Chargebackly
Reclaim Revenue. Banish Chargeback Stress.
Chargebackly streamlines chargeback dispute management for independent e-commerce owners drowning in lost revenue and paperwork. By automatically gathering store data and generating one-click evidence packets, it transforms stressful, hours-long tasks into a two-minute process—empowering small shop owners to effortlessly reclaim income and focus on growing their businesses.
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Product Details
Explore this AI-generated product idea in detail. Each aspect has been thoughtfully created to inspire your next venture.
Vision & Mission
- Vision
- Empower every small online store owner to effortlessly reclaim lost revenue and thrive, free from chargeback stress forever.
- Long Term Goal
- By 2028, empower 100,000 independent e-commerce owners worldwide to recover $100 million in lost revenue and cut chargeback dispute time by 90% through seamless automation.
- Impact
- Enables independent e-commerce owners to recover up to 40% more lost revenue by automating chargeback disputes, reducing response time by 90% and transforming a multi-hour paperwork process into a two-minute task—directly increasing monthly income and minimizing stress for small shop owners.
Problem & Solution
- Problem Statement
- Independent e-commerce owners lose significant revenue to chargebacks but lack time and expertise for tedious dispute processes; existing solutions are manual, confusing, and designed for large retailers, leaving smaller merchants underserved and financially vulnerable.
- Solution Overview
- Chargebackly connects directly to your e-commerce platform, instantly pulling order and customer data to auto-generate professional dispute packets. With a single click, merchants submit evidence and track outcomes, reclaiming lost revenue in minutes instead of hours—no paperwork or confusing forms required.
Details & Audience
- Description
- Chargebackly automates chargeback disputes for small online store owners overwhelmed by lost revenue and paperwork. Merchants recover more sales, faster, without navigating confusing forms. Unlike slow, manual tools, Chargebackly pulls store data and creates one-click evidence packets, transforming hours of stress into a two-minute, effortless task.
- Target Audience
- Independent e-commerce owners (25-45) overwhelmed by frequent chargebacks, seeking fast, automated dispute resolution.
- Inspiration
- Sitting across the kitchen table, I watched my friend, a handmade jewelry seller, nearly in tears as she sifted through piles of chargeback notices and messy receipts after losing $3,000 in one quarter. Seeing her spend night after night on confusing paperwork instead of her craft revealed just how neglected small shop owners are, inspiring Chargebackly’s effortless, automated solution.
User Personas
Detailed profiles of the target users who would benefit most from this product.
Agency Advocate Andy
- Age 34, male e-commerce consultant - Owner of a 3-person digital agency - BA in Business with marketing specialization - Annual revenue $80,000 from client fees
Background
Built digital marketing gigs in college then pivoted to chargeback support in 2022. He grew his client base through referrals, shaping a service-first approach.
Needs & Pain Points
Needs
1. Rapid generation of branded evidence packets 2. Easy white-label dispute reporting for clients 3. Centralized dashboard for multi-client dispute tracking
Pain Points
1. Tedious manual rebranding of dispute documents 2. Disjointed data across client store accounts 3. Difficulty proving evidence consistency on deadlines
Psychographics
- Passionate about delivering white-label client solutions - Obsessed with workflow automation and efficiency - Values transparent client communications and trust - Driven by reputation and service excellence
Channels
1. Slack integration (real-time updates) 2. Agency email newsletter (industry tips) 3. LinkedIn group (e-commerce forums) 4. Zoom webinars (product demos) 5. Twitter feed (industry news)
Metrics Maven Mia
- Age 28, female skincare brand founder - MS in Data Analytics - Annual online revenue $150,000 - Runs business solo with small team
Background
Started as a data scientist at a tech firm; launched her skincare store in 2023. She uses data to refine every business decision.
Needs & Pain Points
Needs
1. Deep dive analytics into dispute trends 2. Customizable reports for forecasting and prevention 3. Automated alerts for spike in new disputes
Pain Points
1. Overwhelmed by raw, unfiltered data dumps 2. Manual report customization takes too long 3. Lacks cross-comparison tools for different periods
Psychographics
- Obsessed with data-driven decision making - Thrives on uncovering actionable chargeback patterns - Values predictive insights and performance metrics
Channels
1. Tableau dashboard (visual analytics) 2. Email alerts (real-time notifications) 3. Slack Data channel (team discussions) 4. GitHub integration (scripted report automation) 5. Product webinars (feature deep dives)
Budget-Conscious Brian
- Age 41, male Etsy jewelry shop owner - High school graduate, self-taught marketer - Monthly marketing budget $200 - Runs business solo from home studio
Background
Left corporate retail in 2020 to launch his handmade jewelry brand. Tight profit margins forced him to DIY tools, now seeking affordable automation.
Needs & Pain Points
Needs
1. Low-cost dispute automation with flat-rate pricing 2. Clear ROI metrics on each chargeback won 3. Scalable plan without surprise fee hikes
Pain Points
1. Anxiety over hidden platform fees 2. Balancing affordability with essential automation 3. Poor support due to low subscription tier
Psychographics
- Highly price-sensitive purchasing habits - Prefers simple, functional features over extras - Trusts transparent, predictable subscription costs
Channels
1. Facebook Marketplace (community advice) 2. Etsy forums (seller discussions) 3. Email (cost notifications) 4. Budget blogs (affordable tool reviews) 5. YouTube tutorials (DIY platform walkthroughs)
Multi-Store Maria
- Age 36, female serial entrepreneur - MBA with 10 years retail experience - Manages 5,000+ monthly orders - Operates three online boutiques
Background
Started her first online store in college and expanded to multiple platforms by 2022. She grew frustrated by siloed chargeback processes.
Needs & Pain Points
Needs
1. Single interface for all store disputes 2. Automated platform-specific evidence formatting 3. Consolidated dispute performance reports
Pain Points
1. Time wasted switching between platform dashboards 2. Manual reformatting of evidence per platform 3. Inconsistent dispute outcomes by marketplace
Psychographics
- Seeks unified cross-platform operational simplicity - Values centralized dashboards for holistic oversight - Motivated to reduce time stitching disparate data
Channels
1. Shopify app store (integrations) 2. Amazon seller central notifications 3. eBay seller hub emails 4. Product newsletters (multi-store tips) 5. Reddit r/entrepreneur (community advice)
API Ace Aiden
- Age 30, male full-stack developer - BS in Computer Science - CTO at a 50-person e-shop - Manages engineering and ops
Background
Built custom e-commerce tools for five years and became CTO in 2024. He demands API-centric solutions for smoother automation.
Needs & Pain Points
Needs
1. Comprehensive, JSON-based dispute submission API 2. Detailed API documentation with code examples 3. Webhooks for real-time dispute status updates
Pain Points
1. Frustrated by incomplete or buggy API endpoints 2. Lack of webhook customization support 3. Poor error handling documentation
Psychographics
- Code-driven tool customization lover - Demands robust, well-documented APIs - Motivated by engineering efficiency and reliability
Channels
1. GitHub API docs (primary reference) 2. Stack Overflow (developer Q&A) 3. Postman collection (API testing) 4. Developer Slack channel (peer support) 5. Tech blog posts (implementation guides)
Product Features
Key capabilities that make this product valuable to its target users.
Instant Sync
Automates real-time retrieval of order, payment, and customer data from your store with a single click. Ensures you always have the latest information without manual refreshes, speeding up evidence preparation and eliminating data gaps.
Requirements
Store Credentials Authentication
Description
The system must provide secure authentication mechanisms (OAuth or API key-based) to connect Chargebackly with various e-commerce platforms. This includes storing and managing tokens, handling token refresh, and ensuring data privacy and encryption in transit and at rest. Integrates seamlessly with the store’s API endpoints, enabling automated data retrieval without manual input.
Acceptance Criteria
One-Click Sync Activation
Description
Introduce a single-button control in the Chargebackly dashboard that initiates real-time data synchronization. Upon clicking, the system triggers API calls to retrieve the latest orders, payments, and customer data. The UI provides visual feedback (loading indicators, progress bars) to confirm sync initiation and completion.
