Correlation Analysis of Client Health Score with Numeric Variables

Feb 13, 2025, 7:13 AM

✨Sales Management System [AI] (1).xlsx

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What factors influence client health scores?

To determine the factors that influence client health scores, we can analyze the correlation between the Client health score and other variables in the dataset. Some potential factors to consider include:
  • Industry: The sector in which the client operates.
  • Client's ARR: The Annual Recurring Revenue from the client.
  • Stage: The current stage of the client relationship.
  • Client's thoughts: The client's expressed intentions or satisfaction.
  • Cumulative value of orders and Cumulative value of opportunity: Financial metrics related to the client.
  • Org size: The size of the client's organization.
Let's calculate the correlation between the Client health score and these variables to identify any significant relationships.
The error occurred because the df.corr() method attempts to calculate the correlation matrix for all columns, including non-numeric ones. The presence of non-numeric data like 'Company A' in the DataFrame caused the conversion to fail.
To resolve this, we need to ensure that only numeric columns are considered for the correlation calculation. Let's explicitly select only the numeric columns before calculating the correlation matrix.
The correlation analysis reveals the following relationships between the Client health score and other numeric variables:
  • Cumulative value of orders: There is a weak positive correlation (0.18) with the client health score. This suggests that higher cumulative order values might be slightly associated with better client health scores.
  • Cumulative value of opportunity: There is a weak negative correlation (-0.065) with the client health score, indicating a slight inverse relationship.
  • Client's ARR: There is a moderate negative correlation (-0.52) with the client health score. This suggests that higher Annual Recurring Revenue might be associated with lower client health scores.
These correlations provide insights into potential factors influencing client health scores. However, it's important to note that correlation does not imply causation, and further analysis might be needed to draw definitive conclusions.
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