Sales analytics that's faster than your weekly forecast call
RevOps spends Mondays building rollups that are wrong by Friday. AnalityQa AI reads your CRM directly, scores deal risk on real momentum signals, and gives you a forecast you can defend.
Where the day goes wrong
Forecasts are submitted by reps and trusted by no one
Commit, Best Case, Pipeline — each rep interprets these categories differently. The aggregate forecast misses by 15-30% every quarter. Sales leadership wants a model-based number; the team doesn't have the data science to build one.
Pipeline reviews discuss the same 10 deals every week
The big ones get scrutinised. The dozens of mid-size deals quietly slip without anyone noticing — close dates push, contact activity goes cold, and by the time the deal is dead it's too late to save it.
Win/loss analysis is a quarterly project that ships once and dies
Someone runs the analysis after Q1 closes. By Q3, nobody remembers what it said. The patterns that drive losses keep recurring because the analysis isn't a continuous practice — it's a one-off deck.
Quota and territory analytics are built once a year and never updated
Territory design, quota allocation, ramp curves — all built before the year starts and rarely revisited. When mid-year reality diverges from the plan, RevOps doesn't have time to rebuild the analysis, so the team operates on stale assumptions.
What you actually ask AnalityQa
Plain English in. Charts, tables, and live dashboards out.
Forecast Q3 booked revenue with a confidence range, weighting each open deal by historical win rate at its current stage.
→ Forecast number with low/mid/high range and the weighting logic shown.
Which open deals over $50K have slipped close dates more than twice or have no contact activity in the last 14 days?
→ At-risk deal list with deal name, AE, last activity, slip count.
Compare win rates by deal size for the past 12 months, and break out by lead source.
→ Win-rate matrix: deal size × lead source with sample sizes shown.
Build a daily pipeline dashboard with stage progression, weighted forecast, and slip alerts by AE.
→ Live dashboard URL with per-AE views, refreshed every hour.
Why did our win rate drop 8pts in the last 60 days?
→ Investigation report breaking the drop into deal-size mix shifts, lead-source mix, and stage-conversion changes.
What does the ramp curve look like for AEs hired in the last 18 months — average attainment by month-since-start?
→ Cohort ramp curve with the median AE's path and the band around it.
How AnalityQa fits your workflow
Five capabilities — every persona uses all of them, in their own way.
Chat with your data
Ask pipeline, forecast, and win/loss questions in plain English. The CRM doesn't have to expose a clever dashboard — AnalityQa AI runs the SQL against the underlying data and answers in seconds.
Auto data prep
Salesforce data is famously messy: inconsistent stage names, blank close dates, deals with $0 amounts. AnalityQa AI's data prep flags these before the rollup so the forecast isn't poisoned by data hygiene problems.
Live shareable dashboards
Pin per-AE pipeline views, the global forecast, the win-rate trend. Each refreshes hourly. AEs see their own dashboard; managers see the rollup; the CRO sees the forecast — all from the same source.
Investigation mode
When attainment misses or win rate drops, ask why. AnalityQa AI runs an investigation across deal-mix, source-mix, stage-conversion, and AE distribution — returns a ranked diagnosis you can read in five minutes.
Data-aware analyst agents
Sales-aware analyst agents flag at-risk deals proactively (slipped close dates, cold activity, stale next-steps), surface forecast risk before the call, and identify AEs whose pipeline coverage is weak. RevOps gets the questions to ask before the meeting starts.
What changes
Use cases relevant to your role
Sales
See where deals stall before they quietly die
Sales
A revenue forecast built on deal data, not gut feel
Sales
Find out why you are losing deals before competitors widen the gap
Sales
Know which reps will hit quota before the quarter ends
Sales
Stop guessing which leads are worth a sales call
Marketing / Finance
LTV and CAC numbers your CFO can actually trust
Frequently asked questions
Does AnalityQa AI integrate with Salesforce and HubSpot?+
It reads from CSV exports of either, and from direct database connections if you have a replica. Most teams export weekly from Salesforce or HubSpot to a CSV (or sync to a warehouse) and AnalityQa AI reads from there. Native CRM connectors are on the roadmap.
How is the forecast model different from the one in our CRM?+
The CRM rolls up rep-submitted commit categories. AnalityQa AI builds a model based on historical win rate at each stage, deal size, deal age, and recent momentum signals (close-date slips, contact activity). The model and the CRM number can be compared side-by-side, and the gap usually identifies which deals are over-committed.
Can AEs see only their own pipeline?+
Yes. Create per-AE dashboards filtered to their owner ID. Each AE sees their pipeline, their forecast, their at-risk list. The manager sees the rollup. The CRO sees the global forecast. Each is a separate dashboard URL with the appropriate scope.
How does it identify at-risk deals?+
AnalityQa AI scores deal risk based on signals you choose: number of close-date slips, days since last contact activity, stage age vs cohort average, lack of recent next-step updates. The scoring is shown so RevOps can audit and adjust. Most teams find the model identifies risks 2-3 weeks earlier than the AE notices.
Can we run win/loss analysis by industry, deal size, or competitor?+
Yes — by any field that's in the CRM data. "Win rate by industry vs competitor X over the past 12 months" returns the matrix. The competitor data needs to be in a CRM field (most teams use a custom "competitor" picklist on the opportunity).
How do we handle quota attainment when reps move territories or join mid-year?+
Define the cohort: "AEs hired in the last 18 months" or "AEs in EMEA who've been in territory more than 6 months" — AnalityQa AI scopes the analysis to that cohort. Ramp curves are normalised on month-since-start so newer AEs aren't unfairly compared to tenured ones.