Sales Data Analysis - Sales Productivity KPIs with Pyhon

Sales teams thrive on performance visibility. Knowing which reps close deals faster, convert more leads, or manage a healthy pipeline helps sales leaders optimize strategy and forecasting.

In this post, we’ll cover the top 10 Sales Productivity KPIs, explain each one, and show you exactly how to calculate them in Python.

Dataset Description:

• Date → The transaction or sale date

• CustomerID → A unique identifier for each customer

• SalesRep → Name or ID of the salesperson responsible for the sale.

• ProductCategory → Category or classification of the product sold

• Region → The geographical area or sales territory where the sale occurred.

• Channel → The sales channel through which the transaction occurred (e.g., Retail, Online, Distributor, Wholesale).

• UnitsSold → The total number of units sold in the transaction. Used for computing volume-based metrics like revenue per unit, profit per unit, etc.

• ListPricePerUnit → Original price before discount

• Discount% → Percentage discount applied

• CostPerUnit → Cost per unit is the total cost of producing one unit of a product or service.

  CostPerUnit=Total Production Cost​ / Number of Units Produced

• PricePerUnit → Final price after discount

• PromotionFlag → 1 if the transaction was during a promotion, else 0

• Revenue → UnitsSold × PricePerUnit

• Cost → UnitsSold × CostPerUnit

• Profit → Revenue – Cost

• Quota → Monthly/quarterly quota assigned to each rep

• Leads → Number of leads handled by each rep

• Opportunities → Number of qualified opportunities

• DealsClosed → Number of deals won

• CloseDate and StartDate → For calculating sales cycle

1.Sales per Rep

Definition: Measures total revenue generated by each salesperson.

Insight: Shows top-performing reps and helps identify underperformers.

sales_per_rep = df.groupby("SalesRep")["Revenue"].sum().reset_index()

print(sales_per_rep)

2.Quota Attainment %

Definition: The percentage of sales achieved relative to the sales quota.

Insight: A key performance indicator in incentive and compensation planning.

quota_df = pd.DataFrame({

"SalesRep": ["A", "B", "C"],

"Revenue": [120000, 90000, 150000],

"Quota": [100000, 100000, 150000]

})

quota_df["QuotaAttainment%"] = (quota_df["Revenue"] / quota_df["Quota"]) * 100

print(quota_df)

3.Lead-to-Sale Conversion Rate

Definition: Measures how effectively leads are converted into paying customers.

Insight: Helps evaluate the quality of leads and sales effectiveness.

conversion_df = pd.DataFrame({

"SalesRep": ["A", "B", "C"],

"Leads": [100, 80, 150],

"DealsClosed": [20, 15, 30]

})

conversion_df["LeadToSaleConversion%"] = (conversion_df["DealsClosed"] / conversion_df["Leads"]) * 100

print(conversion_df)

4.Opportunity-to-Win Ratio

Definition: Compares the number of opportunities pursued to the number of deals won.

Insight: A high ratio indicates strong closing skills and an efficient sales funnel.

opportunity_df = pd.DataFrame({

"SalesRep": ["A", "B", "C"],

"Opportunities": [50, 60, 70],

"DealsClosed": [25, 30, 35]

})

opportunity_df["OpportunityToWin%"] = (opportunity_df["DealsClosed"] / opportunity_df["Opportunities"]) * 100

print(opportunity_df)

5.Sales Cycle Length (Days to Close)

Definition: Average number of days it takes to close a deal from opportunity creation.

Insight: Shorter cycles imply faster conversions and higher productivity.

df["StartDate"] = pd.to_datetime(df["Date"]) # assuming Date = start

df["CloseDate"] = df["StartDate"] + pd.to_timedelta(np.random.randint(10, 60, len(df)), unit="D")

df["SalesCycleDays"] = (df["CloseDate"] - df["StartDate"]).dt.days

avg_cycle_length = df["SalesCycleDays"].mean()

print("Average Sales Cycle Length (days):", round(avg_cycle_length, 2))

6.Pipeline Value

Definition: Total value of all active opportunities in the sales pipeline.

Insight: Represents the total potential revenue in progress.

pipeline_df = pd.DataFrame({

"SalesRep": ["A", "B", "C"],

"OpportunityValue": [200000, 150000, 180000]

})

pipeline_value = pipeline_df["OpportunityValue"].sum()

print("Total Pipeline Value:", pipeline_value)

7.Pipeline Coverage Ratio

Definition: Ratio of pipeline value to the sales target (quota).

Insight: Ideal coverage is typically 3–4x quota to ensure consistent performance.

pipeline_df["Quota"] = [100000, 100000, 150000]

pipeline_df["PipelineCoverageRatio"] = pipeline_df["OpportunityValue"] / pipeline_df["Quota"]

8.Win Rate %

Definition: Measures how many opportunities convert into actual deals.

Insight: A key metric to track overall sales effectiveness and team performance.

win_rate = (opportunity_df["DealsClosed"].sum() / opportunity_df["Opportunities"].sum()) * 100

9.Average Deal Size

Definition: The average revenue generated per closed deal.

Insight: Useful for forecasting and understanding the value of each sale.

total_revenue = df["Revenue"].sum()

total_deals = 200 # example

avg_deal_size = total_revenue / total_deals

10.Number of Deals Closed

Definition: Total number of successful sales closed within a period.

Insight: Core metric to evaluate productivity and goal achievement.

deals_closed = len(df[df["Revenue"] > 0]) # assuming non-zero revenue means closed

Key Takeaways

Tracking Sales Productivity KPIs helps businesses answer critical questions like:

• Who are the most productive reps?

• How long does it take to close a deal?

• Is the pipeline sufficient to meet revenue goals?