Published by NASSCOM 2 Disclaimer The information contained herein has been obtained from sources believed to be reliable. NASSCOM disclaims all warranties as to the accuracy, completeness or adequacy of such information. NASSCOM shall have no liability for errors, omissions or inadequacies in the information contained herein, or for interpretation thereof. The material in this publication is copyrighted. No part of this report can be reproduced either on paper or electronic media without permission from NASSCOM. Request for permission to reproduce any part of the report may be sent to NASSCOM ( Usage of Information Forwarding/copy/using in publications without approval from NASSCOM will be considered as infringement of intellectual property rights.
  3. 3. Foreword India has over 1.2 billion people, over 890 million mobile subscribers, and 213 million internet subscribers. Along with 115 million Facebook users and 24 million LinkedIn users, India is among the global Top 5 in terms of mobile consumers and social media usage. India has over 200,000 factories and an estimated public and private sector employment of 29 million. Government of India is estimated to have spent INR 280 billion on IT in FY2014. There have been over 1 billion eGov transactions and the government aims to achieve 600 million enrollments by 2014 under the Aadhar scheme. It is therefore not surprising that India is generating loads of data, both structured and unstructured. It is estimated that the digital bits captured or created each year in India is expected to grow from 127 exabytes to 2.9 zettabytes between 2012 and 2020. This highlights the scope of applying analytics to this mountain of data and the myriad insights that it could throw up to enable newer and more relevant services. However, the level of adoption in India among enterprises is still low. While there are significant precedents, mainly in the telecom, eCommerce and BFSI sectors, we have but scratched the tip of the iceberg. Industrialising analytics among enterprises in India - that is integrating it into the organisational fabric - is still a long journey ahead. There are challenges to be addressed both from the demand and the supply side – lack of awareness of what exactly is the return on investment, endorsement from top management, cost of investment, data veracity, etc. Keeping these issues in mind and with the aim of promoting adoption, NASSCOM, in partnership with Blueocean Market Intelligence, has put together a report ‘Industrialisation of Analytics in India: Big Opportunities, Bigger Outcomes’. This report analyses the global market size and trends, India’s current scenario, trends in the India market, factors driving adoption, customer expectations, engagement models and challenges faced by both users and services/product firms. More importantly, this report showcases global and India examples of how firms implemented analytics and the benefits gained; further, it sets out a roadmap on what needs to be done by stakeholders involved to industrialise this technology within enterprises in India. This report is the initial step taken by NASSCOM as part of its Analytics Interest Group (AIG) to create awareness for adoption of Analytics in Enterprises. We hope you find this report useful. Please share your feedback at R Chandrashekhar Ashwin Mittal President, NASSCOM President, Blueocean Market Intelligence 3
  4. 4. Executive summary (1/4) Overview Globally, a continuously burgeoning volume of data (40 zettabytes by 2020), data generation from varied channels (Facebook- 800+ million daily users, Twitter-250+ million active users, 9 billion connected devices by 2018) and a corresponding drop in data storage costs per GB (decline of 99 per cent between 2000 and 2014) has enabled unprecedented access to a variety of data. This ever increasing data is a key resource that can be “analysed” to generate insights that can help organisations to take better and faster decisions. Analytics is a process by which insights are extracted from operational, financial and other forms of electronic data, internal or external to an organisation. Analytics can be used to create value using different types of data, both structured (high degree of organisation) and unstructured (lacking pre-defined data model). Global analytics market As firms gain access to greater volumes and newer varieties of data, and as they unearth more innovative ways of generating insights for improved customer engagement, implementing analytics is gaining in importance. The global analytics market (software products and outsourced services) is growing at over 12 per cent since 2012. The 2014 market size is estimated at USD 96 billion and is projected to reach USD 121 billion by 2016. Outsourced services around analytics is growing at a faster CAGR of over 14 per cent vis-à-vis analytics software (CAGR ~10 per cent). This growth is being driven by a host of factors – cloud, in-memory computing; mobile devices, social media; emergence of different business units across an organisation as consumers of analytics, etc. With analytics being consistently recognised as the top priority for CXOs, firms are also industrialising analytics within the organisational culture and this in turn, is seeing the emergence of the Chief Data Officers’ role. India analytics market Compared to the global market, the overall India analytics market size is miniscule and currently accounts for only 1 per cent share. The India market (exports and domestic) is growing at double the rate of global market at 24 per cent CAGR. In FY2014, the total market was USD 954 million and is expected to reach nearly USD 2.3 billion by FY2016. The ratio of exports:domestic is likely to remain steady at 85:15 during this period. 4
  5. 5. Executive summary (2/4) 5 Currently, this segment has over 600 firms offering analytics-related products and services and it employs about 29,000 people. Of this, India is the primary target market for ~50 per cent of these firms. The fact that India’s Top 100 IT-BPM (integrated) firms and about 500+ start-up firms are focused on analytics is statement of proof of this technology’s increasing relevance. India is rapidly emerging as the analytics hub for the world. It has the complete range of ecosystem players from GICs, integrated IT-BPM firms, pure-play analytics firms to BPM-KPOs and a vibrant analytics product firms. In terms of geographic density, Bengaluru has the highest number of analytics firms – 29 per cent, followed by Mumbai and Pune – 24 per cent. Apart from this, many Tier II/III cities are also emerging hubs - Trivandrum, Kochi, Mysore, Indore, etc. 23% 20% 17% 16% 14% 11% Better decision-making Driving sales and revenues Cost control, improve RoI Process efficiency & improvement Greater customer insights Targeted marketing STRATEGIC Impacting top- line OPERATIONAL Impacting performance CUSTOMERS User experience Analytics in the India domestic market There is also a pull factor from the user side – firms in India are beginning to realise the value of implementing analytics. Potential impact can be operational (cost control, process efficiencies), end customers (user insights, targeted marketing) and strategic (driving sales, improved decision making). Firms in the BFSI, telecom and eCommerce verticals have so far been taking the lead in adopting and applying analytics to a wide range of business areas – portfolio analytics, risk & compliance analytics, customer loyalty, subscriber profiling, churn management, etc. Emerging verticals that are still in the pilot phase of adoption include retail, manufacturing and media & entertainment. One of the key verticals that is showing great promise is the Government – SEBI (fraud detection), NATGRID (anti-terrorism) – and state level initiatives - Maharashtra Sales Tax Department and Hyderabad’s intelligent transport system.
