List of Figures

Customer First. Value Next.
The Executive Playbook for AI-Driven Omnichannel Personalization and Customer-Centric Growth

by Mariusz Gromada

Visuals & Diagrams

• Figure 1: The evolution of industrial revolutions: From Steam to AI. [355]
• Figure 2: The evolution of banking models driven by technological revolutions. [34]
• Figure 3: Descriptive Analytics focus: Reporting the past. [47]
• Figure 4: Diagnostic Analytics: Moving from observation to root causes. [48]
• Figure 5: Finding value at the edges: How Predictive Analytics targets high potential and high risk. [49]
• Figure 6: From Hindsight to Foresight. The evolution to Predictive Analytics. [49]
• Figure 7: The whole evolution from passive observation to prescriptive analytics as active decision making. [50]
• Figure 8: The paradigm shift from a mechanical product focus to human-centric personalization. [52]
• Figure 9: The three pillars of value creation via AI Predict, Automate and Generate. [54]
• Figure 10: The transformation path from raw data to executed action. [61]
• Figure 11: The continuous cycle of turning analysis into business execution. [66]
• Figure 12: The end-to-end data supply chain from planning to consumption. [67]
• Figure 13: The evolution of data architecture from centralized Warehouse to decentralized Data Mesh. [69]
• Figure 14: The Cloud Pyramid showing trade-offs between control and convenience. [71]
• Figure 15: Strategies for scaling infrastructure capacity horizontally versus vertically. [72]
• Figure 16: Moving Data Governance from static policy to continuous daily practice (regulatory requirements not covered). [76]
• Figure 17: Removing the IT bottleneck to empower business teams. [78]
• Figure 18: The convergence of Tools, Education, and Culture required for true Data Democracy. [79]
• Figure 19: The complete analytics workflow from setting goals to creating value. [81]
• Figure 20: The iterative learning loop connecting Voice of Customer and Voice of Data. [133]
• Figure 21: The complete Customer DNA combining static attributes and dynamic signals. [138]
• Figure 22: The strategic trap where easy acquisition drives hard retention. [152]
• Figure 23: Building relationship depth to maximize Customer Lifetime Value. [159]
• Figure 24: The method rationale for determining the Regional Fair Share based on local presence. [183]
• Figure 25: Reconstructing the complete customer profile by inferring external relationships. [189]
• Figure 26: Building Multi-Tagging through overlapping behavioral tags. [192]
• Figure 27: Identifying unique customer tribes based on common qualities and profiles. [199]
• Figure 28: The efficiency of capturing 80% of value with just 20% of effort. [204]
• Figure 29: Targeting Persuadables to capture actual incremental value. [210]
• Figure 30: Moving from locating customers for products to discovering products for customers. [214]
• Figure 31: Balancing customer propensity with business value when choosing the Next Best Action. [223]
• Figure 32: Optimizing margin by adjusting prices based on customer sensitivity curves. [231]
• Figure 33: Distributing success credit fairly across channels, assigning Originator, Assist, and Closer roles. [237]
• Figure 34: Swift decline of promotion hunters compared to primary relationships. [240]
• Figure 35: The Retention Dilemma of balancing influence and prediction accuracy. [241]
• Figure 36: Using Geo-Intelligence to map real customer flows and branches’ catchment. [246]
• Figure 37: The strategic risk of depending on manual processing as data volume explodes. [254]
• Figure 38: Moving complexity to the machine to keep things simple for the business and the customer. [264]
• Figure 39: The complete Factory blueprint connecting Senses, Brain, and Voice. [267]
• Figure 40: The varied skills needed in a modern Data SWAT Team. [298]
• Figure 41: The maturity gap across ten dimensions of the CI-RM Customer-Centric Capabilities Model. [303]
• Figure 42: Showing the shift from Firefighting to the Personalization Factory. [305]