53 lines
3.2 KiB
Markdown
53 lines
3.2 KiB
Markdown
# CenturyLink Cisco Quotes Data Analysis
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This data analysis is based on a dataset containing quote details from CenturyLink for Cisco products.
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## Dataset Overview
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The dataset contains 20,082 unique quotes. Each quote includes details such as the quote creation date, quote number, quote contact, quote name, quote status, quote value (sum of extended rev buy curr), and the end customer name.
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## Key Insights
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### 1. High Value Quotes
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The dataset includes a wide range of quote values, from a minimum of $0 to a maximum of approximately $405 million. This large range suggests that some quotes are for significantly higher values than others. Identifying the characteristics of these high-value quotes could provide insights for generating more such quotes in the future.
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### 2. Quote Status
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The majority of quotes (15,826 out of 20,082) are in the 'WON' status. However, a significant number (3,659) are also in the 'EXPIRED' status. Understanding the reasons why these quotes expired could provide insights for improving the quote conversion rate.
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### 3. Top Customers
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Certain customers have significantly higher total quote values than others. The top customer, represented by '-', has a total quote value of approximately $1.9 billion. Other notable customers include 'USDA OCIO ITS TOB' and 'HCA INFORMATION TECHNOLOGY & SERVICES INC'. Focusing on these high-value customers could be beneficial for business growth.
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### 4. Quote Value Distribution
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The distribution of quote values is skewed, with a mean of $122,493 and a median of $1,947.65. This suggests that while most quotes are of lower value, there are a few quotes with very high values that increase the mean.
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### 5. Time Trends
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The total quote value fluctuates over time, with significant spikes on certain dates. Understanding the reasons for these fluctuations could help in predicting future trends and optimizing the timing of quote generation.
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### 6. Missing/Unspecified Data
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A large amount of quote value is associated with '-' in the 'End Customer Name' field. This could represent missing data, and understanding why this data is missing could be important for improving data collection and analysis processes.
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## Recommendations
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Based on the above insights, here are some recommendations:
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1. Investigate the characteristics of high-value quotes to understand how to generate more such quotes.
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2. Analyze expired quotes to identify any common factors that could be addressed to improve the quote conversion rate.
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3. Focus on high-value customers and understand their needs and preferences to maintain and expand business relationships with them.
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4. Consider stratifying the quote value distribution for more detailed insights and to inform the pricing strategy.
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5. Analyze the time trends in more detail to identify any seasonal patterns or other time-related factors that influence quote values.
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6. Improve data collection and cleaning processes to minimize the amount of missing or unspecified data.
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