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Data Visualisation: Empowering Business with Effective Insights

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This virtual experience program is with Tata Insights and Quants or Tata iQ hosted on the platform Forage. We will gain insight into how passionate multi-disciplinary experts solve some of the most complex business problems using data visualization solutions, such as Tableau and Power BI and create amazing stories which are hidden under the mountains of data generated.

The program will show you what kind of problems are solved at Tata Insights and Quants on a day-to-day basis and will attempt to emulate the challenges you will be facing.

As a consultant hired by an online retail store, I was tasked with reviewing their data and providing valuable insights to the CEO and CMO. The objective was to analyze the major contributing factors to the company's revenue to help them plan strategically for the next year. The leadership wanted to view the metrics from both an operations and marketing perspective and seek guidance on areas performing well, so they could focus on what's working. They were also interested in understanding different metrics based on demographic information available in the data.

Task 1: Framing the Business Scenario
Learn how to anticipate the questions your business leaders will need answers to.

To prepare for the upcoming meeting with the CEO and CMO, I approached the task with a business leader's mindset, aiming to analyze the data effectively and present insights that would help evaluate the current business performance and suggest metrics for expansion.

I accessed the provided resources to better understand how business leaders think and approach business performance, which helped me gain valuable perspectives.

To prepare for your meeting, I need to draft questions that will be important and relevant to the CEO and CMO. I have reviewed the provided dataset to identify valuable information and insights. Based on my analysis, I have drafted four questions each for both the CEO and CMO, considering their distinct perspectives.

Questions for the CEO:
  1. What are the top-performing product categories in terms of revenue, and how have they evolved over the past year? Are there any specific trends or seasonal patterns that stand out?
  2. What regions or countries have shown the highest revenue growth, and what strategies have been employed in those regions to achieve such success? How can we replicate these strategies in other markets?
  3. How has the customer retention rate changed over the last year, and what initiatives have been implemented to improve customer loyalty? What can we do to further increase customer retention and lifetime value?
  4. What is the profitability breakdown across different customer segments, and which segments contribute the most to our overall revenue? Are there any untapped customer segments with growth potential?
Questions for the CMO:
  1. How effective have our marketing campaigns been in terms of customer acquisition and conversion rates? Which marketing channels have yielded the highest returns, and how can we optimize our marketing budget allocation?
  2. What are the key demographic insights of our customer base? How do different demographics (age, gender, location, etc.) impact customer behavior and preferences, and how can we tailor our marketing strategies accordingly?
  3. Are there any notable changes in customer sentiment or brand perception based on online reviews and feedback? How can we address any negative sentiment and leverage positive feedback to strengthen our brand image?
  4. What role does social media play in our revenue generation, and which platforms have been most impactful? How can we leverage social media influencers and user-generated content to expand our reach and engage with our target audience effectively?

By addressing these questions during the meeting, I intend to provide the CEO and CMO with actionable insights that will guide their decision-making process, help them evaluate the current business performance, and support their plans for strategic expansion.


Task 2: Choosing the Right Visuals
Learn which visuals are most effective in a given scenario.

In this task, I was tasked with providing visualizations to the CEO and CMO of an online retail store. The goal was to help them understand the company's performance, identify key strengths, and uncover growth opportunities for increased revenue. To ensure accurate representation of the data, I carefully gathered the requirements and created simple yet effective visuals. By using appropriate charts such as line graphs, bar graphs, map charts, I presented insights on sales trends, product category distribution, and correlations between marketing expenditure and revenue. The objective was to enable the senior management to quickly grasp the company's performance and make informed decisions for future growth and success.


Task 3: Creating Effective Visuals
Apply your under and create visuals based on business scenarios.

In this task, I have to create the visuals around four of the questions that the CEO and CMO have requested. In this task I used Tableau for the data visualization and to create a charts for the respective KPI.

Data cleanup
Before proceeding to data analysis there is a need for data cleanup. I observed that there were some negative values present in the quantity & price field that I have removed.
Also the client has provided 2 conditions for the data analysis.
  • Create a check that the quantity should not be below 1 unit
  • Create a check that the Unit price should not be below $0

Question 1
The CEO of the retail store is interested to view the time series of the revenue data for the year 2011 only. He would like to view granular data by looking into revenue for each month. The CEO is interested in viewing the seasonal trends and wants to dig deeper into why these trends occur. This analysis will be helpful for the CEO to forecast for the next year.


