Mango Signal

Workforce Intelligence layer that detects engagement, performance, and operational risk across the workforce, recommends action to managers, drives close-the-loop change, and proves whether the organization improved.

MangoApps

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Overview

Mango Signal is the Workforce Intelligence layer for MangoApps. Most companies collect employee feedback but rarely act on it — surveys produce dashboards, comments pile up, managers do not follow through, employees never see what changed, trust drops, participation falls. Mango Signal closes that loop. It detects engagement, sentiment, theme, and risk signals across Surveys, Recognitions, Performance, News Feed, Shift Feedback, and other native MangoApps sources, computes a continuous engagement health score for the company, segment, manager, team, or location, recommends opinionated next actions, drives a close-the-loop "you said, we did" flow, and proves whether the organization improved by capturing pre/post measurement on every completed action. Standalone listening platforms must integrate with HR systems to access workforce context — Mango Signal already has it in the same tenant. Premium add-on, v1 ships disabled-by-default and license-required, with a built-in AI assistant that answers culture, engagement, and risk questions via Ask AI.

Productivity Utility

Highlights

Continuous engagement health score across the company, every segment, every manager team — computed nightly, query-cached, never stale.

Cross-signal intelligence: sentiment from Surveys, recognition rate from Recognitions, performance pulse from EPMS, engagement from News Feed, shift sentiment from Shift Feedback, and more.

Theme clustering surfaces the recurring concerns hidden in free-text payloads — ranked by signal count, polarity-tagged, and linked back to the underlying signals.

Risk segments at a glance: which teams, locations, or segments are sliding below the configurable engagement threshold, sorted worst-first.

AI-recommended next actions per risk segment — fall back to a stable heuristic recommender when the LLM is unavailable; never block the dashboard.

Close-the-loop flow: completing an action automatically posts a "you said, we did" update to News Feed (or Broadcast for high-reach scopes) and seeds the Impact baseline.

Pre/post measurement on every action proves what worked — average delta, positive/negative counts, and per-action drill-through.

Mango Signal AI assistant answers culture, engagement, and risk questions through Ask AI — risky writes (create_action, update_status) require explicit confirmation.

Use cases

CHRO weekly briefing

CHROs open the Mango Signal dashboard each Monday to see the rolling engagement score, top three risk segments, and the actions in progress against them. The Impact tab confirms which prior interventions moved sentiment.

Manager 1:1 prep

A manager whose team has slipped below the engagement threshold pulls Mango Signal to see contributing themes (e.g., scheduling friction, recognition drop), creates a recommended action, and runs targeted listening 1:1s in the next two weeks.

People Analytics deep-dive

People analytics teams export 90 days of normalized cross-signal data via the CSV/XLSX export, slice by segment, and feed it into their downstream BI for cross-cutting workforce insights.

VP Operations risk monitoring

A VP Ops watches the Risk Segments panel for warehouse and field locations dropping below threshold, drills into the underlying signals (shift feedback, attendance trends), and assigns coverage interventions.

Internal Communications close-the-loop

IC leads use the close-the-loop flow to make sure every action visibly returns to the team that surfaced the original concern — News Feed posts, scoped to the right audience, automatically.

Resources

Mango Signal app overview

overview

What it is, who it is for, primary surfaces, role-based behavior.

Manager dashboard tour

guide

How to read the engagement score, themes, and AI-recommended actions.

Configuring risk thresholds

guide

Tuning the alert threshold and the score recompute window.

Close-the-loop flow

guide

How completing an action becomes a News Feed post and starts Impact measurement.

Mango Signal AI assistant

guide

Tools available in Ask AI, confirmation flow for risky writes, and example prompts.

Mango Signal strategy plan

strategy

Positioning, differentiator, phased roadmap, and competitive landscape.

FAQ

Surveys collects feedback. Mango Signal interprets cross-signal context, recommends action, drives change, and measures impact. It sits on top of Surveys (and many other apps) — it does not replace it.

Report Agent answers ad-hoc natural-language reporting questions. Mango Signal is opinionated, continuously computed, and recommendation-driven — it tells managers what to do next, not just what the data says.

No. The agent and dashboards always aggregate to team, segment, or location scope. Individual sentiment is never surfaced. The strategy plan calls this out explicitly as a privacy boundary.

Mango Signal works with whatever upstream apps you have. The more apps emit signals (Surveys, Recognitions, EPMS, News Feed, Shift Feedback, Tasks, Workspace, etc.), the richer the cross-signal interpretation. v1 ships writers for the most common sources.

The agent is gated by the per-tenant `agent_enabled` setting. When enabled, it appears in the Ask AI sidebar and answers culture / engagement / risk questions. Risky tools (creating or completing actions) require explicit confirmation before persisting.

Mango Signal aggregates the last 30 days of normalized signals (sentiment, recognition rate, performance pulse, engagement, themes), applies per-type weights, and returns a 0..1 score. The window and weights are admin-configurable.

The close-the-loop service fires: a baseline Impact row is captured (current engagement score for the action's scope), a News Feed (or Broadcast) post is queued, and a follow-up measurement runs after the configured window (default 30 days).

Yes. The Analytics tab and Export controls produce CSV and XLSX exports of raw signals with all dimensions, plus a template CSV for one-time historical imports.