Customer Analytics features

Solutions for analyzing customer data to gain insights into behavior, preferences, and opportunities for engagement.

We ask vendors to mark their product against our feature list. It is not available for all products and in some cases we have derived data from publicly available sources. The list of features follows. Vendors can add a further 10 key features of their own.

Advanced Analytics & Insights

Features related to extracting deeper insights from customer data using analytical models and visualizations.

Attrition/Churn Prediction: Uses analytics to forecast which customers are at risk of leaving.
Custom Analytics Models: Users can upload or define their own mathematical/statistical models.
Customer Lifetime Value (CLV) Calculation: Calculates the expected lifetime value of each customer.
Machine Learning Model Support: Supports training, deployment, and use of machine learning models on customer data.
Next Best Action Recommendations: Suggests optimal follow-up actions for each customer based on data.
Predictive Analytics: Capability to predict customer behaviors such as churn or product interest using advanced models.
Propensity Modeling: Models to estimate a customer's likelihood to buy or use a particular product.
Scenario Analysis Tools: Test the impact of potential strategies or market conditions on customer analytics.
Sentiment Analysis: Analyzes and reports on customer sentiment from multiple data points such as feedback, calls, or social media.
Speed of Analytics Processing: Average time taken to run predefined analytical models on the full customer dataset.

Collaboration & Sharing

Collaboration-focused features to help teams share insights, work on analytics together, and coordinate customer strategies.

Access Request Workflow: Built-in mechanisms for users to request and be granted additional report or data access.
Change Tracking & Version Control: Track changes and revert to previous versions of reports or analyses.
Collaborative Workspaces: Multiple users can work together on analytics projects or dashboards.
Commenting on Reports/Dashboards: Users can add notes or discuss insights directly within analytics tools.
Export to Knowledge Repositories: Push reports or insights to organizational repositories (SharePoint, Confluence, etc.).
Integration with Messaging Platforms: Ability to share analytics artifacts directly into common business chat tools (Teams, Slack, etc.).
Notification and Alert System: Team members are notified of new insights, anomalies, or required actions.
Number of Simultaneous Collaborators: How many team members can work together in real time on a single project.
Role and Permission-based Sharing: Share data, reports, and dashboards securely based on user permissions.
Scheduled Delivery to Distribution Lists: Send reports and dashboards on a schedule to user groups.

Customer Segmentation

Capabilities that allow financial institutions to segment and profile customers for targeted analytics and personalization.

AI-driven Segmentation: Uses AI/ML algorithms to automatically discover and suggest segments.
Behavioral Segmentation: Identify customer groups based on transaction patterns and behavior.
Custom Segment Creation: Users can define and create custom segments with multiple filters and logic.
Demographic Segmentation: Segment customers based on demographic information such as age, income, occupation, etc.
Dynamic Segment Updates: Segments are automatically refreshed based on new data.
Loyalty and Profitability Segmentation: Classifies customers based on loyalty or profitability scores.
Number of Segments Supported: Maximum number of customer segments the system can handle simultaneously.
Segment Overlap Analysis: Ability to identify and analyze overlapping members of different segments.
Segment-driven Targeting Tools: Enables marketing or outreach based on segment membership.
Visualization of Segments: Graphical representation and dashboarding of customer segments.

Data Integration

Features that enable seamless gathering and aggregation of data from various sources for comprehensive customer analytics.

APIs for Data Access: Availability of APIs to facilitate integration with external or custom data sources.
Automated Data Quality Checks: System can automatically detect duplicates and errors in imported data.
Batch Data Processing: Support for larger scheduled imports of data sets.
Data Refresh Rate: Frequency at which data can be updated in the analytical system.
Data Validation Rules: Pre-built and customizable data validation rules.
Data Volume Capacity: Maximum volume of data that can be integrated and processed efficiently.
ETL (Extract, Transform, Load) Capabilities: Includes built-in ETL tools to prepare and transform data for analysis.
Encryption During Data Transfer: Ensures data is encrypted during transfer between systems.
Multi-Source Data Connectivity: Ability to connect and import data from diverse sources such as core banking systems, CRM, social media, transaction databases, etc.
Real-time Data Ingestion: Capability to process and analyze data as it is generated, minimizing delays.

Data Visualization & Reporting

Tools that allow users to interpret, share, and present customer analytics in a clear and actionable manner.

Ad-hoc Query Builder: Create custom queries on customer data without coding.
Automated Insights: System automatically surfaces interesting trends, anomalies, or patterns.
Custom Dashboard Templates: Build and reuse dashboard designs across projects.
Drill-down Capabilities: View granular details from summaries within reports and dashboards.
Exportable Reports: Allows users to export reports in various formats (PDF, Excel, etc.).
Interactive Dashboards: Customizable dashboards with drill-down capability.
Role-based Report Access: Control which users have access to which reports.
Scheduled Reports: Automatic generation and delivery of reports at regular intervals.
Sharing & Collaboration Tools: Features for sharing dashboards and collaborating on reports.
Visualization Types Supported: Number of different visualization types (charts, graphs, heatmaps, etc.) available.

Integration & Extensibility

Technical features supporting the extension, customization, and connection of the analytics platform to other systems.

