What Is Contact Center Campaigns?

What Is Contact Center Campaigns?

Contact center campaigns are batched outbound or proactive customer interactions — calls, SMS, email, or chat — driven by a target list, a script or template, and an outcome goal. In AI reliability work, they are a production agent workflow pattern: each contact becomes a trace, each script variant becomes a cohort, and each outcome needs evaluation. FutureAGI evaluates the AI side with TaskCompletion, ConversationResolution, CustomerAgentObjectionHandling, and CustomerAgentTerminationHandling, plus voice evaluators when calls are involved.

Why Contact Center Campaigns Matter in Production LLM and Agent Systems

Campaign AI fails publicly. A retention voice agent that argues with a churning customer makes the news. A collections agent that uses the wrong tone breaks TCPA-style compliance. A survey bot that misreads sentiment generates bad data that downstream teams act on. The blast radius of a single failure is whatever the campaign size is — 10K calls, 100K SMS, 1M emails — so a small per-call regression compounds fast.

Operations sees this as conversion rate down or compliance flags up. Engineering sees it as the same model behaving differently across customer segments because the campaign target list shifted. Compliance sees it as recording reviews where the agent failed to disclose, mishandled an objection, or kept talking after the customer asked to be removed.

Unlike a traditional predictive dialer, an LLM campaign can change phrasing based on the customer’s objections, so one prompt edit can alter thousands of conversations without changing the campaign configuration. In 2026 campaign deployments, AI agents are increasingly running both legs of the conversation — the outbound voice agent that places the call and the chat agent that handles the inbound reply when a recipient texts “stop”. That makes regression evaluation across the full campaign footprint, not just the agent prompt, the binding constraint on quality. Trajectory-level evaluation tied to the campaign cohort is the only way to detect when a script change is silently lowering conversion.

How FutureAGI Evaluates Contact Center Campaigns

FutureAGI does not run a dialer or a campaign-management system. FutureAGI’s approach is to evaluate the contact-level agent, not the campaign scheduler. It captures voice calls through the traceAI integration livekit and chat or SMS exchanges through openai-agents, with campaign_id, cohort, script_variant, and outcome_goal recorded as span attributes so engineers can slice evaluator scores per campaign.

Evaluators are tuned for outbound dynamics. TaskCompletion scores whether the campaign goal was reached (renewed, paid, surveyed, agreed). ConversationResolution grades the end-state of the conversation. CustomerAgentObjectionHandling scores how the agent handled pushback — central in retention and collections. CustomerAgentTerminationHandling flags whether the agent ended the call cleanly when the customer asked. CustomerAgentLanguageHandling covers tone and language across cohorts. For voice, ASRAccuracy and AudioQualityEvaluator track the audio surface; for compliance-sensitive flows, IsCompliant and a custom CustomEvaluation against the disclosure script are layered in.

A practical example: a fintech collections team runs an AI voice campaign across 80K accounts. They sample every call into FutureAGI, dashboard TaskCompletion and CustomerAgentObjectionHandling per delinquency tier, and surface a 14% drop in objection handling on the deepest tier after a script change. They roll back, replay the failed cohort with Persona, Scenario, and LiveKitEngine, run a regression eval against a curated 200-scenario objection set, and re-ship the next day. Without per-cohort eval slicing, the script regression would have shown up two weeks later as a conversion miss with no obvious cause.

How to Measure Contact Center Campaigns

For AI-driven campaigns, evaluate each contact and slice the results by campaign cohort, script variant, channel, and outcome goal. Track both transcript-level and audio-level failures so voice infrastructure issues do not masquerade as policy or reasoning defects. The useful signals are the ones that move before conversion reports close:

• TaskCompletion — returns whether the contact reached the target outcome, such as renewal, payment, completed survey, or qualified lead.
• ConversationResolution — grades the final conversation state, not just the last agent message.
• CustomerAgentObjectionHandling — scores pushback handling across retention, collections, and win-back objections.
• CustomerAgentTerminationHandling — detects opt-out, stop, escalation, and callback paths that must end cleanly.
• ASRAccuracy + AudioQualityEvaluator — separate speech recognition errors from agent reasoning errors on voice calls.
• campaign_id, cohort, script_variant, outcome_goal — span fields used to group evaluator failures by operational cause.
• eval-fail-rate-by-cohort — a dashboard signal for when one segment regresses while the aggregate campaign still looks healthy.

from fi.evals import TaskCompletion, CustomerAgentObjectionHandling

t = TaskCompletion().evaluate(conversation=call_transcript)
o = CustomerAgentObjectionHandling().evaluate(conversation=call_transcript)
print(t.score, o.score)

Common mistakes

These mistakes produce clean-looking campaign dashboards with bad underlying conversations. They usually pass aggregate QA because the wrong cohort is averaged away:

• Reporting on conversion only. Conversion is lagging; per-call quality evaluators catch script regressions before revenue dashboards move or campaign managers notice.
• Skipping per-cohort slicing. Aggregates hide when a script works for renewals but fails for collections, deep delinquency, or non-English speakers.
• No compliance evaluator. Regulated campaigns need IsCompliant and disclosure conformance checks on every sampled call, not quarterly transcript review after complaints.
• Letting the agent improvise on stop requests. Termination handling should be deterministic; evaluate every opt-out, callback, escalation, and “do not contact me” path.
• No regression eval before script changes. Outbound campaigns run at scale; test variants with held-out objections before thousands of customers hear them.