According to my analysis, there are some months of the year that experience exceptional growth. According to the data, the first eight months' revenue were relatively stable, with an average of $685k in revenue each month. The revenue rises by 40% over the prior month in September, marking the beginning of the revenue increase. This pattern persisted up until November, when it reached 1.5 million USD, the highest amount of the entire year. Unfortunately, since the data for December has been missing, no inferences can be made from it. This analysis shows how seasonality, which usually happens in the last four months of the year, impacts retail store sales.

Question 2
The CMO is interested in viewing the top 10 countries which are generating the highest revenue. Additionally, the CMO is also interested in viewing the quantity sold along with the revenue generated. The CMO does not want to have the United Kingdom in this visual.


The second visual shows how the top 10 countries which have opportunities for growth are performing. This data does not include the UK as the country already has high demand and I’ve been told you’re more focused on the countries where demand can be increased. The analysis shows that countries such as the Netherlands, Ireland, Germany and France have high volumes of units bought and revenue generated. I would suggest that these countries should be focused on to ensure that measures are taken to capture these markets even more.


Question 3
The top 10 countries with the most potential for growth are represented in the second graph. Since the UK already has a high demand and I understand you are more interested in countries where demand can be increased, the UK is not included in this data. According to the analysis, sales of units and revenue are very high in nations like the Netherlands, Ireland, Germany, and France. To ensure that steps are taken to further seize these markets, I would propose concentrating on these nations.


The top 10 customers who have made the most purchases from the store have been the focus of the third analysis. According to the data, there aren't many differences between the top 10 customers' purchases. The fact that the highest revenue-producing customer only spent 17% more than the second-highest demonstrates that the company does not rely solely on a small number of customers to generate revenue. This demonstrates that the company is in a strong position and that the customer's power of negotiation is low.


Question 4
The CEO is looking to gain insights on the demand for their products. He wants to look at all countries and see which regions have the greatest demand for their products. Once the CEO gets an idea of the regions that have high demand, he will initiate an expansion strategy which will allow the company to target these areas and generate more business from these regions. He wants to view the entire data on a single view without the need to scroll or hover over the data points to identify the demand. There is no need to show data for the United Kingdom as the CEO is more interested in viewing the countries that have expansion opportunities.


The map chart concludes by comparing the regions that have produced the most revenue to those that have not. Apart from the UK, it is clear that nations like the Netherlands, Ireland, Germany, France, and Australia generate high revenue, and the business should invest more in these nations to boost product demand. The map also reveals that the majority of sales occur exclusively in the European region, with only a small number in the American region. Along with Russia, there is no market for the products in Africa or Asia. Sales revenues and profitability could increase with the implementation of a fresh strategy focused on these areas.


Task 4: Communicating Insights and Analysis
Effectively communicate your findings and explain how it relates to each scenario.



In this task I represented my thought process and how I handled the data cleanup and visualisation phase. I clearly presented the analysis of all four questions from the previous task raised by CEO & CMO and provided context for each scenario and how it relates to their questioning.



Technology Topper Wednesday, August 02, 2023
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Maven Northwind Challenge | Maven Analytics Project

Introduction:
This project is a submission for the #mavennorthwindchallenge conducted by Maven Analytics. As a BI developer at Northwind Traders, I have been given the exciting responsibility of creating a comprehensive KPI dashboard for our executive team. This dashboard will serve as a valuable tool for them to gain quick insights into our company's performance across various crucial areas. These areas include:
  • Sales trends
  • Product performance
  • Key customers
  • Shipping costs
By presenting this information in an easily digestible format, the executive team can make informed decisions and steer the company towards continued success.
Talking about dataset it includes 7 tables in the form of csv format which contains Sales & order data for Northwind Traders, food supplier, including information on customers, products, orders, shippers, and employees.

