"At one of my project, I worked on a project where we needed to collect data from different sections of a large factory and deliver it to a third-party company responsible for predictive analytics on product quality and production levels. The challenge was that each department had different data types and structures, and in many cases, direct connections were restricted due to strict security policies.
My responsibility was to design and implement a solution that could gather all these heterogene"
Maryam G. - "At one of my project, I worked on a project where we needed to collect data from different sections of a large factory and deliver it to a third-party company responsible for predictive analytics on product quality and production levels. The challenge was that each department had different data types and structures, and in many cases, direct connections were restricted due to strict security policies.
My responsibility was to design and implement a solution that could gather all these heterogene"See full answer
"One project that stands out involved building a customer segmentation dashboard for our marketing team using Power BI. The goal was to help them target campaigns more effectively by segmenting customers based on purchase behavior and demographics.
Early in the project, I noticed significant data quality issues in the source tables coming from our CRM system. There were missing values in key fields like customer age and region, duplicate customer IDs, and inconsistencies in how product categories"
Tim F. - "One project that stands out involved building a customer segmentation dashboard for our marketing team using Power BI. The goal was to help them target campaigns more effectively by segmenting customers based on purchase behavior and demographics.
Early in the project, I noticed significant data quality issues in the source tables coming from our CRM system. There were missing values in key fields like customer age and region, duplicate customer IDs, and inconsistencies in how product categories"See full answer
"1. Understand the "Why" (Deep Dive) - Before jumping to solutions, as a PM needs to precisely understand why users are unhappy. NPS gives us a score, but not the reasons. (0 -4 weeks)
Analyze Feedback: Go beyond the score. What are Detractors (0-6) saying? What do Promoters (9-10) love?
Qualitative Research:(VOC- voice of the customer) Conduct user interviews, analyze support tickets, and observe product usage. Pinpoint specific pain points (e.g., slow p"
Vishnu G. - "1. Understand the "Why" (Deep Dive) - Before jumping to solutions, as a PM needs to precisely understand why users are unhappy. NPS gives us a score, but not the reasons. (0 -4 weeks)
Analyze Feedback: Go beyond the score. What are Detractors (0-6) saying? What do Promoters (9-10) love?
Qualitative Research:(VOC- voice of the customer) Conduct user interviews, analyze support tickets, and observe product usage. Pinpoint specific pain points (e.g., slow p"See full answer
"Sales Performance Dashboard t for sales leaders to monitor product performance:
Sales Performance Dashboard
Top-Level Metrics (KPI Summary)
Time Filter: [Day] [Week] [Month] [Quarter] [Custom Range]
Total Sales Revenue
Displays total revenue for the selected period.
Units Sold
Breakdown by product category and SKU.
Average Deal Size
Total revenue ÷ Number of deals closed.
Conversion Rate
_Leads to sales conversion ratio."
Syed A. - "Sales Performance Dashboard t for sales leaders to monitor product performance:
Sales Performance Dashboard
Top-Level Metrics (KPI Summary)
Time Filter: [Day] [Week] [Month] [Quarter] [Custom Range]
Total Sales Revenue
Displays total revenue for the selected period.
Units Sold
Breakdown by product category and SKU.
Average Deal Size
Total revenue ÷ Number of deals closed.
Conversion Rate
_Leads to sales conversion ratio."See full answer