Example Analysis

On the following pages you can get acquainted with the analysis and visualization functions of Insight A2 through some examples.

Examples : 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9

Sales and profit analysis

In this example Peter, a general manager of a software development company, analyses sales and profit results of the first quater of the year.

To do this he opened the company's sales database in Insight A2 and created the following visualization just by dragging and dropping database fields onto the configuration panels of Insight A2.

Each mark on the visualization represents a completed project. The projects are displayed on a scatter plot showing the pre-sales costs and the final contracted amounts along two axes (pre-sales costs usually grow as a sales representative spends more time with a customer). The color and the shape of the marks encode the sales representative of the project (Bill, Josh and Mary).

Reviewing the visualization Peter can reveal the following:

• Similarly colored and shaped marks are forming distinct groups on the scatter plot, which means that each of the 3 sales representatives has different methods of getting and closing a sale.
• There are two main outlier groups on the scatter plot (shown in red frames) where the final contracted amount is way below the average of all the contracted amounts. Actually, the contract in the rightmost outlier has a higher pre-sales cost than the complete contracted amount.

Bill's contracts (blue squares) usually have a modest pre-sales cost (under $450) and have a high contracted amount. At the same time Mary (green circles) closes sales with higher pre-sales costs (around $800) and somewhat lower contracted amounts than Bill. Peter also notices that in one of the outlier groups Bill has three contracts with very low contracted amounts compared to his other sales.

Josh`s (orange crosses) results are halfway between Mary`s and Bill`s both in terms of pre-sales costs and contracted amounts.

At first glance it seems that Bill closes the best deals in the company, with the smallest pre-sales costs and highest contracted amounts.

The company database also contains total project costs and profit numbers for each project. Peter creates a new visualization by quickly replacing the Contracted Amount field with the Profit field on the scatter plot and on the Color panel.

The second visualization displays the same projects as the previous scatter plot. Peter immediately notices that there are projects with negative profit, and sees that nearly all of these projects belong to Bill as all of them are represented by a square. He also sees that hardly any of the projects of Josh and Mary have losses.

How is that possible that Bill who has projects with the highest contracted amounts and lowest pre-sales costs has so many projects which end up with losses?

Peter knows that projects that end up with losses usually involve a lot of re-work during the project implementation due to the unsatisfactory discovery of customer needs during the pre-sales phase. He sees that all of Bill's projects which have losses have a very small pre-sales cost (and therefore activity) with respect to Josh's and Mary's projects.

This insight indicates that even though Bill achieves the highest contracted amounts, a great percentage of these projects make a loss as a result of improper pre-sales activity.

To validate this theory Peter turns to Insight A2 again and changes the visualization to show a profit summary by categories of pre-sales cost ranges.

The new visualization validates Peter's theory that Bill has the lowest pre-sales hours and costs which accounts for nearly all the project losses in the development department. The visualization also shows that Mary's projects are the real winners when talking about final profits.

Peter notices for the future, that based on past performance he should make sure that all of his sales representatives will take enough time for gathering customer requirements (at least $600 worth of man-hour), otherwise there is a high chance of small or no profit at the end.

Finally, to double-check all his new findings Peter creates a summary report with Insight A2, showing both the sum and average values for pre-sales costs, project costs and profits.

In this small example we showed how visual analysis can reveal important information and help you make better decisions using your existing databases.