Big Data Analytics Shaping the business of the future
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1 Big Data Analytics Shaping the business of the future
2 Agenda Big Data Analytics Accenture Point of View Copyright 2013 Accenture. All rights reserved. 2
3 Agenda Big Data Analytics Accenture Point of View Copyright 2013 Accenture. All rights reserved. 3
4 External Internal What is Big Data? A smoothly integrated collection of diverse data-sources: Traditional Enterprise Data Machine Generated / Sensor Data Customer information from CRM systems Transactional ERP data Web store transactions General ledger data Call Detail Records Weblogs Smart meters Manufacturing sensors Equipment logs Trading Systems data Social Media Data Customer feedback streams Micro-blogging sites Social Media platforms Macroeconomic / Public Data Unemployment Interest Rate Consumer Confidence Index Inflation Rate Income Consumer Price Index Big data is defined by four key characteristics: Volume Velocity Variety Value Copyright 2013 Accenture. All rights reserved. 4
5 Industry analysis shows that there is huge value to be gained from Big Data Analytics US Health Care $300 billion value per year ~0.7% annual productivity growth Europe Public Sector 250 billion value per year ~0.5% annual productivity growth Personal Location Data 100 billion revenue for SPs 700 billion for end users US Retail 60+% increase in net margin available Manufacturing 50% decrease in prod dev and assembly 7% reduction in working cap. Copyright 2013 Accenture. All rights reserved.
6 Data Velocity, Variety, Volume & Complexity Technology advances underpin how enormous data volumes can now be processed in real-time Structured & unstructured data (e.g. clickstream data, multi-device usage data, mash-ups from multiple industries) Real-time big data analytics (e.g. real-time voice-to-text mining) Next Generation underpinned by Technology innovations Traditional structured data (e.g. CDRs, CRM, transactions) Data age: Weeks to months Descriptive analytics (e.g. segmentations) Static reports 1 Reactive Business Intelligence Augmented structured data (e.g. CDRs, CRM, transactions, lifestyle, lifestage) Data age: Days to weeks Predictive analytics (e.g. forecasting, churn propensity) Dynamic reports 2 Predictive Business Intelligence Structured & unstructured data (e.g. location, social interaction) Data age: Days to weeks Big data analytics (e.g. social network analysis, social media analytics) 3 Current Generation of Big Data 4 Next Generation of Big Data Copyright 2013 Accenture. All rights reserved. Time
7 What is Big Data Analytics? Big Data Analytics is: a shift in the mindset of how we think about analytics as an internal component to the organization. a way to foster a culture around our organization that focuses on integrating data from a diverse variety of sources in such a way that drives meaningful insights in a rapid fashion. Hence, it leads to enhanced productivity, stronger competitive position and greater innovation. The way that these benefits are ensured is via simultaneously addressing: Missed Opportunities: Lack of sufficient computing power often prevent traditional analytics tools from analyzing all data that is available. Missed Opport unities Latency require ments Management Comple xity - Enhanced productivity - Stronger competitive position - Greater innovation Latency requirements: Lack of an effective computing model may hinder traditional analytics tools from taking into account data this is dynamically updated from a multitude of different data-sources. Management complexity: Lack of a successful integration between different organizational departments data-sources typically impedes traditional analytics tools to infer meaningful inter-departmental insights. Copyright 2013 Accenture. All rights reserved. 7
8 Gradual Progression to Big Data Analytics Last Generation Analytics Current Generation Analytics Next- Generation Analytics/ Big Data Analytics Data type Transactions Call detail records Transactions Call detail records Transactions Call detail records Location Click-streams Social data Channel interaction Device usage Data format Structured Structured Structured Unstructured Data age Weeks to months Days to weeks Real-time Data volume Low Low to medium Immense Analytics Descriptive Predictive Real-time predictive Accenture Proposition Data + Technology People + Culture Process + Organization Next-Gen Analytics Capability Agile Segmentation with Predictive Analytics Social Media Analytics Channel Usage Analytics Social Network Analytics Location Based Analytics Real Time Decision Analytics Next Gen Customer Profile Next-Gen Customer Service Next-Gen Business Innovation Next-Gen Business Service Copyright 2013 Accenture. All rights reserved. 8
9 Typical Big Data Analytic Architecture Big Data Analytics Structured Silo #1 Aggregations Ads Structured Silo #2 Pattern Mining Big Data Transformations Offers Structured Silo #3 New Features Feature Repository Recommendations Propensity Unstructured Data Copyright 2013 Accenture. All rights reserved. 9
10 Agenda Big Data Analytics Accenture Point of View Copyright 2013 Accenture. All rights reserved. 10
11 Analysis shows that the industries that would most benefit from Big Data analytics are Retail, Media, Financial Services and Healthcare Value Potential of Big Data and Ease of Value Capture, Europe, 2011 High Manufacturing Utilities Health Care Finance & Insurance Scale of Data Usage Scientific & Tech activity Construction Mining Retail (Trade / Distribution) Accommodation & Food Transport & Storage Real Estate Comms, Media & Information Government / Public Services Industry Prioritisation 1. Retail & FMCG 2. Advertising/Comms/ Media 3. Financial Service 4. Government & Transport 5. Healthcare Low Low Value Potential Generated by Big Data High Size of bubble represents potential economic value added by industry sector Source: Accenture analysis based on IDC, Eurostat, Gartner and McKinsey estimates Copyright 2013 Accenture. All rights reserved.