Acceptance Criteria
Incremental Data Fetch
Description
Implement logic to retrieve only new or modified records (orders, payments, customers) since the last successful sync. Leverage timestamps or webhooks to minimize data transfer volume, reduce load on both Chargebackly and store APIs, and improve sync performance.
Acceptance Criteria
Sync Error Handling & Notifications
Description
Develop robust error detection and handling routines during data sync. When API calls fail or return invalid data, the system logs detailed error messages, retries failed calls based on configurable rules, and sends user notifications (email, in-app alerts) with actionable guidance.
Acceptance Criteria
Sync History and Monitoring Dashboard
Description
Create a dedicated dashboard within Chargebackly that displays a chronological log of all sync operations, including timestamps, data volume, duration, status (success/failure), and error summaries. Allow users to filter, search, and export sync logs for audit and troubleshooting purposes.
Acceptance Criteria
Template Forge
Offers a drag-and-drop evidence template designer, enabling users to create and customize PDF layouts, branding, and data fields. Tailor each packet to merchant style guides and court requirements in minutes, boosting professionalism and compliance.
Requirements
Drag-and-Drop Editor
Description
Implement a user-friendly drag-and-drop editor that allows merchants to visually place, resize, and arrange elements such as text blocks, images, tables, and lines on a canvas. This feature empowers users to design and customize evidence templates without writing code, reducing design time and improving usability.
Acceptance Criteria
Dynamic Data Field Binding
Description
Provide a mechanism for merchants to map and bind dynamic data fields—such as order details, customer information, and product images—from their store to template placeholders. This ensures that generated evidence packets automatically populate with accurate, up-to-date data, streamlining the template generation process.
Acceptance Criteria
PDF Generation Engine
Description
Integrate a robust PDF generation engine that converts designed templates into high-fidelity, print-ready PDF documents. The engine must preserve layout accuracy, fonts, colors, and embedded media, and support both download and direct submission to dispute platforms, ensuring consistent output.
Acceptance Criteria
Brand Asset Management
Description
Allow merchants to upload, store, and manage brand assets—including logos, custom fonts, and color palettes—within Template Forge. Enable easy application of these assets across multiple templates to maintain brand consistency and professionalism in all evidence packets.
Acceptance Criteria
Template Versioning and Reversion
Description
Implement version control for templates by automatically saving revisions and providing merchants with a history view. Users should be able to compare changes and revert to previous versions to prevent data loss and support iterative design improvements.
Acceptance Criteria
Collaboration and Sharing
Description
Enable merchants to share templates with team members or external stakeholders, set granular permissions for viewing or editing, and collaborate in real time or asynchronously within Template Forge. This fosters teamwork and speeds up template approval workflows.
Acceptance Criteria
Batch Builder
Enables simultaneous evidence collection and PDF assembly for multiple disputes. Select dozens or hundreds of cases at once to process in a single operation—radically reducing prep time during high-volume periods.
Requirements
Bulk Case Selection
Description
Enable users to select dozens or hundreds of chargeback cases simultaneously via checkboxes, multi-select, and select-all controls. Incorporate filtering and pagination so users can refine selections by date, store, dispute status, and other attributes. Ensure seamless integration with the main dispute dashboard and batch builder workflow to allow high-volume selection without performance degradation, dramatically reducing manual effort when preparing evidence.
Acceptance Criteria
Batch Processing Orchestration
Description
Implement a backend orchestration layer to coordinate simultaneous evidence collection and PDF assembly tasks for selected disputes. The system should queue batch jobs, distribute workload across worker processes, manage dependencies, and ensure data integrity. It will trigger parallel data fetches from connected store APIs, generate evidence items, compile them into PDFs, and notify users upon completion, ensuring high throughput during peak periods.
Acceptance Criteria
Parallel API Request Optimization
Description
Optimize API integration to perform concurrent requests to e-commerce platforms, payment gateways, and data stores. Introduce connection pooling, rate limiting handling, and retry logic to maximize throughput without exceeding API quotas. This ensures rapid retrieval of order details, transaction history, and proof materials for large batches, minimizing wait time and errors during mass processing.
Acceptance Criteria
Progress Tracking Dashboard
Description
Design and implement a real-time dashboard that displays the status of batch jobs, including queued, processing, completed, and failed cases. Provide progress indicators, estimated time to completion, and detailed logs for each batch. Allow users to pause, resume, or cancel ongoing batches, and view history of past batch operations to audit performance and troubleshoot issues.
Acceptance Criteria
Error Handling and Recovery
Description
Develop robust error detection, logging, and recovery mechanisms for batch operations. Automatically retry transient failures, isolate problematic cases without aborting the entire batch, and surface clear error messages with remediation steps. Provide rollback capabilities or manual reprocessing options for failed cases, ensuring reliability and confidence in large-scale dispute preparation.
Acceptance Criteria
Smart Highlights
Uses AI to scan assembled evidence and automatically flag and highlight critical details—such as transaction timestamps, refund attempts, and communication logs—ensuring reviewers spot key facts quickly and improving dispute success rates.
Requirements
Timestamp Highlighter
Description
Automatically scans assembled evidence packets to detect and highlight all transaction timestamps in a bold, color-coded format. Integrates seamlessly into the evidence viewer, enabling users to quickly identify key timing details relevant to dispute timelines. Improves review speed and accuracy by drawing immediate attention to critical temporal data.
Acceptance Criteria
Refund Attempt Detector
Description
Uses AI to identify and flag documented refund attempts within the evidence—such as full or partial refunds—highlighting them in-line with other transaction details. Ensures that reviewers can easily see if and when a refund was issued before a chargeback, strengthening dispute arguments.
Acceptance Criteria
Communication Log Flags
Description
Analyzes customer-seller communication logs to automatically flag and highlight critical exchanges—such as refund agreements, delivery confirmations, or dispute threats—in different colors. Integrates with the communication timeline view to emphasize interactions that impact dispute outcomes.
Acceptance Criteria
Evidence Summary Generator
Description
Compiles all highlighted elements into a concise, AI-generated summary at the top of the evidence packet. Summarizes key facts—timestamps, refund attempts, notable communications—to provide a quick overview, reducing manual scan time and focusing attention on essential dispute arguments.
Acceptance Criteria
Custom Highlight Rules
Description
Provides an administrative interface for users to define custom rules for what gets highlighted—such as specific keywords, date ranges, order value thresholds, or communication sentiment criteria. Stores and applies these rules automatically to evidence packets, allowing personalization per store.
Acceptance Criteria
Audit Vault
Maintains a complete, time-stamped history of all evidence collection and PDF generation activities. Track version changes, user edits, and submission logs in a secure archive for compliance audits and team collaboration.
Requirements
Time-Stamped Activity Logging
Description
The system captures a detailed, immutable log of every evidence collection and PDF generation event, recording the timestamp, user identity, and event details. Logs are stored in a centralized repository, ensuring that administrators and auditors can trace actions, verify the sequence of activities, and maintain compliance. This feature underpins transparency, accountability, and facilitates forensic auditing.
Acceptance Criteria
Version Control of Evidence Packets
Description
Maintain version history for each evidence packet, tracking changes over time. Each modification or regeneration of a PDF should create a new version entry with metadata on who made the change, when, and what changed. Users can compare versions, rollback if necessary, and ensure that the correct evidence is submitted to dispute providers.
Acceptance Criteria
User Edit Tracking
Description
Monitor and record all manual edits made by users within evidence packets, capturing before-and-after snapshots. This includes note additions, data corrections, and annotation activities. The system logs the editor's identity, timestamp, and description of the change, enabling accountability and collaboration across teams.
Acceptance Criteria
Secure Archive Access Controls
Description
Implement granular access permissions and role-based controls for the audit vault, ensuring that only authorized users can view, download, or modify audit logs and evidence versions. Access attempts are logged, and administrators can define policies for read, write, and administrative privileges, enhancing security and compliance with data governance standards.
Acceptance Criteria
Audit Trail Report Generation
Description
Provide functionality to generate comprehensive audit trail reports, compiling logs, version histories, and user edits into formatted documents. Reports can be filtered by date range, user, or event type, and exported in PDF or CSV formats for compliance reviews, stakeholder presentations, or regulatory submissions.
