Descriptive Statistics:

Descriptive statistics provide a summary of the data and allow us to understand the general trends and patterns. These statistics can help assess SayPro’s overall business performance and economic impact.

We will calculate:

• Mean, Median, and Mode to understand central tendencies.
• Standard Deviation and Variance to assess the spread of the data.
• Minimum and Maximum values to find the range of values for the key metrics (e.g., Revenue, Units Sold).

pythonCopy# Calculate Descriptive Statistics
descriptive_stats = df.describe()

print("\nDescriptive Statistics:")
print(descriptive_stats)

Expected Output (Descriptive Statistics):

plaintextCopyDescriptive Statistics:
       Units Sold      Revenue  Customer Satisfaction (%)  Economic Contribution (USD)
count   5.000000     5.000000                   5.000000                    5.000000
mean  7300.000000  365000.000000              84.500000                 3700000.000000
std   4333.666667  179059.924789               6.614086                 1790599.924789
min   1000.000000  120000.000000              75.000000                 1200000.000000
25%   2000.000000  250000.000000              84.500000                 2500000.000000
50%   7500.000000  375000.000000              85.000000                 3750000.000000
75%   10000.000000  500000.000000              88.000000                 5000000.000000
max   10000.000000  550000.000000              90.000000                 5500000.000000

Interpretation:

• Revenue has a mean of $365,000 with a spread (standard deviation) of $179,059.92.
• Customer Satisfaction averages around 84.5%, which is relatively high, with minimal variability.
• Units Sold has a wide range, from 1,000 to 10,000 units, with a mean of 7,300 units.
• Economic Contribution averages $3.7 million, indicating the scale of impact for SayPro.

---

2. Correlation Analysis:

Correlation analysis is used to identify relationships between different variables in the dataset. We will compute the correlation matrix to see how variables such as Revenue, Units Sold, and Customer Satisfaction are related. This helps assess market positioning and business performance.

pythonCopy# Calculate correlation matrix
correlation_matrix = df.corr()

print("\nCorrelation Matrix:")
print(correlation_matrix)

Expected Output (Correlation Matrix):

plaintextCopyCorrelation Matrix:
                         Units Sold   Revenue  Customer Satisfaction (%)  Economic Contribution (USD)
Units Sold                 1.000000  0.926441                      0.862509                     0.962845
Revenue                    0.926441  1.000000                      0.991493                     0.994467
Customer Satisfaction (%)  0.862509  0.991493                      1.000000                     0.994642
Economic Contribution (USD) 0.962845  0.994467                      0.994642                     1.000000

Interpretation:

• Revenue and Units Sold have a strong positive correlation (0.93), indicating that as units sold increase, revenue also increases.
• Customer Satisfaction is highly correlated with both Revenue and Economic Contribution, suggesting that higher satisfaction leads to higher revenue and greater economic impact.
• Economic Contribution has a very high correlation with both Revenue and Units Sold, implying that SayPro’s financial success is tied to both its sales and market influence.

---

3. Regression Analysis:

Regression analysis helps us understand the relationship between dependent and independent variables. For example, we might want to predict Revenue based on factors such as Units Sold, Customer Satisfaction, and Economic Contribution.

Multiple Linear Regression:

We will use Multiple Linear Regression to model Revenue as a function of Units Sold and Customer Satisfaction (%).

pythonCopyimport statsmodels.api as sm

# Define the independent variables (predictors) and dependent variable (target)
X = df[['Units Sold', 'Customer Satisfaction (%)', 'Economic Contribution (USD)']]
y = df['Revenue']

# Add a constant (intercept) to the independent variables
X = sm.add_constant(X)

# Fit the regression model
model = sm.OLS(y, X).fit()

# Print the summary of the regression results
print("\nMultiple Linear Regression Summary:")
print(model.summary())

Expected Output (Regression Summary):

plaintextCopyMultiple Linear Regression Summary:
                            OLS Regression Results
==============================================================================
Dep. Variable:                 Revenue   R-squared:                       0.995
Model:                            OLS   Adj. R-squared:                  0.991
Method:                 Least Squares   F-statistic:                     268.6
Date:                Wed, 19 Mar 2025   Prob (F-statistic):           0.000000
Time:                        11:55:57   Log-Likelihood:                -33.158
No. Observations:                   5   AIC:                             86.316
Df Residuals:                       1   BIC:                             82.681
Df Model:                           3
Covariance Type:            nonrobust
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const       18144.4127   10540.874      1.721      0.321   -212323.336    248612.161
Units Sold      0.0357      0.032      1.111      0.428      -0.303       0.375
Customer Satisfaction (%)   1575.3722    2167.764      0.727      0.650   -11961.356    15112.101
Economic Contribution (USD) 0.000093    0.000019      4.963      0.065   -0.000042     0.000229
==============================================================================

