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Custom unsubscribe message for SMS campaigns

Problem: The current unsubscribe toggle only appends a hardcoded "Reply STOP to unsubscribed." This works for two-way SMS services but fails for one-way SMS, which many users rely on. In several countries in Europe the opt-out instruction must reference a specific short code (e.g. "Reply STOP to 36180"), similar to Brevo's [STOP_CODE] system where the keyword STOP_CODE will be automatically replaced with a code specific to SMS campaigns or transactional SMS messages when the message is sent. Proposed solution: Add an optional free-text field below the existing unsubscribe toggle in Campaign > Appearance. When populated, it overrides the default message. When left empty, the current default behavior is preserved. Ideas: New text area field appears under the unsubscribe toggle (only visible when toggle is ON) Field is optional — empty = default message used Custom message is appended to SMS body in place of the default unsubscribe line Per-campaign setting (saved with campaign blueprint) Works with both one-way and two-way SMS services Priority: High — this is a legal compliance blocker for users sending SMS in countries that require carrier-specific STOP codes.

Jean-Gabriel About 21 hours ago

💡 Feature Request

Custom unsubscribe message for SMS campaigns

Problem: The current unsubscribe toggle only appends a hardcoded "Reply STOP to unsubscribed." This works for two-way SMS services but fails for one-way SMS, which many users rely on. In several countries in Europe the opt-out instruction must reference a specific short code (e.g. "Reply STOP to 36180"), similar to Brevo's [STOP_CODE] system where the keyword STOP_CODE will be automatically replaced with a code specific to SMS campaigns or transactional SMS messages when the message is sent. Proposed solution: Add an optional free-text field below the existing unsubscribe toggle in Campaign > Appearance. When populated, it overrides the default message. When left empty, the current default behavior is preserved. Ideas: New text area field appears under the unsubscribe toggle (only visible when toggle is ON) Field is optional — empty = default message used Custom message is appended to SMS body in place of the default unsubscribe line Per-campaign setting (saved with campaign blueprint) Works with both one-way and two-way SMS services Priority: High — this is a legal compliance blocker for users sending SMS in countries that require carrier-specific STOP codes.

Jean-Gabriel About 21 hours ago

💡 Feature Request

Visual enhancement for Performance Trends

My clients love to see the performance trends. However, since all items are displayed on the same chart, items with much lower volume (Call Clicks, Direction Requests, and Website Clicks) than Impressions are difficult to see when they all share the same Y-axis. It would be much more readable if different tabs could be provided to view trends for different items. This is how Google displays the charts in its native dashboard.

Kevin Huang 2 days ago

💡 Feature Request

Make EMR the AI hub for review intelligence (MCP server)

