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Find out why you are losing deals before competitors widen the gap
Loss reasons in your CRM are free-text fields filled in inconsistently, if at all. AnalityQa AI AI turns your closed opportunity history into a structured analysis of why deals close or slip — by competitor, deal size, rep, and discount level — so you can act on the pattern, not the anecdote.
→Loss reason fields in Salesforce and HubSpot are optional picklists that reps fill in after a deal is already lost — the data is incomplete, inconsistent, and rarely reviewed at scale.
→Win rate looks acceptable overall but varies significantly by competitor, segment, and deal size in ways that only appear when you slice the closed data and no one currently does.
→Discount data lives in the opportunity record but is never joined to win/loss outcome at scale, so no one knows whether discounting is closing deals or just eroding margin without improving close rate.
→Competitive intelligence is anecdotal — sales leaders hear 'we keep losing to Competitor X' in deal reviews but cannot quantify the trend or identify which segments are most exposed.
Why the usual approach breaks down
Salesforce loss reason data is almost always incomplete
Most Salesforce orgs have a loss reason picklist on the opportunity object, but it is either not required or requires a workflow to enforce. In practice, reps close-lose a deal and move on — fill rates for loss reason are often below 50%. Analysing an incomplete field produces misleading conclusions unless you account for the missing data explicitly.
HubSpot does not have a native competitor tracking object
HubSpot stores competitor information in free-text fields or custom properties with no standard picklist enforcement. Extracting competitive win rate requires parsing free-text competitor names from deal exports — a task that needs either NLP or manual cleanup before any aggregation makes sense.
Excel pivot tables cannot normalise inconsistent loss reason text
When loss reasons are entered as free text — 'Price', 'Too expensive', 'Budget constraints', 'Pricing' — pivot tables treat each as a separate category. The analysis fragments into dozens of micro-categories instead of a usable taxonomy. Normalising them requires either a lookup table maintained by hand or a text-matching query that Excel cannot run.
Joining discount fields to win/loss outcomes requires multi-table queries
Discount percentage might live on the opportunity, on a quote object, or in a custom field added during contract review. Joining it to win/loss outcome and controlling for deal size, segment, and rep requires a query that crosses multiple CRM objects — straightforward in SQL, but beyond what native CRM reports support without a BI connector.
How AnalityQa AI AI solves it
Upload your data — or connect it live — and ask in plain English.
01
Upload your closed opportunity history from any CRM
Export closed-won and closed-lost opportunities from Salesforce or HubSpot — including loss reason, competitor field, discount, deal size, segment, and rep — and upload as a CSV. AnalityQa AI AI detects the schema and handles incomplete fields without requiring you to clean the data first.
02
Calculate win rate by competitor in plain language
Ask 'What is our win rate by competitor for deals over $20k?' and AnalityQa AI AI groups your closed history by competitor field, calculates win rate for each, and flags the competitors where your win rate has declined over the last two quarters.
03
Trend loss reasons over time to spot emerging patterns
Ask 'Show me how loss reasons have trended quarter over quarter' and AnalityQa AI AI normalises common free-text variations, groups them into a coherent taxonomy, and plots the frequency of each reason by quarter so you can see whether 'price' is growing as a loss driver or stabilising.
04
Quantify the impact of discounting on win rate and margin
Ask 'Do deals with more than 15% discount close at a higher rate?' and AnalityQa AI AI segments your closed history by discount tier and calculates win rate and average deal size for each bucket, showing whether discounting is improving close rate enough to justify the margin cost.
05
Break down win and loss rates by deal size, segment, and rep
Any win/loss metric can be sliced by deal size band, industry segment, or individual rep. Ask 'Which reps have the lowest win rate on enterprise deals?' or 'What is our win rate in financial services versus manufacturing?' and get a ranked table in seconds.
You askedGenerated in 4.2s
"Show me win rate by competitor for the last four quarters."
Pipeline
€2.1M+8.7%
Win rate
27%+2pp
Avg deal
€18.4k+€1.2k
Line chart: win rate by competitor — Q2 2025 to Q1 2026
Last 12 mo
Stacked area chart: loss reason frequency by quarter — 2025 to Q1 2026
Table: win rate and average ACV by discount tier
A dashboard built in AnalityQa AI — from question to chart, no SQL.