  6. 6. To do this, the industry must first address various challenges. The demand side survey conducted indicated that proving the business value from analytics is the top challenge among users. This was followed by the firms’ ability to share data across business units and the total cost of ownership. For non-users, the top three challenges were budget constraints, RoI and costs. Additionally, both users and non-users expressed lack of endorsement from top management as another key challenge. From the supply-side perspective, the lack of awareness of this technology among potential users and lack of top management commitment is the primary challenge. This is followed by challenges with regard to data collection, siloed data, and a greater focus on short-term, output based results rather than on long-term, outcome focus. The journey to industrialisation involves three broad phases: • Phase 1: Discovery (firms pilot a few short-term analytics projects) • Phase 2: Establish (analytics becomes a little more centralised with organisation buy-in and this phase would also see more standardised tools and processes • Phase 3: Industrialise (where analytics is a core strategy linked to business outcomes). Executive summary (3/4) 6 Industrialising analytics in India Although adoption of analytics is very nascent in India, its relevance is not lost on end-user industries. A survey of ~600 demand-side firms (both users and non-users of analytics) indicated that while >60 per cent of user firms recognised the importance of analytics, the corresponding percentage among non-users was ~40 per cent. Significantly, only 14 per cent of non-users indicated that analytics was not important. A further analysis of user firms showed that 80 per cent had either uncoordinated or localised initiatives. Hence, for long term value gain, there is a need to industrialise use of analytics – that is integrate analytics into the organisational fabric. 19% 25% 34% 40% 43% 46% 53% 58% 64% Insufficient in-house expertise Where do I start? Poor data quality Budget constraints Management support, vision Analytics tool cost Vendor costs Sharing data across BUs, siloes Proving the RoI, business value Key challenges for analytics deployment (Users)
  7. 7. Executive summary (4/4) For each of these stages, the supply-side would need to address specific issues: • Phase 1: Showcase RoI, use cases - value add in learnings from global and Indian peers • Phase 2: Address entire analytics value chain - integrate multiple data sources, optimise data utilisation, data security, data maintenance • Phase 3: Industrialise: Greater demand for functionality across business value chain; after sales services – a key differentiator Industry stakeholders will need to work on a 6 point agenda which involves: Six point agenda for Industry stakeholders 1. Raising awareness 2. Creating talent 3. Variabilising cost of offerings 4. Standardising tools and technologies 5. Setting up cross functional analytics teams 6. Getting C-level buy in, to drive industrialisation of analytics in India 7
  8. 8. Table of Contents Acknowledgements 9 Setting the context 11 Global analytics market and trends 13 Indian analytics market and trends 18 Industrialising analytics in India & Way forward 36 8
  9. 9. Acknowledgements This report has been prepared through a collaborative effort by several institutions and individuals. First, we would like to extend our thanks to NASSCOM member firms who provided us with a detailed overview of trends and insights in the analytics industry, both global and Indian. Additionally, we would like to acknowledge the support and guidance of NASSCOM’s Analytics Interest Group. Second, we are also grateful to the customer side of the business. blueocean conducted a detailed survey of nearly 600 user firms and conducted in-depth interviews with firms in the advertising, auto, CPG, eCommerce, healthcare, oil & gas, telecom verticals and a large firm with diversified businesses. We deeply appreciate the effort put in by Mr. Vinay Kulkarni, the independent research consultant who conducted the supply and demand side interviews. Finally and not the least, we would like to thank the team from Blueocean Market Intelligence, particularly Ajith Sankaran, Ishma Siddiqi and Dhanya Vishwanath, who were instrumental in producing this study. A special acknowledgement to the NASSCOM research team for their efforts and contribution towards this report. 9
  10. 10. Study methodology NASSCOM carried out this study in partnership with Blueocean Market Intelligence: • Online survey among analytics users/non-users: blueocean conducted an online survey among Indian firms covering both users and non-users of analytics across ~600 firms • In-depth interviews with users (demand side): blueocean and an independent research consultant jointly interviewed nearly 15 analytic user firms to get deeper insights about analytics adoption, drivers and challenges. These firms represented various verticals including advertising, healthcare, telecom, auto, eCommerce, microfinance, energy & utilities, CPG, etc. • In-depth interviews with analytics vendors (supply side): NASSCOM’s internal team and an independent research consultant conducted in-depth interviews with analytics vendors (20 firms representing service providers and analytics product firms) to understand their perspectives on the domestic market potential • Secondary research: blueocean researchers looked at business, industry, and technology publications for information 10
  12. 12. What is analytics? Gartner defines “Analytics” as a process by which insights are extracted from operational, financial and other forms of electronic data, internal or external to an organisation. These insights can be: • Historical, real-time or predictive and can also be risk-focused (e.g., control effectiveness, fraud, waste, abuse, policy/regulatory non-compliance) • Performance-focused (e.g., increased sales, decreased costs, improved profitability, etc.) • Frequently provide the “how?” and “why?” answers to the initial “what?” questions often found in the information initially extracted from the data Analytics can be used to create value using different types of data: Structured data - Information with a high degree of organisation, such that inclusion in a relational database is seamless and easily searchable Examples - relational and legacy databases, spreadsheets, flat files Unstructured data – Information lacks a pre-defined data model, is unorganised and raw and can be textual or non-textual Examples - social media, blogs, web pages, multimedia, chat, call centre data Source: Gartner, blueocean, NASSCOM 12
