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Rethinking it for digital transformation
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From Customer Insights to Action

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The customer journey could essentially be divided into 7 elements. We’ll touch upon the issue of ‘Privacy’ and how one balance social and commercial value. Practical examples of
customer analytics at its best will be discussed as well as the importance of the eco-system.

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From Customer Insights to Action

  1. 1. From Customer Insights to Action Ruurd Dam, November 2015
  2. 2. 2Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date In a world of connected people and things … 1,820TB of Data created # Source: World Economic Forum *Source: Gartner 168 Million+ emails sent 98,000+ tweets 11Million instant messages 217 new mobile web users 25 Billion Connected "Things" in use in 2020* 3,5 Billion Cars 13,2 Billion consumer devices 695,000 status updates 698,445 Google searches 2.5 Billion social network users in 2018
  3. 3. 3Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date … the new data landscape is the centerpiece of digital change …… IoTMobile Cloud Social Media New Data Landscape No limit to volume No limit to structure No limit to analyzing No limit to timing
  4. 4. 4Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Worldwide we see a four strategic ‘data plays’ for businesses and organizations Insights-as-a-Service Platform BI factories, MDM 1 Big Data 2 Insights 3 4 BusinessTechnology Reporting (looking back) Insights (predictive, prescriptive) Existing Data Landscape New Data Landscape
  5. 5. 5Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Today we will do a deep dive into 3 out of 7 Insights & Data Principles Unleash Data and Insights as-a-service Make Insight-driven Value a Crucial Business KPI Empower your People with Insights at the Point of Action Develop an Enterprise Data Science Culture Master Governance, Security and Privacy of your Data Assets Enable your Data Landscape for the Flood coming from Connected People and Things Embark on the Journey to Insights within your Business and Technology Context 1 2 3 7654
  6. 6. 6Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Master Governance, Security and Privacy of your Data Assets H U R D L E S HOWTOGETTHERE? Source: Capgemini Consulting Report – Cracking the data conundrum: How successful companies make Big Data operational. Lack of strong data management and governance mechanisms is one of the greatest obstacles to the success of insight-driven business. 54% no IT-business joint projects for Big Data initiatives 47% scattered pockets of resources / follow a decentralized model for analytics initiatives 53% no top-down approach for Big Data strategy development 54% 47% 53% Industrialize & mature your data processes & organization, using industry best practices, to increase productivity, agility & manageability Develop a healthy risk appetite to ensure end- to-end security and privacy of your data assets, while staying outcome-focused Engineer a governance mix that fits your culture, balancing central and de-central, top-down and bottom-up Define policies and procedures for management of data assets Develop Big Data competencies Set up the technological base for Big Data initiatives Establish a robust governance framework Big Data Operating Model Data is an invaluable asset. You need a high-performance data organization that embraces privacy and security, is equipped to meet both current and future enterprise needs and mirrors the dynamics of the enterprise. ORGANIZATION
  7. 7. 7Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date • Save people life’s • Better maintenance • Higher traffic security • Bigger yields • Invest smarter •……. • Invasion of privacy • Lack of transparancy • Monetization of data •… •… •….. ..however..we are strange people…. EU Privacy regulations: are data blessing or curse? Source: Wij zijn Big Data, Sander Klous, Nart Wielaard
  8. 8. 8Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date The answer to privacy has perhaps something to do with profit ánd people and planet.. Source: Wij zijn Big Data, Sander Klous, Nart Wielaard Achmea geeft premiekorting voor data van klant, FD, vandaag
  9. 9. 9Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Develop an Enterprise Data Science Culture H U R D L E S HOWTOGETTHERE? Data science – a relatively unknown area Skills and competencies are scarce To change the enterprise mindset to leverage data science Enterprise Data Science Framework SOLUTIONS Value of analytics often stays purely conceptual - to the uninitiated A data-embracing culture does not come naturally, even when it’s part of the strategy Data Prep Selection & Cleansing Modeling Design & development Define Objectives & Levers Simulation optimization & Evaluation Data inventory collection & Understanding Deployment solution within the system Combine business acumen, analytical skills and technology expertise Systematically build and acquire the required data science capabilities Provide hands-on experience with analytics - in real action – to get stakeholders involved and committed Speeding up business value through affordable real-time analytics Analytics Accelerator Data science unlocks insights. Making everybody in the enterprise a bit of a ‘data scientist’ requires nothing less than culture change. An organization that has a data science -led culture, can truly become insight-led CULTURE
