Measuring the Sales-Led Funnel in Mid-Market SaaS

Integrating Data for Full-Funnel Visibility

Measuring a sales-led funnel end-to-end requires connecting data from multiple systems. Marketing captures leads and MQLs (often in a marketing automation platform), Sales handles SQLs and opportunities (in a CRM), Finance tracks revenue and expansion (in a billing/subscription system), and Product data might signal upsell potential.

To achieve a single source of truth, data teams should integrate these sources (e.g., via a data warehouse or BI tool) so that every stage of the funnel can be analyzed together. This unified dataset lets RevOps and leadership see the whole customer journey and identify where handoffs or bottlenecks occur.

With integrated data in place, let's walk through each stage of the sales funnel, key metrics to track, benchmarks to consider, and how to take action on the insights.

Stage 1: Lead Acquisition

Key Metrics to Track:

• Number of New Leads: Total leads acquired in a given period, often segmented by source (content downloads, webinars, paid ads, outbound prospecting)
• Lead Source Breakdown: Distribution of leads by channel or campaign to identify which marketing channels yield the most leads
• Cost Per Lead (CPL): Average cost to acquire one lead, calculated by marketing spend divided by number of leads
• Lead-to-MQL Conversion Rate: Percentage of leads that become Marketing Qualified, indicating lead quality and nurturing effectiveness

Interpreting Lead Metrics:

Monitor lead volume in context of quality. A surge in leads is great, but if the lead-to-MQL conversion is low, it means many leads are unqualified or not ready to engage. For example, if you acquired 1,000 leads from a new eBook campaign but only 5% converted to MQL, you may be attracting the wrong audience or need better follow-up.

Cost Per Lead is another lens on efficiency – a high CPL might be justified for very high-quality leads, but generally you'll want to compare CPL across channels to double down on the most cost-effective sources. As a benchmark, mid-sized B2B SaaS firms might see only 1-3% of website visitors turn into leads, so optimizing landing pages and offers to improve that is key.

Actionable Steps for Lead Acquisition:

Example Analysis:

To see which sources are bringing in the most leads and how they convert to MQLs, you might run a query on your unified data like:

SELECT 
    lead_source,
    COUNT(*) AS total_leads,
    SUM(CASE WHEN mql_date IS NOT NULL THEN 1 ELSE 0 END) AS total_mqls,
    ROUND(
      SUM(CASE WHEN mql_date IS NOT NULL THEN 1 ELSE 0 END)::decimal 
      / COUNT(*) * 100, 
      2
    ) || '%' AS lead_to_mql_rate
FROM analytics.leads
WHERE lead_created_date BETWEEN '2025-01-01' AND '2025-12-31'
GROUP BY lead_source
ORDER BY total_leads DESC;

This SQL snippet aggregates leads by source for the year 2025, counts how many became MQLs, and calculates the lead-to-MQL conversion rate (as a percentage) for each source. Use it to pinpoint which channels produce quality leads versus just volume.

Stage 2: Marketing Qualified Lead (MQL)

Key Metrics to Track:

• MQL Volume: Number of MQLs generated in a period. This is a key output of marketing and often a target for marketing teams
• MQL Rate (Lead-to-MQL): Percentage of total leads that become MQLs, reflecting both lead quality and the strictness of your MQL definition
• MQL to SQL Conversion Rate: The percentage of MQLs that sales accepts and further qualifies into Sales Qualified Leads (SQLs)
• Response Time to MQL: How quickly sales reps follow up with MQLs after they are flagged. A fast response greatly improves conversion chance

Interpreting MQL Metrics:

High MQL volume is positive, but only if those MQLs are converting to SQLs. MQL-to-SQL conversion rate is a critical health indicator of the funnel's middle. For example, if you delivered 100 MQLs but only 20 became SQLs (20% conversion), it may signal a disconnect: perhaps marketing's criteria are too lenient, or sales follow-up is lacking.

Ideally, your MQL-to-SQL conversion might be in the range of 30-50% for a well-aligned sales-marketing process (some industry benchmarks put average around 20% but aim higher if your teams are in sync). A low conversion here means wasted effort at the top of funnel; a very high conversion (say 70%+) could mean marketing is only passing the most obvious easy wins.