In today's highly competitive business landscape, companies rely on data-driven insights to make informed decisions and stay ahead of the curve. Northwind Traders, a leading global import and export company specialising in gourmet food products, understands the value of data analysis in driving success. As a BI developer for Northwind Traders, I had the opportunity to build a comprehensive KPI dashboard that provides the executive team with a bird's-eye view of the company's performance across crucial areas. In this blog post, we will delve into the key features and insights offered by this powerful dashboard.

Maven Northwind Challenge Tableau Dashboard
Maven Northwind Challenge



Sales Trends:
Understanding sales trends is vital for any business. Northwind Traders' KPI dashboard provides a clear visualisation of sales performance over time. Executives can quickly identify patterns, seasonal fluctuations, and growth areas. By analysing sales trends, they can make strategic decisions regarding inventory management, marketing campaigns, and sales projections.
Total sales done by Northwind Traders were $1.35M from Q3 of 2013 to Q2 of 2015, with total orders of 830. The highest sale was done in 2015 Q1, i.e., $57K, with the highest orders of 182.
The total sales for the Northwind Traders are increasing year over year.

Year

Total Sales

2014

$82,791.05

2015

$77,930.02

2013

$26,231.00


Product Performance:
The success of individual products can significantly impact a company's overall revenue. With the KPI dashboard, Northwind Traders' executives gain a comprehensive understanding of product performance. They can assess sales volume, revenue generated, and profitability for each product category. Armed with this information, they can optimise the product portfolio, identify top-performing items, and strategically allocate resources to drive growth.
There were a total of 77 products sold in 8 product categories. The highest product sold was Raclette Courdavault, with $76.3K. According to sales, the Cote de Blaye is the product with the highest sales of $149.98K. The most popular product category was beverages, with a total quantity sold of 404 and total sales of $286.55K.

Key Customers:
Maintaining strong relationships with key customers is crucial for sustainable business growth. The KPI dashboard highlights the company's most valuable customers based on various metrics such as revenue generated, repeat purchases, and customer loyalty. This insight allows executives to focus on nurturing these relationships, tailoring offerings to their specific needs, and identifying potential upsell or cross-sell opportunities.
Northwind Traders has a total of 91 customers across 21 countries. The United States has the most customers, followed by Germany. The top customer for the Northwind Traders was Hanari Carnes, with total sales of $18.3K.

Shipping Costs:
Efficient supply chain management is a critical factor in maximising profitability. The dashboard provides a detailed breakdown of shipping costs, enabling executives to identify areas of potential cost savings, optimise logistics, and negotiate better terms with shipping providers. This information helps Northwind Traders streamline operations and enhance overall profitability.
There are three shippers who supply the food products for the Northwind Traders. United Package Shipper Company supplied the maximum 326 products with the highest total sales of $89.94K, followed by Federal Shipping with total sales of $50.50K in 2nd place and Speedy Express in 3rd place with total sales of $46.51K.

Conclusion:
Northwind Traders' KPI dashboard equips the executive team with a comprehensive overview of the company's performance in key areas. By leveraging data visualisations and actionable insights, executives can make informed decisions, drive growth, and maintain a competitive edge in the gourmet food industry. This powerful tool empowers Northwind Traders to monitor sales trends, evaluate product performance, nurture key customer relationships, and optimise shipping costs. With a data-driven approach, Northwind Traders is well-positioned to continue its success and expand its global reach.



Technology Topper Sunday, May 21, 2023
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Maven Slopes Challenge | Maven Analytics Project

This project is a submission for the #mavenslopeschallenge conducted by Maven Analytics. The aim of the challenge is to build a one-page dashboard that will help users choose their ideal ski resort based on their budget, location, and various other factors. I enjoyed the process of doing data analysis and creating this dashboard using #powerbi.

About the dataset
  • This dataset contains 2 tables in CSV format
  • The Resorts table contains information on 499 ski resorts around the world, including their location, slopes, lifts, prices, and ski season
  • The Snow table contains supplemental data on the surface of the earth covered by snow for each month in 2022, by latitude & longitude
While working on this challenge, I found some helpful insights that will help users find their ideal ski resort destination.
Power BI Dashboard


This dataset included 499 resorts all across 5 continents: Europe, North America, South America, Oceania, and Asia. 72% of global ski resorts are located in Europe, where the average price per person per day is €41.55. North America ranks second, with 19.64% of global ski resorts and an average price per person per day of € 76.97.