  10. 10. 10Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Data Science is the interplay of data, business process, technology and statistics 1 Industry/Vertical expertise and Customer focused 2 Domain / Functional Expertise (DCX, Sales, Customer Service, Marketing) 3 Business Analysis 4 Solution Design 5 Pre-sales skills/ Presentation and Communication Business Skills 6 Ability to build Prototypes (Presales engineer) 7 Machine Learning / Modeling 8 Statistics /Mathematics 9 Simulation and optimization 10 Data profiling 11 Data Preparation and Mining tools (e.g. SAS Base, EG, R, SQL) 12 Visualization and design (Microstrategy, Tableau, SAS VA 13 Ability to perform data science on Big Data, Hadoop (Pivotal/Cloudera) 14 Digital Analytics (Adobe, Google Analytics, IBM digital analytics) Research Skills Technology Skills
  11. 11. 11Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Empower your People with Insights at the Point of Action H U R D L E S HOWTOGETTHERE? Making the right insights available at the right time at the right place to the right person is critical Organizations face the scarcity of data science, deep sector knowledge & technological expertise Perception of the digital transformation ambitions and challenges may not be uniform across all business units Understanding of how insights can be seamlessly integrated at the point of action is rare Make sure you leverage your market and industry expertise at the business side to select key Insights Validate the value of your selected Insights by piloting them quickly – right at the point of action Never stop your quest for insights: Build on your experience to find more and better opportunities Identify insights to incite action on the spot to drastically improve customer experience, optimize operations and reinvent business models All organizations are a series of decision points, both at the macro and micro level. Empower your people with timely insights to make those decisions better and transform your business. Mastering ‘Insights Inside’ is the essence of the journey. INSIGHTS
  12. 12. 12Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Customer Value Analytics (CVA) – to make a fact based and decisive impact on customer journeys More selling, more effective marketing and improved customer experience Global Swedish Retailer ACQUIRE Acquire new customers by implementing innovative customer sourcing/profiling models AVOID FADING AND ATTRITION Retain customers by identifying churn drivers and building churn propensity models GROW SHARE OF WALLET Increase profitability of the existing customer base by building cross sell, up sell or next best action models Digital Channel Migration Pre-qualification & Customized Offering Acquisition Customer Service Retention Promotion Awareness Transactions Information Activation Receive Service Cross Sell/Up SellAdvice/Need Renew Explore/ Experience Lead Generation/ Campaign Management
  13. 13. 13Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date CVA is the science and art of converting data into insight that can be used to drive a great customer experience “I can explore my customer data, but is it correct?” “How frequently and recently are customers buying?” “How likely is a customer to respond to my offer?” “What is the next best action for a customer?” Value extracted from Information CompetitiveAdvantage The five stages of analytics maturity “Why do the performance reports from my teams disagree?” Reporting Descriptive Analytics Predictive Analytics Prescriptiv e Analytics
  14. 14. 14Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date With clients we make a first selection of applicable use cases using Customer Value Analytics prioritization matrixBUSINESSVALUE Phase 1Phase 2 Phase 3 High Low Difficult EasyEASE OF IMPLEMENTATION 5 23 2 11 1 9 4 8 7 6 12 3 19 18 15 14 13 20 16 10 43 38 29 28 27 26 25 24 22 2117 39 37 36 35 31 3042 41 40 32 34 Business Value Levers: Increase Sales, Reduce Cost, Improve Working Capital Ease of Implementation Levers: Data / Tool readiness, dependency on 3rd party, organizational readiness & alignment etc # Insights & Data Capability # Insights & Data Capability 1 Standardized Reporting 23 Program ROI 2 Ad-hoc Analytics 24 Shopper Targeting 3 Household Segmentation (Protect, Recover, Develop Strategies) 25 Sweepstakes 4 Store Segmentation 26 Net Value Cost Analysis 5 Trip Mission / Market Basket 27 Exclusivity & Loyalty 6 Shopper Insights 28 Effective Pricing 7 Test & Control Identification 29 Promo Decomposition 8 Retail Labs (R&D) 30 Household Segmentation 9 Assortment Optimization 31 Category / HH Targeting 10 Brand & Pkg Switching 32 Assortment Optimization 11 Out-of-Shelf Analytics 33 Product Lifecycle Management 12 Plan-o-gram optimization, Development & Space Management 34 Loyalty Analytics 13 New Product Introduction / Adoption 35 Customer Group Analysis 14 Replenishment Planning 36 Neural Net Demand Forecasting 15 Top Shopper Identification 37 SC Network Optimization 16 Product Affinities 38 Promotion Decomposition with ROI 17 Household Exclusivity & Loyalty 39 Vendor Performance 18 Digital Analytics (e-com, social media etc). 40 Working Capital Analytics 19 Campaign Analytics 41 Store Layout Optimization 20 Customer Lifetime Value 42 Predictive Asset Maintenance 21 Customer Churn 43 Recruitment Effectiveness 22 Cross Sell / Up Sell
  15. 15. 15Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date PALLAS- Capgemini selecting use cases 0 1 day Business & Data Value1 3 weeks Proof of Value2 8 weeks 'Fail fast, fail early' A Silicon Valley mantra An Insights Driven Journey is “to think big, start small and grow fast”