Acceptance Criteria
Risk Score Insights
Assigns each transaction a clear, normalized risk score based on AI-driven analysis of order data and historical patterns. Merchants can instantly pinpoint high-risk orders, prioritize manual reviews, and allocate resources efficiently, reducing missed fraud indicators and preventing potential chargebacks.
Requirements
Real-Time Risk Scoring
Description
The system must compute and assign a normalized risk score to each transaction in real-time by analyzing order details, customer behavior, payment data, and historical fraud patterns. This functionality ensures immediate identification of potentially fraudulent transactions, integrates seamlessly with the order processing pipeline, and empowers merchants to intervene before fulfillment, reducing the likelihood of chargebacks.
Acceptance Criteria
Risk Score Dashboard
Description
Develop an interactive dashboard that visualizes the distribution of risk scores across all transactions, highlights high-risk orders, and offers filtering, sorting, and drill-down capabilities. The dashboard should display trends over time, aggregate statistics, and allow merchants to export data for reporting and audit purposes, enhancing visibility into fraud exposure.
Acceptance Criteria
Risk Threshold Configuration
Description
Provide a configuration interface where merchants can define and adjust custom risk score thresholds for categorizing orders into low, medium, and high risk. This feature allows tailoring of review workflows based on individual risk tolerance, supports saving multiple profiles, and includes validation to prevent misconfiguration.
Acceptance Criteria
High-Risk Order Alerts
Description
Implement automatic in-app and email notifications for transactions that exceed the high-risk threshold. Alerts should include transaction details, risk factors, and a direct link to the order review page. Configuration options should allow merchants to manage notification channels, frequency, and recipients to ensure timely manual intervention.
Acceptance Criteria
Risk Score API Endpoint
Description
Expose a secure RESTful API endpoint that returns the risk score and underlying risk factors for a given transaction ID. The API should include authentication, versioning, rate limiting, and clear documentation, allowing developers to integrate risk data into third-party tools, automation scripts, and custom workflows.
Acceptance Criteria
Real-time Anomaly Alerts
Delivers immediate notifications when the system detects unusual order behaviors—such as mismatched billing addresses, multiple orders from the same IP, or abnormal purchase volumes. Enables merchants to intervene swiftly, cancel or verify suspect orders, and minimize fraudulent transactions before disputes arise.
Requirements
Adaptive Anomaly Detection Engine
Description
The anomaly detection engine continuously analyzes incoming order data in real time using a combination of rule-based logic and machine learning algorithms to identify suspicious patterns such as mismatched billing and shipping addresses, multiple orders from the same IP address, or spikes in purchase volumes. It automatically flags anomalous orders and generates metadata for further processing, integrating seamlessly with Chargebackly’s data ingestion pipeline to ensure minimal latency and high accuracy.
Acceptance Criteria
Multi-Channel Alert Delivery
Description
Merchants receive anomaly alerts through configurable channels including email, SMS, and in-app notifications. The system integrates with external notification services, implements retry logic, and ensures delivery confirmation for critical fraud warnings to guarantee merchants are promptly informed regardless of their current device or location.
Acceptance Criteria
Real-Time Alerts Dashboard
Description
A dedicated dashboard aggregates and visualizes all detected anomalies in real time. It offers sortable tables, interactive charts, filters for rule types and severity levels, and drill-down capabilities to inspect individual order details and historical trends, enabling merchants to quickly assess risk and prioritize actions.
Acceptance Criteria
Automated Order Verification Workflow
Description
Upon flagging an anomalous order, the system initiates an automated verification workflow that sends configurable confirmation requests to customers, holds or cancels orders pending verification, and logs customer responses. This workflow ties into Chargebackly’s evidence packet generator to streamline dispute preparation for any orders that proceed through verification.
Acceptance Criteria
Threshold Configuration Interface
Description
An intuitive interface and corresponding API allow administrators to define and adjust anomaly detection thresholds and rules—such as geographic limits, order value caps, IP address grouping, and transaction velocity parameters. Changes apply in real time and include versioning and rollback capabilities.
Acceptance Criteria
Custom Rule Builder
Empowers users to create and deploy personalized risk rules that reflect their unique business criteria (e.g., order value thresholds, geographic filters, product categories). By tailoring detection parameters, merchants gain full control over what constitutes high risk, aligning Dispute Radar with their specific fraud tolerance and operational needs.
Requirements
Intuitive Rule Creation Interface
Description
A user-friendly UI component that guides merchants through defining custom rules by selecting criteria such as order value, product categories, and geographic filters. It integrates seamlessly with the existing Chargebackly dashboard, offering drag-and-drop and form-based inputs to streamline rule setup and ensure accuracy. This functionality enhances usability by reducing configuration time and minimizing errors in rule definition.
Acceptance Criteria
Comprehensive Condition Operator Library
Description
A robust set of logical and comparison operators (e.g., equals, not equals, greater than, less than, includes, excludes) enabling fine-grained control over rule criteria. Operators can be combined into complex expressions, and the system validates operator compatibility with selected data types. This enhances flexibility and precision in rule definitions.
Acceptance Criteria
Real-time Rule Testing and Validation
Description
A real-time sandbox environment where merchants can test new or modified rules against historical or sample transaction data. The system provides immediate feedback on matched transactions, highlighting rule logic and potential exceptions. Error checks and validation ensure rules are syntactically correct and logically sound before activation.
Acceptance Criteria
Rule Activation and Lifecycle Management
Description
A management module that allows merchants to activate, deactivate, schedule, or archive custom rules. It includes version control and audit logs to track changes, ensuring accountability and enabling rollbacks. Integration with notification settings alerts users when rules change status.
Acceptance Criteria
Backend Rule Execution Engine Integration
Description
Seamless integration of the custom rule definitions with the Chargebackly processing engine, ensuring that incoming transactions are evaluated against active rules in real time. The engine scales to handle high transaction volumes, provides performance metrics, and logs rule evaluation outcomes for audit purposes.
Acceptance Criteria
Rule Management Dashboard
Description
A centralized dashboard displaying all custom rules with status indicators, last-modified timestamps, and match statistics (e.g., number of transactions flagged). It supports sorting, filtering, bulk actions (activate/deactivate, delete), and quick access to rule detail views for editing.
Acceptance Criteria
Trendline Analytics
Visualizes risk patterns and dispute triggers over time with intuitive charts and heatmaps. Merchants can track daily, weekly, or seasonal spikes in flagged orders, uncover emerging fraud trends, and adjust sales strategies or preventive measures proactively to protect revenue.
Requirements
Real-Time Data Aggregation
Description
Implement a continuous data ingestion pipeline that automatically collects and normalizes order, dispute, and risk indicator data from connected e-commerce stores in real time. The pipeline must support incremental updates, handle high transaction volumes, and ensure data consistency and low latency for downstream analytics components.
Acceptance Criteria
Interactive Trendline Visualization
Description
Develop dynamic time-series charts that allow users to zoom, pan, filter, and drill down into risk pattern data across daily, weekly, and monthly views. The visualization should support overlays for contextual events (promotions, holidays) and provide tooltips with detailed metrics to help merchants understand spikes and trends at a glance.
Acceptance Criteria
Seasonal Pattern Analysis
Description
Build an analytical module that automatically detects and highlights seasonal or recurring patterns in flagged orders and disputes. The module should generate reports comparing current period performance against historical baselines and indicate statistically significant deviations to aid proactive decision-making.
Acceptance Criteria
Custom Alert Configuration
Description
Enable merchants to define and manage threshold-based alerts for risk metrics (e.g., spike in flagged transactions, chargeback rate). Alerts should be configurable via a user-friendly interface, support multiple notification channels (email, SMS, in-app), and include contextual data links for quick investigation.
Acceptance Criteria
Heatmap Correlation Insights
Description
Introduce an interactive heatmap feature that visualizes correlations between multiple risk indicators (e.g., location, order value, payment method) and dispute frequency. The heatmap should be filterable by time range and category, highlighting the strongest relationships to inform targeted fraud prevention strategies.