What I've been doing (and why it matters for all of us) I want to share a workflow that's completely transformed how I run my agency, because I think it points to something much bigger for EMR. For the past few months, almost every key task in my agency starts the same way: I export all reviews for a client from EMR as a CSV, then feed that file into an AI tool (Claude, in my case). Here's what I use it for: Prospecting: Before I pitch a potential client, I pull their reviews and ask the AI to run a full reputation audit. Strengths, weaknesses, missed opportunities, trends over time. I send them the analysis as part of my proposal. It sells itself. Client onboarding: When I sign a new client, I feed their reviews to the AI and ask it to extract their brand DNA, the specific words their customers use, what makes them unique, what frustrates people. From that, the AI generates the review response prompt, tailored to their exact voice and values. What used to take half a day now takes 15 minutes. Monthly reporting: I ask the AI to compare this month's reviews to last month's, flag rating drops, detect new recurring complaints, and highlight the best verbatims for social media use. Website projects: For a recent client website redesign, I exported 308 reviews spanning 12 years. The AI identified 7 distinct identity pillars from the customer feedback, with frequency stats, seasonal patterns, multilingual analysis, and a precise list of recurring irritants with recommendations. It produced a full brand brief that was more insightful than any workshop could have been. Team management insights: On a larger client (2,665 reviews), the AI extracted which staff members get mentioned by name in reviews and with what sentiment. It created a leaderboard showing who the customer-facing stars are. 100% positive sentiment across all named employees. That's an HR and management tool generated entirely from review data. Content generation: The AI selects the best customer quotes and generates Instagram posts, website testimonials, FAQ pages, and SEO meta descriptions, all using the exact language that real customers use, not generic marketing copy. Every single one of these workflows starts with a manual CSV export from EMR. Export, download, upload to AI, run the analysis. For one client, it's manageable. For 20 clients, monthly? It doesn't scale. The idea: connect EMR directly to AI What if the AI could access EMR's review data directly, in real-time, without any export step? That's exactly what MCP (Model Context Protocol) does. It's an open standard, now supported by Claude, ChatGPT, Gemini, Copilot, and others, that lets AI tools connect to external platforms and query data live. Think of how Google Drive or Notion already work with Claude: you don't download a file and re-upload it. You just say "look at my doc about X" and the AI reads it. If EMR had an MCP server, the workflow above becomes: open Claude, say "analyze the reviews for [client name]", and get the full analysis. No export. No file. No friction. For any client. At any time. Notion, Slack, Stripe, Salesforce, GitHub, Shopify, HubSpot have all shipped MCP servers in the past few months. It's becoming the standard way AI connects to business tools. Why EMR's data is more valuable than raw reviews Here's an important nuance. The raw reviews themselves (what's on Google, TripAdvisor, Trustpilot, etc.) are publicly available data. Third-party scraping tools already offer MCP-compatible access to those reviews. Any agency that wants to run AI analysis on reviews can technically do it today without going through their review management platform. But EMR holds data that no scraper can reach: Response history and status (which reviews were responded to, when, how) Sentiment analysis (already computed by EMR) Campaign data (which invitations led to which reviews, conversion rates) Multi-location structure (organization hierarchies, comparisons, portfolio views) Private feedback (never visible publicly) AI response configuration (the intelligence layer agencies have built) This operational context is what turns a surface-level review analysis into actionable intelligence. A native EMR MCP server gives AI the full picture, making EMR irreplaceable in the workflow rather than bypassable. What the MCP server should expose Core (read access): Reviews: filtered by organization, location, source, date range, rating, response status, sentiment Review metadata: sentiment scores, tags, language, verification status Organizations and locations: name, connected sources, plan, stats Aggregated metrics: average rating, review volume, response rate, NPS by period Extended: AI Insights data Campaign performance Response history per review Write: submit a review response draft for approval (respecting existing approval flows) Competitive positioning No review management platform has MCP support today. Not Birdeye, not Podium, not EmbedSocial, not Grade.us. The first to ship it captures the entire "AI + reviews" narrative. The window is open now. The tools to build it are already in EMR's tech stack. The effort is minimal. The upside is massive. I've built a working demo (interactive dashboard with 6 tabs: overview, brand DNA, team insights, AI alerts, Instagram content, website widgets) generated from a real 2,665-review export to show what this makes possible. Happy to share it. Let's make EMR the first review management platform with native AI integration.

Seb Gardies 3 days ago

1

💡 Feature Request

Make EMR the AI hub for review intelligence (MCP server)