  14. 14. Pre-relational Descriptive What is happening? Diagnostic Why/How did it happen? Predictive What may happen? Prescriptive What should we do? Analytics: Progress from reactive to proactive, real- time response Evolution: From static to real-time, dynamic analytics Examples: • Sales reports • Customer segmentation Examples: • Web analytics • Location intelligence Examples: • Propensity modeling • Sales forecasting Examples: • Price optimisation • Marketing mix optimisation Complexity,usageofstatisticalmethods 1990s 2000-2004Pre 1970s Examples: • Data generation • Storage 2005-2010 2011 onwards Technologies Relational Databases OLAP tools Web analytics tools Data virtualisation Packaged analytics tools Mobile BI NoSQL Hadoop, Hive Cloud BI Big data HANA ETL tools BI suites Dashboard tools Data integration platforms Machine learning AI Source: blueocean, NASSCOM 14
  15. 15. Analytics: Globally, one of the fastest growing technology markets… • Increasing maturity: Firms worldwide expanding analytics adoption from descriptive and diagnostic analytics to predictive and prescriptive analytics • Data and analytics - no longer sole responsibility of IT: Organisational culture changing from a siloed ownership of data and insights to cross-enterprise approach • Firms driving cloud-based BI: New scenarios such as collaboration with customers and outside-the- firewall mobile access also accelerating adoption 35 42 51 42 54 71 2012 2014E 2016P • Self-service analytics becomes the norm at fast-moving firms: Businesses are beginning to expect flexibility and usability from their dashboards. Monolithic infrastructure stacks will pass in favor of solutions that can work with new data sources • In-memory computing providing an opportunity to rethink information systems: In-memory computing will have a long-term impact by changing users’ expectations, application design principles, and vendors strategies • Emergence of the CDO: While it rolled up under CIO priorities earlier, with analytics pervading across business units, the role of Chief Data Officer (CDO) is emerging Software Services CAGR 14.3% CAGR 9.7% Notes: E: Estimate; P:Projection #: Represents only outsourcing services market Source: Cloudtech, Gartner, IDC Global analytics market USD billion 76 96 121 # 15
  16. 16. …Driven by data explosion, access to affordable computing & business imperatives Volume of data is exploding 10x increase Zettabyte 16 4 40 2013 2020 10X 2000 2014 Data comes from increasing Variety of sources; with increasing Velocity: Social media, cloud, IoT, eCommerce, cloud computing Significant reduction in cost of acquiring, storing, managing data Increased need for analytics: Firms realising the value of analytics to compete and operate efficiently Data storage cost USD/GB 800 mn+ daily active users 6 billion hours of video watched every month 250 mn+ active users; 100 mn+ tweets every day 1 Exabyte data stored in cloud, growing rapidly 9 billion connected devices in 2018, up from 2 billion today Increased vendor activity: Analytics is emerging as one of the top revenue opportunities Analytics: A top priority for CXO’s CIO priority surveys (2012-2014) Priority rank for Analytics & BI 1 C–Suite survey, 2013 1 CIO Conference Poll, 201 1 State of CIO survey, 2014 1 99% Source: cdn.business2community, Gartner, IDC 0.1 – 0.5 10
  17. 17. RoI: New revenue streams, cost savings, operational efficiencies UPS’ On-Road Integrated Optimisation and Navigation (ORION) system captures data on 16.3 mn+ packages per day for 8.8 mn customers, with an average of 39.5 mn tracking requests per day. “Big data” comes from telematics sensors in 46,000+ trucks (speed, direction, braking, etc.) Commonwealth Bank of Australia used SAS and Teradata solutions to migrate siloed information onto one platform to analyse customer transactions in real-time Professional society for physician assistants: Quatrro created a big data platform by analysing over 250 types of data sources, leveraged descriptive and inferential analytics, database building, data cleansing, and standardisation by leveraging cloud infrastructure Central Government client uses analytics to uncover hidden relationships across 1 billion internal and external data items and to streamline risk and compliance operations Analytics engine for a global financial services firm to predict customer behavior and for trade analytics: Altimetrik leveraged Hadoop, Hive, Sqoop, and Pig. Cloudera distribution of Hadoop was installed and cluster was setup. A prototype was built for customer data, cash flow and credit card history data for reporting and analytics Deployed IBM’s predictive analytics for understanding customer behavior and to proactively reach out to customers with a high potential to churn • Saving of >8.4 million gallons of fuel • 2X level fraud detection • Fraud alerts for internet banking increased by 60% Reduced: • Membership churn by 42% • Research and analytics cost by 80% • Generated £1.4 billion additional yield to-date • Client’s Customer Experience Index up from 20 to 35 • Development cycle down from 9 months to 3 months, reduced time to market • Increased customer retention, subscription revenues • Average annual RoI - 376%; annual profit - USD 3.8 mn Source: blueocean, IBM, Informs, SAS, NASSCOM Organisation Analytics application areas Benefits 17
  19. 19. India market: CAGR of ~25 per cent to cross USD 2 billion by FY2018 92 163 375 521 791 1,900 FY2012 FY2014E FY2018P Domestic Exports Indian analytics market USD million 613 954 2,275 CAGR 24% CAGR 26% Notes: #: Represents only outsourcing services market *: The break-up given includes over-laps; hence a direct total can't be calculated **: Top 100 IT-BPM firms who offer analytics services and products Source: NASSCOM India: Player landscape* nos. • ~USD 1 billion revenue; to grow >2X over 4-5 years • Domestic market still nascent; to double by FY2018 • Total employees: ~29,000; domestic market: ~5,000 • Number of analytics firms: 600+ nos • Top 100 IT-BPM firms offering analytics services/products • >300 analytics firms focusing on India market • Tier-II analytics hubs: Trivandrum, Kochi, Mysore, Indore, etc. • Emerging firms : Crayon Data, Germin8, Padma, Gazelle, Cognowise, Datapub, Clarisnow, etc. Key trends FY2014E Analytics firms Start-ups Product firms Integrated firms** 600+ 500+ 200+ 100+ # • Indian businesses increasingly seeing transformative benefits of improving resource management and market intelligence • Noticeable shift toward cloud-based solutions • Domain specific players: Indian product firms developing best- in-class products, platforms and enabling tools; smaller firms specialising in analytics for niche domains • Product firms redefining core offerings and exploring service differentiators in conventional and emerging business models 19
