Saga

[Githubberr1]

Subject: A huge thanks for your help! Hey [Colleague's Name], I wanted to take a moment to express my sincere gratitude for your help during my onboarding process. Your guidance and in-depth knowledge of data and analytics methods were invaluable in helping me get up to speed. I especially appreciate your ability to explain complex problems in a clear and concise way. You've become my go-to person for any questions or thoughts I have, and I feel fortunate to have you as a colleague. Thanks again for everything! Best, [Your Name]

[Githubberr1]

Subject: Thanks for being a great teammate! Hey [Colleague's Name], I wanted to express my appreciation for your collaboration on all the projects we've tackled together. Your willingness to jump in and help whenever needed is truly valuable. I especially appreciate the supportive and collaborative environment we've created. It's been a pleasure working alongside you and learning from each other's strengths. Thanks for being a great teammate! Best, [Your Name]

[Githubberr1]

Subject: Appreciation for your leadership Dear [Manager's Name], I wanted to take a moment to express my sincere appreciation for your leadership style. Your understanding nature and encouragement for innovation have created a truly positive and inspiring work environment. I especially appreciate your willingness to listen to new ideas and provide valuable feedback. It's motivating to work under a leader who fosters a culture of growth and collaboration. Thank you for everything! Best regards, [Your Name]

[Githubberr1]

SELECT cast(to_char(date_trunc('month', current_date) - interval '1 day', 'YYYYMMDD') AS INTEGER) AS last_day_prev_month_int;

SELECT -- Get today's date in YYYYMMDD format to_char(current_date, 'YYYYMMDD') AS today_yyyymmdd, -- Get today's date as an integer cast(to_char(current_date, 'YYYYMMDD') AS INTEGER) AS today_int;

[Githubberr1]

Analyzing Customer Subscription Patterns for Product Cross-Selling Understanding the Problem Your goal is to identify customers who are likely to subscribe to additional products. This involves analyzing subscription patterns over time to uncover potential opportunities for cross-selling. Data Requirements To effectively conduct this analysis, you'll need the following data points:

• Customer ID: Unique identifier for each customer.
• Product ID: Unique identifier for each product.
• Subscription Date: Date when the customer subscribed to a product.
• Unsubscription Date (Optional): Date when the customer unsubscribed from a product (if applicable). Analytical Approach
• Data Cleaning and Preparation:
  • Handle missing values: For example, if the Unsubscription Date is missing, assume the customer is still subscribed.
  • Check for inconsistencies: Ensure data integrity and accuracy.
  • Create a time series: Transform the data into a time series format, organizing it by customer and month.
• Calculate Monthly New Subscriptions:
  • Group by customer and month: Group the data by customer and month.
  • Count new subscriptions: Count the number of new products subscribed to by each customer in each month.
• Identify High-Potential Customers:
  • Calculate average subscriptions per month: Determine the average number of products subscribed to by each customer per month.
  • Set a threshold: Define a threshold based on the average or other relevant criteria.
  • Identify high-potential customers: Customers with a subscription rate above the threshold are considered high-potential for cross-selling.
• Analyze Product Co-Occurrence:
  • Create a co-occurrence matrix: Determine which products are frequently subscribed to together.
  • Identify product bundles: Discover common combinations of products that customers tend to subscribe to.
• Time Series Analysis (Optional):
  • Detect trends and patterns: Use time series analysis techniques (e.g., ARIMA, Prophet) to identify trends, seasonality, and other patterns in subscription behavior.
  • Forecast future subscriptions: Predict future subscription behavior for high-potential customers. Recommendation Based on the analysis, you can recommend the following strategies:
• Targeted marketing campaigns: Focus marketing efforts on high-potential customers, promoting products that they are likely to subscribe to based on their past behavior and co-occurrence patterns.
• Product bundling: Offer bundled subscriptions for products that are frequently subscribed to together, providing incentives for customers to subscribe to multiple products.
• Personalized recommendations: Use customer data and analysis insights to provide personalized product recommendations, increasing the likelihood of conversion.
• Customer segmentation: Segment customers based on their subscription patterns and tailor marketing and product offerings to each segment. By following these steps and leveraging the insights gained from the analysis, you can effectively identify high-potential customers and develop strategies to drive additional product subscriptions. Would you like to delve deeper into any specific step or technique?