12 Building products for these industries based on geo-location use cases, gives significant potential market revenues Retail Footfall and Segmentation Real-time footfall analysis products can be created from mobile network events (e.g. connecting to a cell tower, voice, texts), Wi-Fi data, GPS coordinates and other forms of geo-location big data. Such products allow accurate and frequent tracking of population movements, especially with regards to footfalls in retail catchment areas Retailers can use this information to devise targeted marketing campaigns and optimise store locations Media Outdoor Media Planning Real-time footfall analysis products can be created from mobile network events (e.g. connecting to a cell tower, voice, texts), Wi-Fi data, GPS coordinates and other forms of geo-location big data This can be used for the advertising industry to more accurately calculate marketing ROI for the out-of-home media channel (e.g. billboards, transport signage) Financial Services - Fraud Banks are also partnering with mobile network operators to improve their realtime fraud detection and credit scoring Fraud detection algorithms can be augmented by the location of a customer s mobile phone when making online transactions Smart Cities / Traffic Optimisation Smart Cities topic utilises multiple forms of Big Data (e.g. M2M, street light sensors, telematics sensors) to aid governments in building a real-time dynamic view of an urban population Big Data from telematics devices can also enable real-time optimisation of traffic flows in a city Copyright 2013 Accenture. All rights reserved.
13 The three beachhead products for Marketing and Media Sector Outdoor Media Platform Mobile Usage Tracking CPS / Sales Conversion Offer Definition Provide direct outdoor audience measurement and expand into digital signage infrastructure Target Customers Outdoor & Event Marketers Offer Definition Provide aggregated mobile Internet, App, Device and Ad usage metrics based on actual consumer usage on the Telco network Target Customers Digital Marketers Offer Definition Generate customers for advertisers by providing customer value insight and running multi-channel campaigns Target Customers Digital Marketers Outdoor & Event Marketing Agencies Digital Advertising Agencies Outdoor Publishers Digital Publishers Event Venue / Properties Mobile Device OEMs Copyright 2013 Accenture. All rights reserved.
14 Big Data Applications and Solutions Indicative Applications 1.Modeling true risk 2.Customer churn analysis 3.Recommendation engine 4.Ad targeting 5.PoS transaction analysis 6.Failure Prediction 7.Threat analysis 8.Trade promotion effectiveness Solution Patterns 1.Predictive Modeling 2.Data Visualization 3.Cluster Partitioning 4.Outlier Analysis 5.AB Testing 6.Markov Chains Copyright 2013 Accenture. All rights reserved. 14
15 Big Data Solution Overview Internal Customer Data Social Media & External Data Transactional Social Media 1. Acquire Machinegenerated / Sensor-data ETL TERADATA BIG DATA ETL Macroeconomic 2. Organize DATA INTEGRATION Predictive Analytics 3. Analyze SENTIMENT ANALYSIS, TEXT MINING and PREDICTIVE ANALYTICS PERSONALISED OFFERINGS Cosine Similarity Method Neural Networks 4. Evaluate Copyright 2013 Accenture. All rights reserved. Web Mobile Internal dashboards and analytics Decision Tree Customer Micro-Segments 15
16 Big Data requires separate and scalable technology infrastructure (e.g. Hadoop, EMR) to traditional BI to cope with scale, velocity and variations BI Analysis Advanced Analytics Data Ingestion Hive HDFS Map/Reduce Rack Node Disk CPU Rack Node Disk CPU HBase Compute/Storage Rack Node Disk CPU MPP Agile Data Marts NoSQL Data Integration (REST) Real-time, In-Memory Analytics Customer-Facing Apps Hadoop Ecosystem Illustrative Conceptual Solution Architecture Copyright 2013 Accenture. All rights reserved.