  16. 16. 16Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Marketing Analytics & Campaign Management for UK Retail Client The objective was to design an analytical platform that could enable business and statistical analysts to mine data and derive insights for new marketing campaigns  New platform will drive market position by enabling  Cross-functional data available in one place  Analytics and data mining to understand customer behavior  Complex campaign design and management  Effective and efficient delivery of analytics and campaign management services via Capgemini’s Right Shore framework  The client, wanted to enhance their marketing analytics and campaign management platform for two of their in- house brands  Current campaign management platform lacks analytical capability and can run only basic campaigns. Campaign tracking and response modeling is also limited  No integrated source of marketing and customer information. Several disparate sources  Analytical insights and models to be integrated with campaign management platform  Integrated platform to design campaigns, allocate budgets, coordinate implementation and measure response  IBM SPSS for analytics; IBM UNICA for campaign management, Oracle 11g for Marketing DB  Analytic enablers to build Response models, Churn models and share of wallet analysis  Analytic enablers in building behavior analysis for effective target segment identification  Analytic enablers to design end to end campaign management platform BUSINESS / DATA CHALLENGES SOLUTION BENEFITS Tools used  Model Development – IBM SPSS Modeler  Model Deployment – Oracle, IBM Unica Campaign High level architecture design for the new environment
  17. 17. 17Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Propensity Models and facilitated Event-Based Marketing to identify Home & Car Insurance Prospects for a leading Norwegian Insurer The objective was to predict the propensity of a Bank/Pensions customer to purchase home or car insurance products in order to enhance the conversion rate of cross-sales efforts  A set of 8 distinct logistic regression models that predicted the probability for each Bank and Pensions customer to purchase a home or car insurance product  The model indicated that 35% of non- customers could be potential purchasers of home or car insurance products  Data insights around the profile of existing home and car insurance customers vs. non- customers  Hypothesis testing based on variables that reflect the customers’ decision journey  The propensity to purchase was calculated in the form of probability for each customer using binary logistic regression BUSINESS / DATA CHALLENGES SOLUTION BENEFITS 22% 78% No. of Customers (1000s) by Gender Male Female Holders Non-Holders 9% 30 % 40 % 21 % No. of Customers (1000s) by Age Group <= 28 28 - 44 Data Cleaning & Structuring Missing Value Treatment Hypothesis Testing Active Only Customers Predicted Non- Customers Predicted Customers Non-Customers 62% 35% Customers 1% 2% N = 1 Customer has Car/Home Insurance N = 0 Customer does not have Car/Home Insurance 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0 0.5 1 1.5 2 2.5 3 Before I/A Grouping After I/A Grouping After Dropping Vars After Dropping Vars 2After Dropping Vars 3 Lift(Training) Lift(Testing) ROC Model Performance Analysis Feature Importance Model Accuracy 17% 83% Car Insuranc e 11% 89% House Insuranc e
  18. 18. 18Copyright © Capgemini 2015. All Rights Reserved Presentation Title | Date Digital Analytics helped a leading Global CPG player achieve a 2x improvement in online campaign reach and effectiveness The objective was to monitor online visitor behavior through real-tile listening and help the client team to fine- tune its online messages and content. This helped the client to improve the campaign reach and engagement  The analysis enabled the client to fine- tune website content in real-time and thereby engage better with their target audience  The analysis identified which segments and geographies responded best to the campaign  The analysis helped the client allocate more content to sites with higher traffic and also coordinate channel integration  Resulted in 2.5x increase in reach and 2x increase in engagement levels  Resulted in lowest cost per engagement for online channel relative to traditional media channels  The client was undertaking multiple online campaigns across its micro site and social media channels for the a marketing event spanning 4 days.  The Analytics team had to work closely with the client’s media-buying, creative and brand teams to analyze real-time online visitor communication and suggest appropriate response strategies  The methodology involved creation of customized profiles for the micro site using Google Analytics & configuration of social media listening tools to capture visitor behavior on the micro site (with a lag) and on social media channels (real- time)  The listening tools also identified potential influencers on a real time basis BUSINESS / DATA CHALLENGES SOLUTION BENEFITS
  19. 19. Thank You
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The customer journey could essentially be divided into 7 elements. We’ll touch upon the issue of ‘Privacy’ and how one balance social and commercial value. Practical examples of customer analytics at its best will be discussed as well as the importance of the eco-system.


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