[Githubberr1]

Analyzing Customer Subscription Patterns for Product Cross-Selling Understanding the Problem Your goal is to identify customers who are likely to subscribe to additional products. This involves analyzing subscription patterns over time to uncover potential opportunities for cross-selling. Data Requirements To effectively conduct this analysis, you'll need the following data points:

• Customer ID: Unique identifier for each customer.
• Product ID: Unique identifier for each product.
• Subscription Date: Date when the customer subscribed to a product.
• Unsubscription Date (Optional): Date when the customer unsubscribed from a product (if applicable). Analytical Approach
• Data Cleaning and Preparation:
  • Handle missing values: For example, if the Unsubscription Date is missing, assume the customer is still subscribed.
  • Check for inconsistencies: Ensure data integrity and accuracy.
  • Create a time series: Transform the data into a time series format, organizing it by customer and month.
• Calculate Monthly New Subscriptions:
  • Group by customer and month: Group the data by customer and month.
  • Count new subscriptions: Count the number of new products subscribed to by each customer in each month.
• Identify High-Potential Customers:
  • Calculate average subscriptions per month: Determine the average number of products subscribed to by each customer per month.
  • Set a threshold: Define a threshold based on the average or other relevant criteria.
  • Identify high-potential customers: Customers with a subscription rate above the threshold are considered high-potential for cross-selling.
• Analyze Product Co-Occurrence:
  • Create a co-occurrence matrix: Determine which products are frequently subscribed to together.
  • Identify product bundles: Discover common combinations of products that customers tend to subscribe to.
• Time Series Analysis (Optional):
  • Detect trends and patterns: Use time series analysis techniques (e.g., ARIMA, Prophet) to identify trends, seasonality, and other patterns in subscription behavior.
  • Forecast future subscriptions: Predict future subscription behavior for high-potential customers. Recommendation Based on the analysis, you can recommend the following strategies:
• Targeted marketing campaigns: Focus marketing efforts on high-potential customers, promoting products that they are likely to subscribe to based on their past behavior and co-occurrence patterns.
• Product bundling: Offer bundled subscriptions for products that are frequently subscribed to together, providing incentives for customers to subscribe to multiple products.
• Personalized recommendations: Use customer data and analysis insights to provide personalized product recommendations, increasing the likelihood of conversion.
• Customer segmentation: Segment customers based on their subscription patterns and tailor marketing and product offerings to each segment. By following these steps and leveraging the insights gained from the analysis, you can effectively identify high-potential customers and develop strategies to drive additional product subscriptions. Would you like to delve deeper into any specific step or technique?

[Githubberr1]

Delving Deeper into Customer Subscription Analysis Let's focus on the key steps and techniques involved in identifying high-potential customers for cross-selling:

1. Data Cleaning and Preparation:
   • Handling missing values:
   • Imputation: Fill missing values with appropriate values (e.g., mean, median, mode, or interpolated values) based on the data distribution.
   • Deletion: Remove rows with too many missing values if they significantly impact the analysis.
   • Outlier detection and handling:
   • Statistical methods: Use techniques like Z-scores or IQR (Interquartile Range) to identify outliers.
   • Visualization: Create box plots or histograms to visually inspect outliers.
   • Treatment: Decide whether to remove or correct outliers based on their impact on the analysis.
2. Calculating Monthly New Subscriptions:
   • Time granularity: Consider the appropriate time granularity (e.g., monthly, weekly) based on your data and business objectives.
   • Subscription status: Define how to handle subscriptions that are still active at the end of the analysis period.
3. Identifying High-Potential Customers:
   • Threshold determination:
   • Percentile-based: Set a threshold based on a specific percentile (e.g., top 20%) of customers.
   • Statistical analysis: Use statistical tests (e.g., t-test, ANOVA) to compare subscription rates between different customer segments.
   • Business knowledge: Consider industry benchmarks or company-specific goals.
   • Customer segmentation:
   • RFM analysis: Segment customers based on Recency, Frequency, and Monetary value.
   • Churn analysis: Identify customers at risk of churning and prioritize them for cross-selling.
4. Analyzing Product Co-Occurrence:
   • Association rule mining: Use algorithms like Apriori or FP-growth to discover frequent itemsets and association rules between products.
   • Market basket analysis: Analyze shopping cart data to identify product combinations that are frequently purchased together.
5. Time Series Analysis:
   • Seasonal patterns: Identify seasonal variations in subscription behavior (e.g., increased subscriptions during specific months or holidays).
   • Trend analysis: Detect long-term trends in subscription rates (e.g., increasing or decreasing over time).
   • Forecasting: Use time series forecasting models (e.g., ARIMA, Prophet) to predict future subscription behavior for high-potential customers. Would you like to delve deeper into any specific technique or aspect of the analysis?

[Githubberr1]

You're looking to segment clients based on their subscription patterns over time. This is a classic problem in customer analytics and can be approached using various techniques. Here are some algorithms and statistical methods that can help:

1. Clustering algorithms:
   • K-Means Clustering: Group clients based on similarities in their subscription patterns.
   • Hierarchical Clustering: Create a tree-like structure to visualize client segments.
2. Time Series Analysis:
   • Dynamic Time Warping (DTW): Compare subscription patterns across clients, allowing for variations in timing.
   • Longest Common Subsequence (LCSS): Identify similar patterns in subscription sequences.
3. Sequence Analysis:
   • Markov Chain Analysis: Model client transitions between subscription states.
   • Sequence Clustering: Group clients with similar subscription sequences.
4. Machine Learning:
   • Supervised Learning: Train a model on labeled client data to predict subscription patterns.
   • Unsupervised Learning: Use techniques like Principal Component Analysis (PCA) or t-SNE to identify patterns in subscription data.
5. Statistical Methods:
   • Chi-Square Test: Identify significant differences in subscription patterns across clients.
   • Survival Analysis: Model client subscription duration and identify segments with similar patterns.

To get started, consider the following steps:

1. Prepare your data: Ensure your subscription data is in a suitable format, with client IDs, product IDs, and timestamped subscription events.
2. Explore and visualize: Use plots and heatmaps to understand the subscription patterns and identify potential segments.
3. Choose an algorithm: Select a suitable algorithm based on your data characteristics and desired outcome.
4. Evaluate and refine: Assess the quality of your segments and refine your approach as needed.

Remember to consider domain knowledge and business context when interpreting your results.

[Githubberr1]

Analyzing Client Subscription Patterns for Product Recommendations Understanding the Problem: Your goal is to identify patterns in small business client subscriptions to increase product sales. You have client-level data, including unique identifiers (clientid, implementation_id, pwid) and likely information about subscription dates and product types. Grouping Clients Based on Subscription Patterns: Here are some potential approaches to group clients:

1. RFM Analysis (Recency, Frequency, Monetary Value):
   • Recency: How recently did the client make a purchase?
   • Frequency: How often does the client make purchases?
   • Monetary Value: How much does the client spend on average?
   • Calculation:
     • Recency: Days since last purchase
     • Frequency: Number of purchases in a given period
     • Monetary Value: Total spending in a given period
   • Grouping: Cluster clients based on RFM scores using techniques like K-means clustering or hierarchical clustering.
2. Cohort Analysis:
   • Definition: Group clients by a common characteristic (e.g., onboarding date, subscription type) and analyze their behavior over time.
   • Analysis: Track metrics like product adoption rate, churn rate, and average purchase value for each cohort.
   • Grouping: Identify cohorts with similar patterns and target them with relevant product recommendations.
3. Sequence Mining:
   • Purpose: Discover frequent patterns of product purchases over time.
   • Technique: Algorithms like Apriori or FP-growth can identify common sequences of product subscriptions.
   • Grouping: Group clients based on their identified sequences, suggesting products that frequently follow their current subscriptions.
4. Product Affinity Analysis:
   • Goal: Determine which products are often purchased together.
   • Technique: Use association rule mining to find products that frequently co-occur in transactions.
   • Grouping: Group clients based on their current product subscriptions and suggest products that are frequently purchased by similar clients.
5. Machine Learning-Based Clustering:
   • Approach: Use algorithms like DBSCAN, HDBSCAN, or Gaussian Mixture Models to cluster clients based on their subscription patterns and other relevant features (e.g., company size, industry).
   • Benefits: Can handle complex patterns and non-linear relationships. Additional Considerations:
   • Feature Engineering: Create additional features to enhance clustering accuracy, such as time between purchases, product category, or customer demographics.
   • Evaluation: Use metrics like silhouette score, Calinski-Harabasz index, or Davies-Bouldin index to evaluate the quality of the clusters.
   • Visualization: Create visualizations (e.g., heatmaps, dendrograms) to understand the relationships between clients and products.
   • A/B Testing: Experiment with different product recommendation strategies to measure their effectiveness. By combining these approaches and leveraging the unique characteristics of your data, you can effectively group clients and identify targeted product recommendation opportunities. Would you like to delve deeper into any of these methods or explore other possibilities?

[Githubberr1]

=IF(AND(COUNTIF(A:A,A2)>1,COUNTIF(B:B,"quarter1",A:A)=COUNTIF(A:A,A2)),1,0)

[Githubberr1]

Analyzing the Scatterplots Based on the provided scatterplots, it appears that there is a weak negative correlation between volume reduction and complaints increase, especially in the 3 to 6-month timeframe. Here's a breakdown of what we can observe:

• 3 to 6 Months:
  • The scatterplot points show a general trend where as volume reduction increases (moving left on the x-axis), there's a slight tendency for complaints to decrease (moving down on the y-axis). However, this correlation is not very strong, as there is still a significant amount of scatter in the data.
• 6 to 12 Months:
  • The correlation is even weaker in this timeframe. The points are more scattered, and there's no clear trend suggesting a strong relationship between volume reduction and complaints increase. Overall: While the scatterplots hint at a possible negative correlation between volume reduction and complaints increase, especially in the shorter timeframe, the relationship is not particularly strong. Other factors might be influencing the data, and more analysis would be needed to draw definitive conclusions. Additional Considerations:
• Correlation Coefficient: To quantify the strength of the correlation, calculating the correlation coefficient (e.g., Pearson's correlation coefficient) would be helpful. A value closer to -1 would indicate a stronger negative correlation.
• Other Factors: There might be other factors influencing the relationship between volume reduction and complaints. Consider exploring these factors to gain a more comprehensive understanding.
• Data Size: A larger dataset might reveal a clearer correlation or uncover additional patterns. In conclusion, while the scatterplots suggest a possible negative correlation, it's not a strong one. Further analysis is needed to confirm the relationship and understand its implications.

Interpreting the Correlation Matrix Heatmap Understanding the Heatmap: A correlation matrix heatmap is a visual representation of the correlation coefficients between different variables. The color intensity and direction of the color (red or blue) indicate the strength and direction of the correlation. Key Points:

• Diagonal: The diagonal elements are always 1, as a variable is perfectly correlated with itself.
• Color Scale: The color scale on the right side shows the range of correlation coefficients, from -1 to 1.
• Red: Warmer colors (red) generally indicate a positive correlation, meaning the variables increase or decrease together.
• Blue: Cooler colors (blue) generally indicate a negative correlation, meaning one variable increases as the other decreases.
• Intensity: The intensity of the color represents the strength of the correlation. A darker color signifies a stronger correlation, while a lighter color indicates a weaker correlation. Analyzing the Given Heatmap: Based on the provided heatmap, here are some observations:
• Volume Reduction vs. Complaints Increase:
  • 3 to 6 Months: There's a weak negative correlation between volume reduction and complaints increase in this timeframe, as indicated by the light blue color.
  • 6 to 12 Months: The correlation is even weaker, with a near-zero correlation coefficient.
• Volume Reduction vs. Volume Reduction:
  • The diagonal elements are all 1, as expected.
  • The off-diagonal elements for volume reduction are relatively high, suggesting a strong positive correlation between volume reduction in the two timeframes.
• Complaints Increase vs. Complaints Increase:
  • Similar to volume reduction, the diagonal elements are 1, and the off-diagonal elements show a strong positive correlation between complaints increase in the two timeframes. Overall: The heatmap confirms the findings from the scatterplots. While there's a slight negative correlation between volume reduction and complaints increase in the shorter timeframe, it's not very strong. The stronger correlations observed between the same variables across different timeframes suggest a consistent trend in those variables over time.

[Githubberr1]

Summary of the EMM Client Analysis Slide Three Most Important Points:

• Overall Balance Increase: Despite a significant number of clients experiencing balance declines, the overall balance change for the analyzed period shows a net increase of $787,240,579.
• Notable Balance Increases: A small number of clients (3) experienced substantial balance increases exceeding $50 million, contributing significantly to the overall positive balance change.
• Distribution of Balance Changes: The majority of clients saw smaller balance changes, with a significant number experiencing increases between 0 and 1 million, while others experienced declines in the same range. Additional Observations:
• The slide provides a detailed breakdown of client balance changes categorized by size and direction.
• The total number of clients with balance increases (5,342) slightly outnumbers those with balance declines (4,990).
• The total volume of balance increases ($3,032,455,138) is greater than the total volume of balance declines ($2,245,214,560).
• The slide includes a visual representation of the overall balance change using a bar chart. In summary, the slide highlights the overall positive balance change for the analyzed period, despite a significant number of clients experiencing balance declines. The analysis also identifies a small group of clients with substantial balance increases that contributed significantly to the positive net balance.

[Githubberr1]

WITH cte_balances AS ( SELECT a.eci, d.party_name, c.mst_acct_num, c.mst_avail_bal, c.as_of_date FROM db_edhsb_midofc.emmeci a LEFT JOIN db_edh.ref_account_party_relationship b ON lpad(cast(b.eci AS varchar), 10, '0') = lpad(cast(a.eci AS varchar), 10, '0') LEFT JOIN db_edh.ref_wholesale_party d ON lpad(cast(d.eci AS varchar), 10, '0') = lpad(cast(b.eci AS varchar), 10, '0') LEFT JOIN ( SELECT DISTINCT mst_acct_num, mst_avail_bal, as_of_date FROM db_edh.account_icdw_lda_csdep_acct_daily WHERE as_of_date BETWEEN 20240601 AND 20240901 ) c ON lpad(cast(c.mst_acct_num AS varchar), 20, '0') = lpad(cast(b.account_id AS varchar), 20, '0') WHERE a.peo_flag = '' AND a.fre_flag = '' AND a.approve_datetime YEAR IN (2023, 2024) AND b.application_cd IN ('B010', 'T24') AND a.pit_client_status = 'Active Cust Rel' AND a.platform = 'CONNECT' ), cte_dates AS ( SELECT row_number() OVER () - 1 AS date_offset FROM (SELECT 1) AS dummy LIMIT 60 ), cte_pivot AS ( SELECT eci, party_name, as_of_date + date_offset AS as_of_date, mst_avail_bal FROM cte_balances JOIN cte_dates ON 1 = 1 ) SELECT eci, party_name, MAX(CASE WHEN as_of_date = 20240601 THEN mst_avail_bal END) AS "20240601", MAX(CASE WHEN as_of_date = 20240602 THEN mst_avail_bal END) AS "20240602", -- ... (continue for all 60 dates) MAX(CASE WHEN as_of_date = 20240901 THEN mst_avail_bal END) AS "20240901" FROM cte_pivot GROUP BY eci, party_name;

[Githubberr1]