Out of 499 resorts 99.20% of resorts are child friendly. If you are planning to ski in the summer season, 29 (5.81%) resorts are available with a summer ski option, and 295 (59.12%) resorts are present where you can plan for night skiing.
Breckenridge is the resort located at the highest point (3914 m) in the United States, with an average daily price of € 140. In Canada, the "Le Massif" resort is located at the lowest point of 36 meters and costs € 51.

Looking for the longest ski run?
The resorts with the longest 16-kilometer runs are Alpe d'Huez (France), Bansko (Bulgaria), and Les Deux Alpes (France). If you are looking for the ski resort with the most difficult slopes, Big Sky Resort (USA) has 126 difficult slopes. Les Sybelles-Le Corier (France) has the most intermediate slopes (239), while Les 3 Vallees (France) has the most beginner slopes (312).



If you're looking for resorts with free entry, consider the following options:
Yellowstone Club(United States)
Alpika Service(Russia)
High1 Resort(South Korea)
Palandoken-Ejder 3200 World Ski Center(Turkey)
Perisher(Australia)
Pragelato(Italy)
Puigmal(France)
Sun Mountain-Yabuli(China)
Uludag-Bursa(Turkey)

If you are planning to visit a ski resort, the best time would be from December to April because the most snow falls during this season.



Some key insights from the above analysis include:
  • Out of the 499 resorts, 99% are child-friendly, 40.88% offer night skiing, and 5.81% offer summer skiing.
  • Europe has 72% of the world's ski resorts, with North America coming in second with 19.64%.South America is at the bottom with seven (1.40%) resorts.
  • Oceania, with 58% of beginners' slope, is suitable for people who are looking for beginners' slope. North and South America are the continents with the most difficult slopes. whereas Europe is suitable for intermediate slopes.
  • Resorts in North America, with an average price of €77, are more expensive as compared to other continents and Asia, with the lowest average price of €33.
  • The best time to plan a visit to a ski resort is between December and April, when the most snow falls.
  • If this is your first trip, Europe is an excellent choice because the majority of the ski resorts are located there, are reasonably priced, and are suitable for intermediate skiers.



Technology Topper Sunday, February 19, 2023
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120 years of Olympic history | SQL Project | MySQL Workbench
SQL Workbench

In this SQL project, I worked on a 120-year Olympic history dataset, where I tried to find some insight by writing SQL queries. I used MySQL Workbench to analyze the dataset for this project. I have analyzed the dataset in the form of questions and answers by writing SQL queries where i used joins, common table expression, subqueries, window function, etc.

This dataset contains 2 tables, in CSV format:
  • The Athlete Events table contains over 270,000 Olympic performances across history
  • Each record represents an individual athlete competing in an individual event
  • Records contain details about the athlete (ID, sex, name, age, height, weight, country) and the event (games, year, city, sport, event, medal)
  • The NOC Region table serves as a lookup table with “NOC” as the primary key
  • Each record represents one country according to the National Olympic Committee.

Loading dataset to MySQL Workbench Database:

athlete_events table:


noc_regions table:



SQL Queries:
I am writing SQL Queries using this data. For each of these queries, you would find the problem statement, SQL Queries and then the screen shot of the output(some screen shot contains few lines of output).


1. How many olympics games have been held?
Problem Statement: Write a SQL query to find the total no of Olympic Games held as per the dataset.

select count(distinct(Games)) as olympics_games
from athlete_events;

Output:



2. List down all Olympics games held so far.
Problem Statement: Write a SQL query to list down all the Olympic Games held so far.

select distinct year, season, city
from athlete_events
order by year;

Output:



3. Mention the total no of nations who participated in each olympics game?
Problem Statement: SQL query to fetch total no of countries participated in each olympic games.

with all_countries as
    (select games, nr.region
    from athlete_events oh
    join noc_regions nr ON nr.noc = oh.noc
    group by games, nr.region)
select games, count(1) as total_countries
from all_countries
group by games
order by games;

Output:



4. Which year saw the highest and lowest no of countries participating in Olympics
Problem Statement: Write a SQL query to return the Olympic Games which had the highest participating countries and the lowest participating countries.

with all_countries as
    (select games, nr.region
    from athlete_events oh
    join noc_regions nr ON nr.noc=oh.noc
    group by games, nr.region),
tot_countries as
    (select games, count(1) as total_countries
    from all_countries
    group by games)
select distinct
concat(first_value(games) over(order by total_countries)
, ' - '
, first_value(total_countries) over(order by total_countries)) as Lowest_Countries,
concat(first_value(games) over(order by total_countries desc)
, ' - '
, first_value(total_countries) over(order by total_countries desc)) as Highest_Countries
from tot_countries
order by 1;

Output:



5. Which nation has participated in all of the olympic games?
Problem Statement: SQL query to return the list of countries who have been part of every Olympics games.

with tot_games as
    (select count(distinct games) as total_games
    from athlete_events),
countries as
    (select games, nr.region as country
    from athlete_events oh
    join noc_regions nr ON nr.noc=oh.noc
    group by games, nr.region),
countries_participated as
    (select country, count(1) as total_participated_games
    from countries
    group by country)
select cp.*
from countries_participated cp
join tot_games tg on tg.total_games = cp.total_participated_games
order by 1;

Output:



6. Identify the sport which was played in all summer olympics.
Problem Statement: SQL query to fetch the list of all sports which have been part of every olympics.

with tot_games as
    (select count(distinct(games)) as total_games
    from athlete_events where season = 'Summer'),
sports_played as
    (select games, sport
    from athlete_events
    group by games, sport),
game_participated as
    (select sport, count(1) as total_participated_games
    from sports_played
    group by sport)
select cp.*
from game_participated cp
join tot_games tg on tg.total_games = cp.total_participated_games
order by 1;

Output:



7. Which Sports were just played only once in the olympics.
Problem Statement: Using SQL query, Identify the sport which were just played once in all of olympics.

with t1 as
    (select distinct games, sport
    from athlete_events),
t2 as
    (select sport, count(1) as no_of_games
    from t1
    group by sport)
select t2.*, t1.games
from t2
join t1 on t1.sport = t2.sport
where t2.no_of_games = 1
order by t1.sport;

Output:



8. Fetch the total no of sports played in each olympic games.
Problem Statement: Write SQL query to fetch the total no of sports played in each olympics.

select Games, count(distinct(Sport)) as total_sports
from athlete_events
group by Games
order by total_sports desc, Games;

Output:



9. Fetch oldest athletes to win a gold medal
Problem Statement: SQL Query to fetch the details of the oldest athletes to win a gold medal at the olympics.

select * from athlete_events
where Medal = 'Gold' and
Age = (select max(Age) from athlete_events where Medal = 'Gold');

Output:



10. Fetch the top 5 athletes who have won the most gold medals.
Problem Statement: SQL query to fetch the top 5 athletes who have won the most gold medals.

with m1 as(
    select Name, Team, count(Medal) as tot
    from athlete_events
    where Medal ='Gold'
    group by Name, Team
    order by tot desc),
m2 as
    (select Name, Team, tot, dense_rank() over(order by tot desc) as rnk
    from m1)
select name, team, tot
from m2
where rnk <= 5;

Output:



11. Fetch the top 5 athletes who have won the most medals (gold/silver/bronze).
Problem Statement: SQL Query to fetch the top 5 athletes who have won the most medals (Medals include gold, silver and bronze).

with m1 as(
    select Name, Team, count(Medal) as tot
    from athlete_events
    where Medal <> 'NA'
    group by Name, Team
    order by tot desc, Team, Name),
m2 as
    (select Name, Team, tot, dense_rank() over(order by tot desc) as rnk
    from m1)
select name, team, tot
from m2
where rnk <= 5;

Output:



12. Fetch the top 5 most successful countries in olympics. Success is defined by no of medals won.
Problem Statement: Write a SQL query to fetch the top 5 most successful countries in olympics. (Success is defined by no of medals won).

