When an agent's production drops below their own historical average, the right response isn't a warning — it's a question. Production data gives you the evidence to open that conversation specifically and early, before a temporary dip becomes a sustained decline. This post covers how to use data to have performance conversations that feel supportive rather than punitive, and how to track whether those conversations are making a difference.

Every production dip looks the same from the outside. An agent who was submitting 10 cases a month is now submitting 4. But the reasons behind that dip — and the right response — are completely different depending on whether it's a personal challenge, a market disruption, a carrier issue or the early sign of an agent disengaging from your agency.

The agencies that navigate these conversations well aren't the ones that wait until the dip has lasted six months. They're the ones that see the change early — while it's still a question rather than a conclusion — and reach out with curiosity rather than criticism.

Production data gives you the evidence to be that kind of proactive, well-informed partner. It tells you when an agent has dropped below their own historical average, how significant that drop is and what else has changed in their production pattern that might explain it.

What "Producing Below Historical Average" Actually Means

Not all production dips are equal. An agent who averages 10 cases a month and submits 7 in November is not necessarily in trouble. November can be a slow month. An agent who was averaging 10 a month all year and has now averaged 5 for three consecutive months has a pattern worth understanding.

Comparing an agent's current production to their own historical average — rather than to a fixed threshold or to the performance of other agents — is the most fair and accurate way to identify a meaningful change. An agent's historical average accounts for their specific market, their product mix and their natural production rhythm.

When your data visualization module tracks rolling averages at the individual agent level, it can surface agents who are producing meaningfully below their own historical pattern — not just agents who fall below a network-wide threshold.

Your Distribution Management System holds the case history for every agent in your network. A comparison of each agent's current 90-day production against their prior 12-month average is available automatically when that data feeds into a connected reporting environment.

Why Individual Baseline Comparison Matters

The problem with evaluating agent performance against a fixed agency threshold is that it treats all agents the same regardless of their production context.

An agent who typically submits 6 cases per month and drops to 3 is showing a 50% decline in their personal production — a meaningful signal. An agent who typically submits 20 cases per month and drops to 17 may be less concerning even though their absolute volume is higher in both cases.

Network-wide threshold reporting misses the former agent (who is below average but not below the threshold) and flags the latter agent unfairly (who is below the threshold but experiencing only a minor personal variation).

Individual baseline comparison gives you the right picture for the right conversation. And it ensures that the agents who actually need a check-in get one — not just the ones who happen to fall below a fixed line.

Approaching the Conversation: What Not to Do

The most common mistake in agent performance conversations is opening with the data before establishing the relationship.

An agent who receives a call that opens with "we noticed your production is down 40% from your average" may immediately feel surveilled, judged or accused — regardless of the tone the caller intended. The data is accurate, but the framing creates defensiveness before the conversation has a chance to start.

The more effective opening acknowledges the relationship and the agent's history: "I wanted to check in with you — I've been thinking about your book and I wanted to understand how things are going." That opening invites a genuine conversation rather than a performance review.

When the agent responds — and most agents will share what's actually happening, if the opening is genuinely curious rather than evaluative — you can bring in the data to deepen the conversation rather than to open it.

"That makes sense — I can see in the production data that the last couple months have been lighter than your usual pace, which is what prompted me to call. What do you need from us to get things moving again?" is a very different conversation from "your production is down and we wanted to talk about that."

Karen Essary of Ideal Producers Group noted that agents "trust" the information her team provides — and that trust comes from a history of data being used to serve agents rather than evaluate them. That foundation makes data-informed performance conversations easier to have and more likely to produce a positive outcome.

What to Listen for in the Conversation

When you open a production check-in conversation well, agents typically tell you exactly what's happening. The most common explanations for production dips fall into a few categories.

Personal or health challenges: the agent or someone in their family has experienced something that has temporarily limited their capacity. This requires empathy, flexibility and a reduced-pressure approach.

Market or carrier changes: a carrier the agent relied on has changed their underwriting guidelines, their rates or their product availability in a way that has disrupted the agent's production flow. This is an operations issue your team may be able to help address.

Client pipeline timing: the agent has been working on a group of larger cases that haven't closed yet. Production appears low, but the pipeline is healthy. This requires patience rather than intervention.

Competitive disruption: the agent has contracted with a second agency and is starting to divide their business. This is the flight risk conversation — addressed in an earlier post in this series — and requires a different kind of response.

The conversation tells you which situation you're dealing with. The production data gives you the evidence that something has changed. Together, they give you what you need to respond appropriately.

Tracking the Conversation's Impact in Production Data

After a check-in conversation, your data visualization dashboard gives you the ability to see whether production recovers in the weeks that follow.

An agent who explained a temporary disruption and responded with improved production in the following 30 to 60 days confirms that the conversation was productive and the situation was temporary. An agent who did not recover — or who declined further — is a signal that the underlying situation is more serious and warrants a follow-up.

Building this production monitoring into the weeks after a check-in conversation is one of the most valuable uses of real-time dashboard data. It removes the need to follow up manually on every conversation and gives your sales managers a data-informed view of which conversations produced results and which agents need additional support.

Want to see how individual agent baseline tracking works in a live dashboard? We would be happy to show you. Talk to our team to see how OneHQ surfaces below-average production trends at the individual agent level.