17 Selection of appropriate predictive analytics techniques Cosine Similarity Method Model customer behaviour using linear algebra For example: customers can be modelled as vectors, merchants as vector components, spending amounts as component magnitude Artificial Neural Networks Neural networks are powerful machine learning algorithms that use complex, nonlinear mapping functions for estimation and classification. Models with more complex topologies may also include intermediate, hidden layers and neurons Decision Trees Decision Trees are perhaps the most popular classification technique Through successive partitions of the initial population, their goal is to produce pure sub-segments, with homogeneous behavior in terms of the output Customer Segmentation Multi-dimensional grouping of customers based on needs, behavior and value dimensions, according to pre-defined business objectives Segment profiling as input to customer strategy around value propositions, products, services, channels & experience Copyright 2013 Accenture. All rights reserved. 17
18 Accenture Point of View 360-degree view of customers Benefits Client Examples Details 1. Improved agility to respond to competitive threats European mobile operator South-East Asian operator Drawing and combining data from real time feeds and traditional historical data enables generation of real time insights, inference of hot spots (i.e. current interests, new location patterns, reactions to competitor promotions), presentation of the right product at the right time to each customer. 2. Richer insights from social media US-based network operator Extracting and combining social characteristics with existing mobile behavioral knowledge, Big Data Analytics create deeper insights to engage and retain existing customers. 3. Differentiated user experience US wireless operator Integration of data coming from IPTV set-top boxes and voice and data usage combined with application of basket analysis generate: a channel affinity map that links channels most likely to be viewed together, recommendation of appealing bundles based on viewing patterns. Copyright 2012 Accenture. All rights reserved. 18
19 Accenture Point of View 360-degree view of customers Benefits Client Examples Details 4. Identification of leaders and followers US based wireless operator Combination of social media information can help to follow actions of super-influencers during all stages of any product s lifetime. This functionality: adds value during early product adoption and service take-up stages, promptly identifies all signs of contagious churn, provides recommendations for customized targeting (i.e. pricing, bundle of offers of interest) to super-influencers. 5. Smarter business decisions Spanish mobile operator Integration of call detail records, location data and usage patterns enables location-based real time marketing offers. 6. Tailor-made real time recommendations for each customer interaction UK mobile operator Application of predictive analysis and historical customer engagement rules on real time customer interaction information enables a client to offer appropriate real time recommendations to each interacting customer. Copyright 2013 Accenture. All rights reserved. 19
20 A case study from a major financial institution Business Challenge Client sought partner to: analyze customer patterns of behavior, provide recommendations to customers for best actions that have proved of help to similar customers, provide recommendations to customers based on their upcoming key life events, Project scoped to span from data collection and integration to modeling and insight generation. Accenture Contribution Integrated a wide variety of internal and external data sources in an efficient way. Developed sophisticated statistical modeling techniques to identify groups of customers with similar behaviors. Implemented cutting edge algorithms to infer insights for customers best courses of action. Developed innovative approaches to provide suggestions to customers, depending on their corresponding key life events. Key Benefits Increased Sales to existing customers: Proactively contact customers based on behavioural triggers and key life stages. Increased retention of existing customers: Proactively target customers with high risk of churn with specific high value services. Increased acquisition of new customers: Provide Personalised pricing and use social data indicators during interactions. Copyright 2013 Accenture. All rights reserved. Illustrative Outputs Correlation and Prediction Analysis and Decision Making Cosine Similarity Method Neural Networks Recommendations and Insights Decision Tree Customer Micro- Segments 20
