Sales & Marketing Dashboard
Persona: Sales / Marketing Portfolio · All SKUsRenewal-narrative KPIs, portfolio adoption & competitive churn signals · Impact: Revenue, Comp. Intel
Questions about this data
Protocol Success Rate (Headline)i
91%
↑ +10%Active Experiments (Headline)i
15K
↑ +5%Queries Triaged / Experimenti
30K
↑ +25%SKU Retention Ratei
84%
↑ +3pp QoQSuccess Rate Trend by SKUi
Trailing 30 days vs. prior period · portfolio panel
Why it matters: A rising or falling success rate is the leading indicator you'll see before a renewal conversation goes well or badly — get ahead of the account instead of reacting to it.
Weekly Active Experiments by SKUi
Adoption trajectories & seasonality
Why it matters: Adoption trend by SKU tells you which product to lead with in the next upsell conversation, and which one is quietly losing momentum.
Churned Protocol Signaturei
Composite: query rate ↑ + deviations ↑ + success ↓
Why it matters: This is your earliest warning that an account is heading toward non-renewal — flag it for outreach weeks before the standard renewal check-in.
Refill Velocity Trendi
Inferred reorder timing · account-level average
Why it matters: A slowing reorder pace predicts a lapsing account before it shows up in your pipeline numbers, giving you time to intervene before the deal is lost.
Competitive Displacement Risk Scorei
Composite: method substitution intent + competitor mentions + refill decline
Why it matters: This single number tells you which accounts are actively evaluating a competitor, so you can prioritize save calls before they switch.
Pre-Failure Behavioral Fingerprinti
Composite voice signal, 3 steps before failure
Why it matters: Surfaces the accounts most likely to churn in the next few runs, before the account itself files a complaint — the earliest possible save opportunity.
Product Development & R&D Dashboard
Persona: Product / R&DExecution quality, UG clarity & design signal · Impact: Quality/R&D, Comp. Intel
Questions about this data
First-Pass Success Ratei
79%
↑ +7%Protocol Deviation Frequencyi
14.6%
↑ +2ppMean Time to Mastery (MTPM)i
7.1 runs
↓ −1.4 runsProtocol Innovation Response Timei
9.2 days
↓ −3.1 daysConfusion Hotspot Indexi
Query rate + pause + retry, per protocol step
Why it matters: Shows you exactly which step to fix first in the next user guide revision — the step generating the most confusion is your highest-ROI edit.
First-Time User Confusion Deltai
Confusion score, run 1 vs. run 3, by account segment · learnability
Why it matters: If confusion isn't dropping between run 1 and run 3, the protocol itself is the problem, not the operator — a signal to redesign rather than retrain.
Learning Curve Velocityi
Success rate (%) vs. run number, new users
Why it matters: A slow climb to proficiency means the kit is harder to learn than it should be — this is your design-quality scorecard, run over run.
Reagent Waste Eventsi
Waste events per 100 runs, trend toward target
Why it matters: Recurring waste points to a packaging or volume-sizing flaw worth fixing in the next kit revision, not a one-off operator mistake.
Protocol Resilience Scorei
Inverse-failure composite: 1 − (deviation × repeat run × query surge)
Why it matters: The earliest warning you'll get that a protocol is degrading — catch it here before it becomes a QC complaint or a churned account.
Protocol Step Entropy Scorei
Execution unpredictability, step × session
Why it matters: High entropy at a step means operators are improvising — a design smell that predicts both quality issues and operator dropout before either happens.
Customer Success Dashboard
Persona: Customer SuccessAccount health, proactive intervention triggers & cohort lifecycle · Impact: Customer Success, Revenue
Questions about this data
Account-Level Protocol Health Scorei
Composite: success rate + deviation rate + query rate
72
Success rate84%
Deviation rate11.2%
Query rateelevated
Why it matters: One number to triage your book of business — start your day with the accounts scoring lowest.
New vs. Active vs. Dormant Cohortsi
Engagement lifecycle, rolling 6 months
Why it matters: A growing dormant bucket is churn that's already happening, just not yet visible in your renewal pipeline — this is where to focus win-back effort.
