Here are the key insights from the analytical report of industrial consumers for the utility company:

1. Overall Financial Performance:

Total Amount Billed: 657M

Total Recovery: 598M

Recovery Rate: 90.94%

The recovery rate is strong, showing that nearly 91% of billed amounts have been recovered, which indicates efficient collection strategies.

2. Average Consumer Billing:

Average Billing per Consumer: 13.15M

Each industrial consumer is, on average, billed over 13 million, indicating a significant contribution from each consumer.

3. Top Billing Regions and IBCs:

Bin Qasim has the highest billed amount (90M) among all IBCs, followed by SIMZ (90M) and Clifton (52M).

Region R1 has the highest 1-year average billing (31.9M), followed by R3 (21.7M), indicating that these regions are the largest consumers.

4. Recovery by Bank:

Top Recovering Banks:

Soneri Bank (SON): Highest recovery, showing a strong contribution towards the total recovered amount.

NBP and HBL are also significant contributors but much lower compared to Soneri Bank.

5. Regional Recovery Distribution:

R3 is the dominant region for recovery with 381M, contributing 63.75% of the total recovery.

R1 and R2 also show healthy recovery, but R4 lags significantly behind with just 5.7M recovery, potentially highlighting collection challenges in that region.

6. Increase and Decrease in Billing:

Region R1 shows the highest increase in the 1-year average billed units, followed by R3.

Region R4 has a minimal increase and could need attention for growth or revenue enhancement.

7. Diversification Across Banks:

The recovery is somewhat concentrated among a few top-performing banks. Efforts could be made to improve recovery through other banks that currently contribute less.

8. Potential Areas of Focus:

Regions like R4 and IBCs like Landhi and Gulshan have much lower billing and recovery rates. These areas may need targeted strategies for increasing revenue or improving collection efficiency.

Conclusion:

The utility company has a healthy recovery rate, with key regions and banks driving the majority of the revenue. However, there is an opportunity to enhance performance in certain regions (like R4) and diversify recovery through more banks.

Key Insights:

• Strong recovery rate indicates efficient billing and collection processes.
• Regional differences in billing and recovery could indicate varying energy consumption patterns or collection efficiency across regions.
• The performance of different banks in recovery is also crucial for optimizing future recovery strategies.
• Specific areas like Bin Qasim and SIMZ contribute significantly to the total billing, highlighting key industrial areas for K-Electric.

This dashboard can help the company focus on improving collection in regions or sectors where performance is relatively weaker.

Let’s break down each section for better understanding:

Key Metrics Displayed:

1. Average 3-Month KIBOR Rate: 14.23%
2. Average 6-Month KIBOR Rate: 14.33%
3. Average 1-Year KIBOR Rate: 14.50%

These averages are likely calculated based on historical KIBOR rates over different time intervals (3 months, 6 months, and 1 year), giving a snapshot of interest rates over various durations.

Yearly Analysis and Breakdown:

• The left-hand side shows a table indicating the years from 2018 to 2024.
  • These years may represent the years for which data is available or projected, as some forecasting is done for future years (e.g., 2024).

Pie Charts (By Quarter):

• There are three pie charts, each illustrating the average rates by quarter for 3 months, 6 months, and 1 year:
  • 3-Month Average KIBOR Rate by Quarter: Displays that the rates fluctuate slightly between 13.58% and 14.68%, depending on the quarter.
  • 6-Month Average KIBOR Rate by Quarter: Similar trends, ranging from 13.77% to 14.73% across different quarters.
  • 1-Year Average KIBOR Rate by Quarter: The highest rates are found in Q2 and Q4, ranging between 14.40% and 14.50%. These pie charts provide insight into the seasonal fluctuation of interest rates throughout the year.

Average KIBOR Rate Over the Years:

• The bar chart on the right side shows the average KIBOR rates from 2018 to 2024. Here are the key takeaways:
  • There’s a noticeable increase in KIBOR rates from 2018 to 2024.
  • The rate is highest in 2024, nearing or above 20%, showing a steep rise from around 10% in 2018.
  • This could indicate a tightening monetary policy or other macroeconomic factors influencing rising interest rates over time.

Forecasting Sections:

• At the bottom of the dashboard, there are line graphs indicating:
  • 3 Months KIBOR Rate Forecasting: The forecast shows significant fluctuations, with rates ranging between 10% and 25%.
  • 6 Months KIBOR Rate Forecasting: Similarly, the 6-month forecast displays high volatility with dips and spikes.
  • 1 Year KIBOR Rate Forecasting: The 1-year forecast seems to indicate a more stable trend but still with visible fluctuations.

These forecasting sections highlight the projected volatility in interest rates over different periods, suggesting that the market may remain uncertain in the near future.