  20. 20. Mobile and internet growth, technology base, growing economy sets the base for analytics growth in India Increasing internet, mobile penetration driving data growth internet users 29 382 2012 2016 Smartphone users in India# (mn) Organised retail growing rapidly, driving need for analytics 2012 2015E 40.5 88.3 Increased awareness at C-suite level Regulatory compliance social media users • Big data & analytics - No. 2 (after cloud) in key areas for investment for CIOs • RBI’s Automated Data Flow initiative (ADF) • International Financial Reporting Standards (IFRS) • eXtensible Business Reporting Language [XBRL] To further push firms to invest in analytics and BI 243 mn 168.7 mn Organised retail* USD million Retail eCommerce is exploding (no of online shoppers) 20mn 2013 40mn 2016 Rapid technology adoption by Indian industry • Limited legacy IT challenges, enable rapid technology adoption • Huge potential for analytics as large number of Indian firms get increasingly IT enabled Growing economy, competition, globalisation • India: One of the fastest growing economies • Stabilising economy, globalization, increased on- data, fact-based decision- making tools Source: Accelpartners, Avendus, blueocean, CIOandleader, Gartner, IBEF, IBM 20
  21. 21. Mumbai16% Bengaluru29% Chennai 8% Pune8% Delhi11% Hyderabad11% Gurgaon7% Noida 7% Kolkata 4% India: Vibrant analytics vendor ecosystem • 1.5X: Growth in number of analytics firms in India in last two years • ~3.6X: Increase in average employees of analytics firms in India from 76 in 2012 to 270 in 2013 …Geographic spread in India per cent Analytics vendor landscape in India… Global in-house centres 50+ Pure-play analytics service providers 80+ BPMs & KPOs 80+ Integrated firms 100+ Analytics software / product firms 200+ Source: Analytics India magazine, blueocean, NASSCOM 21
  22. 22. Key drivers: Data driven decision making, customer insights, improved performance and efficiencies Base 286; multiple responses allowed, value indicates number of respondents ranking these in top 3 Primary factors driving Indian firms to deploy analytics No. of responses • Implementing analytics – being explored by both large firms and SMEs; many still in pilot phase • Analytics – enabling competitive advantage • eCommerce firms and MNCs, esp. in BFSI and telecom, leading the charge • Firms in India looking to analytics to formulate business-driven strategies and derive value… • …And better manage costs, optimise operational processes, earn higher returns • Unlike globally, firms in India currently not using analytics for fraud prevention, pricing strategies • Distinct vertical trends: o Banks-fraud detection o eCommerce-web analytics o Manufacturing, retail - supply chain optimisation 23% 20% 17% 16% 14% 11% Better decision-making Driving sales and revenues Cost control, improve RoI Process efficiency & improvement Greater customer insights Targeted marketing STRATEGIC Impacting top-line OPERATIONAL Impacting performance CUSTOMERS User experience Source: blueocean, NASSCOM 22
  23. 23. Telecom, BFSI, eCommerce – trendsetters in analytics adoption Source: blueocean, NASSCOM Verticals Adoption rate Analytics services areas Telecom High Network monitoring & optimisation, capacity planning analytics, forecasting, customer scoring, customer acquisition, churn analysis, real-time analytics, pricing models, campaign management, churn prevention, fraud prevention BFSI High Customer analytics, marketing analytics, portfolio, credit risk, fraud & risk analytics, pricing models, demand forecasting, cross- and up-sell models, churn modelling, loyalty & claims analytics eCommerce High Web analytics, customer & data analytics, logistics and distribution analytics, inventory management, real time analytics Retail Medium Merchandising analytics, customer analytics, churn analytics, inventory management, supply chain optimisation, sales performance analysis, market mix modeling, CRM analytics, fraud detection and prevention Manufacturing Medium Supply chain, logistics and inventory management, demand forecasting and SKU rationalisation, process and quality analysis, engineering analytics, predictive maintenance Media & advertising Medium Demand signaling, sentiment analysis, targeted advertising analytics, customer acquisition Healthcare Low Portfolio performance analysis, treatment flow analytics, clinical analytics, supply chain analytics Education Low Learning analytics, relationship mining, predictive analytics 23
  24. 24. Government of India: Ripe ground for analytics • NATGRID, India’s national intelligence network invested USD 16 million to use real-time analytics in anti- terrorism efforts • SEBI implemented a comprehensive SAS Business Analytics Platform including an enterprise data warehouse for fraud detection • Karnataka Vocational Training and Skill Development Corporation is using IBM’s employability analytics to understand demand and supply trends • Aarogyasri Health Care Trust partnered with SAS to leverage predictive analytics to optimise performance and reduce operating expenses. SAS analytics solution provides real-time access to AHCT and aids in understanding the lead time between pre-authorisation approval and surgeries, forecasting funds requirement, preventing fraud, spotting disease trends, and taking preventive health measures • Maharashtra Sales Tax Department is using SAS Business Intelligence and SAS Analytics with the help of Capgemini to tackle fraud and improper payments • Hyderabad implemented an intelligent transport system provided by CMC. Data gathered from GPS in public buses is being used to track the frequency and delay of buses in each route, allowing city officials to plan deployment of more buses Source: blueocean, Economic Times, futuregov, IBM, Informatics, SAS 24
  25. 25. eCommerce: Leading in analytics adoption Faced a pressing need to improve inventory utilisation. Using a solution from QlikView , Flipkart was able to integrate complex data from disparate sources and deliver analytical data to the staff in various departments Wanted to ensure that the top stores got the pick of merchandise due to their size, catchment and customer profile. However, owing to overstocking at some stores, merchandise allocation to smaller stores was less than optimal. Usage of BI stack helped overcome this problem Using IBM’s ExperienceOne and SPSS Predictive Analytics solutions, the firm was able to identify customers and target them with individual campaigns. It also provided access to clients’ real time data and analysed it - helped in innovative marketing efforts •Inventory utilisation up by 5 per cent •Optimise stock levels •Lower costs •Optimise merchandise allocation more accurately, based on actual sales throughput • Helped utilise subscriber data better to discover perfect match credentials • Deliver integrated marketing messages to target potential partners Source: Secondary sources Organisation Analytics application areas Benefits 25
