Importance of predictive analytics to business agility
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The document discusses the importance of predictive analytics for business agility. It outlines how predictive analytics can help businesses address challenges in areas like customer satisfaction, consumer behavior, pricing, and inventory management. The document also provides examples of how TCS has helped clients in various industries like retail, banking, manufacturing, and media leverage predictive analytics to improve outcomes in domains such as marketing ROI, risk assessment, demand forecasting, and customer churn. TCS's analytics as a service model aims to provide an integrated and global team to help clients derive value from analytics.
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Importance of predictive analytics to business agility
- 1. 0Copyright © 2014 Tata Consultancy Services Limited Importance of Predictive Analytics to Business Agility TCS Predictive Analytics Event N. Sarma Vice President & Head of Analytics and Insights
- 2. 1 TCS Public Agenda Business Challenges & Applications What’s Next with Predictive Analytics TCS Case Studies
- 3. 2 TCS Confidential 2 Data: The New Natural Resource Market Trends Social Media Customer Satisfaction & Loyalty Consumer Behavior Price & Promotions Sustainability Issues• Demand and supply forecast accuracy • Inventory Management • Effective pricing • Managing losses • Sourcing efficiency • Capacity Utilization • Market Share • Share of voice • Brand health • Demand growth Utility enterprise
- 4. 33 TCS Sensitive Moving up the Analytics Value Chain BusinessValue Solution Sophistication Data Management Reporting Descriptive Analytics Predictive Modeling Optimization
- 5. 44 TCS Sensitive Pertinent Challenges that Plague most Businesses Personalization Stock out Fraud Loss Optimal Pricing Credit Loss Marketing ROI Customer Engagement IMPERATIVES FOR BUSINESS AGILITY
- 6. 55 TCS Sensitive BFS Insurance Energy & Utilities Hi- Tech Life Sciences MfgMedia Retail Telecom Travel Industry Predictive Analytics, a Game Changer for every Industry •Anticipate Risk & Fraud •Manage credit lines and collections •Customer profitability & churn • Improve Marketing ROI •Optimal Maintenance Scheduling •Routing and Load Factor Optimization •Demand & Sales Forecasting •Service Plan Optimization •Churn Analytics •Preventive Asset Maintenance •Network Optimization •Inventory optimization •Optimal Pricing •Campaign Analytics •Loyalty & Churn analysis • Demand based pricing • Optimal Program Planning • Market Sentiment • Marketing ROI • Set Premiums • Improve Customer Loyalty •Improve Marketing ROI •Customer Life-time Value •Improve Collections •Cost Assessment •Optimal Tariff •Load Planning •Optimize Production •Pricing Optimization •Supply Chain Optimization •Demand Planning •Inventory Forecasting •Sales force Optimization •Call Profitability •Risk based site monitoring •Demand Forecasting •Demand Forecasting •Optimal Pricing •Warranty data analytics •Failure Prediction Prediction is key to the success of any business
- 7. 6 TCS Confidential 6 TCS Provides end to end Analytics to a large global market research company 6 Advanced Analytics Covers Predictive Modeling of Market Mix, Price, Promotion & Assortment Measurement Science Delivers Statistical Analysis and Data Management Client Services Provides Insights, Foresights & Trends in Business & Consumer Watch Services Analyzes & tracks TV, Mobile, Internet, Social Media to measure brand performance Analytics as a Service 50% Saving in Client Service Executive time >30% Improvement in Cycle Time Global Hubs Created for Top 5 Customers Design Centre Built to create new methodologies & experiment techniques Training For New Joiners
- 8. 7 TCS Confidential 7 TCS Analytics Helps Shape a Large Australian Bank’s Strategy 7 Sales Reports Provide insights on branch performance, sales trends & profitability BASEL Helps manage Capital Risk Marketing Analytics Enables identification of target customers and promote the right offers Service Analytics Supports call center capacity planning that determines right response time to satisfy customer Data Management Consolidates data, cleanses & hosts it on a platform for a single version of truth Analytics COE Contributes significantly in shaping the bank’s strategy across Revenue growth, Profitability, Loss reduction, Service excellence
- 9. 8 Decision Support ChurnAnalyticsCampaignAnalytics SocialMediaListening&Analytics Consumer Insights Predictive Analytics Sales&MarketingAnalytics Supply Chain Media Planning Market Basket Spend Analytics Capacity Analytics SKU Rationalization Loyalty Analytics Assortment Planning Pricing Analytics Inventory Optimization Demand Forecasts Marketing Mix Customer Segmentation $ 15 MM Opportunity identified for new product launch through price optimization 200% Increase in campaign response rate $ 70 MM Additional revenues by increasing response to marketing campaigns 2-3% Inventory reduction through accurate demand forecasting and right pricing $ 5 MM Opportunity identified by marketing budget optimization HR Analytics Delivering value through analytics 30% Increase in chargeable premium for maintenance of risky assets, by a Turbine Manufacturer possible with TCS’s stochastic forecasting models , 70% Reduction in scheduling TAT . $ 120 M Credit Loss Saves and $ 450 M in revenues for Top 5 Bank
- 10. 9 • Steering group • Program management office • Delivery structure • 3rd Party management • Business metrics impact • “Prove performance” • Business analysis • Change management • Value demonstration • Business development• Business process expertise • Training - modeling, statistical analysis, tools • Knowledge sharing AaaS Organization strategy Business engagement Technology foundation Governance Analytics development Service delivery model Outcome measuremen t Competency Incubation & Innovation • Data integration • Capacity planning • Environment management • Data management • Data quality • Stewardship • Knowledge management • Analytical methods • Analytical models & Validation • Re-use of components • Software tools • Analytics Maturity plan • Process / Workflow • Agility & Service levels • Standards / Consistency • Complex problem solving • Emerging technologies • Disruptive applications • Next gen Insights delivery & Visualization INTEGRATED TEAM - GLOBAL HUB – VALUE DRIVEN - ENHANCED END USER EXPERIENCE Analytics as a Service – Engagement Model
- 11. Thank You
Notas do Editor
- The era of Big Data has arrived. The amount of enterprise data, and the rate at which it’s being accumulated, is rising exponentially. The proliferation of mobile devices, artificial intelligence, Web analytics, social media and other types of emerging technologies is creating new data streams that only add to traditional data stores, such as transaction records and financial data.