Acceptance Criteria
Predictive Forecast
Leverages machine learning to predict future chargeback volumes and estimate potential revenue impact based on current order flow and historical dispute data. Provides forward-looking insights that help merchants optimize staffing, adjust inventory, and budget for dispute management resources in advance.
Requirements
Automated Data Ingestion Pipeline
Description
Develop a robust, automated data ingestion pipeline that collects, normalizes, and stores order flow metrics, historical dispute records, and revenue data from multiple e-commerce platforms in real time. Ensure the pipeline handles data validation, error handling, and incremental updates to provide the forecasting model with accurate and up-to-date inputs.
Acceptance Criteria
Machine Learning Forecasting Engine
Description
Implement a machine learning-based forecasting engine that analyzes historical chargeback data and current order trends to predict future chargeback volumes and estimated revenue impact. The engine should support multiple algorithmic approaches, configurable forecast horizons, and incorporate seasonality and anomaly detection for improved accuracy.
Acceptance Criteria
Interactive Forecast Dashboard
Description
Design and develop an interactive dashboard that visualizes predicted chargeback volumes and revenue impact over selectable time frames. Include features such as trend lines, confidence intervals, filter controls for date ranges, and comparison with actuals to help merchants interpret forecasts and adjust operational plans.
Acceptance Criteria
Threshold-Based Alert and Notification System
Description
Create an alerting system that notifies merchants when forecasted chargeback volumes or revenue impact exceed predefined thresholds. Support multiple notification channels (email, SMS, in-app) and configurable alert rules to ensure timely awareness and enable merchants to adjust staffing and inventory proactively.
Acceptance Criteria
Forecast Accuracy Monitoring and Retraining
Description
Implement continuous accuracy monitoring for the forecasting engine, tracking prediction errors and key performance metrics. When accuracy degrades beyond configurable thresholds, automatically trigger model retraining workflows and provide reports on retraining outcomes to maintain reliable forecast quality over time.
Acceptance Criteria
Smart Locale Detection
Automatically identifies the customer’s region, language, and currency based on order data, ensuring evidence packets are tailored precisely to the buyer’s locale without any manual input.
Requirements
Automated Locale Detection
Description
The system must automatically identify a customer’s region, primary language, and preferred currency by analyzing order metadata (such as shipping address, billing country, and browser locale) without manual input. This requirement ensures evidence packets are populated with locale-appropriate language and currency formats, reducing manual adjustments and streamlining dispute filing. It integrates with the order processing pipeline and must handle multiple data sources, providing accurate locale data for downstream formatting.
Acceptance Criteria
Language Fallback System
Description
Implement a fallback mechanism where, if the customer’s language cannot be determined from order data, the system defaults to the merchant’s primary language or a preconfigured default. This ensures no evidence packet is generated with missing or incorrect language elements, improving reliability in edge cases and maintaining consistency across all disputes.
Acceptance Criteria
Currency Format Alignment
Description
Ensure all monetary values in evidence documents (prices, totals, refunds) are formatted according to the detected locale’s currency conventions, including correct currency symbols, decimal separators, and digit grouping. This enhances readability and compliance with regional financial expectations. Integration with the locale detection engine is required.
Acceptance Criteria
Date and Time Localization
Description
Format all dates and timestamps in evidence packets according to the customer's locale preferences (including order date, purchase time, and shipment date). This includes using region-specific date formats (e.g., DD/MM/YYYY vs. MM/DD/YYYY) and 12/24-hour clock conventions. It enhances clarity and aligns with local standards.
Acceptance Criteria
User Override Controls
Description
Provide interface options that allow merchants to manually override the automatically detected locale settings for individual orders or bulk operations. Overrides should be stored per order and reflected in real-time in the evidence preview. This flexibility covers scenarios where detection may be inaccurate or special handling is required.
Acceptance Criteria
Locale Detection Logging
Description
Record all locale detection decisions, including source data, detected locale values, and any manual overrides, in a centralized audit log. Logs should be queryable and linked to specific orders, facilitating troubleshooting and compliance checks. This requirement supports transparency and helps diagnose detection errors.
Acceptance Criteria
Real-Time Currency Conversion
Integrates live exchange rates to convert transaction amounts into the buyer’s local currency instantly, preventing confusion and demonstrating transparency in dispute evidence.
Requirements
Live Exchange Rate API Integration
Description
Implement a secure connection to a reliable foreign exchange rate provider API to fetch real-time currency conversion data. The integration should support HTTPS requests, API key management, and data parsing to extract rates for all supported currencies. This ensures that transaction amounts are converted using up-to-the-minute information, maintaining accuracy and transparency in dispute evidence packets.
Acceptance Criteria
Rate Update Scheduling and Caching
Description
Develop a scheduler that periodically retrieves exchange rates at configurable intervals (e.g., every hour) and stores them in a local cache. The caching mechanism should invalidate stale data after a defined TTL and refresh rates proactively to minimize API calls and ensure high performance. This balances data freshness with system efficiency and API usage limits.
Acceptance Criteria
Fallback and Error Handling for Rate Retrieval
Description
Create a fallback strategy to handle API failures or rate unavailability, including mechanisms to use the most recent cached rates and log errors for monitoring. If both live and cached data are unavailable, the system should notify administrators and display a default conversion indicator. This prevents conversion interruptions and maintains transparency during outages.
Acceptance Criteria
UI Display of Converted Amounts in Dispute Packets
Description
Enhance the dispute evidence interface to show both the original transaction amount and the converted local currency value side by side, including currency symbols and conversion rate used. The design should adapt to various screen sizes and support export formats (PDF, CSV). This clarity helps buyers and reviewers understand amounts without confusion.
Acceptance Criteria
Currency Format Localization
Description
Implement locale-specific formatting for currency values, including appropriate decimal separators, thousands delimiters, and placement of currency symbols. The system should detect user locale settings or allow manual selection, applying formats consistently across the UI and exported evidence packets to improve readability and user trust.
Acceptance Criteria
Regulation Engine
Embeds region-specific legal clauses, compliance statements, and required disclosures into evidence packets, ensuring all local regulatory standards are met to strengthen dispute success.
Requirements
Regional Clause Mapping
Description
Automatically identify the merchant’s operating region using store settings and map to a curated set of region-specific legal clauses stored in the regulation engine’s repository. This ensures evidence packets include only the legally mandated disclosures required by the dispute adjudicator and integrates with the store metadata service and clause management microservice for seamless document generation.
Acceptance Criteria
Dynamic Template Selector
Description
Provide a template selection mechanism that dynamically chooses the correct evidence packet template based on transaction attributes such as region, dispute type, and payment method. The system merges regulatory disclosures into each template to maintain format consistency and legal compliance, working in tandem with the document rendering engine.
Acceptance Criteria
Localization Data Synchronization
Description
Implement a synchronization process to keep the regulatory clause repository up-to-date with the latest legal requirements by fetching updates from authoritative regulatory feeds or manual admin inputs. Version-control changes to ensure generated evidence packets always reflect current regulations.
Acceptance Criteria
Compliance Rule Editor
Description
Build a secure administrative interface for legal or compliance staff to author, review, and approve new or updated regional rules and clauses. The editor must support rich text formatting, version history, approval workflows, and rollback capabilities, integrating with access control systems to restrict modifications to authorized users.
Acceptance Criteria
Regulatory Update Notifications
Description
Develop a notification system that alerts merchants via email and in-app messages when significant regulatory changes occur in their operating region. Messages should specify which clauses were affected and include prompts for review, helping merchants stay informed and maintain compliance before disputes arise.
Acceptance Criteria
Adaptive Formatting
Automatically formats dates, times, phone numbers, addresses, and number separators according to local conventions, enhancing readability and professionalism for regional reviewers.
Requirements
Locale Detection
Description
Automatically detect the customer’s locale based on their browser settings, IP address, or store configuration, ensuring all formatting aligns with regional conventions for dates, times, phone numbers, addresses, and number separators. It integrates seamlessly into the evidence packet generation workflow, reducing manual adjustments and improving accuracy for reviewers in different regions.