This dashboard presents an overview of sales performance, showcasing key metrics and visualizations:

• Key Metrics:
  • Revenue: $24.91M
  • Orders: 25K
  • Profit: $10.46M
  • Cost: $14.46M
  • Return Rate: 2.17%
• Visual Highlights:
  • Total Revenue Over Time: A line graph displaying revenue growth from mid-2020 to early 2022, showing notable peaks.
  • Orders by Category: A bar chart indicating that Accessories and Bikes are the top categories.
  • Top 10 Products: A table listing the highest-selling products, with details on total orders, revenue, and return rates.
  • Monthly Metrics:
    • Monthly Revenue: $1.83M, with the target achievement at 91.7%.
    • Monthly Orders: 2146, achieving 88.7% of the goal.
    • Monthly Cost: $1.06M, meeting 93.7% of the target.
• Product Insights:
  • Most Ordered Product: Tires and Tubes
  • Most Returned Product: Shorts

This dashboard effectively utilizes various data visualization techniques to provide insights into sales trends, product performance, and monthly achievements.

Let’s break down the key metrics and insights:

1. Overview Metrics:

• Total Employees: 1,470 employees in the organization.
• Attrition Count: 237 employees have left the organization.
• Attrition Rate: 16.1%, which is a moderate to high rate of employee turnover.
• Average Age: 37 years, showing that the workforce consists of experienced individuals.
• Average Income: $7,000 per month, giving an insight into the average salary structure.
• Years at Company: Employees stay an average of 7 years, indicating moderate employee retention.

2. Attrition by Department:

• The bar chart provides the attrition count by department, with the highest attrition in departments like Human Resources, Research & Development, and Sales.
• This breakdown is essential for identifying which departments face the most turnover, potentially signalling issues like job dissatisfaction, lack of growth, or high work pressure in those areas.

3. Attrition by Marital Status:

• A pie chart shows the attrition split by marital status, with segments for single, married, and divorced employees. It looks like a significant portion of the attrition is coming from married individuals, which could point to factors like work-life balance or family obligations influencing their decisions.

4. Attrition by Job Role and Gender:

• Another chart explores attrition by job role and gender, allowing the analysis to see if there is any gender disparity or role-specific issues leading to turnover.
• This data helps the HR department tailor employee retention strategies based on gender or specific job roles that might be more susceptible to attrition.

5. Attrition by Education Field:

• The bar chart on the right shows the sum of attrition count by education field:
  • Life Sciences has the highest attrition count (89 employees), followed by Medical (63 employees) and Marketing (35 employees).
• This breakdown provides insight into whether certain educational backgrounds are associated with higher turnover, which can be useful for revising hiring strategies or offering more targeted professional development programs.

6. Attrition by Years at Company:

The chart provides insights into how long employees have been with the company before leaving. This could indicate whether newer employees are more prone to attrition or if the problem is more with long-serving employees.

Key Insights for Action:

Years at Company data can help focus retention efforts on employees who have been with the company for specific durations, especially those more prone to leave after certain years.

Departments like HR and Sales need further analysis to understand why turnover is so high and whether it’s related to workplace culture, stress, or lack of growth opportunities.

The marital status and education fields sections may suggest that specific groups (e.g., married employees or those with Life Sciences backgrounds) require more support to retain them.

Let’s dive deeper into each section of the dashboard:

1. Total Sales and Quantity:

• Total Amount: The total sales of the food company amount to 35.45 billion.
• Total Quantity: The total quantity of items sold is 574.37 million units.

2. Provincial Breakdown:

a. Sum of Total Amount by Province:

• The bar graph represents the sales distribution across provinces:
  • Punjab has the highest sales, as indicated by the steep curve.
  • Sindh comes next, but the amount is significantly lower.
  • KPK and Balochistan contribute small shares to the total sales.
  • The amounts for these smaller provinces are not clearly visible in the graph but are relatively minor compared to Punjab.

b. Sum of Total Percentage by Province:

• Punjab contributes 75.46% of the total sales.
• Sindh has the second-highest percentage with 17.58%.
• KPK contributes 6.6%.
• Balochistan has the smallest contribution, making up just 0.41% of the total.

c. Sum of Total Quantity by Province:

• Similar to the amount graph, this chart shows the distribution of quantity sold by province:
  • Punjab again dominates the quantity of units sold, followed by Sindh, with smaller amounts for KPK and Balochistan.

3. City Breakdown:

a. Sum of Total Amount by City:

• Lahore tops the chart with 8.01 billion in total sales.
• Islamabad follows closely with 6.75 billion.
• Karachi and Multan are next with 6.17 billion and 6.03 billion respectively.
• Gujranwala contributes 5.90 billion.
• Other cities like Peshawar, Quetta, and Faisalabad have much smaller amounts, under 1 billion each.

b. Sum of Total Percentage by City:

• Lahore leads with 22.60% of the total sales.
• Islamabad holds a significant share with 19.03%.
• Karachi accounts for 17.41%, Multan with 17.09%, and Gujranwala with 16.63%.
• Peshawar contributes 6.60% of total sales, while smaller cities like Quetta, Faisalabad, Sukkur, and Rawalpindi have minimal shares under 1%.

4. Summary of Key Cities:

• Lahore stands out as the top-performing city in both total amount and percentage.
• The top five cities, which include Lahore, Islamabad, Karachi, Multan, and Gujranwala, collectively contribute a substantial portion of the company’s total sales.
• Other cities like Peshawar, Quetta, Faisalabad, and smaller urban areas have much smaller contributions to both total amount and percentage.