- Descriptive analytics—already in wide use in many ad hoc ways—looks at historical data, helping companies answer such basic questions as what happened, why it happened, and how much it helped or hurt results. Predictive analytics extends those findings using sophisticated statistical modeling, forecasting and optimization algorithms to anticipate the impact of various actions, such as promotions, price changes and advertising, on business outcomes. Predictive analytics is about more than simple linear what-if exercises. It enables complex, dynamic research with multiple variables and often involves flurries of concurrent, low-cost, fast-turn market experiments. Forecasts become more about facts than about hunches.
- Personalization: The younger customers demand highly customized products and services to a point where businesses need to personalize their offerings to stay in the game.Stock out: Improper planning and forecasting of inventory and production is causing business losses that run into millions.Fraud Loss: Loss owing fraud is growing at an alarming rate making it imperative for businesses to predict such a transaction in order to mitigate the loss. I also important as it has a bearing on the company’s reputation.Optimal Pricing: To be able to understand the market forces, customer purchase behaviour and competition moves and develop a price point that is attractive and competitive is to be ahead in the game. Credit Loss: Credit loss is plaguing the financial services industry. From policy rebuilding to predicting possible defaults, Fis are working towards minimizing the amount of credit loss they suffer.Marketing ROI: Increased pressure to improve marketing ROI makes marketers look at best channels and optimal output which analytics can help with.Customer Engagement: Predicting future customer behaviour from past engagement with the brand can enable marketers to develop targeted marketing campaigns to improve Customer Lifetime Value.
- Gone are the days of data mining. With real-time, predictive analytics, companies have the ability to derive customer insights, mitigate risk and make better decisions. BFS:Credit card companies use predictive analytics to manage credit lines and collections as well as to target customers with exactly the right direct mail campaigns. Insurance companies use predictive analytics to set premiums. Banks, insurance companies and even government agencies have turned to analytics to root out fraud. Personalizing Customer Experiences through real time analytics – predicting behavior on the spot and providing customized solutions. Insurance: Predictive analytics is imperative to insurance organizations, which are particularly reliant on predicting future activities. An insurer’s ability to forecast a policy’s ultimate cost determines how accurately it prices its product and, in turn, the extent to which it can avoid adverse selection. In the fight for market share going on today, accurate pricing based on policy performance is one of the critical areas.ERU: Without real-time and historical knowledge of energy usage and demand data, options to reduce load and cost are limited. Analytics provides the understanding of where improvements are needed, the measurement and verification of improvements performed, and optimization of energy programs to solve for best outcome and predict results.Hi-Tech: The high tech business is constantly changing, customer demands are very high, and product life cycles are getting shorter. Big data, cloud computing, social media, and mobile devices are impacting the way they operate. Theyneed to leverage predictive analytics to run business with real time insight, predict business outcomes, and immediately adapt processes to changing conditions.Life Sciences: Payer restraints, more complex products and more compact sales forces have sharpened the imperative to get the right drug to the right doctor with the right message at the right time for the right patient. Analyzing and predicting need and demand is key in the pharma arena.Manufacturing: The role of data in manufacturing has traditionally been understated. Manufacturing generates about a third of all data today, and this is certainly going increase significantly in the future. Data forms the backbone of all Digital Manufacturing technologies, which will be the centerpiece of the strategy for advancing Manufacturing in the 21st century. Predictive analytics and big data techniques will be key enablers to best leverage this new data.Media: While media companies are generating a huge amount of data themselves, it is key for them to analyse this data to plan, design and deliver their programs and services to customers in the form they appreciate the most.Retail: This industry has traditionally used analytics to create their strategies. Only now they are looking at more sophisticated analytics to predict the future and make intelligent business decisions.Telecom: Telecom services are becoming increasingly commoditized, and telecom companies or telcos are trying to break out of this impasse both strategically and operationally. Using predictive analytics techniques could be the solution.Travel: While heaps of big data exist in the travel sector – from how seasonality affects bookings to the types of travel packages that receive the highest conversion rates among consumers – leveraging some of the more unstructured streams into effective predictive modeling can be a challenge.