Six Sigma Sales Rep Analysis
Lean Six Sigma Tools
Cause & Effect Diagram
Comparison Analysis (Box & Whisker Chart)
This manufacturing/distribution company had 8 sales reps that covered specific geographic regions of the northeast United States. The company manufactured several components and distributed others to a very specific industry-niche.
- We started the project with a Cause & Effect Diagram of everything that impacts sales and close rate. Download Customer Analysis PowerPoint Presentation.
- The team voted that we focus the analysis on sales reps and the customers they serve. This sales rep / territory approach allowed us to do a very interesting comparison analysis.
- The first graph is a Pareto Chart of gross sales dollars by sales rep. Each sales reps’ initial are below the bar. This chart yielded no great information. Everyone knew who the best and worst sales rep was. Seeing it in graphical form provided a few “raised eye-brows”, but no break-through. So we decided to go deeper and examine the data “within the Pareto Chart”.
- Looking at the data within the Pareto Chart means that the “spread” within each sales reps’ territory is analyzed.
- We switched from gross sales to net profit per order (customer gross profit dollars – (order overhead cost of $115 per order x # of orders customer placed this year)).
- As you can see on Slide 3 the data is surprising and troubling.
- It is important to note that we did not show outliers on this graph as some very profitable customers (this is good!) change the scale on the graph and do not allow us to properly analyze the core of the data.
- Over half of KT’s (our highest sales dollar rep) customers are net money losers. Over 75% of DB’s (our lowest sales dollar rep) customers are net money losers.
- We acted on this information. The two reasons for these unprofitable customers were; 1) They had too many low volume customers who also negotiated very low prices and 2) Even good customers were ordering very frequently in small quantities, that increased my Client’s order-processing overhead.
- Low volume / low price customers prices were increased.
- Sales reps worked with good customers on reducing order-frequency and increasing order size.