How do insights get smarter as I run more analyses?

TL;DR

Every analysis you run feeds a workspace-wide evidence base, and that base now works harder for you: insight trends are dated by when the calls actually happened, every insight shows how well-supported it is and how it co-moves with your win rate, /insights can contrast won vs lost deals directly, and the AI assistant can answer layered questions like "what separates our losses from our wins in Healthcare over the last 6 months?" or "how do I win this open deal?" (Growth and up.)

How it works

Discera doesn't treat each report as a one-off. Every completed analysis — regular reports, Win/Loss deal diagnoses, competitor scans, pipeline reviews — deposits its findings into your workspace's shared insight set within minutes of finishing. The more you run, the more history each insight accrues, and the more Discera can tell you about it: when it started, whether it's growing, how many real conversations back it, and whether deals that carry it win more or less often than your average deal.

None of this needs configuration, and none of it consumes call quota. It compounds in the background as you work.

Trends reflect when calls happened, not when you analyzed them

All insight time-series — the sparklines and trend charts on /insights — are keyed to the date of the underlying calls (or the deal's close date), not the date the analysis ran. That means you can analyze last quarter's calls today and the trend lands in last quarter, where it belongs. Back-filling six months of history builds you six months of honest trend, not a spike on today's date.

Judging an insight: provenance and impact

Two additions on each insight card (and in its detail drawer) tell you how seriously to take it:

• ·Provenance — a grounding line like "12 calls across 7 deals over 4 months." An objection heard on twelve calls across seven deals over four months is a pattern; one heard on two calls last week is a lead worth watching. The numbers let you tell them apart at a glance.
• ·Impact chip — where the data supports it, a chip like "−22pp win rate when present." It compares the win rate across deals where the insight appears against your workspace's baseline win rate. Chips only appear once an insight spans at least 5 deals with known outcomes, and they're recomputed nightly.

One honest caveat, and the UI repeats it: impact is co-movement, not causation. A "−22pp" chip means the insight travels with worse outcomes — it doesn't prove the insight caused them. Use chips to decide what to investigate first, then read the deals and quotes behind the insight to judge why.

Contrast won vs lost deals

The /insights tab can now put your won and lost deals side by side and contrast their attributes — which themes, objections, and competitors concentrate in the deals you lose versus the ones you win. It's the fastest way to move from "what are buyers saying?" to "what's different about the deals we lose?"

Ask layered questions in chat

The AI assistant can now run analytical drill-downs over your deal corpus — slicing by industry, rep, deal-size band, loss driver, competitor, outcome, and month, with date windows — so you can narrow a broad question step by step instead of composing one perfect query:

You: What are the attributes of losing vs winning deals? You: Now just Healthcare. You: Now the last 6 months.

Each turn refines the previous slice, and the assistant answers with the numbers and the deals behind them. Win-rate questions can be scoped to a close-date window too — "what was our win rate in Q2 vs Q1?"

Two more things it can do with the same corpus:

• ·Find similar deals. Point it at a deal — or just describe a situation — and it retrieves the most similar historical won and lost deals, with their outcomes and drivers:**You:** Show me past deals like the Acme opportunity — mid-market, security-review heavy, competing against Competitor X.
• ·Coach an open deal. On a deal still in your pipeline, ask it how to win:**You:** How do I win the Northwind deal?The assistant assembles the deal's current health and risks, the most similar historical deals you won and lost, the workspace insights that match it, and the win-rate drivers that apply — then writes concrete, cited next-best-actions and the red flags to defuse. You can also ask it to list your open pipeline deals and drill into any of them.

Where to start

If you've only run a handful of analyses, start by widening your coverage — a Win/Loss run over the last quarter gives the corpus outcomes to correlate against, which is what powers impact chips, won-vs-lost contrast, and deal coaching. From there, everything above deepens on its own with every run.