I like that idea that [inaudible] AI basically from that customer journey [inaudible] makes it better. A little more easy. A little more [Inaudible] for the customer and for the brand. Mike, what would you add to that?
Michael Miller: (18:29)
earlier, which is getting more proactive. So we’ve got nearly four billion shipping events on our platform right now, and we’ve built machine learning models to crawl all over those specifically so that we can predict when an estimated delivery date or a promise date is going to be missed.
So for example, just last week,
we identified over 300,000 shipments that were going to miss their promise date and we did it up to 36 hours before the carrier even reported the problem. So you’re talking about up to a day and a half before you would otherwise know there’s a problem. When you can reach out to the customer, you can reassure them, you can appease them, you can reset expectations, you can talk to the carrier about the issues. So it’s really for us all about identifying stuff that the carriers aren’t telling you and that you can’t otherwise as explicitly see in the network so that you can get out in front of these issues and create better customer experiences. That’s the biggest place where we’re deploying it.
Gabe Larsen: (19:33)
Yeah, that’s incredible. The 36 hours. That’s a long time before obviously the carriers knew about it. Well, let’s wrap, guys, a lot of fun conversations, obviously costa rica phone number list challenging times need to figure out the best way to do that. Specifically, thinking about this idea of, “Where is my order.” Before we leave, advice for customer service leaders. Give me kind of your summary or your takeaway. Vikas, let’s start with you.
Vikas Bhambri: (19:58)
Yeah. I mean, my advice to customer service leaders is cloud security challenges you have a once in a lifetime opportunity, right? For the last few years, every leader betting email list I speak to, not just in the customer service, but the C level in the boardroom has said, “