Actionable Steps for MQL Optimization:

Example Analysis:

To analyze MQL performance, here's a sample SQL query that joins marketing and CRM data to calculate how many MQLs turned into SQLs each month:

SELECT 
    DATE_TRUNC('month', m.mql_date) AS mql_month,
    COUNT(DISTINCT m.lead_id) AS mql_count,
    COUNT(DISTINCT s.lead_id) AS sql_count,
    ROUND(
      COUNT(DISTINCT s.lead_id)::decimal 
      / NULLIF(COUNT(DISTINCT m.lead_id), 0) * 100, 
      1
    ) || '%' AS mql_to_sql_rate
FROM crm.marketing_leads m
LEFT JOIN crm.sales_leads s 
    ON m.lead_id = s.lead_id AND s.sql_date IS NOT NULL
WHERE m.mql_date IS NOT NULL
GROUP BY mql_month
ORDER BY mql_month;

This query groups by month, counting MQLs and how many of those became SQLs, then computes the conversion rate. Such analysis helps you spot trends – for instance, if April's MQL-to-SQL was 50% but May's was 25%, you know something changed in lead quality or follow-up in May.

Stage 3: Sales Qualified Lead (SQL)

Key Metrics to Track:

• SQL Volume: Number of Sales Qualified Leads per period. This is a direct feed into pipeline creation
• SQL Conversion Rate (MQL-to-SQL): How well sales accepts marketing leads. We expect a healthy percentage here if definitions are aligned
• SQL-to-Opportunity Rate: Percentage of SQLs that turn into pipeline (opportunities). Often this is very high if the process is simply that every SQL becomes an opportunity
• Average Qualification Time: How long it takes for an MQL to be worked and converted to an SQL. Faster is generally better, showing efficiency in engaging the lead

Interpreting SQL Metrics:

SQL volume reflects the true yield of your marketing efforts after sales vetting. If SQL counts are low relative to MQLs, it indicates a filtering effect – which could be good (if marketing was passing too many unqualified leads) or bad (if sales is too slow or strict, leading to potential opportunities being dropped).

Additionally, track how long it takes to move leads through qualification. A long qualification time might show resource constraints (e.g., not enough sales development reps to handle incoming leads promptly) or process inefficiencies. Best-in-class teams often achieve qualification within 1-3 days for inbound leads.

Actionable Steps for SQL Stage:

Example Analysis:

To keep tabs on your SQL stage, you can run analysis queries. For example, to see how quickly leads are being qualified by sales:

SELECT 
    s.owner_id AS sales_rep,
    COUNT(*) AS sql_count,
    ROUND(AVG(DATE_PART('day', s.sql_date - m.mql_date)), 1) AS avg_qualify_time_days,
    ROUND(
      COUNT(*)::decimal 
      / NULLIF((SELECT COUNT(*) FROM crm.marketing_leads x 
                WHERE x.mql_date BETWEEN '2025-07-01' AND '2025-09-30'), 0) * 100, 
      1
    ) || '%' AS pct_of_mqls_converted
FROM crm.marketing_leads m
JOIN crm.sales_leads s 
    ON m.lead_id = s.lead_id 
WHERE m.mql_date BETWEEN '2025-07-01' AND '2025-09-30' 
  AND s.sql_date IS NOT NULL
GROUP BY s.owner_id;

This sample query looks at Q3 2025 and calculates per sales rep how many SQLs they created, the average time (in days) it took them to qualify those leads, and what percentage of all MQLs each rep converted. This can highlight star performers or reps who may need help.

Stage 4: Opportunity Creation

Key Metrics to Track:

• Number of Opportunities Created: The count of new opportunities opened in a given period. This is a primary indicator of pipeline generation
• Opportunity Value (Pipeline $$): The total dollar value of pipeline created. Could be summed up as Expected Revenue or simply the sum of the initial opportunity values
• Average Opportunity Size: The average deal value of newly created opportunities. This gives insight into the caliber of deals entering the funnel
• Opportunity Source/Segment: Breakdown of opportunities by source or by segment. This helps attribute pipeline to different efforts and ensure diversification
• Conversion Rate to Opportunity: If measuring from earlier stages, track Lead-to-Opp or MQL-to-Opp conversion, encapsulating the entire qualification process efficiency

Interpreting Opportunity Metrics:

Opportunity creation is the bridge between lead generation and revenue. One useful way to interpret it is via the Pipeline Coverage ratio – compare the total pipeline value created (or currently open) against your future sales targets. Mid-market SaaS sales often use a pipeline coverage of 3x to 4x quota.

Another aspect is quality: if you see a high number of opps but low average value or many opps from segments that rarely close, the pipeline may be inflated with low-quality deals. It's better to have slightly fewer but well-qualified opportunities that have a solid chance, rather than many long-shot deals.

Actionable Steps for Opportunity Creation:

Example Analysis:

To get the total and average value of opportunities created each month:

SELECT 
    DATE_TRUNC('month', opp.created_date) AS month,
    COUNT(*) AS opps_created,
    SUM(opp.amount) AS total_pipeline_amount,
    ROUND(AVG(opp.amount)) AS avg_deal_size
FROM sales.opportunities opp
WHERE opp.created_date >= '2025-01-01'
  AND opp.stage = 'New'  -- newly created opps initially in stage "New"
GROUP BY month
ORDER BY month;

This query groups new opportunities by month of creation in 2025, counting how many were opened, summing their dollar amount, and computing the average deal size. It helps you see trends in pipeline generation and can proactively address pipeline gaps.