WITH cte_balances AS ( SELECT a.eci, d.party_name, c.mst_acct_num, c.mst_avail_bal, c.as_of_date FROM db_edhsb_midofc.emmeci a LEFT JOIN db_edh.ref_account_party_relationship b ON lpad(cast(b.eci AS varchar), 10, '0') = lpad(cast(a.eci AS varchar), 10, '0') LEFT JOIN db_edh.ref_wholesale_party d ON lpad(cast(d.eci AS varchar), 10, '0') = lpad(cast(b.eci AS varchar), 10, '0') LEFT JOIN ( SELECT DISTINCT mst_acct_num, mst_avail_bal, as_of_date FROM db_edh.account_icdw_lda_csdep_acct_daily WHERE as_of_date BETWEEN 20240601 AND 20240901 ) c ON lpad(cast(c.mst_acct_num AS varchar), 20, '0') = lpad(cast(b.account_id AS varchar), 20, '0') WHERE a.peo_flag = '' AND a.fre_flag = '' AND a.approve_datetime YEAR IN (2023, 2024) AND b.application_cd IN ('B010', 'T24') AND a.pit_client_status = 'Active Cust Rel' AND a.platform = 'CONNECT' ), cte_dates AS ( SELECT row_number() OVER () - 1 AS date_offset FROM (SELECT 1) AS dummy LIMIT 60 ), cte_pivot AS ( SELECT eci, party_name, as_of_date + date_offset AS as_of_date, mst_avail_bal FROM cte_balances JOIN cte_dates ON 1 = 1 ) SELECT eci, party_name, MAX(CASE WHEN as_of_date = 20240601 THEN mst_avail_bal END) AS "20240601", MAX(CASE WHEN as_of_date = 20240602 THEN mst_avail_bal END) AS "20240602", MAX(CASE WHEN as_of_date = 20240603 THEN mst_avail_bal END) AS "20240603", MAX(CASE WHEN as_of_date = 20240604 THEN mst_avail_bal END) AS "20240604", MAX(CASE WHEN as_of_date = 20240605 THEN mst_avail_bal END) AS "20240605", MAX(CASE WHEN as_of_date = 20240606 THEN mst_avail_bal END) AS "20240606", MAX(CASE WHEN as_of_date = 20240607 THEN mst_avail_bal END) AS "20240607", MAX(CASE WHEN as_of_date = 20240608 THEN mst_avail_bal END) AS "20240608", MAX(CASE WHEN as_of_date = 20240609 THEN mst_avail_bal END) AS "20240609", MAX(CASE WHEN as_of_date = 20240610 THEN mst_avail_bal END) AS "20240610", MAX(CASE WHEN as_of_date = 20240611 THEN mst_avail_bal END) AS "20240611", MAX(CASE WHEN as_of_date = 20240612 THEN mst_avail_bal END) AS "20240612", MAX(CASE WHEN as_of_date = 20240613 THEN mst_avail_bal END) AS "20240613", MAX(CASE WHEN as_of_date = 20240614 THEN mst_avail_bal END) AS "20240614", MAX(CASE WHEN as_of_date = 20240615 THEN mst_avail_bal END) AS "20240615", MAX(CASE WHEN as_of_date = 20240616 THEN mst_avail_bal END) AS "20240616", MAX(CASE WHEN as_of_date = 20240617 THEN mst_avail_bal END) AS "20240617", MAX(CASE WHEN as_of_date = 20240618 THEN mst_avail_bal END) AS "20240618", MAX(CASE WHEN as_of_date = 20240619 THEN mst_avail_bal END) AS "20240619", MAX(CASE WHEN as_of_date = 20240620 THEN mst_avail_bal END) AS "20240620", MAX(CASE WHEN as_of_date = 20240621 THEN mst_avail_bal END) AS "20240621", MAX(CASE WHEN as_of_date = 20240622 THEN mst_avail_bal END) AS "20240622", MAX(CASE WHEN as_of_date = 20240623 THEN mst_avail_bal END) AS "20240623", MAX(CASE WHEN as_of_date = 