Data visualization is the process of gathering your data from different company silo’s, putting it in a usable form, and graphing out key indicators and metrics. The benefits include:
- Visualize distribution of a variable and see patterns (e.g., sales in ‘000 over 1 year) with line charts, histograms and trend lines.
- See relationships between variables with scatter plots (E.g., relationship between calls and number of sales) and heat maps.
- Classify variables (e.g., percent of gross revenue by salesperson) with bar charts.
Here are a few examples:
You Peak at Your Customers Number of Visits
You look at the Average Sale Over Time
The Percentage of Customers that Never Purchase Again, then ask why!
You Run an RFM Analysis
Foreclosure Home Buying Company:
- When we met: Company bid on auctioned/foreclosed houses. Company had home attributes and the final sale disposition data.
- What we did: Coded and labeled data in suitable format.
- The result: Created charts showing trends of homes sold by bank and time.
- The result: Table 1 showed the home buying company which banks were selling homes for relative to the assessed value. This intelligence told the company how much to consider bidding for a $100,000 from Bank 7 compared to Bank 1.
- An executive summary: Key findings of the analyses, opportunities and threats, and competitive advantages.
- Explanation of each figure and table.
- Actionable recommendations.
- Key variables.Key variables drilled down by region, sales person, time, product, or other variables that make sense (e.g., most and least profitable customers)
- Distribution.Visualize distribution with line charts, histograms and trend lines.
- Relationships. Relationships between variables with scatter plots and heat maps.
- Classifications. Classify variables with bar charts.