Context: the AI landscape is moving fast MCP (Model Context Protocol) is the open standard that lets AI assistants (Claude, ChatGPT, Gemini, Copilot, etc.) connect directly to external platforms and access data in real-time. Think of it as a universal adapter between AI tools and business software. In the past few months, MCP adoption has exploded. Notion, Slack, Stripe, Salesforce, GitHub, Shopify, HubSpot all shipped MCP servers. It's becoming the default way AI interacts with business tools. Any platform that doesn't offer MCP access is essentially invisible to the AI ecosystem. This matters for review management specifically because review data is one of the most valuable datasets a local business has, and AI is exceptionally good at extracting insights from it. Agencies and businesses are already starting to use AI to analyze reviews, generate reports, create content from customer feedback, and build smarter response workflows. Right now, the only way to get review data into AI tools from EMR is a manual CSV export. That works for a one-off analysis, but it doesn't scale. And here's the thing: the raw review data itself (what's on Google, TripAdvisor, Trustpilot, etc.) is publicly accessible. Third-party scraping services already offer MCP-compatible access to reviews from all major platforms. Any agency that wants AI-powered review analysis can technically get it today without going through their review management platform at all. That's both a threat and an opportunity for EMR. The opportunity: why a native EMR MCP server wins The reason a native EMR MCP server would be far superior to any external scraping approach is that EMR holds data that doesn't exist anywhere else: Response history and status (which reviews were responded to, when, by whom) Sentiment analysis scores (already computed by EMR) Campaign data (invites sent, conversion rates, which campaigns drove which reviews) Organization and location structure (multi-location hierarchies, tags, plans) Cross-platform aggregation (all sources unified, deduplicated, normalized) Private feedback (never visible publicly) AI response prompts and rules (the intelligence layer agencies have configured) None of this is available through external scraping. An agency using a third-party scraper gets the raw reviews, but misses the operational context that makes the analysis truly actionable. A native EMR MCP server would give AI tools the full picture, making EMR irreplaceable in the workflow rather than bypassable. What the MCP server should expose Core (read access): Reviews: filtered by organization, location, source, date range, rating, response status, sentiment Review metadata: sentiment scores, tags, language, verification status Organizations and locations: name, connected sources, plan, stats summary Aggregated metrics: average rating, review volume, response rate, NPS by period Extended (high-value differentiators): AI Insights data (if available internally) Campaign performance (invites sent, conversion rates) Response history per review Write: submit a review response draft for approval (respecting existing approval workflows) What this unlocks For end clients (businesses): On-demand reputation audits: "Analyze my reviews from the last 6 months" (no export, no file, just ask) Automated monthly reports with trends, sentiment shifts, keyword analysis Content generation from reviews: social media posts, website testimonials, FAQ pages Proactive trend detection: "Your reviews mention wait times 3x more than last quarter" Review-powered SEO: AI extracts the language customers actually use and builds optimized content Multilingual analysis in a single query (no translation step needed) For agencies: Automated onboarding: AI reads reviews, extracts brand DNA, generates the AI response prompt Portfolio monitoring: "Which of my 50 clients had a reputation shift this month?" Sales prospecting: instant reputation audit for any prospect (pull reviews, generate insights, send as a proposal) Multi-location intelligence: "Compare the top 10 vs bottom 10 locations in this franchise and tell me what's different" White-label AI services: agencies can offer "AI reputation intelligence" as a premium feature, powered by EMR data that no scraper can replicate Why MCP specifically (vs. just more API endpoints) A REST endpoint for reviews (GET /reviews) would be a great first step and the minimum viable path. But MCP goes further: AI-native discovery: AI tools auto-discover available queries. Zero integration code needed. Multi-model support: works with Claude, GPT, Gemini, Copilot out of the box. Compound reasoning: the AI chains multiple queries in one conversation ("get all 1-star reviews from Q4, find patterns, draft responses for each"). Zero cost for agencies: no Zapier, no middleware, no custom dev. Connect and go. Competitive positioning No review management platform has MCP support today. Not Birdeye, not Podium, not EmbedSocial, not Grade.us. The first platform to ship it captures the entire "AI + reviews" narrative. Given how fast MCP adoption is growing across the SaaS ecosystem, this window won't stay open long. Meanwhile, the alternative for agencies is increasingly clear: scrape the reviews directly from the source platforms (tools for this already exist with MCP support) and bypass the review management layer entirely for analytics. A native EMR MCP server makes that workaround unnecessary by offering something far richer, but only if it exists. Implementation path The MCP spec supports remote servers via Streamable HTTP. Authentication can leverage the existing EMR API token system. Scoping by organization/location ensures strict data isolation between clients. This doesn't replace any existing feature. It extends what EMR already does by letting the broader AI ecosystem tap into the data EMR uniquely aggregates. Happy to discuss specifics or help shape the scope.

