Exemplo de análise com gráfico de dispersão em serviços

FM2S Educação e Consultoria

22 Jul 202214:14

Summary

TLDRThe script discusses a case study on utilizing scatter plots and linear regression analysis to improve sales processes. It highlights a company selling safety sensors where the high cost of visits leads to low returns. By analyzing data through scatter plots and regression, the company identifies a strong correlation between the number of visits and total expenses, leading to insights on reducing costs without negatively impacting sales. The case emphasizes the importance of data analysis in decision-making and process optimization.

Takeaways

📊 The use of a scatter plot graph and linear regression analysis can provide valuable insights into sales processes and customer behavior.
🔍 A real-case study was used to demonstrate how data analysis can help understand and improve sales performance and reduce costs.
💡 The company in the case study sells safety sensors and uses technical salespeople to visit clients' factories to assess their needs and provide quotes.
📈 The scatter plot graph helped identify a clear relationship between the number of visits and total expenses, showing that more visits lead to higher costs.
📊 There was no clear correlation between the number of visits and total sales, indicating that increasing visits does not necessarily lead to higher sales.
🛠️ The case study company made changes to their sales process, such as improving communication and using photo submissions, which led to a reduction in the number of visits and costs without negatively impacting total sales.
📈 Linear regression analysis showed that 86.90% of the variation in total expenses can be explained by the variation in the number of visits.
🔑 Stratification of products revealed that some products were less influenced by the number of sales, while others had a more significant impact on sales and profitability.
🚀 Focusing efforts on products that have a higher impact on sales and profitability can lead to a more efficient and cost-effective sales process.
💼 The importance of understanding customer needs and tailoring the sales approach accordingly was emphasized as a key takeaway for improving sales performance.

Q & A

What is the main topic of the script?
-The main topic of the script is the use of scatter plots and linear regression analysis in sales data to identify patterns and improve business processes.
What was the issue the company faced with their sales process?
-The company faced a high cost of prospection relative to their faturamento (revenue), with some sales visits being very costly and yielding low returns.
How did the company's sales process work initially?
-Initially, a technical salesperson would visit the customer's factory to analyze their needs and provide a quote for the required safety sensors. If the customer accepted the quote, the company would make the sale and revenue.
What data did the company have about their sales process?
-The company had data on the total revenue from each customer, the number of sales made to each customer, the total cost spent on visits, and the number of visits made.
What type of analysis did the speaker use to understand the data?
-The speaker used scatter plots and linear regression analysis to understand the relationship between various variables such as total revenue, number of visits, and total cost.
What was a key insight gained from the scatter plots?
-A key insight was that there was a clear relationship between the total cost and the number of visits, indicating that increasing the number of visits led to increased costs.
What did the linear regression analysis reveal about the relationship between the number of visits and total cost?
-The linear regression analysis revealed that 86.90% to 95% of the variation in total cost could be explained by the variation in the number of visits.
How did the company improve their sales process based on the analysis?
-The company improved their sales process by enhancing communication before visits, using photos and detailed information in forms to reduce the need for visits, which in turn reduced costs without negatively impacting total revenue.
What was the impact of these changes on the company's sales and costs?
-The changes led to a reduction in the number of visits, lower total costs, and did not negatively impact the total revenue, indicating a more efficient sales process.
What other insights were gained from further analysis of the data?
-Further analysis revealed that different types of products had different levels of impact on revenue per sale, with some products being more lucrative when sold in larger quantities and others being less affected by the number of sales.
What was the overall lesson learned from the data analysis?
-The overall lesson was that understanding the relationship between various sales variables through data analysis can lead to strategic improvements in the sales process, making it more efficient and cost-effective.

Outlines

00:00

📊 Introduction to Sales Data Analysis

The paragraph introduces a case study on the use of scatter plots and linear regression analysis in understanding sales data. The context is set for a company selling safety sensors where a salesperson visits clients to analyze their needs and provide a quote. The goal is to understand the effectiveness of this process and how data analysis can help improve it. The company's sales data, including the number of visits, costs, and revenue, is mentioned as a starting point for analysis.

05:02

📈 Scatter Plots and Their Insights

This section delves into the use of scatter plots to identify patterns and relationships within the sales data. It highlights the correlation between total revenue and the number of sales, as well as the total cost and number of visits. The analysis reveals that there is a significant relationship between the total cost and the number of visits, suggesting that increasing visits leads to higher costs. The concept of using regression analysis to predict the impact of visits on total cost is introduced, with an example of a regression report that shows 86.9% of the variation in total cost can be explained by the number of visits.