with cte as(
    select r.Region, count(a.Medal) as Total_Medal
    from athlete_events a join noc_regions r
    on a.noc = r.noc
    where a.Medal <> 'NA'
    group by r.Region
    order by Total_Medal desc)
select Region, Total_Medal, row_number() over(order by Total_Medal desc) as rnk
from cte limit 5;

Output:



reference :
https://techtfq.com/blog/practice-writing-sql-queries-using-real-dataset
https://www.mavenanalytics.io/blog/maven-olympics-challenge





Technology Topper Sunday, January 08, 2023
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Movie Ratings Analysis | Python Project | Pandas, Seaborn, Matplotlib

In this Python Data Analysis Project, I worked on a dataset that contains the different genres of movies, ratings received by the expert and audience, year of release, and budget in millions. While working on this project, I used the Pandas, Seaborn, and Matplotlib python libraries. By using these libraries, I built different types of graphs and charts, which helps us make the analysis easier. In this project, I have analyzed which movie genres are popular among the audience based on their ratings. We can suggest which genre would be best if the producer or director wanted to make a movie at the end of this project.

Python Code
        Most of the movies belong to the action, comedy, and drama genres, which shows that audiences are more interested in this type of movie. Movies with very high audience and critic ratings are considered to be highly liked. That happens mostly for action, drama, and thrillers. By analyzing this dataset, domain experts can make their decisions for upcoming movies that are popular among the audience as per their ratings. So, if a producer or director wants to make a film, the above-mentioned genre will be most preferred in order to get the best response from the audience and earn the most profit from that film.
Movie Ratings Analysis | Python Project




Technology Topper Wednesday, December 28, 2022
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Olist Store Analysis | Power BI Project

Preview
The OLIST STORE is an e-commerce business headquartered in Sao Paulo, Brazil. This firm acts as a single point of contact between various small businesses and the customers who wish to buy their products. In this project We are given multiple tables in CSV format and a schema depicting how these tables are connected. After connecting all the 8 tables, we analyze the entire dataset. It contains multiple categorical and numerical columns and information about 100k orders made at multiple marketplaces between 2016 to 2018.
In this project we are provided with 5 KPI’s on which we have to work & provide answers & solutions by analyzing the dataset. During this project we worked in different phases & tools. Steps involved in this process were data cleaning using power query, data modeling for fact & dimensions table based on the basis of primary & foreign key relationship present in the reference image provided. By using MYSQL Workbench we found the answers for particular KPI by joining the tables using joins concept. Finally to present the in depth analysis for each KPI in the form of an interactive dashboard we used Power BI.

Dataset:
Olist Store Analysis | Power BI Project

Problem Statements(KPI):
  1. Weekday Vs Weekend Payment Statistics
  2. Number of Orders with review score 5 and payment type as credit card
  3. Average number of days taken for Pet Shop
  4. Average price and payment values from customers of Sao Paulo city
  5. Relationship between shipping days Vs review scores
After importing the files in Pier BI I did the analysis on the above given KPI and tried to represent analysis & answers in the form of a dashboard using Power BI.

Main dashboard which represents the analysis for the 5 KPI
Olist Store Analysis | Power BI Project

1. Weekday Vs Weekend Payment Statistics
Olist Store Analysis | Power BI Project
  • Total orders, Total Sales, Payment Value is more on weekdays as compared to weekends.
  • Maximum sales($1.91M), count of orders(17.80K) & customers(15.54K) are from Sao Paulo city during weekdays & weekends.
  • Credit cards are the payment type used by most of the customers(75.24%) with a total payment value of $12.54M.
  • Maximum orders are placed during March to August(60.38K) so sales for this month($8.34M) are higher. There is a positive correlation between count of orders & sales.

2. Number of Orders with review score 5 and payment type as credit card
Olist Store Analysis | Power BI Project
  • Maximum payment value is done through payment value credit card(78.34%) with payment value $12.54M followed by boleto(17.92%) with payment value $2.87M.
  • More than 77% of the orders received review score more than 4 because of which overall review score is 4.09.
  • If the payment value of the product is higher then customers preferred installment option which is available on credit card only this is one of the main reasons customers preferred credit card as payment type.
  • Bed bath table, health beauty, sports leisure, furniture decor, computer accessories, housewares & watches gifts are the products which were most recommended by customers with average reviews score more than 4.