SKU Retention Rate (Account)i
84%
↑ +3ppRepeat Run Rate (Operator)i
9.4%
↑ +0.6ppUnresolved Query Ratei
6.2%
↑ +1.1ppSession Interruption Ratei
11.3%
↑ +1.9ppQuery Surge Events (7-Day Rolling)i
Real-time alert overlay at flagged steps
Why it matters: A spike here is your cue to reach out before the account files a support ticket or quietly gives up.
Protocol Success Rate (Account) Trendi
vs. contract SLA target
Why it matters: Falling below SLA is your trigger to schedule a proactive check-in before the account raises it first.
Voice Sentiment Drift Indexi
LLM-classified frustration / neutral / confidence, rolling 7-run avg
Why it matters: Frustration builds before an operator ever files a complaint — this catches the mood shift while there's still time to help.
Protocol Abandonment Ratei
Mid-run stops by inferred cause
Why it matters: Mid-run abandonment is a stronger dropout signal than a support ticket — these are the operators who gave up silently and need outreach.
Best-Performing SKUi
83LQ033-48
Health score 81Needs Attentioni
40H8710-96
Health score 54Portfolio Avg Health Scorei
69
of 100Portfolio Avg Dormancy Ratei
8.7%
↑ +0.8ppAccount Health Score by SKUi
Avg composite health score, by SKU
Why it matters: Tells you which SKU's customer base needs a proactive-outreach push, not just which single account does.
Dormancy Rate by SKUi
Share of accounts inactive 30+ days, by SKU
Why it matters: A SKU with rising dormancy across many accounts is a product-experience problem, not a handful of unlucky renewals — worth flagging to Product.
Technical Support Dashboard
Persona: Tech SupportTriage priorities & ticket-deflection targets · Impact: Ops Efficiency, Customer Success
Questions about this data
Query Surge Events (Step-Level)i
3.2×
vs. 30d avgRepeat Query Ratei
18%
↑ +2.4ppInstrument Error Verbalization Ratei
5.4%
StableInvalid Result Rate (Spike)i
4.1%
↑ +0.9ppQuery Surge Events (Control Chart)i
Surge events per step, toward zero-surge target
Why it matters: Tells you which step is about to flood your queue — get ahead of the ticket volume instead of reacting to it.
Step Retry Count at Failure Stepi
Highest-retry step per run, for triage
Why it matters: The step with the most retries is where your next troubleshooting article or proactive outreach will deflect the most tickets.
Instrument Error Verbalization Ratei
By instrument model
Why it matters: Points to which instrument model is driving support load, so you know whether the fix is a doc update or an equipment compatibility note.
User Guide Ambiguity Score (Spike)i
Sudden clarity deterioration, flagged for revision
Why it matters: A sudden spike flags a step that just got confusing — often the first sign of a bad lot or outdated instructions, before tickets pile up.
Operator Recovery Patterni
Median time to next productive step, by deviation class
Why it matters: Slow recovery after a deviation means the user guide isn't helping operators self-serve — a clear case for a clearer troubleshooting note at that step.
Step-Level Cognitive Load Indexi
Query rate + pause + retry, normalized per step
Why it matters: Flags the steps about to generate a ticket wave before the queries actually arrive, so you can staff or pre-empt accordingly.
Accounts in Triage Queuei
7 accounts
↑ +2 vs. last wkTotal Query Volume (7-Day)i
412
↑ +18%Avg Resolution Lagi
2.1 days
↓ −0.4 daysRepeat Contact Ratei
22%
↑ +3ppAccount Triage Queuei
Ranked by composite support-signal score
| Account | Primary SKU | Signal Score | Top Issue | Status |
|---|
Why it matters: Your daily call list — this is the order to work triage in in, ranked so the accounts closest to a formal complaint get called first.
Support Signal Volume by Accounti
Top 6 accounts, 7-day signal count
Why it matters: Shows you at a glance whether this week's load is concentrated in a few accounts or spread thin — that changes whether you fix a doc or make a phone call.
