As we grow, we look for ways to serve our customers better and become more efficient. One of the best ways to do this is be proactive – spotting potential problems before they happen.
We implemented a new race review process over the past several months based on taking our best practices and implementing them in our system – think Artificial Intelligence with the system automatically highlighting potential trouble spots.
In the past, we had our Account Managers review each race as it was created by hand. They would have to pop thru a number of different pages on the race’s dashboard to look for common mistakes, or places where race directors can get confused. In January we had 1,561 new races created and 1,586 races renewed – that is a total of 3,153 races we have to review – over 100 per day! So increased automation and speed are required.
Here is a look at some of the races that were created on Saturday that the system is calling out:
The three red boxes highlight setting that were made where race directors sometimes need help – Fundraisers, USAT and Team Pricing. There are lots of options in each of those areas. On Monday the account managers for each of these races will check out that page (they will click on the link in the red box and go directly to that setup page for that race). If there is an issue, they will get in contact with the race director and let them know how they can make their race better. We get a lot of pleasantly surprised race directors!
We obviously can’t be responsible for every race being set up right, but this helps our customers be just a bit better. And hopefully saves them (and us) time in the future when there are problems.
Here is a list of the current items we use our little AI best practices engine on:
Bob,
Are you guys using anything like TensorFlow or PyTorch in RSU?
Not at this point. We are just coding best practices into the platform. We want to start some projects in the next year that are more advanced and use something like those. We are probably going to do a lot with the AWS tools for ML and things like Sagemaker.