Real examples
Paste your data. Ask. Ship.
You
Show me win rate by competitor for the last four quarters.
AI
AnalityQa AI AI groups your closed-won and closed-lost opportunities by the competitor field, calculates win rate per competitor per quarter, and renders a trend line for each so you can see which competitors are gaining ground.
Line chart: win rate by competitor — Q2 2025 to Q1 2026
You
What are the most common loss reasons, and how have they trended this year?
AI
AnalityQa AI AI normalises free-text loss reason variations into a condensed taxonomy, calculates the frequency of each category by quarter, and plots the trend so growing loss drivers are immediately visible.
Stacked area chart: loss reason frequency by quarter — 2025 to Q1 2026
You
Do deals with more than 20% discount have a higher win rate than non-discounted deals?
AI
AnalityQa AI AI buckets your closed opportunities by discount tier (0%, 1-10%, 11-20%, 20%+), calculates win rate and average deal size for each bucket, and returns a table showing whether deeper discounts correlate with better close rates.
Table: win rate and average ACV by discount tier
You
Which reps have the highest win rate on competitive deals?
AI
AnalityQa AI AI filters deals where a competitor is recorded, groups by rep, calculates each rep's win rate on those deals, and ranks them. It flags reps with statistically meaningful sample sizes to distinguish signal from noise.
Bar chart: win rate on competitive deals by rep
You
Show me average deal size for wins versus losses by segment.
AI
AnalityQa AI AI groups closed opportunities by industry segment and outcome, calculates average deal size for won and lost deals in each segment, and renders a side-by-side comparison to show where lost deals tend to be larger or smaller than won ones.
Bar chart: average deal size — won vs. lost by segment
What teams get out of it
✓Competitive exposure becomes quantifiable — sales leaders can show which competitors are winning more deals and in which segments, rather than citing anecdotes from deal reviews.
✓Loss reason analysis moves from an ignored CRM field to a quarterly strategy input, because the pattern across hundreds of deals is visible in minutes rather than hours.
✓Discount policy decisions are grounded in actual win-rate data by tier, replacing gut-feel negotiations about whether deeper discounts are justified.
✓Reps with high win rates on competitive deals are identifiable, enabling targeted knowledge-sharing on what they do differently in those situations.
Frequently asked questions
Can AnalityQa AI AI connect directly to Salesforce instead of a CSV export?+
A native Salesforce connector is on the roadmap. Today, the recommended path is to export your closed-won and closed-lost opportunities as a CSV — including loss reason, competitor, discount, and deal attributes — and upload it. Teams that mirror Salesforce to PostgreSQL or Google Sheets can connect those directly.
Our loss reason field is only filled in about 40% of the time. Will the analysis still be useful?+
Yes. AnalityQa AI AI flags the fill rate for any field it uses in an analysis so you know how representative the data is. For the 40% of deals with a loss reason, the analysis is valid — it just comes with an explicit caveat about coverage rather than silently assuming the field is complete.
Can it handle free-text competitor entries where reps have spelled things differently?+
Yes. You can ask AnalityQa AI AI to group variations — for example, 'treat Competitor X, CompetitorX, and Competitor-X as the same entry' — and it applies the grouping before calculating win rates. You can do this in a follow-up message without re-uploading the file.
Is this useful for product teams who want to understand why the product loses deals?+
Yes. Product teams often need exactly this analysis — loss reasons by segment, deal size, and competitive context — to prioritise feature gaps. AnalityQa AI AI gives them a direct path to that data from CRM exports without needing a data analyst to write the query.
Is our closed deal data private?+
Your uploaded files are stored in your isolated tenant, encrypted in transit and at rest, and are never used to train models.
Can win-loss dashboards be shared with product or marketing stakeholders outside the sales team?+
Yes. Dashboards built in AnalityQa AI AI can be shared with read-only viewers. Product managers, marketing, and executives can view the latest competitive win rate and loss reason trends without uploading files or writing queries.
What does it cost?+
Pricing is based on the number of users and data volume, not the number of queries. A free trial is available with no credit card required — details are on the pricing page.