Weekly Support Signal Trendi
Portfolio-wide, 8-week trend
Why it matters: A rising trend tells you to staff up or ship a fix now, before next week's queue looks like this week's plus more.
Signal Source Breakdowni
Triaged accounts by primary issue type
Why it matters: Tells you whether this week's load needs a documentation fix, an equipment note, or a QC escalation — the right owner for the fix.
Commercial Strategy Dashboard
Persona: Commercial Strategy Portfolio · Category viewPortfolio positioning, category benchmarks & premium-tier intelligence · Impact: Comp. Intel, Revenue
Questions about this data
Active Experiment Volume Indexi
112
vs. 100 category avgCategory Success Rate Ranki
#3
of 14 SKUsFastest-Growing Assay (Emerging)i
+34%
YoY volumeKit-to-Kit Upgrade Conversioni
61%
↑ +4ppEmerging Assay Adoption Curvei
Active experiment volume by assay type — category tailwinds
Why it matters: Shows which assay categories are growing months before published research confirms it — the earliest signal for where to invest next.
Kit-to-Kit Upgrade Pathwayi
Observed SKU adoption sequence over operator tenure
| From SKU | To SKU | Transition Vol. | Signal |
|---|
Why it matters: Reveals the upsell sequence customers already follow on their own — build your expansion motion around the path that's already working.
Cross-manufacturer benchmarks
Anonymized across the manufacturer pool — available once enough manufacturers are onboarded in a given assay category to preserve anonymity.
Category Success Rate League Tablei
Anonymized ranking within assay category
Why it matters: Your rank against anonymized competitors is the clearest evidence for whether quality is a selling point or a liability in the next deal.
Active Experiment Volume Index vs. Categoryi
Own share of category-level weekly volume
Why it matters: Tells you whether you're gaining or losing share of actual lab usage — a truer signal than sales volume alone.
Workflow-Adjacent Kit Gap Analysisi
Kits from other manufacturers consistently paired with own SKU
| Paired Kit (Anon.) | Co-occurrence | Category |
|---|
Why it matters: Shows exactly which product categories to bundle or acquire next, based on what customers already use alongside your kit.
Operator Retention Curve Benchmarki
Own operator retention, runs 1–5, vs. category median
Why it matters: If operators drop off faster than the category median, that's a product experience gap sales data alone will never show you.
QC & Manufacturing Dashboard
Persona: QC / Mfg Portfolio-wide · Last 60 daysLot-level quality signals from field voice data · Impact: Quality/R&D
Questions about this data
Lot-to-Lot Success Variancei
±7.4pp
↑ HighStep Completion Ratei
97.8%
Target ≥99%Instrument Wait Time (Trend)i
4.2 min
↓ −0.6 minComponent Run-Out Frequencyi
3.1%
StableSuccess Rate by Lot Numberi
Field-derived · red = below 85% threshold
Why it matters: Tells you exactly which lot to investigate or quarantine before it generates a wave of formal complaints.
Shelf-Life Performance Curvei
Success rate vs. days since manufacture
Why it matters: Shows whether your expiry dating is too conservative or too aggressive — a direct input to your next stability study.
Lot Watchlisti
Ranked by field deviation signal strength
| Lot # | Success Rate | Deviations | Field Age | Status |
|---|
Why it matters: Your prioritized action list — start with the lot at the top before it becomes a customer-facing quality issue.
Cross-Lot Mixing Event Ratei
Contamination risk incidents by lot pair
Why it matters: A rising rate signals a labeling or SOP gap in the field that's putting data integrity at risk, before it shows up in a formal deviation report.
Lot Health Index (LHI)i
Success rate + deviation fingerprint + instrument error rate, composite
Why it matters: The earliest number you have that a lot is failing — days or weeks before a formal QC complaint or churn event confirms it.
Cross-Step Reagent Dependency Mapi
Co-use frequency between kit components, by step pair
| Component Pair | Co-Use Freq. | Risk |
|---|
Why it matters: Shows which components are informally being combined in the field — a direct input to updating SOP or lot-sizing guidance before it causes a failure.