Total euro value of open sales tickets that have been sitting more than 14 days. Bands show how the pipeline is split by age — the longer a lead sits, the lower the chance it ever closes; 60+ day is effectively written off.
Sales tickets worth €500 or more where no agent has replied within 4 hours. The big number is the count; the table shows the worst breaches by value — click a ticket to open it.
Open tickets where the last message was >30 days ago. Split by who went silent first — agent left needs a re-engagement, customer ghosted usually means archive.
Open tickets where the agent answered last and the customer has been silent for 3–30 days. These are the highest-leverage re-engagement targets — sorted by value.
How long unclaimed tickets are sitting before someone touches them. Red bars are over the 24-hour SLA target. The 14d+ band is the hard backlog — mostly already cold.
Open ticket count per agent, paired with their 90-day conversion rate. Capacity badge: green ≤50 healthy, amber 51–100 stretched, red >100 drowning.
Open tickets bucketed by age (columns) × value tier (rows). Cell color intensity = relative € exposure. Hot cells in older bands are where the money is rotting.
Conversion rate by how fast we replied to the customer's first message. The strongest single lever in the data — replying within an hour roughly doubles the close rate vs replying after 48 hours.
Per-agent first-reply time distribution. p25 = fastest 25% of their tickets, p50 = median, p90 = slowest 10%. Big gap between p25 and p90 means the agent is bimodal — fast for some, ignored for others.
Percentage of each agent's tickets that received a first reply within the 4-hour target. Threshold: 60% is the team-wide goal — bars below that are the coaching targets.
Conversion rate per agent over the last 90 days, with tenure column. Tenure separates “new and learning” from “just slow” — helps decide who to coach vs reroute traffic away from.
Conversion rate by agent (rows) × product (columns). Cell shows %; color intensity = relative strength. Hot intersections are specialists — route similar tickets to them.
For each ticket: gap between the customer’s second message (their reply to our first) and our second agent reply. Long tail in 48h+ is where conversations go to die — if we don’t follow up within 24h after they respond, the lead usually doesn’t close.
Conversion rate by how many agent replies the ticket got. Engagement is intent — tickets that get to 5+ agent replies close at roughly twice the rate of single-reply ones. Worth investing the labor when leads are warming up.
Three-stage drop-off: every sales ticket received → how many got a proposal stored in the sidebar → how many converted to orders. Hero shows the order rate among tickets that got a proposal — that’s the sidebar’s effectiveness signal.
Of all the € entering the pipeline, where does it land? Unchecked = sales tickets we never followed up on — if those converted at the average rate, that’s the upside math. New lead = still open. Converted/Returning = closed deals.
Per-product conversion rate over the period. Headline drag is usually Embroidered Patches (~15%) vs Woven Labels (~33%) — routing decisions and pricing tweaks go here.
Conversion rate split by whether at least one intent on the ticket was tagged quote vs anything else. Counter-intuitive finding: Rest often beats Quote — a hint that pricing-only tickets aren’t where the highest-intent customers actually live.
Everything the AI determined a human must do, across all tickets — the supply-side mirror of the intent stream.
Action types by 30-day volume, done-rate and dismissal. High volume + high done + low dismiss = ripe to automate. The dismissal column is the AI's false-positive rate, visible at last.