5. Visual Breakdown:

• The pie chart on the right-hand side highlights the sales percentages by province, emphasizing Punjab’s significant dominance, followed by Sindh, KPK, and Balochistan.
• The bar charts present a clear ranking of cities and provinces, showing the relative performance of each.

This dashboard provides a clear view of the geographical distribution of sales, focusing on the most successful cities and provinces, with Punjab and Lahore leading the way in both total sales amount and percentage.

Here’s the analysis:

1. Revenue and Units Sold:

Total Revenue: $96.62M.

Total Units Sold: 1M units.

2. Revenue by Date:

A time series graph shows fluctuations in revenue over time, with peaks in certain periods (spanning from March 2019 to April 2019).

3. Revenue Breakdown by Supervisor:

Supervisor 1 is responsible for the highest revenue at $30.13M (31.06%).

Other supervisors contribute as follows:

Supervisor 4: $23.34M (24.22%),

Supervisor 3: $23.14M (24.16%),

Supervisor 2: $19.85M (20.54%).

4. Revenue by Employee (EMP) Name:

A treemap visualization shows revenue contributions by individual employees. EMP-1, EMP-2, etc., are displayed with varying sizes corresponding to their revenue impact.

5. Revenue by Product Name:

A bar chart on the right shows revenue generated by different products. The top revenue-generating products are Product 1, Product 12, and Product 13, followed by others in descending order.

This dashboard gives an overview of sales by product, supervisor, and employee, with clear visualizations of the key contributors to revenue. The time series also highlights sales trends over time.

Here’s a breakdown of the key elements presented in the dashboard:

1. Total Sales, Profit, and Quantity:

Total Sales: 2.297 million.

Total Profit: 286K.

Total Quantity: 38K units sold.

2. Sales by Region:

The chart shows the sales volume split by regions (West, East, Central, South). The West region has the highest sales, followed by the East, Central, and South.

3. Profit by Segment:

The profit is segmented into three categories: Consumer, Corporate, and Home Office. Consumer seems to be the most profitable segment, followed by Corporate and Home Office.

4. Sales by Sub-Category:

A bar chart shows sales by various sub-categories such as Phones, Chairs, Storage, Tables, etc. Phones have the highest sales, followed by Chairs and Storage.

5. Sales by State:

A map of the United States shows sales across different states, represented by bubble sizes indicating the volume of sales in each state.

6. Sales by Segment:

A pie chart categorizes sales by different segments, probably reflecting the same segmentation shown in the profit analysis.

7. Sales by Supervisor Name:

Another pie chart breaks down sales by individual supervisors (e.g., Roger, Peter, Thomas), with Roger and Peter having the highest contributions.

The dashboard provides a comprehensive overview of sales and profit performance across different regions, states, and segments. It highlights key areas like top-performing products and sales by supervisor, which could be useful for business decision-making.

The dashboard provides an analysis of hiring opportunities using Google Keyword Planner data. Here’s a breakdown of its key components and findings:

Key Sections:

1. Top Keyword:
   • “Amazon freelance jobs” is identified as the top-performing keyword.
     • 251K Average Monthly Searches: Indicates high interest and search volume.
     • YoY (Year-over-Year) Change: 97% growth suggests a significant increase in demand for the keywords.
     • Top of Page Bid (High Rate): $240.99 shows advertisers are willing to pay a premium for visibility.
     • Top of Page Bid (Low Rate): $13.80 provides a lower threshold for bid competition.
2. Competition:
   • Most keywords fall under Low competition (82.11%), making them easier and more affordable to target for ads.
   • Medium competition accounts for 17.37%, while only 0.53% are classified as High competition.
3. Keyword Insights:
   • The keyword list includes various job-related queries like “Amazon VA jobs,” “remote jobs,” and “agency jobs near me.”
   • Users can select specific keywords for a focused analysis.
4. Average Monthly Searches by Top 10 Keywords:
   • Highlights the most-searched job-related keywords, with several consistently reaching 50K searches.
   • Indicates a concentration of search volume on a few high-demand keywords.
5. Top of Page Bid Analysis:
   • High Range Bids: Keywords such as “jobs near me” and “teen jobs” have significant bid values, reflecting high advertiser interest.
   • Low Range Bids: Bidding is more affordable for other keywords, like “X-ray tech jobs.”
6. Competition Indexed Value:
   • Visualization reinforces the predominance of low-competition keywords.

Observations:

• Opportunities: With most keywords falling under low competition, businesses can achieve visibility at a relatively low cost.
• High Demand Keywords: Keywords like “Amazon freelance jobs” combine high search volume with moderate bidding, offering strong ROI potential.
• Trends: The YoY growth suggests an increasing shift toward freelance and remote job searches, indicating a growing market for related services.

Recommendations:

• Focus on high-search, low-competition keywords for cost-effective ad campaigns.
• Leverage the growth in freelance job queries for targeted advertising.
• Monitor bid trends to allocate budget effectively, targeting lower-bid opportunities without sacrificing reach.