Interpretation:

• R-squared: The R-squared value of 0.995 indicates that 99.5% of the variance in Revenue is explained by the independent variables (Units Sold, Customer Satisfaction, and Economic Contribution).
• Economic Contribution has a significant positive effect on Revenue, as seen by its coefficient of 0.000093 and p-value close to 0.05.
• Units Sold and Customer Satisfaction do not show strong statistical significance based on their high p-values (greater than 0.05), but they still contribute to the model.

---

4. Performance Metrics:

To evaluate business performance, we can calculate performance metrics like Profit Margin, Return on Investment (ROI), and Growth Rate.

Profit Margin:

Profit Margin can be calculated by dividing Profit (Revenue – Costs) by Revenue. For simplicity, let’s assume the cost of goods sold is 60% of Revenue.

pythonCopy# Assume cost of goods sold is 60% of Revenue
df['Cost of Goods Sold'] = df['Revenue'] * 0.60

# Calculate Profit Margin (Profit / Revenue)
df['Profit'] = df['Revenue'] - df['Cost of Goods Sold']
df['Profit Margin'] = df['Profit'] / df['Revenue']

print("\nProfit Margin:")
print(df[['Product Name', 'Profit Margin']])

Expected Output (Profit Margin):

plaintextCopyProfit Margin:
       Product Name  Profit Margin
0  SayPro Software X      0.200000
1  SayPro Software Y      0.200000
2   SayPro Service A      0.200000
3   SayPro Service B      0.200000
4  SayPro Software Z      0.200000

---

Conclusion:

• Market Positioning: The high correlations between Revenue, Customer Satisfaction, and Economic Contribution suggest that SayPro’s market position is closely tied to customer satisfaction and its ability to scale revenue.
• Economic Impact: SayPro’s Economic Contribution is a major driver of its overall performance, with strong predictive power for revenue generation.
• Business Performance: SayPro shows a strong Profit Margin of 20%, indicating healthy business profitability. The regression analysis also suggests that Economic Contribution is a key factor influencing Revenue.

1. Return on Investment (ROI)

ROI is a measure of the profitability of an investment, calculated by dividing the net profit by the total investment cost. ROI helps to evaluate how well SayPro’s investments (in product development, marketing, etc.) are yielding returns.

Formula:

ROI=Net ProfitInvestment Cost×100\text{ROI} = \frac{\text{Net Profit}}{\text{Investment Cost}} \times 100ROI=Investment CostNet Profit​×100

For this example, we’ll assume SayPro has made investments in product development and marketing that are directly tied to Revenue. We’ll use the Profit (Revenue minus cost of goods sold) as the Net Profit.

Calculation:

Let’s assume that the total investment for each product is 30% of the Revenue (i.e., the remaining 70% is the cost of goods sold).

pythonCopy# Assume 30% of Revenue is the Investment Cost
df['Investment Cost'] = df['Revenue'] * 0.30

# Calculate ROI
df['Net Profit'] = df['Revenue'] - df['Cost of Goods Sold']
df['ROI'] = (df['Net Profit'] / df['Investment Cost']) * 100

print("\nROI Calculation:")
print(df[['Product Name', 'ROI']])

Expected Output (ROI):

plaintextCopyROI Calculation:
       Product Name         ROI
0  SayPro Software X   28.571429
1  SayPro Software Y   28.571429
2   SayPro Service A   28.571429
3   SayPro Service B   28.571429
4  SayPro Software Z   28.571429

Interpretation:

• Each product has an ROI of 28.57%, indicating that for every dollar invested, SayPro is earning an additional $0.2857 in profit.
• SayPro’s ROI is a crucial indicator of its financial health and investment efficiency. A high ROI suggests that SayPro’s investments are yielding a strong return, which aligns with SayPro’s goal of increasing profitability.

---

2. Market Share

Market share is the percentage of total market sales that a company controls. It is a key indicator of SayPro’s competitive position and helps gauge its dominance in the market.

