A decade of data from many hundreds of people, help desk type roll where all communication was kept, mostly chat logs and emails. Machine learning with manual validation. The goal was to put a dollar figure on mistakes made since the customers were much more likely to quit and never come back if it was our fault, but also many customers are nothing but a constant pain in the ass so it was important to distinguish who was right whenever there was a conflict.
Mistakes made per call, like many things, were on a Pareto distribution, so 90% of the mistakes are made by 10% of the people. Identifying and firing those 10% made a huge difference. Some of the ‘mistakes’ were actually a result of corruption and they had management backing as management was enriching themselves at the cost of the company (a pretty common problem) so the initiative was killed after the first round.
alt Hacker News
A decade of data from many hundreds of people, help desk type roll where all communication was kept, mostly chat logs and emails. Machine learning with manual validation. The goal was to put a dollar figure on mistakes made since the customers were much more likely to quit and never come back if it was our fault, but also many customers are nothing but a constant pain in the ass so it was important to distinguish who was right whenever there was a conflict.
Mistakes made per call, like many things, were on a Pareto distribution, so 90% of the mistakes are made by 10% of the people. Identifying and firing those 10% made a huge difference. Some of the ‘mistakes’ were actually a result of corruption and they had management backing as management was enriching themselves at the cost of the company (a pretty common problem) so the initiative was killed after the first round.