  26. 26. AdNear: Air traveller behavioural insights for targeted marketing campaigns Analyse air traveller behavior in India for targeted marketing campaigns Client & business challenge • AdNear used Apache Spark and Hadoop frameworks, data structure server Redis, and Python • Locational analytics, customer segment analysis and cluster analytics were used • AdNear tracked 650,000 travellers - all mobile users - across six airports in India: Mumbai, Delhi, Chennai, Bengaluru, Kolkata and Hyderabad Analytics solution deployed & methodology • Customer segmentation and behavioral insights • Improved campaign planning for maximum impact and sales conversion Benefits to customer 26
  27. 27. Airtel’s churn management initiative Airtel wanted to identify pointers that would help reduce customer churnClient & business challenge Analytics solution deployed & methodology • Enabled Airtel to develop an automated tool to mine customer call texts and calculates aggregate probability for churn • Whenever the probability crosses a certain threshold point, Airtel takes up special tele-calling to these customers to ensure quicker resolution of their issues and save the churn • Helped reduce churn significantly over last one month Benefits to customer How to use customer care calls’ data in churn reduction: • Step 1: To quantify the importance of customer care in a customer’s life cycle journey • Step 2: To do text mining and figure out early churn and therefore save the customer • Airtel looked through multiple customer touch points and figured out that Customer care plays one of the most impactful role in case of trouble shooting • Used analytical tools to figure out the specific texts used by customer or call agents which might indicate possible churn • Also checked calls of customers who have not churned for the same texts and identify differences in the usage of same text between churn vs non-churn groups • Ran a stochastic modelling to attach a probability to each of these texts which indicates probability of churn OBJECTIVE METHODOLOGY 27
  28. 28. BRIDGEi2i: For an Indian conglomerate, measure effectiveness of promotions on trade revenue Large Indian firm with major CPG focus wanted to measure effectiveness of various trade and consumer promotions on trade revenue while segregating impact of promotion from impact of price change, competitive actions, SKU cannibalisation and halo effects Client & business challenge • Outlet segmentation based on similar responsiveness and product assortment • Creation of price indices, promo calendar, competition and cross-category interaction indices • Data treatment trend, seasonality, outlier etc. • (Mixed Effect) Regression modeling of volume sold against price, promotion, competition, interaction indices • Decomposition of volume realised into base volume, cannibalisation, promotion net impact, competition impact etc. • Analyse RoI of promotion spends based on incremental value • Identify optimal promotion and price for each channel and segment DATA APPROACH SEGMENT DATA, CREATE INDICES MODELLING DECOMPOSITION OF IMPACT SCENARIO ANALYSISSegment level data for outlets Historical sales by volume and value Historical price and promotion data Channel and competition data Model results and scenario analysis • Improved promotion strategy and RoI of promotion spend across segments Benefits to customer 28
  29. 29. Essar Oil: ‘EOL Dashboards’ for operational excellence At Essar Oil, after setting up a 20 MMTPA refinery, the focus is on maximising returns from the newly and substantially enhanced capacity of the high-complexity refinery in next 3-4 years Client & business challenge Objective: Set up Operational Intelligence tool (EOL Dashboard) to measure, monitor, improve KPIs across business value-chain (crude to product): • Enable sharing of information across the firm, quickly, easily and appropriately • Allow users to consume large amounts of information in a simple, graphical view • Enable management to monitor organisation-wide KPIs in real-time with complete drill-down facility • Align users to a single version of the truth Analytics solution deployed & methodology Solution: EOL implemented the Oil & Gas specific analytics tool of Siemens XHQ across four key business transaction systems: •SAP ERP •AspenTech’s Manufacturing Execution System •Commodity trading platform of ‘The Bulldog’ and •A set of custom-made Microsoft technology-enabled solutions The data generated from these systems is approximately to the tune of 1.5 Terabytes Benefits to customer • Covered all KPIs of firm’s Balanced Scorecard and those monitored by functional heads and their line managers • Operations visibility through functional & cross-functional overview, KPIs, analysis and opportunity cost optimisation through pushed exceptions, drill-downs to lowest detail, on demand • Enabling business users take quicker decisions and reduce dependency on people • Helped gain important insights in the refinery operations and also in responding quicker to any challenges faced • Unlocked automation opportunities in some business processes • Has been extended to users with access to tablets making it easy for them to track KPIs in real-time 29
  30. 30. 2014 Lok Sabha election analysis in real time by Gramener (A Data Visualisation firm) Client & business challenge • Gramener -CNN-IBN-Microsoft partnership for historic & real-time update of 2014 Lok Sabha election • 300 parties, 8,000 candidates, ~800 million voters, ~21,000 votes/sec on live counting day – A Big Data Problem in every sense Analytics solution deployed & methodology • Client received data from Nielsen • Gramener installed it’s Counting day ETL scripts (Python script) which pulled data every 10 seconds from SQL server and converted it into 2 JSON files: one storing the candidates and another storing their status/votes • These files were copied via rsync every 10 seconds to, which is an Azure VM in Singapore – a 4 core 7GB RAM Ubuntu system • The server had a copy of the Gramener visualisation server installed which had built-in analytics and visualisation capabilities • Results rendered in real-time using a visualisation template Benefits to customer • The system was able to analyse over 10 million pages worth of election results in real-time, enabling CNN-IBN to stay ahead of other channels or websites 30