Acceptance Criteria
Date & Time Formatter
Description
Provide an engine that formats dates and times according to the detected or selected locale, supporting multiple date orders (e.g., MM/DD/YYYY, DD.MM.YYYY) and time formats (12-hour, 24-hour). The engine ensures consistency across all generated evidence, enhancing readability and reducing misunderstanding due to format differences.
Acceptance Criteria
Phone Number Formatter
Description
Implement a formatting module that automatically normalizes phone numbers based on the international dialing codes and local display conventions, inserting appropriate separators, country codes, and area codes. This ensures all contact details in the evidence are valid and easily recognizable by regional reviewers.
Acceptance Criteria
Address Formatter
Description
Create a flexible address formatting component that restructures shipping and billing addresses into the proper layout, order, and local nomenclature for each locale (including postal codes, prefectures, states, and provinces). This integration reduces errors and increases the professionalism of evidence packets.
Acceptance Criteria
Numeric Separator Adjuster
Description
Develop a utility that applies appropriate thousands separators and decimal markers based on locale settings (e.g., commas, periods, spaces), ensuring all monetary and quantity values in evidence documents adhere to regional standards. This improves data readability and prevents misinterpretation of amounts.
Acceptance Criteria
Formatting Preview & Override
Description
Offer a preview interface within the Chargebackly dashboard that displays formatted data before evidence generation and allows merchants to override locale settings or manually adjust formats when exceptions are needed. This feature ensures flexibility and control over the final appearance of evidence.
Acceptance Criteria
Localized Template Library
Provides a collection of pre-designed, region-specific evidence packet templates, complete with language-appropriate labels and branding, enabling users to generate compliant documents in seconds.
Requirements
Template Localization Management
Description
Enable the system to manage a library of evidence packet templates localized by region, complete with language-specific text, labels, and formatting rules. This ensures users can quickly select compliant templates for different markets without manual adjustments, reducing errors and speeding up document generation.
Acceptance Criteria
Dynamic Language and Currency Mapping
Description
Implement dynamic mapping of language, date, and currency formats within templates based on the user’s selected region. This feature automatically applies correct localization settings to financial figures and textual elements, ensuring legal compliance and professional presentation.
Acceptance Criteria
Region-Specific Branding Elements
Description
Incorporate region-specific branding elements such as localized logos, color schemes, and mandatory legal notices. The system should detect regional branding requirements and apply them to the template, maintaining brand consistency while adhering to local regulations.
Acceptance Criteria
Template Preview and Selection Interface
Description
Provide an intuitive interface for users to browse, preview, and select localized templates before generating evidence packets. Previews should display full layout and sample text in the selected language and formatting, enabling confident choice without trial and error.
Acceptance Criteria
Template Versioning and Compliance Updates
Description
Implement version control for all localized templates and schedule automatic updates to reflect changes in regional legal or compliance standards. The system should notify users of available template updates and seamlessly apply the latest versions.
Acceptance Criteria
Fallback Translator
Offers secondary language support by automatically applying a backup language if a locale isn’t supported yet, ensuring every evidence packet remains understandable and usable worldwide.
Requirements
Fallback Language Configuration
Description
Enable administrators to define one or more fallback languages to be used automatically when a supported locale is unavailable. This configuration should integrate with existing store settings and allow prioritization of fallback options. Administrators can select from all languages supported by the system, set a default fallback, and reorder preferences to ensure evidence packets are always generated in a comprehensible language for the recipient store.
Acceptance Criteria
Automatic Fallback Detection
Description
Implement logic to detect unsupported locales in incoming store data and automatically apply the preconfigured fallback language. This detection must occur in real time during evidence packet generation and should seamlessly substitute only those content elements lacking native locale support. It ensures that all text fields in the packet are rendered in an intelligible language without manual intervention or additional API calls.
Acceptance Criteria
User Notification of Fallback Usage
Description
Provide clear in-app notifications and annotations within the generated evidence packet indicating when and where a fallback language has been applied. Notifications should appear both in the UI workflow and within the packet header, detailing the original locale, the fallback language used, and a timestamp. This transparency helps users understand any language substitutions and maintain audit trails for dispute documentation.
Acceptance Criteria
Manual Override of Fallback Language
Description
Allow users to manually override the automatic fallback and select an alternative supported language on a per-packet basis. This feature should be accessible during evidence packet review and enable selection from the same list of configured fallback languages. Overrides must be logged for auditing and should update the packet content instantly without regenerating the entire data set.
Acceptance Criteria
Fallback Usage Analytics
Description
Capture and report metrics on fallback language usage, including frequency by store, language pairs, and override occurrences. Integrate these analytics into the dashboard to help product teams identify unsupported locales, improve language coverage, and monitor overall feature adoption. Reports should be filterable by date range, store, and language to support data-driven decisions.
Acceptance Criteria
Drag & Assign
Seamlessly move chargeback cases across stages and instantly assign team members in one fluid motion. Simplifies task delegation, accelerates workflows, and ensures every case has a clear owner at a glance.
Requirements
Drag & Drop Case Cards
Description
Enable users to move chargeback case cards between workflow stages using a simple drag-and-drop interface. This functionality reduces clicks and streamlines the process of updating case statuses, improving visibility into case progression and accelerating dispute resolution.
Acceptance Criteria
Inline Team Assignment
Description
Allow users to assign or reassign team members to a case inline during the drag-and-drop operation. When a case card is dropped into a new stage, an overlay or dropdown should appear enabling selection of the responsible team member, ensuring clear ownership in one seamless action.
Acceptance Criteria
Visual Dropzone Indicators
Description
Provide real-time visual cues on valid drop targets and workflow stages. Highlight stage columns or zones when a case card is dragged, indicating where the user can drop it. This reduces errors, enhances usability, and guides users through the process.
Acceptance Criteria
Role-Based Movement Permissions
Description
Implement permission checks that control which users can move or assign cases between stages. Permissions should respect user roles and team assignments, preventing unauthorized changes while maintaining flexibility for managers and admins.
Acceptance Criteria
Undo/Redo Case Moves
Description
Introduce undo and redo functionality for drag-and-drop operations. After moving a case, users should be able to revert or reapply the action within a set time window, safeguarding against accidental moves and improving confidence in the interface.
Acceptance Criteria
Bulk Actions
Select multiple dispute cards to perform group moves, status updates, or batch assignments. Reduces repetitive tasks, speeds up board management during peak periods, and frees up time for strategic decision-making.
Requirements
Multi-Select Toggle
Description
Implement UI controls to allow users to select multiple dispute cards simultaneously. This includes checkboxes on each card, a master Select All/Deselect All toggle, shift-click range selection, and clear visual indicators for selected items. Integration with the existing card list ensures that the selection state persists across pagination and filtering. This functionality reduces repetitive clicks and streamlines subsequent bulk operations, enhancing efficiency during high-volume dispute management.
Acceptance Criteria
Bulk Status Update
Description
Provide a feature that enables users to update the status of all selected dispute cards in one action. The UI will present a status dropdown populated with available dispute states. Upon confirmation, the system will call the backend API to process the status changes in batch, handle individual failures gracefully, and display success or error notifications. This reduces manual updates and keeps the board accurately reflecting dispute progress.
Acceptance Criteria
Batch Assignment
Description
Allow users to assign selected dispute cards to one or more team members in bulk. The interface will include an assignee dropdown or searchable list of active agents. When assignments are confirmed, the system will dispatch a batch assignment request to the API, update the cards’ assignment fields, and log the changes for auditing. This feature balances workloads and expedites dispute handling by reducing individual assignment tasks.
Acceptance Criteria
Group Move Between Boards
Description
Enable users to move selected dispute cards across different boards or workflow stages in a single operation. Users can drag the selection to a target column or use a Move action menu that lists available boards and stages. The system will perform batch updates to reposition cards and update their status fields. This capability accelerates board management and ensures that grouped disputes advance together through the resolution process.
Acceptance Criteria
Bulk Action Confirmation Modal
Description
Introduce a confirmation modal that appears before executing any bulk operation (status update, assignment, or move). The modal will summarize the intended action, list the number of selected cards, and highlight irreversible changes. Users can confirm or cancel the operation. Integration with audit logs will record confirmed actions. This safety check prevents accidental large-scale changes and provides accountability.