20240624 THEN mst_avail_bal END) AS "20240624", MAX(CASE WHEN as_of_date = 20240625 THEN mst_avail_bal END) AS "20240625", MAX(CASE WHEN as_of_date = 20240626 THEN mst_avail_bal END) AS "20240626", MAX(CASE WHEN as_of_date = 20240627 THEN mst_avail_bal END) AS "20240627", MAX(CASE WHEN as_of_date = 20240628 THEN mst_avail_bal END) AS "20240628", MAX(CASE WHEN as_of_date = 20240629 THEN mst_avail_bal END) AS "20240629", MAX(CASE WHEN as_of_date = 20240630 THEN mst_avail_bal END) AS "20240630", MAX(CASE WHEN as_of_date = 20240701 THEN mst_avail_bal END) AS "20240701", MAX(CASE WHEN as_of_date = 20240702 THEN mst_avail_bal END) AS "20240702", MAX(CASE WHEN as_of_date = 20240703 THEN mst_avail_bal END) AS "20240703", MAX(CASE WHEN as_of_date = 20240704 THEN mst_avail_bal END) AS "20240704", MAX(CASE WHEN as_of_date = 20240705 THEN mst_avail_bal END) AS "20240705", MAX(CASE WHEN as_of_date = 20240706 THEN mst_avail_bal END) AS "20240706", MAX(CASE WHEN as_of_date = 20240707 THEN mst_avail_bal END) AS "20240707", MAX(CASE WHEN as_of_date = 20240708 THEN mst_avail_bal END) AS "20240708", MAX(CASE WHEN as_of_date = 20240709 THEN mst_avail_bal END) AS "20240709", MAX(CASE WHEN as_of_date = 20240710 THEN mst_avail_bal END) AS "20240710", MAX(CASE WHEN as_of_date = 20240711 THEN mst_avail_bal END) AS "20240711", MAX(CASE WHEN as_of_date = 20240712 THEN mst_avail_bal END) AS "20240712", MAX(CASE WHEN as_of_date = 20240713 THEN mst_avail_bal END) AS "20240713", MAX(CASE WHEN as_of_date = 20240714 THEN mst_avail_bal END) AS "20240714", MAX(CASE WHEN as_of_date = 20240715 THEN mst_avail_bal END) AS "20240715", MAX(CASE WHEN as_of_date = 20240716 THEN mst_avail_bal END) AS "20240716", MAX(CASE WHEN as_of_date = 20240717 THEN mst_avail_bal END) AS "20240717", MAX(CASE WHEN as_of_date = 20240718 THEN mst_avail_bal END) AS "20240718", MAX(CASE WHEN as_of_date = 20240719 THEN mst_avail_bal END) AS "20240719", MAX(CASE WHEN as_of_date = 20240720 THEN mst_avail_bal END) AS "20240720", MAX(CASE WHEN as_of_date = 20240721 THEN mst_avail_bal END) AS "20240721", MAX(CASE WHEN as_of_date = 20240722 THEN mst_avail_bal END) AS "20240722", MAX(CASE WHEN as_of_date = 20240723 THEN mst_avail_bal END) AS "20240723", MAX(CASE WHEN as_of_date = 20240724 THEN mst_avail_bal END) AS "20240724", MAX(CASE WHEN as_of_date = 20240725 THEN mst_avail_bal END) AS "20240725", MAX(CASE WHEN as_of_date = 20240726 THEN mst_avail_bal END) AS "20240726", MAX(CASE WHEN as_of_date = 20240727 THEN mst_avail_bal END) AS "20240727", MAX(CASE WHEN as_of_date = 20240728 THEN mst_avail_bal END) AS "20240728", MAX(CASE WHEN as_of_date = 20240729 THEN mst_avail_bal END) AS "20240729", MAX(CASE WHEN as_of_date = 20240730 THEN mst_avail_bal END) AS "20240730", MAX(CASE WHEN as_of_date = 20240731 THEN mst_avail_bal END) AS "20240731", MAX(CASE WHEN as_of_date = 