3. Average number of days taken for Pet Shop
Olist Store Analysis | Power BI Project
  • Orders for the Pet Shop category are delivered between 7 to 15 days which brings the average delivery days to approximately 11 days for pet shops.
  • Out of 1710 ordered products 1688 products(98.71) are delivered successfully with average delivery days as 11.31 resulting 4.24 as positive review score for pet shop product category.
  • Maximum orders for the pet shop category products are placed during April to August months(1058) with total price $131.10K, average delivery day 9.90 & review score 4.31.
  • In the month of august delivery days went from 11 to 8 days, in turn rating went up to 4.38 which is above average(4.24).

3. Average price and payment values from customers of Sao Paulo city
Olist Store Analysis | Power BI Project
  • The maximum crowd is from Sao Paulo city resulting in 15.62% of total orders being placed from Sao Paulo which contributes to 15.39% of overall sales.
  • Around 97.63% of the total order had a price in between $0 to $700, Which brings the overall average price approx. $120.
  • Average Review Score is 4.16 because more than 77% of total orders received review score 4 and above.
  • Credit card is the most proffered payment type in Sao Paulo with $135.83 average payment value & $1.91M total sales which is 81.51% as compared to other payment types.
  • Contribution of Sao Paulo is more as compared to other cities in overall sales($1.91M) and payment value($2.20M).

5. Relationship between shipping days Vs review scores
Olist Store Analysis | Power BI Project
  • Over 76% of orders got more than 3.5 star rating, which is closer to overall average rating of 4.09.
  • Delivery days are directly influencing review scores. When delivery days are more than 30, the average review score is 1.87, which is 2.21 units lower than the overall average of 4.09.
  • Review score can be increased by working on delivery days. My stats review score is 0.70 points higher when delivery days are less than 11.
  • Weight & surface area of product are also influencing delivery days. More than 71% of orders has 12 delivery days, as they weigh from 0gm to 2000 gm & have surface area from 250sq.cm to 3500sq.cm
  • As delivery days increase, delivery costs also increase with the exception of a few products.

Conclusion
  • Maximum orders are placed between March to August(60.38K) because of which sales during this month are higher($8.34M) which is 61.36% of total sales. Which shows that the store is performing well during this month.
  • Currently credit cards are the most preferred payment type by customers which contributes a major role in overall sales(78.44%). To improve sales & to promote other payment types, Olist Store can provide discounts & offers on other payment types.
  • Olist Store needs to work more during the last quarter to improve the sales as there are less orders placed during September to December months which also results in less average review score. Store needs to focus on advertisement & different offers on products during the period.
  • Maximum customer crowd is from Sao Paulo & Eastern side of the country as compared to other regions. To improve the sales in other regions stores need to focus on campaign & promotion in these regions.
  • After observing the comment section we got to know that Bed bath table, health beauty, sports leisure, furniture decor, computer accessories, housewares & watches gifts are the products which receive maximum of the recommendation from customers with average review score 4.08 which shows these are most demanding products among the customers.
  • From 5th KPI we can conclude that if sellers take longer days to deliver the product then customers provide less review score which shows it is one the factor that influences review score.
  • Sellers should improve on delivery days & should keep customers informed about status of delivery it will make time go much faster for customer and will create personalize experience because they know what's happening.



Technology Topper Tuesday, November 29, 2022
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Maven Pizza Challenge - Maven Analytics | Power BI Project

For the Maven Pizza Challenge, playing the role of a BI Analyst by Plato's Pizza, a Greek-inspired pizza place in New Jersey. Designed a dashboard to analyze & help the restaurant use data to improve operations. Found some useful insight & answer for the question provided in the challenge by creating the interactive dashboard in Power BI.
The data was provided by Maven as a part of the challenge. It was contained in 4 .csv files, named Orders, Order Details, Pizzas, and Pizza type. The dataset was already clean so there was no need to do anything else in this step of the process.