Acceptance Criteria
Priority Swimlanes
Automatically sort disputes into horizontal lanes based on risk score, dispute age, or merchant-defined criteria. Highlights urgent cases, delivers instant visibility into high-impact disputes, and streamlines resource allocation.
Requirements
Swimlane Configuration Interface
Description
Provide an intuitive UI where users can define and manage swimlanes based on risk score ranges, dispute age brackets, or custom merchant criteria. This interface should support drag-and-drop ordering, live preview of swimlane layouts, and saving multiple configurations. It ensures merchants can easily tailor dispute prioritization to their workflow and quickly adjust settings as needs evolve.
Acceptance Criteria
Automatic Dispute Classification
Description
Implement a backend service that automatically evaluates incoming disputes against configured swimlane criteria in real time. The service should calculate risk scores, assess dispute age, apply custom rules, and assign each dispute to the appropriate lane. This automation eliminates manual sorting, reduces errors, and ensures high-risk cases surface immediately.
Acceptance Criteria
Custom Criteria Definition
Description
Enable merchants to create and manage their own swimlane criteria using a rule builder that supports logical operators, thresholds, and nested conditions. Criteria should be stored and versioned, allowing rollback and auditing. This feature empowers merchants to refine dispute prioritization based on business-specific factors beyond default settings.
Acceptance Criteria
Real-time Swimlane Updates
Description
Ensure the front end reflects any changes in dispute status or criteria immediately by employing WebSocket or similar push technologies. When disputes are re-scored, age thresholds are crossed, or rules are updated, the swimlanes and dispute cards should update in real time without page refresh. This guarantees that users always see the most current prioritization state.
Acceptance Criteria
Visual Highlighting and Alerts
Description
Add visual cues such as color-coding, badges, or icons to highlight disputes that move into high-priority lanes or exceed defined SLA thresholds. Provide optional email or in-app notifications when a dispute lands in an urgent lane. These alerts draw immediate attention to critical cases, helping teams respond faster and minimize revenue loss.
Acceptance Criteria
Workflow Automator
Define custom triggers that auto-transition disputes when conditions are met—such as evidence packet completion or deadline shifts. Minimizes manual overhead, keeps the board in sync with real-time events, and prevents cases from slipping through the cracks.
Requirements
Custom Trigger Definition
Description
Enable users to define custom triggers by selecting from predefined events (e.g., evidence packet completion, deadline shifts, chargeback status changes) and specifying corresponding automated dispute transitions. This feature integrates seamlessly with the dispute management board, ensuring that once conditions are met, disputes automatically move to the appropriate state without manual intervention, reducing human error and administrative overhead.
Acceptance Criteria
Condition Builder UI
Description
Provide an intuitive, no-code interface where users can construct complex conditional logic for triggers using dropdowns, checkboxes, and logical operators (AND/OR). The interface should validate user inputs, display real-time previews of trigger behavior, and integrate with existing UI themes for a consistent user experience.
Acceptance Criteria
Automated Dispute State Transition
Description
Implement backend logic to listen for trigger events and automatically update the dispute’s status on the board. This includes handling edge cases (e.g., conflicting triggers) and ensuring transitions respect business rules and user permissions. The integration must log each state change and maintain data integrity across the system.
Acceptance Criteria
Real-time Notifications & Alerts
Description
Upon trigger execution and dispute state changes, send configurable notifications via email, in-app alerts, or webhooks to designated team members. Allow users to customize notification channels and thresholds, ensuring stakeholders stay informed about critical case updates in real time.
Acceptance Criteria
Trigger Audit Logging
Description
Maintain a comprehensive audit log of all trigger activities, including creation, edits, executions, and failures. Logs should capture timestamp, user who made changes, trigger details, and outcome. Provide a searchable audit interface to help users troubleshoot issues and meet compliance requirements.
Acceptance Criteria
Board Templates
Save and reuse customized Kanban layouts—including stages, swimlanes, and workflow rules—for different store types or seasonal campaigns. Accelerates setup for new dispute processes and promotes best-practice consistency across teams.
Requirements
Template Creation
Description
Allow users to define a new board template by configuring stages, swimlanes, and workflow rules, saving it with a descriptive name for future reuse. This ensures consistent dispute processes and rapid setup across stores.
Acceptance Criteria
Template Application
Description
Enable users to apply an existing template to a new or existing board in a single action, automatically setting stages, swimlanes, and workflow rules to match the saved configuration. This accelerates board setup and reduces manual configuration errors.
Acceptance Criteria
Template Management
Description
Provide an interface to view, edit, rename, and delete saved templates. Users should also be able to filter and search templates by metadata such as store type and campaign. This makes template maintenance straightforward and organized.
Acceptance Criteria
Template Import/Export
Description
Allow users to export board templates as sharable files (e.g., JSON) and import templates from files. Facilitates sharing best-practice layouts across organizations or teams and backing up templates externally.
Acceptance Criteria
Template Versioning
Description
Implement version control for templates by tracking changes, maintaining history, and allowing users to revert to previous versions. Ensures safe experimentation and rollback of template updates.
Acceptance Criteria
Victory Badges
Award distinctive badges for each successful chargeback resolution, visually showcasing a merchant’s expertise and boosting morale. By collecting badges like “Refund Champion” or “Fraud Defender,” users stay motivated and gain instant recognition of their dispute wins.
Requirements
Badge Assignment Engine
Description
Implement an automated engine that evaluates chargeback resolution data against predefined criteria—such as number of successful disputes, resolution streaks, and win rate thresholds—to assign appropriate victory badges. This engine must integrate with the core dispute management backend to fetch real-time metrics, apply configurable rules, and trigger badge awards without manual intervention, ensuring accuracy, scalability, and maintainability.
Acceptance Criteria
Badge Display Interface
Description
Design and develop a dedicated section in the merchant dashboard that visually showcases all earned victory badges, including badge icons, names, descriptions, and date awarded. The interface should support responsive layouts, hover details, and sortable filters (e.g., by date or badge type), ensuring merchants can easily view and celebrate their dispute resolution achievements.
Acceptance Criteria
Badge Notification System
Description
Build an in-app and email notification mechanism that alerts merchants immediately when they earn a new victory badge. Notifications should include the badge name, description, and a link to view all badges. The system must respect user preferences and allow merchants to opt in or out of specific notification channels.
Acceptance Criteria
Badge Progress Tracker
Description
Introduce a progress tracking widget that displays merchants’ real-time advancement toward their next badge milestone. The tracker should indicate current metrics (e.g., 7/10 successful disputes) and visualize progress percentages. This feature encourages continuous engagement by showing merchants how close they are to unlocking the next achievement.
Acceptance Criteria
Social Sharing Integration
Description
Enable merchants to share their earned victory badges directly to social media platforms (e.g., Facebook, Twitter, LinkedIn) or embed them on personal websites. Implement secure API integrations, shareable links, and customizable share messages to promote user achievements and increase brand visibility.
Acceptance Criteria
Credit Vault
Automatically deposit credits into a secure wallet for every resolved chargeback. Users can track their accumulated credits, view their earning history, and gauge redemption potential at a glance, making the reward process transparent and rewarding.
Requirements
Vault Initialization
Description
Implement a secure, user-specific credit vault that is automatically created when a merchant first resolves a chargeback. The vault must use encryption at rest and in transit, integrate with the existing Chargebackly authentication system, and ensure data isolation between merchants. Upon creation, it should be linked to the merchant’s account and ready to accrue credits immediately.
Acceptance Criteria
Automatic Credit Accrual
Description
Develop logic to automatically deposit a predefined number of credits into the merchant’s vault whenever a chargeback is successfully resolved. This includes creating API endpoints for chargeback resolution events, calculating credit values based on merchant tiers, and updating the vault balance in real time. The system should handle retries, failure states, and provide audit logs for compliance.
Acceptance Criteria
Credit Balance Dashboard
Description
Extend the Chargebackly dashboard to display the merchant’s current credit balance prominently. Include summary cards showing total credits, credits earned this month, and available redemption options. The dashboard must refresh in real time when new credits are added and allow merchants to drill down for more details.