  31. 31. GrayMatter: Consolidation of information across enterprise for GMR Group • One of the largest infrastructure players in India, GMR wanted to integrate data across multiple airport locations and have a corporate level single view dashboard of airport business Client & business challenge • Management gets a quick snapshot of entire airport business at the corporate level on live data • Online current receivables and payables status improved finance decision making abilities • Commercial department can easily identify and eliminate leakages in revenue on the retail side • Advise airlines on optimising their flight frequencies at the airport, thereby increasing traffic Benefits to customer • GrayMatter’s pre-built stack on SAP BO BI platform used to quickly customise solutions around customer’s unique requirements • Key data elements identified from all data sources relevant to the pre-built and customised metrics • Created single version of truth with a centralised data repository • Implemented GrayMatter’s unique user interface and dashboards • High reaction time due to lag in consolidation of information across enterprise • Manual intensive consolidation process consumed critical resource time and effort • Integration of data across multiple airport locations • Corporate level single view dashboard of airport business, a critical need • Exorbitantly high cost of alternate solutions • Data correctness across disparate data sources Key challenges Analytics solution deployed & methodology Solution 31
  32. 32. Persistent: Social media and digital analytics for Chennai Express, a Bollywood movie • Digital team wanted to use social media as the focus of their marketing strategy • Objectives: maximising customer reach, build campaigns to translate social media traffic into increased audience in theatres • Persistent and eMee evaluated online presence and implemented “Actionable Insights”, a recommendation system that uses analytics, big data tools and calibrated metrics to gauge what people like and their engagement levels • Big data analytics, using a variety of in-house and off-the shelf tools and their unique approach based on 4A model of engagement • Cross-pollination of social networks - Twitter, YouTube, Facebook, etc. • 1 billion cumulative impressions, 750K+ tweets over 90-day period • Made Twitter history in India as the first film to be listed in the top trends for more than 10 consecutive days • Became first Indian film in twitter history to trend at worldwide No. 1 with two separate hashtags #ChennaiExpress, #ChennaiExpressWeekend • Box office collections mirroring social media buzz Client & business challenge Analytics solution deployed & methodology Benefits to customer 32
  33. 33. Serco: Product affinity analysis for a leading retail chain in India • Improve inventory management through cross-selling by identifying basket of products or brands which are most likely to sell together • Identify key profitable product categories and impact of promotions on the product sales Client & business challenge Analytics solution deployed & methodology • 3 per cent overall promotional revenue impact • Identified key profitable product categories to optimise profits • Helped in crafting successful cross-sell Benefits to customer • Data Mining – Data was cleaned and necessary data transformation techniques used for getting the derived variables for data mining • Product Affinity Analysis was done and association strength of various products and brands was derived • Price sensitivity Analysis - Price elasticity of each product was derived pre-promo and post- promo and price sensitivity analysis was carried out • Customer unaware about basket of products or brands most likely to be bought together • No clarity on key profitable product categories on which to focus their marketing efforts • Unable to identify impact of promotions on sales of product Before Analytics Scenario • Client rolled out the cross-sell strategy with the help of insights received from Product affinity analysis • Marketing efforts were focused on Key profitable categories in order to maximize the profits • Suggested more effective promotional offers by deriving impact of promotion on sales of product through price sensitivity analysis After Analytics Scenario 33
  34. 34. Tata Motors: Applying analytics to track direct material costs • Manage direct material costs • Trace millions of cost reduction/cost increases in price amendments back to product variant level • Monitor on a monthly basis changes in cost of product variant and also of critical aggregates, components Client & business challenge Analytics solution deployed & methodology • Operational Impact: 80% of cost is at design stage so it gives visibility to the designers on the cost, thus help design lower cost variants • Strategic Impact: Visibility on how movements of cost would make pricing more responsive to market needs • Customer Impact: Ability to price more competitively and be more responsive to changes in commodity price fluctuations Benefits to customer• Material cost contributes 65% of total cost • Biggest problem was to link cost reduction/increases to changes in cost of product variants • Tata Motors implemented SAP Business Objects/BW to help analyse nearly 480 lakh data points a year • Manual compilation of data over 3- 4 weeks • Reporting done once in six months and confined to a department/BU • Impact of price rise across the BUs/firm could not be ascertained • Decision making was largely reactive (post impact) Before Analytics Scenario • Direct Material Cost tracking solution rolled out to all business planning agencies across manufacturing locations, material price panel, designers, new product introduction teams • Monthly analysis/reporting enabled across the firm giving critical insight to materials, business planning & R&D • Data reported is factual enabling pro-active decision making and monitoring price increase/cost reduction at item level After Analytics Scenario 34
  35. 35. WNS: Customer segmentation for an Indian apparel retailer • One of the top three Indian apparel retailers wanted to customise marketing messages based on customer preferences, needs and habits • Had large loyalty card base but no understanding of purchase patterns Client & business challenge • Holistic customer segmentation proposed based on customer value, discount hunting behavior, lifestyle preferences, life stage • Transaction and demographic data analysed and fed into segmentation algorithms • Large volumes of data > 45 GB churned for analysis • Decision trees and clustering algorithms generated customer segments - five segments with distinct profiles were unearthed • Segments profiled extensively on transaction frequency, monetary value, typical products purchased, etc. to create a holistic picture Analytics solution deployed & methodology • Complete segmentation solution for all loyalty card customers • Customer file creation and maintenance to track segment behavior • Segmentation implemented at client site in CRM system • working with client to use segmentation to customise various direct marketing campaigns Benefits to customer 35