21 Improving the performance on a US online media content provider Business Challenge The client is one of the leading online video streaming companies in the US The available titles are currently over 8000 and are offered either in SD or in HD format. The client offers titles from movie companies such as EPIX, Lionsgate, NBCUniversal, Paramount Pictures, Relativity and Sony Pictures The client was to become the leader of the market and obtain a market share greater than that of Netflix The recommendations are currently static and not generated though an automated process The client wanted to evaluate the increase in the market share and bottom line an automated intelligent recommender system can bring in. Outcomes The recommendations of ARE were compared to those of the client using Accenture s offline validation approach and it was found that they improve performance by 10% in terms of recall and precision. Currently a live validation is performed via the channel: ARE s recommendations are offered to clients and their responses are recorded in order to assess the effect on the performance. Most recent results present an increase of 10-15% in views (rentals, purchases and subscriptions) How Accenture Helped Accenture engaged in a PoC with the client to assess the sales lift that ARE can achieve. Accenture used multiple data sources spanning different dimensions of the customer DNA and applied user-based collaborative filtering techniques to identify the best possible user-movie matches The solution is hosted on the cloud and it operates in real time offering targeted recommendations to the subscribers of the customer Copyright 2013 Accenture. All rights reserved. 21
22 Helping the Leading Chinese online retailer improve their recommender system Business Challenge leading B2C ecommerce portal (35% market share) in China. The Client has expanded beyond their core IT and consumer electronics products, into general merchandize and books, and has also penetrated the C2B2C channel via their public open platform. Its leading position has attracted investors, and they are now planning a US IPO. Its aim is to be the Amazon.com of China. Its annual revenue was approx. 6BN USD in The client is now exploring alternative growth areas, beyond category expansion. As a key strategic thrust in 2012, they seek to grow revenues via online product recommendations and web page optimization. The current recommender system contribution to sales is below industry benchmarks. Client is growing rapidly: Number of transactions double every 5 months and Long tail: 80% of their products account for only 5% of their sales Outcomes Accenture developed approximately 40 machine learning algorithm variants to give us the results. In online testing ARE outperformed the internal recommendation engine of the client by up to an estimated 30% This is translated to revenue uptick for the client, which is estimated to be initially up to ~$100M USD per year (we expect this to increase over time). We have also applied out robust repeatable agile testing platform to rapidly prototype algorithm performance. The test platform logic is derived from the field of information retrieval (IR) where we calibrated algorithm performance according to their impact on precision and recall. How Accenture Helped Accenture is helping the client improve its recommendation capability, with a focus on driving transactions via better use of data and improved algorithms, leveraging Big Data analytics and web page optimization. Accenture used a global team consisting of experts in machine learning and big data from Adelaide, Athens, Beijing and San Francisco to deliver results for Implementing our recommendation engine algorithms (ARE) Copyright 2013 Accenture. All rights reserved. 22
23 Leading US Discount Coupons Company Business Challenge The client is a leading coupons company in the US offering discount coupons to consumers. The discount coupons are issued by manufacturers who want to use this channel to increase their sales. The customer is one of the leading players in the coupons industry and they want to increase their market share by applying intelligent, real time automated targeted methodologies The client is affiliated with many grocery stores chains all over the country The challenge is to combine heterogeneous data from many different sources and to generate insights and provide personalised coupon recommendations to customers using various means: web site, digital receipt, s, etc. Outcomes ARE has been empowered with new algorithms that cater for the peculiarities of the coupons industry. ARE s algorithms recommend not only coupons to customers, but also products for which new coupons should be issued The site has been re-organised and coupons are presented to customers based on the corresponding customer to coupon estimated interest During the first months of operation the coupon activation rate has increased up to 20%, depending on the product category Identification of most desired/recommended products contributes in the design of more targeted coupons. How Accenture Helped Developed an in-house big data platform that collects transactional data from affiliated grocery stores, processes them and generates automated personalised recommendations using ARE Accenture used a global team consisting of experts in machine learning and big data from Adelaide, Athens, Chicago and San Francisco to deliver results for Implementing our recommendation engine algorithms (ARE) ARE is estimated to process 2-3 TB on a daily basis in order to deliver updated, real-time recommendations Copyright 2012 Accenture. All rights reserved. 23
24 Thank you! Copyright 2013 Accenture. All rights reserved. 24
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