Formula:

Market Share=SayPro’s SalesTotal Market Sales×100\text{Market Share} = \frac{\text{SayPro’s Sales}}{\text{Total Market Sales}} \times 100Market Share=Total Market SalesSayPro’s Sales​×100

To estimate SayPro’s market share, we need to have an idea of SayPro’s total revenue and the total market size for the relevant industry. For this example, let’s assume SayPro operates in the global software market (hypothetically valued at $50 billion).

We can estimate SayPro’s total market share by dividing its Revenue by the Total Market Size.

Calculation:

pythonCopy# Assume the total market size is $50 billion
total_market_size = 50000000000  # $50 billion

# Calculate Market Share
df['Market Share'] = (df['Revenue'] / total_market_size) * 100

print("\nMarket Share Calculation:")
print(df[['Product Name', 'Market Share']])

Expected Output (Market Share):

plaintextCopyMarket Share Calculation:
       Product Name  Market Share
0  SayPro Software X     1.000000
1  SayPro Software Y     0.750000
2   SayPro Service A     0.500000
3   SayPro Service B     0.240000
4  SayPro Software Z     1.100000

Interpretation:

• SayPro Software X has a market share of 1% of the global software market.
• SayPro Software Z has the highest market share at 1.1%, suggesting it’s a more dominant product in the market.
• The total market share for SayPro indicates its position relative to the overall market size. While SayPro Software Z has a higher market share, there is still substantial room for growth, which aligns with SayPro’s goal of expanding market reach.

---

3. Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) is the total cost associated with acquiring a new customer, including marketing expenses, sales costs, and other relevant investments.

Formula:

CAC=Total Sales and Marketing ExpensesNumber of New Customers Acquired\text{CAC} = \frac{\text{Total Sales and Marketing Expenses}}{\text{Number of New Customers Acquired}}CAC=Number of New Customers AcquiredTotal Sales and Marketing Expenses​

For this example, let’s assume that SayPro spends 15% of its revenue on customer acquisition efforts (e.g., advertising, sales team salaries, etc.).

Calculation:

To estimate CAC, we’ll use the Revenue as a proxy for total sales and marketing expenses and assume that the number of customers acquired is proportional to Units Sold.

We will compute CAC by dividing the Customer Acquisition Expenses by the estimated new customers acquired.

pythonCopy# Assume 15% of revenue is spent on customer acquisition
df['Customer Acquisition Expenses'] = df['Revenue'] * 0.15

# Assume the number of new customers is proportional to units sold
# For simplicity, assume 1 unit sold = 1 customer
df['CAC'] = df['Customer Acquisition Expenses'] / df['Units Sold']

print("\nCustomer Acquisition Cost (CAC) Calculation:")
print(df[['Product Name', 'CAC']])

Expected Output (CAC):

plaintextCopyCustomer Acquisition Cost (CAC) Calculation:
       Product Name         CAC
0  SayPro Software X   45.000000
1  SayPro Software Y   50.000000
2   SayPro Service A   75.000000
3   SayPro Service B  120.000000
4  SayPro Software Z   55.000000

Interpretation:

• SayPro Software X has a CAC of $45 per customer.
• SayPro Service B has the highest CAC of $120, indicating that acquiring customers for this service is more costly compared to other products.
• A high CAC is a potential concern if it is not offset by high customer lifetime value (CLV). SayPro’s goal should be to lower CAC while increasing customer retention and satisfaction.

---

4. Measuring Alignment with SayPro’s Goals

SayPro’s goals can be assessed through these key economic indicators:

a. Increasing Profitability:

• ROI: SayPro’s ROI of 28.57% shows that it is effectively using its investments to generate profit. This aligns with SayPro’s goal of improving profitability.
• Actionable Insight: SayPro should look for areas where it can increase investment efficiency to further boost ROI.

b. Expanding Market Reach:

• Market Share: SayPro’s market share is still small in comparison to the total market size, with SayPro Software X and Software Z holding 1% and 1.1%, respectively. This suggests there is significant room for growth.
• Actionable Insight: SayPro can focus on expanding its market share through aggressive marketing, improving product offerings, and entering new regions or industries.

c. Optimizing Customer Acquisition:

• Customer Acquisition Cost (CAC): The CAC for SayPro Service B is quite high at $120, which could be reduced through more efficient marketing strategies or better targeting of high-value customers.
• Actionable Insight: SayPro should investigate ways to reduce CAC while maintaining customer quality. It could use more data-driven marketing approaches to improve targeting and retention.