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Per-campaign dialer sentiment & outcome card

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What the card shows

• The four-way tally — the share of attempts classified positive, neutral, negative, plus a Pending score bucket for calls whose post-call scoring hasn’t landed yet. Each bucket shows its count and, where scored, its mean sentiment score.
• Average sentiment score — a single mean over all scored attempts on a −1.0 to +1.0 scale, shown to two decimal places. A campaign with no scored attempts yet renders an em dash, never 0.00 — a literal zero would read as “dead-neutral,” which is wrong for an unscored population.
• Per-outcome breakdown — every carrier outcome the campaign produced (connected, no_answer, busy, voicemail, amd_detected, in-flight pending, and so on), each with its positive/neutral/negative/pending-score counts and mean score. Rows sort by attempt volume, so the outcome most of your traffic produced leads the table.

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How the aggregation works — and why hangup bias is avoided

1. The pacing scheduler stamps one call_logs row per originated attempt, keyed off the attempt’s call identifier.
2. The post-call synthesis service scores that row — sentiment_label (positive / neutral / negative) plus sentiment_score (float from −1 to +1).

• Unanswered outcomes don’t drag the score down. Without the join, unanswered attempts would all pile into a “negative” bucket purely because the call hung up before anyone spoke. Because the join only counts attempts with a real scored call row, no_answer / busy outcomes sit in their own rows with a pending-score count, and the sentiment split reflects only calls that actually connected.
• Scoring lag shows up honestly. A call whose synthesis hasn’t landed yet folds into Pending score rather than being silently and wrongly scored as neutral. If Pending score dominates the card ten minutes after a batch ran, the scoring pipeline is behind — don’t read the split as neutral-heavy.

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Where the card lives

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Triaging a negative-skew spike

1. Check the target segment. Pull the current list’s segment/filters. A list refresh that re-queued previously burned contacts, or a geographic/demographic shift in the audience, reads as negative skew the moment it lands — before any campaign-side change is in play. If the segment changed, re-screen the list and pause.
2. Check the content. Open the campaign’s script and the recent audio/broadcast asset. A new offer line, a changed opening question, or a recording rework correlates with a skew step-change the day it shipped. If the skew starts at a content change, revert or rewrite that change before touching anything else.
3. Check RNG pacing. Look at the predictive pacing knobs and the abandon ceiling. A pacing ratio that creeps up puts calls in front of agents with less setup time, agents rush the opener, and recipients push back — that cascade shows up as negative sentiment on connected calls. Also check the abandon-rate trend: rising abandonment often walks in with a negative skew because more calls land with no agent ready.
4. Correlate with local caller-ID coverage. Open the campaign’s caller-ID coverage read on the same detail page. If the negative skew concentrates specifically where the origin number isn’t local, the issue is recipient trust, not the pitch — raise coverage for the affected region and re-measure.

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Export the read — GET /api/v1/dialer/campaigns/:id/sentiment

{
  "data": {
    "campaign_id": "camp_abc123",
    "campaign_name": "Sales outbound — August",
    "analytics_window_days": 30,
    "attempts_scanned": 18540,
    "attempts_scored": 9120,
    "avg_score": 0.4,
    "tally": [
      { "sentiment": "positive", "count": 5270, "avg_score": 0.61 },
      { "sentiment": "neutral", "count": 3310, "avg_score": 0.04 },
      { "sentiment": "negative", "count": 540, "avg_score": -0.48 },
      { "sentiment": "unscored", "count": 9420, "avg_score": null }
    ],
    "by_outcome": [
      {
        "outcome": "connected", "total": 9120,
        "positive": 5270, "neutral": 3310, "negative": 540, "unscored": 0,
        "avg_score": 0.4
      },
      {
        "outcome": "no_answer", "total": 6800,
        "positive": 0, "neutral": 0, "negative": 0, "unscored": 6800,
        "avg_score": null
      }
    ],
    "attempts_scan_capped": false
  }
}

• avg_score — null when nothing has been scored yet. Parse as nullable.
• tally — always all four buckets, zero-padded so your consumer doesn’t need to handle missing keys.
• by_outcome[].unscored — the counts in a row sum: total = positive + neutral + negative + unscored.
• attempts_scan_capped — true when the campaign’s 30-day attempt volume exceeded the server-side scan bound; the rollup then describes the most recent cap of attempts rather than the whole window. Treat that flag as coverage metadata in any report you build off this.

curl -s "https://api.orbit.devotel.io/api/v1/dialer/campaigns/$CAMPAIGN_ID/sentiment" \
  -H "X-API-Key: $ORBIT_API_KEY" | jq '.data'

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Pair with the manual-dial view

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See also

• Launch an outbound dialer campaign — the pacing, lists, dispositions, and post-call scoring the card rolls up
• Dialer dispositions — the carrier-outcome vocabulary the per-outcome breakdown uses
• Conversation intelligence — how post-call synthesis, sentiment, and outcomes apply across inbound and outbound calls
• Dialer API — full request/response schema for the sentiment endpoint