Topic: Banking-Customer & Marketing Management
Report type: Banking Digital Operations Research Report (银行数字化运营研究报告)

With the continuous advancement of banking digitalization and financial technology, digital operations have become a core capability connecting customers, products, channels, and services. Centered on the customer lifecycle, banks are gradually building digital operation systems characterized by data-driven decision-making, process automation, and refined management. However, the actual effectiveness and inherent constraints of these systems remain to be systematically evaluated.

Please conduct an analysis of the banking digital operation system and, based on concrete case studies, produce a structured research report addressing the following aspects:

1. Current Practices and Implementation  
Systematically review current digital operation practices across key stages such as customer acquisition, activation, conversion, and retention. Analyze how data-driven approaches, process automation, and refined management are applied in these contexts.

2. Effectiveness and Challenges  
Evaluate the effectiveness of these digital operation practices in improving customer value, operational efficiency, and service experience, and identify the main constraints and challenges they face.

3. Optimization Pathways and Future Directions  
Considering technological advancements and evolving customer behavior, propose optimization directions and actionable pathways for future banking digital operation models from the perspectives of operational models, organizational mechanisms, and system capabilities.

## Evaluation Criteria for Research Report on a Bank’s Digital Operations System for Online Financial Business

This evaluation framework is used to provide a unified, executable, and quantitative assessment of analytical reports on a “bank’s digital operations system for online financial business,” balancing consistency between human review and large-model (LLM) automated scoring. The framework contains **4 primary dimensions and 12 secondary dimensions**, with a total score of **100 points**. Scoring focuses on: whether the end-to-end lifecycle closed loop is complete; whether evaluation is verifiable; whether solutions are implementable in real banking scenarios; and whether the optimization path has actionable implementation granularity and risk/compliance awareness.

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### 1. Quality of Operations Framework and Current-Practice Mapping (30 points)
#### (1) Completeness of Full Lifecycle and Closed-Loop Framework (10 points)
- **9–10 points**: Builds a clear closed loop (e.g., “data collection/governance → insights & segmentation → outreach & journey orchestration → conversion & monetization/operations → retention & repeat purchase/cross-sell → review & iteration”), and maps it to typical online-finance business journeys (account opening, card binding, credit approval/loan drawdown & utilization, wealth product subscription, payment activation, etc.); identifies key inputs/outputs and metric definitions for each stage.
- **6–8 points**: Framework is largely complete, but provides insufficient detail for stages such as review/iteration, post-investment/post-loan operations, and cross-channel collaboration; or mapping to online-finance business characteristics is weak.
- **0–5 points**: Fragmented framework that remains at concept listing (e.g., “use big data/AI for operations”), lacking an end-to-end journey and an executable structure.

#### (2) Depth of Practice Decomposition for Acquisition (10 points)
- **9–10 points**: Covers and differentiates major acquisition methods and channels (owned app/mini-program, online account opening, payment scenarios, content/live streaming, external platform partnerships, offline-to-online traffic, etc.); explains the funnel from **spend/delivery → reach → account opening/KYC → first transaction**; discusses cost and quality (CAC, account-opening conversion rate, anti-fraud/anti-abuse) and segment differences.
- **6–8 points**: Describes main tactics, but lacks funnel decomposition, channel differentiation, or quality controls (e.g., only discusses acquisition but not anti-fraud and “effective accounts”).
- **0–5 points**: Only lists channel buzzwords or copies generic internet growth jargon, lacking banking constraints and key steps in acquisition.

#### (3) Depth of Practice Decomposition for Activation, Conversion, and Retention (10 points)
- **9–10 points**: Separately provides concrete operational actions and trigger mechanisms for activation (MAU/DAU, key behavior completion), conversion (first purchase/credit utilization, cross-sell, asset uplift), and retention (repeat purchase, churn warning, lifecycle operations), such as journeys/event triggers, contact frequency capping, benefits/loyalty system, and relationship-manager collaboration; reflects differences by segment/product line (deposits/loans/FX/wealth, credit cards, wealth management, funds, insurance, etc.).
- **6–8 points**: Covers most links, but the linkage mechanism from “activation → conversion → retention,” product-specific strategies, or trigger rules are not concrete enough.
- **0–5 points**: Incomplete coverage or generic content; lacks executable operational actions and scenario-based description.

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### 2. Data-Driven and Fine-Grained Operations Effectiveness Evaluation (25 points)
#### (1) Fit of Data Foundations, Tags/Profiles, and Data Governance (8 points)
- **7–8 points**: Clearly defines data sources and boundaries (transactions/behavior/channels/marketing touchpoints/risk control/customer service, etc.); explains unified ID, data quality, master data management, real-time/offline pipelines; tags/profiles map to bank-accessible data and compliance requirements (data minimization, consent/authorization, audit trails).
- **4–6 points**: Mentions tags/profiles and governance, but lacks key implementation details (e.g., ID stitching, consistent metric definitions, real-time requirements) or has weak compliance boundary discussion.
- **0–3 points**: Unclear data sources; overly generic profiling; or proposes clearly unavailable/non-compliant data usage.