Custom Object Model Support: Ability to extend the system’s data model with custom objects or fields.
Custom Script Execution: Run custom business logic scripts within the tool (e.g., Python, R).
Data Export Options: Ability to export data into various file formats or external databases.
Integration Through iPaaS Solutions: Works with integrator platforms such as MuleSoft, Dell Boomi, or Zapier.
Maximum API Call Rate: Peak allowed frequency of API calls.
Open APIs: APIs available for third-party system integration and automation.
Plug-in or Add-on Support: Developers can build and install extensions to the analytics product.
Pre-Built Integrations: Out-of-the-box connectors to popular banking and marketing platforms.
Scheduling of API Calls: APIs can be scheduled to pull or push data at defined intervals.
Webhook Support: Integration with external workflow or event-based systems via webhooks.

Personalization & Recommendation Engines

Features that enable tailored offers, communication, and product recommendations for customers based on analytics.

A/B Test Management: Supports running A/B tests to optimize recommendations and personalization strategies.
Multi-channel Personalization: Supports personalization across channels (email, app, web, SMS, etc.).
Personalization Performance Metrics: Tracks how well personalization strategies perform (e.g., lift, conversion rate).
Personalization Rule Engine: Lets users set business rules to govern personalization tactics.
Personalized Offer Generation: Automatically creates and delivers personalized offers based on analytics.
Real-time Recommendations: Generates recommendations for customers in real time based on their behavior.
Recommendation Algorithm Selection: Allows administrators to choose from multiple algorithms (collaborative filtering, content-based, etc.).
Scalability of Recommendations: Maximum number of customers who can receive personalized recommendations simultaneously.
Self-service Configuration: Business users can configure personalization strategies without IT intervention.
User Profile Enrichment: Ability to augment customer profiles with external and behavioral data.

Scalability & Performance

Capabilities ensuring the platform can handle growth in users, data, and analytical complexity without loss of performance.

Analytics Query Response Time: Average response time for complex analytics queries.
Concurrent User Support: Maximum number of users who can be active simultaneously without performance degradation.
Data Storage Scalability: System can scale data storage as required, up to a specified maximum.
Disaster Recovery RPO: Recovery Point Objective – age of backup data on recovery.
Disaster Recovery RTO: Recovery Time Objective in minutes for catastrophic failures.
Elastic Resource Allocation: Dynamically allocate more computing resources as needed to meet analytic demand.
High Availability (HA): Built-in features to ensure system uptime and resilience.
Hot/Cold Data Archiving: Automatic segregation and management of frequently and infrequently accessed data.
Performance Monitoring Tools: Real-time and historic reporting on system performance.
Scheduled Maintenance Windows: Advance notification period before planned system maintenance.

Security & Compliance

Security and regulatory compliance capabilities to protect sensitive customer data and ensure banking regulation adherence.

Audit Logging: Comprehensive tracking of all data access and manipulation activities.
Automatic Security Updates: System automatically applies patches and security updates.
Data Encryption At Rest: Customer data is encrypted when stored in the system.
Data Masking & Anonymization: Sensitive data elements can be masked or anonymized for analysis.
Data Residency Controls: Specify and enforce where customer data is physically stored.
GDPR/CCPA Compliance: Features to support compliance with regional privacy regulations such as GDPR or CCPA.
Multi-Factor Authentication: Requires additional authentication factors to access customer analytics.
Number of External Security Certifications: Count of reputable third-party security certifications attained by the product.
Penetration Testing Certification: System is regularly tested for vulnerabilities and has certification.
Role-based Access Control: Granular permissions for different user roles and responsibilities.

Support & Training

Customer support and education features to ensure buyers can effectively use and benefit from the analytics platform.

24/7 Support Availability: Round-the-clock technical and business support.
Community Forums: Peer support via active online community forums.
Comprehensive Documentation: Depth and breadth of user and technical documentation provided.
Custom Training Curricula: Can provide organization-specific training programs.
Dedicated Account Manager: Assigned contact for ongoing support and relationship management.
In-person/Virtual Training Options: Availability of classroom or live virtual training sessions.
Onboarding Assistance: Guided setup and implementation support for new clients.
Online Knowledge Base: Self-service repository for FAQs, how-to guides, and troubleshooting.
Regular Product Training Webinars: Scheduled online training sessions for users.
SLA Guaranteed Response Time: Support contract's maximum time to first response.

User Experience & Usability

Features focused on ensuring the analytics tool is intuitive, user-friendly, and accessible for a range of banking staff.

Accessibility Compliance: Meets accessibility standards (WCAG, ADA, etc.) for users with disabilities.
Customizable Workspace: Users can tailor dashboards, workflows, and notifications.
Mobile Access: Usable from smartphones and tablets.
Multi-language Support: Interface can be displayed in various languages.
Search and Navigation Tools: Robust search capabilities and intuitive menu organization.
Self-service Features: Non-technical users can create and manage analytics tasks without IT help.
User Onboarding/Tutorials: Built-in guides and tutorials for fast learning.
User Satisfaction Score: User-rated ease of use (out of 10).
Web-based Interface: Accessible from modern web browsers without additional installation.
Workflow Automation: Automate repetitive tasks, such as report generation or alerts.

Features in Customer Analytics

for Analytics and Business Intelligence