10:03

💡 Improving Sales Process with Data

The final paragraph discusses the application of data analysis insights to improve the sales process. It mentions a specific project where changes were made to reduce the number of visits and total cost without negatively impacting total revenue. The importance of communication and professionalism in sales visits is emphasized, and the idea of focusing on selling more profitable products is introduced. The analysis also uncovers that the relationship between the number of sales and total revenue is not straightforward and varies by product type. The paragraph concludes by summarizing the learnings from the data analysis and the potential for further exploration and application in sales strategy optimization.

Mindmap

Keywords

💡Scatter plot

A scatter plot is a graphical representation used to display values for two variables for a set of data. In the video, the scatter plot is used to visualize the relationship between the number of visits made by salespeople to clients and the total revenue generated. It helps identify any trends, correlations, or patterns in the data that can inform business decisions.

💡Linear regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In the context of the video, linear regression is used to predict the total cost based on the number of visits, with the aim of understanding how much of the variation in cost can be explained by the variation in the number of visits.

💡Data analysis

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data to discover useful information, suggest conclusions, and support decision-making. In the video, data analysis is crucial for understanding the sales process, identifying inefficiencies, and making improvements to increase revenue and reduce costs.

💡Sales performance

Sales performance measures the effectiveness and efficiency of a company's sales efforts. It involves evaluating metrics like revenue, number of sales visits, and cost per visit to determine how well the sales team is achieving its targets. In the video, the speaker is interested in understanding and improving sales performance by analyzing data from past sales activities.

💡Cost-effectiveness

Cost-effectiveness refers to the value obtained in relation to the money and resources spent. It is a key consideration in business decision-making, as it helps determine whether the benefits of a particular action or investment outweigh the costs. In the video, the speaker is looking for ways to make the sales process more cost-effective by reducing the number of unnecessary visits and lowering overall costs without sacrificing revenue.

💡Customer visits

Customer visits refer to the act of a salesperson physically going to a client's location to discuss products, services, or to conduct business. In the video, the number of customer visits is a significant factor being analyzed to understand its impact on sales revenue and cost, and to find ways to optimize this aspect of the sales process.

💡Business process improvement

Business process improvement involves the evaluation and enhancement of a company's procedures to increase efficiency, effectiveness, and competitiveness. In the video, the speaker uses data analysis to identify inefficiencies in the sales process and suggests changes to improve it, such as reducing unnecessary visits and improving communication with customers.

💡Data-driven decision making

Data-driven decision making is the process of making choices based on the analysis of relevant data and statistical evidence. It is a critical approach in modern business management, as it minimizes guesswork and maximizes the likelihood of successful outcomes. In the video, the speaker emphasizes the importance of using data analysis to make informed decisions about the sales process and to implement changes that will lead to better results.

💡Regression analysis report

A regression analysis report is a document that presents the results of a regression analysis, typically including statistical measures such as the R-squared value, which indicates the proportion of variance for the dependent variable that's explained by the independent variables. In the video, the regression analysis report is used to quantify the relationship between the total cost and the number of visits, providing evidence for further business analysis.

💡Product stratification

Product stratification is the process of categorizing products into different groups or strata based on certain characteristics, such as profitability, sales volume, or customer preference. In the video, the speaker identifies two types of products based on their impact on sales revenue and cost, which helps in tailoring the sales strategy to maximize profitability.

💡Sales strategy

Sales strategy refers to the plan and approach used by a company to achieve its sales targets. It encompasses various aspects such as target markets, sales channels, pricing, and promotional activities. In the video, the speaker analyzes data to inform the sales strategy, aiming to make it more efficient and cost-effective by understanding the relationship between visits, sales, and revenue.

Highlights

The use of scatter plots and linear regression analysis in a real-case scenario to extract valuable data insights.

The case study revolves around a company selling safety sensors and the analysis of their sales process.

The high cost of prospection relative to the low faturamento due to excessive visits, exemplified by a São Paulo to Goiás sales trip resulting in a USB sale.

The importance of understanding the objective of change and the context of the problem through data analysis.

The variable Y representing the faturamento of each account and variable X representing the number of visits and cost of visits.

The use of a scatter plot matrix to analyze the performance of the commercial process and identify patterns.

The discovery that the total expense is highly related to the number of visits, with almost 86.90% of the variation explained by the regression model.

The lack of a clear correlation between the number of visits and the total faturamento, indicating that reducing visits does not necessarily impact total sales.

The stratification of products into two groups based on their sensitivity to the number of sales, with some products being less impacted and others more lucrative.

The implementation of changes such as improving communication before visits and using photos and detailed descriptions to reduce the need for on-site visits.

The reduction in the total expense without negatively impacting the faturamento total by focusing on more efficient use of visits.

The insight gained from data analysis leading to more efficient sales processes and better client management.

The practical application of scatter plot analysis and linear regression in improving a company's sales strategy and cost management.

The potential for further analysis and refinement of the sales process through deeper understanding of product categories and their respective impacts on sales.

The emphasis on the value of data analysis in decision-making and strategic planning for businesses.