#### (2) Verifiability of KPI System and Evaluation Methods (9 points)
- **8–9 points**: Establishes a layered metric system (business outcome metrics + process metrics + quality/risk metrics) and provides verifiable methods: funnel analysis, cohort/segment control comparisons, A/B testing or quasi-experiments, incremental attribution (multi-touch/marketing attribution), long-term value metrics (LTV/CLV, net AUM growth, risk cost); clarifies metric definitions and observation windows.
- **4–7 points**: Has metrics and methods, but metric definitions/windows/control-group setup are unclear; or uses correlation narratives instead of causal evaluation.
- **0–3 points**: Lacks a metric system and evaluation methods; effectiveness judgments are mainly subjective.

#### (3) Effectiveness and Reproducibility of Fine-Grained Strategies (8 points)
- **7–8 points**: Explains how fine-grained strategies drive outcomes: tiering/segmentation, personalized recommendation/ranking, customer journey orchestration, frequency capping and fatigue management, benefit/cost control; discusses trade-offs between “short-term conversion” and “long-term retention/experience,” and proposes strategy modules that can be replicated across multiple product lines.
- **4–6 points**: Has fine-grained ideas, but lacks strategy granularity (missing trigger conditions, frequency rules, cost constraints) or has weak reproducibility.
- **0–3 points**: Stays at slogan level (e.g., “precision marketing/personalization for everyone”), lacking details and constraints.

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### 3. Process Automation, System Capabilities, and Organizational Mechanisms (20 points)
#### (1) Coverage and Business Value of Process Automation (8 points)
- **7–8 points**: Clearly decomposes automatable objects (automated journey triggers, lead routing, outbound calling/message orchestration, operations dashboards and alerts, RPA handling, ticket/work-order routing, etc.), and evaluates impact on efficiency, timeliness, experience, and risk (productivity gains, shorter SLA, fewer complaints, etc.).
- **4–6 points**: Lists automation scenarios, but lacks quantified business value, or ignores key nodes requiring human involvement (compliance review, relationship-manager service).
- **0–3 points**: Equates automation with “fully unmanned,” ignoring real constraints of banking service and risk control.

#### (2) Match Between System Architecture and Required Capabilities (7 points)
- **6–7 points**: Breaks down system capabilities and dependencies: CDP (customer data platform), CRM, marketing automation/journey orchestration, recommendation engine, content platform, risk control and anti-fraud, data platform/real-time computing, permissions and audit; explains integration with legacy core systems and channels (app/mini-program/SMS/enterprise WeChat, etc.) and key challenges.
- **3–5 points**: Mentions system modules, but architecture relationships, data flows/APIs, real-time and stability requirements are unclear.
- **0–2 points**: Generic system description; lacks real banking IT constraints (transformation cycles, interfaces, data consistency, etc.).

#### (3) Organization, Governance, and Collaboration (5 points)
- **4–5 points**: Clearly defines operations-role division (HQ/branch; product/operations/data/technology/risk/compliance/customer service; relationship-manager collaboration), processes and access rights, approval and audit-trail mechanisms, KPIs and incentives (balancing scale and quality), and cross-line collaboration mechanisms.
- **2–3 points**: Mentions organizational collaboration but remains general, without clarifying key mechanisms (e.g., access/approval/performance conflicts).
- **0–1 point**: Ignores organizational governance and attributes issues entirely to technology or data.

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### 4. Gap Diagnosis, Trend Integration, and Implementable Optimization Path (25 points)
#### (1) Depth of Diagnosing Performance Gaps and Underlying Constraints (8 points)
- **7–8 points**: Systematically identifies and explains sources of constraints and impact chains: data silos and inconsistent definitions, insufficient real-time capability, model non-interpretability/bias, outreach noise and customer fatigue, channel fragmentation, conflicts between operations and risk-control goals, compliance restrictions (privacy, consent, suitability), system debt and delivery cycles, etc.
- **4–6 points**: Points out problems but lacks root-cause analysis or impact mechanisms; or only describes phenomena without constraint boundaries.
- **0–3 points**: Overly generic diagnosis, lacking real constraints in a banking context.

#### (2) Degree of Linking Tech Trends and Customer Behavior Changes (8 points)
- **7–8 points**: Clearly ties trends to operations transformation: boundaries and risk control for AIGC in content production/customer service/investor education; real-time data and event-driven operations; privacy computing/federated learning for data collaboration; MLOps/feature platforms improving model iteration efficiency; effects of customer behaviors (cross-device, multi-touchpoint, low patience, preference for immediate feedback) on journey design and frequency capping.
- **4–6 points**: Mentions trends but links weakly to specific operational steps, or lacks actionable landing points.
- **0–3 points**: Lists trends without direct relevance to optimizing the operations system in this topic.

#### (3) Executability of Optimization Directions and Implementation Roadmap (9 points)
- **8–9 points**: Provides an implementable path and phased objectives (e.g., 0–3 months / 3–6 months / 6–12 months), milestone deliverables, resources and dependencies (data, systems, organization), cost-benefit and key risk controls; clarifies prioritization and the sequencing logic of “what first, what next” (e.g., unify ID and metric definitions → build journey orchestration and experimentation system → build intelligent recommendation and closed-loop optimization).
- **4–7 points**: Proposes directions and some measures, but lacks a phased plan, assessment of resource constraints, or quantified benefit expectations.
- **0–3 points**: Remains at vision-level recommendations, lacking implementable steps and constraint handling.