Stage 5: Pipeline Progression

Key Metrics to Track:

• Stage-to-Stage Conversion Rates: The percentage of opportunities that move from one stage to the next. This highlights specific drop-off points
• Pipeline Attrition (Drop-Off Rate): How many opportunities are lost at each stage. You might track what fraction of opps that entered a stage ultimately did not progress
• Average Time in Stage (Sales Cycle by Stage): How long opportunities tend to stay in each stage or overall sales cycle length
• Win Rate by Stage Reached: What is the win rate of opportunities that make it to each stage. Often, an opp that reaches a late stage has a higher probability to win
• Pipeline Velocity (Sales Velocity): A composite metric showing how quickly value is moving through the pipeline

Sales Velocity Formula:

Interpreting Pipeline Progression Metrics:

These metrics together tell the story of pipeline health and efficiency. Stage-to-stage conversions let you pinpoint where deals struggle. For example, if 90% of opps go from initial meeting to demo, but only 30% go from demo to proposal, that suggests prospects lose interest after the demo.

Average time in stage is essentially your sales cycle breakdown; a long sales cycle can hurt velocity and tie up resources. In mid-market SaaS, a typical sales cycle might be anywhere from 1 to 3 months, and win rates on well-qualified pipeline could be around 20-30%.

Actionable Steps for Pipeline Progression:

Example Analysis:

To dive into pipeline progression data, consider a query like the following to examine stage conversion and aging:

SELECT 
    stage,
    COUNT(*) AS opp_count,
    ROUND(AVG(days_in_stage)) AS avg_days_in_stage,
    ROUND(
      COUNT(*)::decimal 
      / NULLIF((SELECT COUNT(*) FROM sales.opportunities 
                WHERE stage_order = 1), 0) * 100, 
      1
    ) || '%' AS conversion_from_initial
FROM (
    SELECT 
        opp.id,
        opp.stage,
        opp.stage_order,  -- numeric representation of stage sequence
        DATE_PART('day', NOW() - opp.stage_entered_date) AS days_in_stage
    FROM sales.opportunities opp
    WHERE opp.is_open = TRUE
) sub
GROUP BY stage, stage_order
ORDER BY stage_order;

This example counts open opps by stage, calculates the average days they've been in that stage, and calculates roughly what percentage of initially created opps are still alive at each stage. This helps spotlight where deals accumulate and age.

Stage 6: Win/Loss Analysis

Key Metrics to Track:

• Win Rate (Close Rate): The percentage of opportunities that convert to a sale. This can be calculated overall and also segmented by factors like source, sales rep, deal size, or product
• Number of Wins and Losses: Simply count how many deals were won and lost in a period. This gives context to win rate and indicates volume
• Average Deal Size (Closed-Won): The average revenue per won deal (e.g., average first-year contract value). This helps in revenue planning
• Sales Cycle Length (for Wins vs Losses): The average time it took to close won deals versus lost deals. Often, won deals might move faster if there's strong alignment
• Win/Loss Reasons: Qualitative data often captured by reps at close. Common categories might include "Price too high," "Lost to competitor X," "No budget," "Missing feature," etc.

Interpreting Win/Loss Metrics:

Win rate is one of the clearest indicators of how well your sales process and product offering are performing in the market. For mid-market B2B SaaS, win rates can vary widely, but a general benchmark might be around 20-30% for opportunities that have passed initial qualification.

If your win rate is significantly lower, it could point to problems such as strong competition, misalignment in what you qualify as an opportunity, or sales execution challenges. A very high win rate (50%+ consistently) might mean your team is cherry-picking only the easiest deals, indicating missed opportunity to pursue more pipeline.

Actionable Steps for Win/Loss Improvement:

Example Analysis:

To calculate win rates and analyze outcomes, here's an example SQL snippet that summarizes closed deals:

SELECT 
    rep.name AS sales_rep,
    SUM(CASE WHEN opp.status = 'Closed Won' THEN 1 ELSE 0 END) AS deals_won,
    SUM(CASE WHEN opp.status = 'Closed Lost' THEN 1 ELSE 0 END) AS deals_lost,
    ROUND(
      SUM(CASE WHEN opp.status = 'Closed Won' THEN 1 ELSE 0 END)::decimal 
      / NULLIF(SUM(CASE WHEN opp.status IN ('Closed Won','Closed Lost') THEN 1 ELSE 0 END), 0) * 100, 
      1
    ) || '%' AS win_rate,
    ROUND(AVG(CASE WHEN opp.status = 'Closed Won' THEN DATE_PART('day', opp.close_date - opp.created_date) END)) AS avg_sales_cycle_won_days,
    ROUND(AVG(CASE WHEN opp.status = 'Closed Lost' THEN DATE_PART('day', opp.close_date - opp.created_date) END)) AS avg_sales_cycle_lost_days,
    ROUND(AVG(CASE WHEN opp.status = 'Closed Won' THEN opp.amount END)) AS avg_win_deal_size
FROM sales.opportunities opp
JOIN sales.reps rep ON opp.owner_id = rep.id
WHERE opp.close_date BETWEEN '2025-01-01' AND '2025-12-31'
  AND opp.status IN ('Closed Won','Closed Lost')
GROUP BY rep.name
ORDER BY win_rate DESC;

This query groups closed opportunities by sales rep, counting wins and losses, and calculating each rep's win rate. It also compares average sales cycle time for won vs lost deals per rep, and average deal size of wins. This can illuminate which reps are most effective and whether some reps might be hanging onto lost causes too long.

Stage 7: Revenue Expansion

Key Metrics to Track:

• Expansion Revenue (Upsell/Cross-sell Bookings): The amount of additional revenue generated from existing customers in a given period
• Net Revenue Retention (NRR) Rate: The percentage of recurring revenue retained and grown from your customer base, usually annually. NRR considers starting revenue plus expansions minus churn
• Gross Revenue Retention (GRR) Rate: The percentage of revenue retained excluding any expansions (just looking at churn/downgrades)
• Expansion Rate per Customer: What fraction of customers expanded their account in a period
• Average Expansion Deal Size: The average value of an upsell/cross-sell deal
• Time to First Expansion: How long on average it takes a new customer to make their first expansion purchase

Interpreting Expansion Metrics:

High expansion revenue and NRR indicate a strong product-market fit and customer success motion – customers are finding value and investing more. In mid-market SaaS, a common benchmark for NRR is around 100-110% for solid performance, and best-in-class companies achieve 120%+ NRR.

If your NRR is below 100%, it means churn is outweighing upsells – a warning sign to improve retention or find more upsell opportunities. If expansion revenue is a small portion of total growth, your company may be too dependent on new sales – unlocking expansion could be a growth lever.

Actionable Steps for Driving Expansion:

Example Analysis:

To keep track of expansion and retention, your data warehouse can aggregate billing and CRM info. For example, to compute Net Revenue Retention by cohort year:

SELECT 
    start_year,
    ROUND(
      SUM(CASE WHEN year = start_year THEN arr END)::numeric, 2
    ) AS starting_arr,
    ROUND(
      SUM(CASE WHEN year = start_year + 1 THEN arr END)::numeric, 2
    ) AS arr_after_1_year,
    ROUND(
      SUM(CASE WHEN year = start_year + 1 THEN arr END) 
      / NULLIF(SUM(CASE WHEN year = start_year THEN arr END), 0) * 100, 
      1
    ) || '%' AS NRR_after_1_year
FROM (
    -- Example subquery: account ARR by year
    SELECT 
      account_id, 
      MIN(EXTRACT(YEAR FROM start_date)) AS start_year,
      EXTRACT(YEAR FROM year_month) AS year,
      SUM(mrr) * 12 AS arr  -- assuming we have monthly MRR data per account
    FROM finance.account_mrr_history
    GROUP BY account_id, year
) sub
GROUP BY start_year
ORDER BY start_year;

This example assumes you have a table of monthly recurring revenue (MRR) per account over time. It groups accounts by the year they started and then compares the ARR from that cohort in the year they started vs one year later, calculating an NRR percentage. Monitoring NRR by cohort tells you if your retention and expansion strategies are improving for newer customers versus older ones.

Conclusion

Measuring a sales-led funnel end-to-end provides the insights needed for RevOps, Data teams, and Leadership to drive predictable growth. By instrumenting each stage – Lead Acquisition, MQL, SQL, Opportunity, Pipeline Progression, Win/Loss, and Revenue Expansion – you create a common language across Marketing, Sales, and Customer Success.

Remember that metrics are most powerful when they prompt action: track them consistently, benchmark against industry standards and your own goals, and dig into the "why" behind the numbers. For a mid-market SaaS organization, the funnel metrics help allocate resources (e.g., where to invest marketing dollars, how to coach sales, where to improve product or onboarding).

Finally, iterate and improve: a sales-led motion is dynamic. As your company introduces new products or targets new markets, conversion rates and benchmarks will shift. Use your data (and the example SQL queries) to continuously monitor the health of your funnel.