20240801 THEN mst_avail_bal END) AS "20240801", MAX(CASE WHEN as_of_date = 20240802 THEN mst_avail_bal END) AS "20240802", MAX(CASE WHEN as_of_date = 20240803 THEN mst_avail_bal END) AS "20240803", MAX(CASE WHEN as_of_date = 20240804 THEN mst_avail_bal END) AS "20240804", MAX(CASE WHEN as_of_date = 20240805 THEN mst_avail_bal END) AS "20240805", MAX(CASE WHEN as_of_date = 20240806 THEN mst_avail_bal END) AS "20240806", MAX(CASE WHEN as_of_date = 20240807 THEN mst_avail_bal END) AS "20240807", MAX(CASE WHEN as_of_date = 20240808 THEN mst_avail_bal END) AS "20240808", MAX(CASE WHEN as_of_date = 20240809 THEN mst_avail_bal END) AS "20240809", MAX(CASE WHEN as_of_date = 20240810 THEN mst_avail_bal END) AS "20240810", MAX(CASE WHEN as_of_date = 20240811 THEN mst_avail_bal END) AS "20240811", MAX(CASE WHEN as_of_date = 20240812 THEN mst_avail_bal END) AS "20240812", MAX(CASE WHEN as_of_date = 20240813 THEN mst_avail_bal END) AS "20240813", MAX(CASE WHEN as_of_date = 20240814 THEN mst_avail_bal END) AS "20240814", MAX(CASE WHEN as_of_date = 20240815 THEN mst_avail_bal END) AS "20240815", MAX(CASE WHEN as_of_date = 20240816 THEN mst_avail_bal END) AS "20240816", MAX(CASE WHEN as_of_date = 20240817 THEN mst_avail_bal END) AS "20240817", MAX(CASE WHEN as_of_date = 20240818 THEN mst_avail_bal END) AS "20240818", MAX(CASE WHEN as_of_date = 20240819 THEN mst_avail_bal END) AS "20240819", MAX(CASE WHEN as_of_date = 20240820 THEN mst_avail_bal END) AS "20240820", MAX(CASE WHEN as_of_date = 20240821 THEN mst_avail_bal END) AS "20240821", MAX(CASE WHEN as_of_date = 20240822 THEN mst_avail_bal END) AS "20240822", MAX(CASE WHEN as_of_date = 20240823 THEN mst_avail_bal END) AS "20240823", MAX(CASE WHEN as_of_date = 20240824 THEN mst_avail_bal END) AS "20240824", MAX(CASE WHEN as_of_date = 20240825 THEN mst_avail_bal END) AS "20240825", MAX(CASE WHEN as_of_date = 20240826 THEN mst_avail_bal END) AS "20240826", MAX(CASE WHEN as_of_date = 20240827 THEN mst_avail_bal END) AS "20240827", MAX(CASE WHEN as_of_date = 20240828 THEN mst_avail_bal END) AS "20240828", MAX(CASE WHEN as_of_date = 20240829 THEN mst_avail_bal END) AS "20240829", MAX(CASE WHEN as_of_date = 20240830 THEN mst_avail_bal END) AS "20240830", MAX(CASE WHEN as_of_date = 20240831 THEN mst_avail_bal END) AS "20240831", MAX(CASE WHEN as_of_date = 20240901 THEN mst_avail_bal END) AS "20240901" FROM cte_pivot GROUP BY eci, party_name;

[Githubberr1]

select c.pli,cmplt_pli_dt,c.lst_op_stat_pli,wo_request_type_newsetup,wo_request_type_enhancement, wo_request_type_testsupport, wo_request_type_addaccounts tim_open_flag,tim_cmplt_flag,tim_worked_sid, tim worked name, tim worked indicator, tim team from db edh.cbco pli c

Script Map

left join db_edh.chco_pli_indicators b on c.plib.pli

left join db_edh.coco_paysource_spec_data a on c.plia.pli where c.pli=19286022'

Results