PROFIT MAKERS
Investigating your Sales Profit/Revenue
This analysis is done to understand the market trend of the company's sales. The analysis is an in-depth study of metrics associated with the growth rate of an organization. Our data is collected from Future-500 companies themselves in order to analyze the organizations' business market, which in this case is understanding their operational expenses and sales performance. Our goal is to help the organization increase its revenue and profit in its entirety. In accord with this focus, this analysis will be providing them with analytical insights and showcase a prediction of profits in the future.
How to lead to higher Profit?
An organization's revenue is the product of the number of goods sold by them at their respective prices. However, the profit is the difference earned that year after clearing all the expenses of that year by that organization.
There may be numerous factors leading to the growth of organizations. Every year, some organizations increase their earnings, and some incur losses. With our data analysis, we intend to detect such factors and help our businesses raise their benefits and reduce their expenses with more profits.
Step-wise Approach:
When skimming through the sample set, linear information on companies' balance sheets to leave was noted. This helped in the choice of making a linear regression model, namely, the Multiple Linear Regression Model for the predictive analysis of the data set on companies from numerous industries. To start with our analysis, it was pivotal to understand the factors involved in making profits. For this, a dig into the variables (like industries type, inception year, location, or hirings per year) involved was important, which was indicated well in our data. Further research brought to light, that data here, denotes cases that could have an inclination towards some major expenses or profits of companies, which in our data terminology is referred to as outliers. These outliers have a tendency of shifting the distribution curve of analysis and affect the model as a whole ambiguously. Hence it became crucial to handle those outliers and carry out our data analysis. We have used R for this purpose, as discovering important variables impacting revenue and profit rise decisions over large data becomes easier. We have also visualized reports from data, and then created a dashboard for easy tracking and reporting through PowerBI.
To apply a modeling technique to predict Profit, we needed to understand the companies' work environment and culture. We categorized the companies into different industry types they render their market, as these sample distributions may give a clear insight into their work life.
Some Insights:
Upon intense analysis, we found that the IT service industry holds the major share in the market.
Approximately 28.38% of companies are IT service providers and nearly 14% are software product companies. Health Industry is standing second in the race, by holding a market share of more than17% overall.
Most IT companies are divided into two sectors: One that focuses majorly on support and services, and the other that solely brings new products or software into the market. Either case has resulted in high growth opportunities. With high revenue and profit margin, the growth in such an industry is comparatively higher globally.
An overall Map chart for all the state-wise growth of Future-500 companies can be visible here.
We can see, that California (CA) had the highest growth of more than 13% over the years. Although there is a decline in the number of employees here across the span of 3 years (2012-2014), CA still stands second to Texas in terms of employee count across all states.
Next to CA, Virginia (VA) with almost 11% growth, followed by Texas (TX) at 9%, Illinois (IL) at 6.2%, and Florida (FL) at 5.4% over the years.
We also noticed that the linear graph here denotes the linear relationships between the revenue and profit earned by organizations among all the industries. However, the variations are seen based on the type of industries served by the company.
Most affected industries are organizations that require larger manpower or human resources, like construction, medicines, and finances.
Results and Reports:
ProfitMarkers.pptxYou can check the full report here...
You can have a glimpse of dashboard here...
Recommendations
By rendering predictive modeling techniques using Linear regression, we can predict profit or loss for companies with at most 88% accuracy. With our predictive analysis, we now know that organizations that indulge in industrial sectors such as Construction, Financial Services, Government Services, and HealthCare, are more affected by monetary matters.
Companies with manual documentation, labor, or manpower, should start digitalizing themselves in order to sustain themselves in the market, as we can say software industries are going to bloom in the market in the future with a growth of a minimum of 20% of the industry.
Cities like San Diego have a rising number of employees, for now, making its competing growing cities a target for the future employment sector.