In this project, I conducted a comprehensive analysis of customer data using Power BI. The objective was to visualize and gain insights from the data, focusing on customer demographics and product categories.

📈The analysis includes the following key visualizations:

Customer Distribution by Age: illustrates the number of customers across different age groups, providing insights into the demographic distribution.

Customer Distribution by Time: This visualization shows the count of customers segmented by year, quarter, month, and day, helping identify trends over time.

Customer Distribution by Gender: displays the distribution of customers by gender, highlighting any significant differences.

Total Amount by Product Category: depicts the total revenue generated by each product category, allowing for easy comparison.

Quantity by Product Category: shows the total quantity of products sold in each category, helping to identify popular items.

The cards display key metrics:

Average Age: 41.39 Total Customers: 1000 Total Quantity Sold: 2514 Total Amount Sold: 465 000$ Total Transactions: 1000 Additionally, I implemented filters for product category, date, gender, quantity, and age, providing users with the ability to refine their analysis.

Findings:

The analysis of customer distribution by age reveals no specific relationship between age and the quantity of products sold. This indicates that purchasing behavior may not be strongly influenced by the customer’s age. There are notable peaks in the quantity sold on May 20, 2023, and again in July, suggesting higher purchasing activity during these periods. The customer distribution by gender shows that 49% of customers are female, while 51% are male. In terms of total amount sold by product category, electronics is the top category, generating the highest revenue, followed by clothing, with beauty ranking last. Similarly, when looking at quantity sold by product category, electronics makes up 33.77%, clothing is slightly higher at 35.56%, and beauty is the smallest category at 3.67%. This project demonstrates the power of Power BI in analyzing customer data and deriving actionable insights. The visualizations created provide a clear understanding of customer behavior and preferences, which can help businesses make informed decisions.

Echo’s Mobility Data package includes attributes that allow it to map the activity around more than 58M+ Points-of-Interest. Visits & visitors are matched to physical locations, enabling companies to gain an in-depth understanding of: - New movement trends - Popular locations - The customers’ journey - Frequency of visits & repeat visitors - And more…

Thanks to these insights, it is possible to: - Assess an area’s growth potential by evaluating its’ Foot Traffic - Identify cross-visitation trends - Evaluate customer loyalty to a specific brand - The length of the buying journey

We run monthly or quarterly maintenance and updates on our existing database to ensure ongoing data accuracy and precision. This data is Non-PII and GDPR- compliant.

It is possible to request Activity Analyses to get further contextualisation of the mobility around a POI. Ask one of our data experts for our: - Cross Visitation Analysis - Customer Journey Analysis

The market size of the Statistics Software Market is categorized based on Deployment Type (On-Premise, Cloud-Based) and Application (Data Analysis, Data Visualization, Predictive Analytics, Statistical Analysis, Reporting) and End-User Industry (Healthcare, Finance, Retail, Education, Government) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Our Location Intelligence Data provides a detailed view of people’s movements across over 14 million physical locations worldwide. This aggregated and anonymized data is utilized to understand visit patterns and volumes at specific sites. Compiled from diverse global data sources, this information offers valuable context for analyzing foot traffic and location engagement.

Location Intelligence Data Reach

Our Location Intelligence Data delivers in-depth insights into Points of Interest (POIs), places, and Out-of-Home (OOH) advertising locations.By leveraging Factori's Mobility & People Graph data, which integrates information from numerous sources globally, we provide accurate foot-traffic attribution. For instance, to calculate foot traffic at a specific location, we combine attributes such as location ID, day of the week, and time of day, generating up to 40 distinct data records for each POI.

Data Export Methodology

We dynamically gather and update data, delivering the most current insights through methods tailored to your needs, whether daily, weekly, or monthly.

Use Cases

Our Location Intelligence Data is essential for credit scoring, retail analytics, market intelligence, and urban planning, offering businesses and organizations critical insights for strategic decision-making and planning.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Mobile Location Data insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Places Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. Clear and compliant privacy notices accompany our data collection process. Opt-in/out management empowers users over data distribution.

Discover the precision of our Mobile Location Data insights with Irys – where quality meets innovation.

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