MARA

Consolidates reviews from disparate sources (Google, Yelp, Facebook, industry-specific platforms) into a single dashboard by implementing platform-specific API connectors that poll review feeds at configurable intervals, normalize metadata (reviewer name, rating, timestamp, platform origin), and deduplicate entries across sources. Uses a centralized data model to abstract platform differences, allowing unified filtering, sorting, and triage without requiring users to visit each platform individually.

Analyzes incoming reviews using NLP to extract sentiment, key topics (service quality, pricing, staff, cleanliness), and urgency signals, then generates contextual response templates using a fine-tuned language model trained on business-specific brand voice examples. The system learns from user-approved responses to refine future suggestions, maintaining tone consistency through a brand voice profile (formal/casual, empathetic/direct) that acts as a constraint during generation. Responses are ranked by relevance and customization effort required.

Enables users to set up custom alerts triggered by specific review conditions (e.g., rating < 3, mentions of health/safety issues, competitor mentions, sudden volume spikes). Alerts are delivered via email, SMS, Slack, or in-app notifications with configurable frequency (immediate, daily digest, weekly summary). Users can define alert rules using a rule builder UI or JSON configuration. Supports alert escalation (e.g., notify manager if responder doesn't reply within 2 hours) and integration with incident management systems.

Ranks reviews using a multi-factor scoring algorithm that weights sentiment (negative reviews prioritized), reviewer influence (high-follower accounts, verified purchasers), platform visibility (Google/Yelp weighted higher than niche platforms), and business impact signals (mentions of staff, pricing, or service quality issues). Allows users to customize weighting rules and set alert thresholds (e.g., notify immediately if rating < 3 and mentions 'food poisoning'). Implements rule-based filtering to surface reviews requiring urgent response vs those that can be batched.

Enables users to compose a single response in the MARA interface and publish it across multiple platforms (Google, Yelp, Facebook, etc.) simultaneously using platform-specific API endpoints. Handles platform-specific constraints (character limits, formatting restrictions, allowed HTML tags) by truncating or reformatting responses automatically. Tracks publication status per platform and provides audit logs showing when responses were published, by whom, and any platform-specific errors. Supports scheduled publishing and bulk response operations.

Aggregates review data over time to generate dashboards and reports showing sentiment distribution (positive/neutral/negative %), average rating trends, topic frequency analysis (which issues are mentioned most often), and platform-specific performance metrics (e.g., Google vs Yelp average ratings). Uses time-series analysis to detect sentiment shifts (e.g., sudden drop in ratings after a specific date) and correlate with business events. Exports reports as PDF or CSV for stakeholder communication. Supports custom date ranges and filtering by platform, location, or topic.

Enables multiple team members to access the review dashboard, assign reviews to specific users for response, and track response status (assigned, in-progress, responded, published). Implements role-based access control (manager, responder, viewer) with different permissions (e.g., responders can draft responses but managers must approve before publishing). Provides activity feeds showing who responded to which reviews and when, and supports comments/notes on reviews for internal team discussion. Integrates with email/Slack to notify assigned users of new reviews.

Analyzes incoming reviews for signals of inauthenticity (bot-generated text, suspicious reviewer patterns, platform ToS violations) using heuristics and machine learning models trained on known spam/fake review datasets. Flags reviews with low authenticity scores for manual review, and optionally filters them from the main dashboard. Detects patterns like multiple reviews from the same IP address, reviews posted in rapid succession, or text matching known spam templates. Integrates with platform-provided verification signals (verified purchaser badges, account age) to supplement detection.

Provides a library of pre-written response templates for common scenarios (thanking positive reviewers, addressing service complaints, responding to pricing objections, handling health/safety concerns). Users can browse templates, customize them for their brand voice, and save custom templates for reuse. Templates are tagged by scenario and sentiment, enabling quick search and filtering. Integrates with the AI response suggestion capability to use templates as starting points for generation, rather than generating responses from scratch.

Allows users to create and maintain a business profile including company name, industry, location(s), brand voice guidelines (tone, values, communication style), and response preferences (e.g., always offer compensation for negative reviews, never mention competitors). The profile acts as context for AI response generation and team collaboration, ensuring all responses align with brand identity. Supports multiple profiles for multi-brand or multi-location businesses. Changes to the profile are versioned and tracked for audit purposes.

Enables bulk export of review data (aggregated reviews, responses, analytics) in standard formats (CSV, JSON) for integration with external systems (CRM, business intelligence tools, spreadsheets). Supports scheduled exports (e.g., daily or weekly) delivered via email or uploaded to cloud storage (Google Drive, Dropbox, S3). Provides API endpoints for programmatic access to review data, enabling custom integrations. Exports include metadata (platform, date, reviewer info) and response history.