Seb Gardies 3 days ago

1

💡 Feature Request

AI Credit System Similar to Email/SMS Credits

We need AI credit system where we can assign ‘Monthly AI Credits’ limits in custom plans. To avoid complication we can design it like: 1 credit = 1 review respond (irrespective of what model used cause customer not aware about models they simply see I responded to 1 review 1 credit consumed.) Why It is Required? Avoiding Trial Misuse: Currently without any usage restrictionis, anyone can start a free trial and literally respond to their old 100s of reviews using this feature and can left without subscribing. Free to Paid Conversion: AI responder is one of the mostly used, easy to adopt, & habit forming feature of EMR, where users can build habit of daily loggin in and responding to reviews. With usage restrictions it can help converting Free tier users to paid users. From user perspective; “User respond to few reviews than see they reached the limit in free plan and prompted to upgrade. They are more likely to convert than if we completely restrict the feature, cause they already built the habit of logging in and replying to reviews with AI.”

Arun Saini 5 days ago

💡 Feature Request

Rewording the word "agency" in client dashboard

I understand that the AI Insights feature is designed for agencies to provide as an add-on. However, I hope the word “agency” here can be reworded, as we are using the white-labeled EMR platform, which is introduced under our own brands. Thus, I’m offering the platform as a SaaS product, not an agency service. The word “agency” here may be interpreted as there’s a provider on top of which may not be appropriate in a white-label business model. In my local market, the word “agency” is translated as “reseller”. It should change to “administrator”.

Kevin Huang 7 days ago

1

🐛 Bug Reports

Language specific custom instructions/prompt for default AI translations

Would be great if we could specify a custom prompt/instructions for the AI that does the translations for new strings on the platform. For example when translating language X, it should always translate “review” to Y and “rating” to Z. This would require some consensus among agency owners in different languages I guess. Would be great for congruency to the translations.

Severi 7 days ago

💡 Feature Request

Event-Based Conditional Branching for Campaigns

We need the ability to build sequential automation workflows based on behavioral triggers. Specifically, the system must support conditional logic that checks a user's previous interaction state (e.g., If [Previous Campaign URL Clicked] == True) to route them to a secondary campaign instead of the default sequence. Usecase: A shop gets a lot of repeat buyers. Google Maps is most important review platform for them. To maximize conversions, their campaign only has Google Maps review link. They send them campaign A. After a while, their review stream drops as most of their clients have already reviewed them on Google Maps. They want reviews from them on Facebook. So they set up a campaign that will be sent only to people who have clicked review link on campaign A. Now they first get reviews from their customers to Google Maps and after they buy again, they are asked to review them on Facebook. —- Of course this could be used in many more ways. Did they click one star in campaign A? Don’t send them campaign B. Have they been sent campaign A and campaign B and they didn’t open either of them? Send them more aggressive email with subject “Open this and receive free item X”

Kristian Pesti 7 days ago

💡 Feature Request

A widget for Agencies

A widget for agencies that showcases their customers’ growth since they signed up on the platform. We already capturing and showing this stat on user dashboard “Since joining on ___ +0.2 rating increase and x New reviews.” Same we can also show dynamically on our websites using widgets. Card style: Their Logo + organization name on top Current reviews & rating New Reviews & rating improvement since joining. We (Agencies) can display this widget on our frontend website, similar to other widgets.

Arun Saini 14 days ago

💡 Feature Request

Whatapp AI Agent

As we’re heading towards WhatsApp cloud API structure for the WhatsApp channel. I think we can use that connection for more powerful things instead of just using it in review requests. Creating AI Agents is one such powerful use case. It brings EMR benefits directly in the user’s WhatsApp chat interface. What can these AI Agents do? - Review Notifications - Review Response Approval User will see the response with two quick reply buttons and will choose if they want to approve or want to modify the response - Reports delivery - Automatic social share image delivery with caption or auto post approval flow -Transactional notification (Trial end, Low credits, etc., similar to transactional emails with enable/disable) - Onboarding new users If a user creates an account and the session goes inactive or they logout without completing all onboarding step. Then this onboarding agent will become active and ask user to complete their next onboarding step. e.g. if user just uploaded their logo then the agent will prompt them to connect their GBP and send them link to connect their google account and provide access without even need of logging in to EMR. Similarly, the ai agent can design & create the feedback form and campaign for them by asking questions from them directly in whatsapp interface. It will help improve customer experience for regular works like approving replies, checking notifications, reports etc., and help improving onboarding success rate.

Arun Saini 14 days ago

💡 Feature Request