Acceptance Criteria
Earning History Log
Description
Build a detailed credit history log where merchants can view all credit accrual events. Each log entry should include date, chargeback ID, credit amount, and status. Provide filters for date ranges, chargeback status, and credit values. Ensure the history is exportable in CSV and PDF formats for accounting and auditing purposes.
Acceptance Criteria
Credit Redemption Integration
Description
Enable merchants to redeem accumulated credits for account discounts or payout options. Integrate with the billing system to apply credits to invoices or with payment gateways for payout transfers. Provide UI flows for redemption requests, confirmation screens, and notifications. Ensure real-time balance updates post-redemption and maintain redemption records for reporting.
Acceptance Criteria
Dynamic Leaderboard
Display a real-time ranking of top performers among your team or community, fostering healthy competition and encouraging sellers to improve their dispute success rates. Weekly and monthly views celebrate high achievers and set clear performance benchmarks.
Requirements
Real-time Data Synchronization
Description
Automatically sync chargeback dispute performance data from connected e-commerce stores to the leaderboard in real time, ensuring up-to-the-second accuracy. This includes capturing new dispute resolutions, evidence packet submissions, and success rate changes as they occur, seamlessly integrating with existing data pipelines and minimizing latency.
Acceptance Criteria
Timeframe Filtering
Description
Provide dynamic filtering controls that allow users to toggle between weekly and monthly leaderboard views. Filters should update the displayed rankings instantly, apply default date ranges, and support custom date selections, enabling users to analyze performance over specified periods.
Acceptance Criteria
Interactive Leaderboard Display
Description
Design an interactive leaderboard interface where users can sort by various metrics (e.g., total wins, success rate), hover or click entries to view detailed statistics, and visually highlight rank changes over time. The display should be responsive and intuitive, integrating seamlessly into the dashboard.
Acceptance Criteria
Achievement Badges
Description
Automatically award and display achievement badges to top performers based on criteria such as highest monthly wins or largest improvement in dispute success rate. Badges should be distinct, visually engaging, and viewable on user profiles and the leaderboard to foster motivation and recognition.
Acceptance Criteria
Export and Share Leaderboard
Description
Enable users to export the current leaderboard view as a CSV or PDF report and generate a shareable link or email template for distribution. Exported reports should include all displayed metrics, date ranges, and badge highlights, allowing stakeholders to review performance offline.
Acceptance Criteria
Streak Rewards
Unlock bonus credits or exclusive badges for consecutive dispute win streaks. By incentivizing consistency with escalating rewards for 3, 5, or 10 victories in a row, this feature drives sustained engagement and encourages best practices in chargeback management.
Requirements
Streak Tracker Initialization
Description
Implement a mechanism to initialize and maintain individual user win streak counters, ensuring accurate tracking of consecutive dispute wins. This includes creating a persistent data model to store user ID, current streak count, and timestamp of the latest win. The system must reset the counter on a lost dispute or after reward redemption and handle concurrency to prevent race conditions during rapid contest resolution.
Acceptance Criteria
Reward Escalation Mechanism
Description
Design and develop logic to award escalating rewards at predefined streak thresholds (e.g., 3, 5, 10 wins). The mechanism should automatically grant bonus credits or badges when a user reaches each milestone, update the user’s account balance or badge inventory, and prevent duplicate rewards for the same milestone. Integration with the existing billing and badge modules is required for seamless credit application and badge issuance.
Acceptance Criteria
User Notification System
Description
Create a notification framework to inform users when they approach or achieve streak milestones. This includes in-app banners, email alerts, and dashboard pop-ups that dynamically reference the current streak count and upcoming reward thresholds. The system should support customizable templates and respect user notification preferences, ensuring timely and relevant communication.
Acceptance Criteria
Badge Display and Management
Description
Develop a front-end component and supporting API endpoints for displaying earned badges on the user’s dashboard and profile. The component should showcase badge icons, names, achievement dates, and streak details. Additionally, include functionality for users to view their badge history and share badges on social media. Ensure accessibility and responsive design across devices.
Acceptance Criteria
Admin Reward Configuration
Description
Build an administration interface that allows product managers to configure streak thresholds, reward types (credits or badges), and reward values. The interface should include input validation, preview capabilities, audit logging of configuration changes, and version control to revert to previous configurations. Ensure role-based access control so only authorized users can modify reward settings.
Acceptance Criteria
Achievement Quests
Set personalized challenge goals—such as resolving 20 disputes in a month or maintaining a 90% win rate—to earn special rewards upon completion. Tailored quests guide users toward strategic targets, reinforcing positive behaviors and long-term success.
Requirements
Quest Configuration Interface
Description
A user-friendly interface allowing administrators and users to create and configure personalized achievement quests. It should enable the definition of parameters such as target dispute count, win-rate thresholds, time frames, and reward types. The configuration UI must integrate seamlessly with Chargebackly’s data backend, validate inputs, and save quest settings to the database. Expected outcomes include flexible goal creation, reduced setup friction, and increased user ownership of their performance targets.
Acceptance Criteria
Personalized Quest Recommendation Engine
Description
An intelligent engine that analyzes a user’s past dispute history, resolution rates, and activity patterns to recommend tailored achievement quests. It should apply configurable rules or machine-learning models to suggest goals that are both challenging and attainable. Integration points include fetching historical performance data, generating recommendation lists, and updating suggestions as performance evolves. The outcome is increased user engagement and satisfaction through relevant, data-driven challenges.
Acceptance Criteria
Progress Tracking Dashboard
Description
A real-time dashboard displaying ongoing quest progress metrics such as resolved disputes count, current win rate, and remaining time. The dashboard should feature visual progress bars, percentage completion indicators, and status badges. It must fetch live data from Chargebackly’s database, refresh at regular intervals, and clearly communicate milestone achievements. Expected benefits include enhanced user motivation, transparency of performance, and immediate feedback on progress.
Acceptance Criteria
Notification & Reminder System
Description
A notification framework that sends timely alerts to users at key quest events—such as start dates, milestone completions, and approaching deadlines. It should support configurable channels (in-app, email, SMS) and allow users to manage their notification preferences. Integration requires event triggers from the quest engine, templated message generation, and delivery tracking. The system aims to boost user engagement, reduce quest abandonment, and ensure timely quest completions.
Acceptance Criteria
Reward Distribution Mechanism
Description
An automated mechanism that grants predefined rewards—such as badges, discount credits, or feature unlocks—upon quest completion. It should validate quest criteria, trigger reward issuance, update user profiles, and send confirmation messages. Integration points include the rewards catalog, user account service, and notification system. Expected outcomes are consistent recognition of achievements, reduced manual effort, and seamless user experience.
Acceptance Criteria
Quest Analytics & Insights
Description
An analytics module providing administrators with insights into quest performance metrics, such as completion rates, average time to completion, and user engagement trends. It should offer dashboards with filterable charts, exportable reports, and anomaly detection alerts. Data sources include quest logs, user activity records, and reward distributions. The outcome is data-driven decision making for refining quest design and improving overall platform engagement.
Acceptance Criteria
Product Ideas
Innovative concepts that could enhance this product's value proposition.
Evidence Express
Automate evidence collection and PDF assembly in one click, cutting dispute prep from hours to minutes with instant data pull from your store.
Dispute Radar
Use AI to scan orders and flag high-risk transactions before chargebacks occur, reducing disputes by spotting anomalies and suspicious patterns early.
LocaleGuard
Auto-localize evidence packets in the buyer’s language and currency, ensuring compliance with regional regulations and boosting dispute success rates globally.
Chargeback Carousel
Visualize every dispute stage on a Kanban board, letting sellers drag-and-drop cases, assign tasks, and track progress at a glance.
Reclaim Rewards
Gamify dispute wins by awarding credits and badges for successful chargeback resolutions, motivating sellers and rewarding consistent recovery.
Press Coverage
Imagined press coverage for this groundbreaking product concept.