  37. 37. How can IT-BPM firms differentiate their solutions? 76% 69% 60% 54% 46% 31% 30% Base 286; multiple responses allowed, value indicate number of respondents rating these as top 2 on a 5 point scale Important criteria while selecting analytics solutions & services No. of responses Return on investment Proven cases, references Assistance in setting up analytics system Cost, discounts Vendor brand/reputation Product functionality Responsive after sales service Source: blueocean, NASSCOM Clear need for showcasing business value: • Demand side seeing value add in learnings from global and Indian peers Phase 1: Discover Phase 2: Establish Phase 3: Industrialise Hand-hold across the analytics value chain: • Integrate multiple data sources, optimise data utilisation, data security, maintenance • Lower total cost of ownership As analytics gets further embedded into firms’ business culture: • Greater demand for functionality across business value chain • After sales services – to emerge as a key differentiator 37
  38. 38. Acquirer Target Year Value (USD million) Strategic advantages Facebook Little Eye Labs 2014 10-15 Performance analysis capabilities and acquire monitoring tools for mobile app developers Tech Mahindra FixStream 2014 10 Access to data integration and analytics platforms for datacentre and cloud management Pegasystems MeshLabs 2014 NA Adds social listening, text analytics and natural language processing to enhance customer service & marketing IbiboGroup YourBus 2014 NA has a GPS-based bus tracking & analytics platform that provides real-time data to travelers and bus operators MphasiS Digital Risk 2012 175 Mphasis added highly specialised risk, compliance and transaction management solutions for mortgage industry Crisil Coalition Development 2012 49 Strengthened positioning in high end research and analytics space for leading investment banks globally Wipro Promax App Group 2012 33 Strengthened positioning and capability in management, analytics & optimisation of trade promotions Equifax NettPositive 2012 NA Deepen Equifax’s footprint in the Indian market Moody Amba Services 2012 NA Grow expertise in investment research and quantitative analytics for financial institutions IMS Health PharmARC 2012 NA Acquisition strengthened IMS’s BPM capabilities, services delivery platform, and suite of proprietary technology and apps Cognizant CoreLogic 2011 50 Provide end-to-end business processes and sophisticated analytics solutions across the mortgage industry Genpact EmPower Research 2011 NA Acquisition added social media monitoring and measurement, event impact research, brand tracking, and data management M&As: ~USD 500 million - quicker access to markets, customers, capabilities Not exhaustive Source: Avendus, Saviance, blueocean, NASSCOM 38
  39. 39. Analytics firms’ attracting significant investor interest - ~USD 400 million funding since 2011 Funded to Funded by Year Deal value USD million Capillary Technologies Amex Ventures 2014 4 Sapience Seed Ventures 2014 0.758 Mu Sigma MasterCard 2013 45 Opera Solutions Wipro 2013 30 Fractal TA Associates 2013 25 Qubole Lightspeed, Charles River 2013 7 UNBXD IDG Ventures India, Inventus Capital Partners 2013 2 AbsolutData Fidelity Growth Partners India 2012 20 Manthan Systems Norwest Venture 2012 15 iCreate Sequoia Capital 2012 9 IQR Consulting Seed Ventures 2012 0.525 Mu Sigma General Atlantic 2011 108 Opera Solutions Silver Lake Sumeru 2011 84 Mu Sigma Sequoia 2011 25 • Outbound: Wipro invested in Opera Solutions (USA), Zodius Capital invested in Antuit (Singapore) • PE funds investing in smaller analytics services providers: Demonstrating confidence in their ability to provide strong growth and higher returns • Analytics firms proactively looking for funding to expand portfolio offerings through addition of new verticals and building technology and platforms Not exhaustive Source: Avendus, Saviance, blueocean, NASSCOM 39
  40. 40. Engagement models: Largely hybrid of dedicated internal teams and vendor engagement • In the Indian domestic market, large part of analytical engagements continue to be driven by internal teams. When Indian firms look at outsourcing analytics, large firms prefer annuity/FTE models. Smaller firms opting for ad-hoc engagements to pilot analytics based projects, analyse the RoI, and then make larger investments • In many cases, Indian firms work with analytics software vendors for tools and systems and implementation services, and then have their own internal teams run and manage the analytics services • IT-BPM firms offering analytics as part of the packaged deal; independent pure analytics outsourcing projects yet to gain sufficient traction Internal teams One-off/Project based engagement Annuity/FTE engagement Outcome based models Adoption by Indian analytics users High Moderate Low Negligible Source: blueocean, NASSCOM 40
  41. 41. Supply-side challenges: Proving business case, data veracity • CXO level commitment lacking - Analytics remains a priority to select teams/individuals • In many Indian organisations, there is a lack of understanding of analytics and its potential benefits • Customers focus on short term results vis-à-vis long term growth goals – more “output” focused than “outcome” focused • Data collection capabilities are not robust or standardised in many Indian firms; siloed data available, and these don’t talk to each other; no consolidated view • Reluctance to change existing internal IT structure or some of the existing organisational systems • Difficulty in finding resources with knowledge of statistics and analytical tools plus domain knowledge, business analysis skills and program management skills • Internal analytics teams of customers not exposed to business side – leading to lack in understanding of requirements at both ends Challenges mapped to analytics value chain Source: blueocean, NASSCOM 41
  42. 42. Demand-side challenges: RoI and costs, leading concerns for users, non-users Base: 286-analytics users and 312-non-users of analytics (multiple responses allowed, value indicate number respondents ranking these in top 3) Key challenges for analytics deployment • Proving RoI and TCO – among the top concerns for users/non-users • Both groups highlight need for top management endorsement Source: blueocean, NASSCOM 31% 39% 42% 50% 57% 72% 77% 86% Lack of in-house expertise Where do I start? Lack of domain skills (vendors) Data collection issues Lack of management support Cost of solutions and services Unsure of benefits Budget constraints 19% 25% 34% 40% 43% 46% 53% 58% 64% Insufficient in-house expertise Where do I start? Poor data quality Budget constraints Management support, vision Analytics tool cost Vendor costs Sharing data across BUs, siloes Proving the RoI, business value Users Non-users • Budget constraints – the top challenge for non-users • Need for domain understanding among service providers/product firms 42
