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Report

Enumerated below are a list of our analysis and dashboards pertaining to various business functions and also related to different industry types.

Marketing Analysis Report

This dashboard and underlying report analyze a manufacturing company’s marketing data. It attempts to keep an eye on the industry and the company’s market share, product volume, sales, and sentiment. Historically the company was a market leader in its industry segment but has started to show decline in the sales figure. This reports attempts to discover growth opportunities.

Through our analysis we have shown information about the market share, sales, and sentiment. Data is broken down by region, time, and competition. On market share you can see how the company is performing in different categories, how it is performing in last 12 months and the YoY change on market share. Also you will find the market share of competitors over the years. Also you will find the regions, segments and competitors from which there exists a growth opportunity. Thirdly you will find how the company is performing vis. a vis. top competitors. Finally you will also see comparison of sentiments for the company and competitors across the various districts and segments

eCommerce Analysis Report

Studying top products requires more than just product listings. You also need to know what sells well and what does not. This dataset contains data from www.wish.com. With this, you can finally start to look for correlations and patterns regarding the success of a product and the various components. This should facilitate better understanding of e-commerce sales and also help businesses optimize their stocks and sales.

Through our analysis we have tried to demonstrate how sensitive the customers are to price drop i.e. discounted price to original price. We have also identified the top categories of product by sales, inventory and revenue. Additionally we have also shown the correlation between sale of product and quality of product (ratings). Also we have shown the ration of shipping to pricing distribution across category.

Manufacturing Analysis Report

The Garment Industry is one of the key examples of the industrial globalization of this modern era. It is a highly labour-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories

This dataset includes important attributes of the garment manufacturing process and the productivity of the employees which had been collected manually and also been validated by the industry experts.

Using this data we have done a workforce analysis where you can see the distribution of workers across various teams, the distribution of idle workers across various teams and distribution of idle time across various teams. You will also find average productivity and average productivity gap distribution for different teams. You will also find a comparison of finished products and unfinished products across dates, weekdays and teams. Furthermore, you will also be able to find distribution of task time across various times intervals, distribution of average task time date wise, and distribution of task time for various teams. Finally, you will also be able to see distribution of overtime and incentives across dates and teams.

Supply Chain Report

This dataset is the sales & inventory data for a chain of toy stores . It includes information about products, stores, daily sales transactions, and current inventory levels at each location. Through the analysis we were able to add more dimensions and meaning to the product such as rotation speed, profitability, stock level tiers. Using these dimensions we could create a visual story that demonstrates which products are driving the biggest profit and how this is distributed across the various geographical location. We were also able to demonstrate how much money is tied up in inventory and how long will this last. You can also see holding patterns across product profitability levels for different stores. Additionally you will also see comparison of sales by product rotation speed and the distribution of sales by product rotation speed across stores. We have also shown a relationship between production rotation speed versus profitability. Finally you can also view the requirements versus actual stock of products categorized by product rotation speed.

Retail Store Data Analysis

This dataset contains household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer. It contains all of each household’s purchases, not just those from a limited number of categories. For certain households, demographic information as well as direct marketing contact history are included.

Through our analysis we have tried to show the profile of the customers linking it to their average weekly spending at the retail store. We have also shown the consumer behavior in terms of hour of purchase, stores from where they purchase, the average basket size and how they are motivated by retailer discount. Additionally we have shown the change in the spending of customer over time and how is that related to customer demographics. You will also be able to see the top departments by sales and relationship between top departments sales with customer demographics. Yet another report you will find is how the marketing have affected customers in shaping their spending. Finally you will also see a causal relationship between marketing slots and sales to understand which are performing better.

HR Analytics

Attrition in a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with. This report helps us interpret employee attrition data of an organization. The dataset is created by combining the employee records and exit interview surveys of outgoing employees. The report finds the people-related trends and patterns and allows the HR Department to take the appropriate steps to keep the organization running smoothly and profitably.

In order to create this report we had to clean the data and also add some additional dimensions to make it more insightful. Some of the dimensions that we created for preparing the data for analysis:

  • Age group
  • Percentage of Total Attrition
  • Percentage of Employee
  • Tiers for Distance from Home
  • Work experience tiers
  • Tiers for number of companies worked
  • Tiers for numbers of years spend in a company
  • Tiers for years in a current role
  • Tiers for no of years with current manager
  • Tiers for monthly salary range
  • Tiers for Salary hike levels

Creating these additional dimensions helps us do comparisons between the various subgroups of employee and come up with hidden patterns and trends

  • The analysis that was done on the data are:
  • Demographics Patterns: Compare the attrition by age group, gender and marital status
  • Travel and Daily Commute Trends: Track attrition patterns based on requirement to travel for business purposes and also based on daily commute distance to office
  • Education Background Analysis: Compare the attrition based on education level, education field and also no of trainings received
  • Satisfaction Analysis: Compare the attrition based on satisfaction level of an employee with respect to their job environment, job satisfaction level, work life balance, satisfaction with their personal relationships
  • Work Experience Level: Analyze the attrition trends based on work experience level and also number of companies worked previously
  • Employment History Analysis: Identify attrition patterns based on history in the company such as number of years in the company, number of years since last promotion, number of years in current role and number of years with current manager
  • Compensation Analysis: Evaluate attrition based on monthly salary levels, salary hike levels, stock option levels and performance ratings

Based on the above analysis the dashboards are created and also important findings highlighted in each of reports.