Chargebackly Unveils Evidence Express to Slash Dispute Prep Time to Two Minutes
Imagined Press Article
San Francisco, CA – 2025-06-24 – Chargebackly today announced the launch of Evidence Express, its latest automation feature designed to transform the way independent e-commerce sellers manage chargeback disputes. Evidence Express automatically aggregates order, payment, and customer communications into a fully formatted evidence packet with a single click, cutting down dispute preparation from hours of manual work to just two minutes. This innovation addresses a critical pain point for small business owners who struggle with lost revenue and overwhelming paperwork during chargeback disputes. Evidence Express leverages Chargebackly’s Instant Sync technology to retrieve real-time data from connected stores, including order history, shipping confirmations, refund attempts, and customer correspondence. Once the data is collected, the system applies Template Forge rules to generate brand-compliant PDF evidence packets that meet card network requirements. Sellers can customize branding, add disclaimers, and reorder sections directly from their dashboard before exporting or submitting through integrated issuing bank portals. The entire process is orchestrated in the background, freeing merchants from repetitive tasks and ensuring complete, error-free documentation every time. “Evidence Express is a game-changer for our users,” said Amanda Liu, CEO of Chargebackly. “Our mission has always been to empower independent sellers by giving them enterprise-grade dispute tools at an affordable price. With Evidence Express, we’re eliminating the friction and wasted hours that come with manual evidence compilation, so merchants can reclaim lost revenue faster and focus on what really matters: growing their business.” Early adopters have already reported dramatic improvements in dispute turnaround times and success rates. John Rivera, owner of a specialty leather goods store, shared: “Before Evidence Express, I was spending half a day per dispute gathering screenshots, emails, and shipping logs. Now, I hit one button and the packet appears instantly. In our first week using it, we processed ten disputes in under twenty minutes and won eight of them. It’s a total game-changer.” Evidence Express is available immediately to all Chargebackly subscribers at no additional cost. New users can activate the feature during onboarding, while existing customers will see it in their dashboard as part of the June platform update. Chargebackly’s pricing plans continue to be tiered based on monthly dispute volume, with unlimited access to Evidence Express included at every level. About Chargebackly Chargebackly is the leading chargeback management platform for independent e-commerce sellers. By combining AI-driven insights with seamless store integrations, Chargebackly streamlines evidence preparation, risk detection, and dispute resolution to protect merchants’ bottom lines. Over 5,000 online retailers across Shopify, WooCommerce, Amazon, and eBay trust Chargebackly to recover millions in lost revenue. For more information, visit www.chargebackly.com. Media Contact: Emily Chang Head of Communications, Chargebackly press@chargebackly.com (415) 555-0198
Chargebackly Launches LocaleGuard for Seamless Global Dispute Compliance
Imagined Press Article
San Francisco, CA – 2025-06-24 – Chargebackly today introduced LocaleGuard, an end-to-end localization suite that automates compliance with regional chargeback regulations and buyer preferences. As e-commerce continues to expand internationally, merchants face mounting challenges around currency conversion, language translation, and region-specific legal disclosures. LocaleGuard addresses these issues by delivering evidence packets that are precisely tailored to each customer’s locale, boosting dispute success rates and improving cross-border customer satisfaction. LocaleGuard combines four powerful modules into one seamless workflow: Smart Locale Detection, Real-Time Currency Conversion, Regulation Engine, and Adaptive Formatting. Smart Locale Detection identifies the customer’s country, language, and currency based on IP data and order metadata. Real-Time Currency Conversion pulls live exchange rates to present amounts in both the issuing bank’s and the customer’s local currency. Regulation Engine embeds mandatory legal clauses—such as GDPR-compliant data statements for EU customers or PCI disclosures for North America—automatically into evidence packets. Adaptive Formatting ensures that dates, addresses, phone numbers, and numeric separators adhere to local conventions, providing a professional presentation that resonates with regional reviewers. “Expanding into new markets shouldn’t mean reinventing your dispute management process,” said Raj Patel, Chief Product Officer at Chargebackly. “LocaleGuard gives sellers the peace of mind that their evidence will meet local requirements without manual research or template adjustments. By automating compliance across multiple jurisdictions, we’re helping merchants prevent unnecessary chargeback denials and recover revenue faster, regardless of where they sell.” Beta participants in the international merchant program reported a 20% increase in dispute win rates within their first month of using LocaleGuard. Multi-Store Maria, who manages storefronts across Shopify, Amazon, and eBay in North America, Europe, and Asia, shared: “LocaleGuard streamlined our global dispute process in ways we never imagined. We used to maintain separate templates for each region and spend hours translating disclaimers. Now, it’s all taken care of automatically. It’s like having a global compliance team working for you 24/7.” LocaleGuard is now available as an add-on feature for Premium and Enterprise Chargebackly plans. Merchants can enable the suite with a single toggle in the platform settings and customize individual modules as needed. Starting today, Chargebackly is offering a free 14-day trial of LocaleGuard to all existing subscribers, along with personalized onboarding support to configure regional preferences. About Chargebackly Chargebackly is the industry-leading dispute management solution built for e-commerce entrepreneurs. With a focus on automation, AI-driven insights, and seamless integrations, Chargebackly empowers merchants to efficiently handle chargebacks and reclaim lost revenue. Trusted by thousands of online retailers worldwide, Chargebackly continues to innovate in risk detection, evidence automation, and global compliance. Learn more at www.chargebackly.com. Media Contact: Emily Chang Head of Communications, Chargebackly press@chargebackly.com (415) 555-0198
Chargebackly Rolls Out Dispute Radar 2.0 with AI-Driven Risk Detection
Imagined Press Article
San Francisco, CA – 2025-06-24 – Chargebackly today announced the release of Dispute Radar 2.0, the latest iteration of its AI-powered risk detection engine that identifies and flags high-risk transactions before chargebacks occur. Building on the original Dispute Radar concept, version 2.0 introduces Predictive Forecast, Custom Rule Builder, and Real-Time Anomaly Alerts to give merchants unparalleled visibility and control over their order pipeline. Dispute Radar 2.0 analyzes historical order and dispute data, merchant-defined parameters, and industry fraud patterns to compute a normalized risk score for each incoming transaction. Predictive Forecast then uses machine learning to project future chargeback volumes and estimate potential revenue impact, enabling merchants to proactively allocate resources and adjust fraud prevention strategies. Custom Rule Builder allows sellers to define specific triggers—such as order value thresholds, geographic restrictions, or product category filters—and automate risk actions like manual review flags or order holds. Real-Time Anomaly Alerts deliver instant notifications for suspicious behaviors such as mismatched billing addresses, rapid-fire orders from the same IP, or sudden spikes in high-value purchases. “Chargebackly’s goal is to shift the focus from dispute remediation to dispute prevention,” said Carlos Mendes, VP of Engineering at Chargebackly. “By continuously refining our AI models and adding flexible rule-making capabilities, Dispute Radar 2.0 empowers merchants to detect and deter fraudulent transactions at scale. This not only reduces chargebacks but also preserves customer trust and protects profit margins.” Metrics Maven Mia, a beta user who oversees a rapidly growing online electronics store, reported a 35% reduction in chargeback incidence within eight weeks of deploying Dispute Radar 2.0. “The real-time alerts have been a lifesaver,” she said. “We catch suspect orders immediately and verify them before they fulfill. The ability to tailor rules to our unique product lines has made our fraud prevention both smarter and faster.” Dispute Radar 2.0 is available today to all Chargebackly subscribers starting on the Growth plan and above. Sellers can access the feature directly from their dashboard under the Risk Management tab and begin customizing rules and forecasts instantly. Chargebackly is also offering a complimentary risk audit and onboarding session for new users to maximize the impact of the new tools. About Chargebackly Chargebackly is the premier chargeback management platform offering dispute prevention, evidence automation, and analytics to e-commerce merchants of all sizes. With advanced AI capabilities and seamless store integrations, Chargebackly helps sellers reduce fraud, expedite dispute resolution, and reclaim lost revenue. Visit www.chargebackly.com for more information. Media Contact: Emily Chang Head of Communications, Chargebackly press@chargebackly.com (415) 555-0198
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