The first data we analyzed was which errors occurred most frequently. The above Pareto chart serves to separate the “vital few” errors from the “trivial many”. The first 7 types of errors (from left to right) account for 78% of the total service errors. Concentration on eliminating those types of errors is a good first step in minimizing customer service errors and boosting revenue. If you can eliminate less than half of the error types you can eliminate more than 2/3 of the total errors. Next we looked for correlations between the data above and which errors were most costly.
We again chose Pareto charts to express the relationships between the types of errors and how much they cost the company. The use of Pareto to express the total cost of each error type is valuable to identify which error types are costing the most cumulatively and also offers some correlations. Again we see the first 7 error types (from left to right) make up a large majority of the money spent correcting errors. 79% in fact. We find that 5 error types: Typesetting, Wrong position, Ran in Error, Wrong ad, and Wrong date occur in the “vital few” data of both frequency and total cost of errors.
Further concentration on these 5 error types will not only go a long way in eliminating the frequency of errors, but will also eliminate a large portion of the total cost associated with service errors. Another important finding in this data is that while copy errors occur most frequently (17% of total errors) they are relatively inexpensive to fix (only 6% of the total cost of errors). So eliminating copy errors will go a long way in improving customer service, but will not have the same impact on the cost of fixing service errors.
Examining the cost data further we can see which errors are the most expensive to fix on a per error basis. While Pareto was not necessary to express cost per error (cumulative % is not important in this case), it is the easiest type of chart to read...
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