About the dataset:
  • This dataset contains 4 tables in CSV format
  • The Orders table contains the date & time that all table orders were placed
  • The Order Details table contains the different pizzas served with each order in the Orders table, and their quantities
  • The Pizzas table contains the size and price for each distinct pizza in the Order Details table, as well as its broader pizza type
  • The Pizza Types table contains details on the pizza types in the Pizzas table, including their name as it appears on the menu, the category it falls under, and its list of ingredients

For the analysis I have imported tables in power BI. I have done the data modeling in Power BI by connecting the 4 tables based of primary & foreign key. After connecting the table I did the analysis for the given problem statements by maven analytics. Below is the Power BI dashboard which I have created to show the analysis for the Maven Pizza challenge.

DASHBOARD
  • Plato's Pizzas is open from 9:00 a.m. to 11:00 p.m. every day of the week.They have 4 pizza categories and 32 pizza types in 5 different sizes.
  • A total of 21.35K pizza orders were placed, and a total of 48.75K pizzas were sold, with an average of 2.32 pizzas per order.
  • The busiest day is Friday because the majority of orders are placed on this day, and the busiest time for the store is between 12 and 1 p.m.
  • Classic-type pizza is the category that is ordered by most of the customers (29.9%), followed by supreme, veggie, and chicken. Large-size pizza is preferred by most of the customers, followed by medium, small, XL, and XXL.
  • The bestselling pizza was the "Classic Deluxe Pizza" (2453 orders), and the lowest selling pizza was the "Brie Carre Pizza" (490 orders).



Technology Topper Tuesday, November 29, 2022
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Superstore Sales Analysis Project | Tableau Project

Tableau Superstore Dataset is a sample Dataset provided by Tableau which includes data for the Sales of multiple products sold by a company along with subsequent information related to geography, Product categories, and subcategories, sales, and profits, segmentation amongst the consumers, etc.

Dataset Description:

Column

Data Type

Column

Data Type

Order ID

String

Postal Code

Geographic

Order Date

Date

Region

String

Ship Date

Date

Product ID

String

Ship Mode

String

Category

String

Customer ID

String

Sub-Category

String

Customer Name

String

Product Name

String

Segment

String

Sales

Number

Country

Geographic

Quantity

Number

City

Geographic

Discount

Number

State

Geographic

Profit

Number


Superstore dataset contains the hierarchical data column Order date & ship date in the form of Year-Month-Week. Also for geographical form Region-State-Country-City. In this project I have connected the superstore dataset to the tableau. After importing the dataset in tableau I have represented the analysis in the form of a dashboard.


  • Superstore dataset includes three types of products categories i.e. Technology, Office Supplies & Furniture. Total sales was $2.30M with total 9994 orders & 1862 type of products which ordered by total 793 customers. The total quantity sold was 37873.
  • For the office supplies product category total 6026 orders were placed by 788 customers with total quantity of 22906. Overall sales for the office supplies product category was $719.05K.
  • For the technology product category total 1847 orders were placed by 687 customers with total quantity of 6939. Overall sales for the technology product category was $836.15K which is more than any other category.
  • For the furniture product category total 2121 orders were placed by 707 customers with total quantity of 8028. Overall sales for the furniture product category was $742.00K.
  • Chairs has maximum share in the sales, but there is not much difference between the Chairs and the Book cases. Storage has maximum share in the sales. Additionally, around 50% of the share in Office supplies is accounted by Storage and Appliances. There is a huge gap in the contribution between the first Two and the others. Phones has maximum share in the sales and followed by the Copiers- which together account for 50% of total Technology sales.
  • West region is the most profitable region as compare to other regions. Total sales for the West region was $725.46K with total orders of 3203 ordered by 686 customers.
  • New York City was at the top in terms of profit(62K) with total 256.37K Sales follwed by Los Angeles with 30.44K Profit.
  • I visualized and analyzed various use cases in the superstore dataset. I got some insightful results about profit and sales that can be used to improve future policies. I also found a trend over the year, so preparations in stores and warehouses for the next year can be made accordingly, and sales and profit can increase. Stores must focus on other regions, such as the South and Central, to increase sales in these areas. We can now confidently pick up more datasets to define use cases for and visualize them in Tableau.



Technology Topper Tuesday, November 29, 2022
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