Sales Pipeline Performance
&
Revenue Operations Analysis
Tools: Databricks, SQL, dashboard development
Scope: 8,800 opportunities, 85 accounts, 7 products, and 3 sales regions
​
Project overview
I developed four dashboards to examine sales outcomes, open-pipeline workload, team performance, and product and account opportunities.
The project addresses a practical Revenue Operations question: Where should sales leadership focus to improve commercial performance and pipeline visibility?
My work translates opportunity-level data into management insights: explaining changes in won deal value, evaluating performance in the context of product mix, and identifying data gaps that limit operational decisions.
Data and analytical approach
The analysis combines opportunity records with product, sales-team, and account information. Engagement dates span October 2016 through December 2017, while recorded closes span March through December 2017.
I organized the dashboards around four decision areas:
​
The analysis uses won deal value to describe the value recorded on successful opportunities. Recognized revenue, collections, and profitability cannot be established from these files.
Dashboard Name | Purpose |
|---|---|
Executive Performance | Track won value, closed win rate, deal size, and changes over time |
Pipeline Workload and Hygiene | Identify open opportunities requiring review and incomplete account information |
Sales Execution and Team Performance | Compare sales outcomes, cycle length, and performance across teams |
Product and Account Strategy | Examine product mix, account contribution, and potential commercial opportunities |
Key insights and recommendations
​
1. The Q4 decline involved volume, conversion, and deal size
Won deal value decreased 6.0%, from 2.98 million in Q3 to 2.80 million in Q4.
Three measures declined together:
-
Closed opportunities decreased from 2,047 to 1,985.
-
Closed win rate decreased from 61.4% to 60.3%.
-
Average won deal value decreased from 2,373 to 2,343.
This indicates that the decline warrants investigation across several operating drivers. Lower closing volume, weaker conversion, and smaller won deals all contributed to the result.
Recommendation: Review the Q3–Q4 change by product and manager, prioritizing the segments contributing most to the decline. Use those findings to determine whether the next action should address opportunity throughput, conversion, or commercial mix.
​
2. Missing account information limits open-pipeline visibility
Of 2,089 open opportunities, 1,425—68.2%—lack an account.
This prevents a complete assessment of open opportunities by customer, industry, or account size. It also limits the ability to connect pipeline review with account planning.
Recommendation: Establish an owner-level exception queue and require an account association at an agreed qualification milestone. Track open-opportunity account completeness alongside pipeline workload.
​
3. Win counts alone can misrepresent commercial contribution
MG Special generated 18.7% of wins but only 0.44% of total won value. In contrast, GTX Pro and GTX Plus Pro together generated 61.4% of won value.
A team handling many lower-priced products could lead on wins while contributing less value. This makes product mix essential context for performance comparisons.
Recommendation: Evaluate sellers using won value, win rate, average won value, and sales cycle together. Compare performance within products before drawing coaching or resource-allocation conclusions.
Product prioritization should also consider margin, customer demand, and selling effort, which are unavailable in this dataset.
​
4. Regional performance differences require further diagnosis
Central recorded 2,604 closed opportunities, compared with 1,858 in East. However, East’s average won value was approximately 2,639, compared with 2,054 in Central.
Their closed win rates were similar: 63.0% in East and 62.6% in Central.
The difference therefore warrants investigation into product and account mix before attributing it to seller effectiveness.
Recommendation: Compare regions within common product and customer segments. Use the results to identify whether differences reflect assigned opportunities, commercial strategy, or execution.
​
5. Open-opportunity age supports a review process
Using December 31, 2017 as an illustrative reference date, Engaging opportunities had a median age of 165 days, and 93.1% exceeded 90 days.
These figures identify a substantial review population. Age alone does not establish inactivity or loss risk, and the dataset does not provide a confirmed extraction timestamp.
Recommendation: Review older opportunities to validate ownership, customer engagement, next steps, and continued qualification. Future CRM reporting should capture activity and stage-change dates to distinguish long sales cycles from stalled opportunities.
Analytical judgment and limitations
Several choices are central to interpreting the results responsibly:
-
Closed win rate uses Won ÷ (Won + Lost). Open opportunities are excluded from that denominator.
-
Current status counts describe the opportunity population. They do not establish stage-to-stage conversion.
-
Open opportunities have no recorded value, so actual open-pipeline value and weighted forecasts are unavailable.
-
Q1 contains only March closes, limiting comparisons with complete later quarters.
-
Sales cycle measures engagement-to-close time; it does not measure the full lead-to-close journey.
-
The product naming mismatch between GTXPro and GTX Pro requires explicit reconciliation when joining the files.
-
Account-company revenue is a segmentation attribute and should not be interpreted as sales generated by this business.