  43. 43. Over 60 per cent of users recognise relevance of analytics No opinion 4% Not important 5% Somewhat important 30% Important 38% Very important 23% Importance of analytics Indian firms 42% 37% 18% 3% Central analytical group that closely coordinates analytical activity across the enterprise Central analytical group; some coordination over analytical activity across the enterprise Localised analytical capabilities that are beginning to share tools, data & people Uncoordinated pockets of analytical activity Current state of analytics in Indian firms Users Base: 598; 286 analytics users, 312 non users Base: 286 analytics users Source: blueocean, NASSCOM No opinion 9% Not important 14% Somewhat important 38% Important 26% Very important 13% Users Non-users Focus area for IT-BPM industry in immediate future 43
  44. 44. Need to “industrialise” analytics for sustained long term value • Few firms currently have been able to integrate analytics into organisational fabric and generate sustained long term value. This is largely due to disconnect between business priorities and analytics initiatives, often resulting from: a) Analytical teams that focus most of the resources on theoretical pursuits or the “art” of analytics b) Organisational siloes and lack of investment from the ultimate “users” of analytics • In order to address this challenge, what organisations should aim for is “Industrialisation of Analytics” • This implies analytics programs that are tightly tied to business outcomes, delivered via engagement models that disaggregate analytics process chain, hive out repeatable and standard processes to centralised process teams, and use standardised tools & approaches Businessvalue Phase 1: Discover • Data discovery • More of “art” • Analytics pilots Phase 2: Establish • Deliver initial pilots/models • Organisational buy-in • Standardise tools, processes Phase 3: Industrialise • Link analytics business priorities • Disaggregate analytics, move “heavy lifting” processes to shared services teams/outsource • Standardise tools, processes Source: blueocean, NASSCOM Mid term Long termShort term 44
  45. 45. • >60 per cent of analytics workforce in India has work experience of 3-10 years • Analytics jobs being created at a faster pace as compared to other geographies India advantage: A hub for analytics with mature ecosystem Strong vendor ecosystem • 600+ analytics firms in India; of which ~200 are product firms; ~500 are start- ups, and ~100 integrated firms • 300+ analytics players focusing on the Indian domestic market; of which 100+ firms are analytics product vendors • Tier-II cities like Trivandrum, Cochin, Mysore, Indore, etc. emerging as analytics hubs Scalability and leadership • Mature global delivery market • Best-in-class governance frameworks • Excellent CSAT across IT services Talent pool Strong analytics capabilities • Best practices hub • Centres of Excellence • A primary hub for providing analytics services to global clients 18% 14% 10% 9% 8% 8% 7% 6% 4% 3% 3% 3% 3% 2% 2% Risk Analytics BI/Reporting Others Marketing Analytics Retail Analytics Digital Analytics Financial Analytics Research Big Data Healthcare Analytics Supply chain Analytics HR Analytics Consulting Customer Insight Strategy Analytics Analytics job postings based on functions 2013 Source: Accenture, blueocean, NASSCOM 45
  46. 46. Stakeholders collaborating to develop a sustainable analytics talent pool Specialised analytics training institutes Service providers introducing cross-training to existing talent Launched an open data science and big data analytics training and certification program in 2012 Provides training to its analysts via its MuSigma University programme for core analytical skills Launched Infosys Big Data Certification Programme to validate and build knowledge of big data amongst its employees IMT, Ghaziabad signed an MoU with Genpact to develop and implement analytics elective for a 2-year PGDM program Taken analytics orientation and trainings to academic institutes incl. IIM Bangalore, IIITB, Great Lakes Institute etc. Certificate Programme in Business Analytics Certificate Programme in Business Analytics – IIML Business Analytics and Intelligence (BAI) – IIMB Executive Program in Business Analytics – IIMC Advanced Certificate Program in Business Analytics – IITB Business Analytics and Data Mining – ISI, Pune Academia is making efforts to build talent for analytics Significant improvements in analytics training programs enabling India to produce analytics professionals with relevant skills and domain expertise Hyderabad IIM, Lucknow IIM, Bangalore IIM, Calcutta ISI, Pune IIT, Bombay Source: Analytics India magazine, NASSCOM 46 Not exhaustive
  47. 47. Way forward: 6-point agenda to industralise analytics in India (1/2) Awareness Vendors: - Showcase case studies and highlight customer benefits (global, Indian) NASSCOM: - Leverage Analytics Interest Group, annual summit to showcase case studies, thought leadership - Roadshows across cities; regional events between demand and supply side for exchange of ideas Talent Vendors: - Look beyond existing education system to foster talent - Develop business consultancy/domain expertise skills in addition to core analytical skills NASSCOM: - Partner with academia to define right training, skills needs; provide guidance via Curriculum Advisory Team - Conduct regional and country level hackathons/codefests Academia and government: - Promote domain expertise through vertical specific analytics courses, teach business skills - Collaborate with industry for exposure to real business situations, specialised analytics training programs - Special government initiatives to integrate academicians with analytics technology Cost Vendors: - India specific billing rates/bundled offerings/packaged solutions across value chain Users: - Make necessary investments and focus on long term benefits - Shift from ‘output’ to ‘outcome’ focus and from ‘tools’ to ‘business solution’ focus Short term Mid term Long term Source: blueocean, NASSCOM 47
  48. 48. Organisational focus Users: - CXO level commitment essential for department level adoption - Analytics as core strategy, not driven by individual departments/IT Standardise and leverage processes, tools and technology Vendors: - Analytics firms should try and build analytics systems on top of existing systems - Standardise and leverage common tools and technology Users: - Disaggregate analytics processes - Move standard processes to scalable delivery teams - Standardise and leverage common tools & technology - Integrate analytics with legacy systems and tools Analytics as a cross-functional unit Vendors: - Partner with clients right through various stages of analytics value chain - Help with data collection/audits, data organisation, data engineering Users: - Set up analytics teams which are cross-functional rather than pure play analytics Way forward: 6-point agenda to industralise analytics in India (2/2) Source: blueocean, NASSCOM Short term Mid term Long term 48
  49. 49. 49