Gaurav Rateria: Mohit, last question on the comments that you made around generative AI. So if I just look at how to think about the eventual outcome of early investment on the 3 factors: one, competition or your competitive moat; second, addressable market; and third, the efficiency that can help to lower your cost structure.
Rajesh Magow: It’s a great question, Gaurav. In all 3, the sort of separate variables that we should look to see how this new technology is going to impact. See, it’s going to be a long journey, by the way. And we’ve started, and we started looking at, in fact, looking at all 3 aspects. I think the first 2 will get reflected in either the overall online penetration increasing because the overall experience. See, ultimately, this technology is going to help us improve the customer experience on the platform, making it far more intuitive, making it far more easier, discovery can be a lot better, we can — there are use cases that we are already working on. We’ve done soft launch as well. We can — the intelligent bots can come up with an innovative vernacular based, voice-based interface as well, which would mean that it will sort of expand our reach to the smaller cities beyond the, let’s say, top 20, 25 cities of the country.
And all of that would mean effectively that on one side, the customer experience will continuously improve and people will feel more and more comfortable to come and buy online. And two, it will go beyond 25 cities as well, more and more and trying to obviously also resolve the language issue beyond English. Now this is the — specifically the language issue to crack commerce on vernacular language is going to be a long journey. It is not necessarily going to be an overnight solution. But on the other side, on the experience side, we’ve already started getting some good feedback from customers that this feature is helping and that feature is helping. But I think this overall — this new technology is definitely looking more promising. Now on the third side, on the efficiencies also, there are use cases identified already for at our end.
And one of them is the post-sales experience that we are already looking at further right now or the 1.0 or maybe the 2.0 journey for post-sales experience customer — from a customer journey perspective, was how do we sort of automate and make the transition from, let’s say, contact center servicing to doing the self-service or self-help, most of the use cases that can be done on phone on a click of button, et cetera, which we achieved — which we have achieved quite significantly with a lot of the share is now self-service. And I think the 3.0 is going to be now further in that journey. How could we, on one hand, either the sort of get some more productivity gains for the agents — for the call center agents for the really sort of complex cases by providing a lot of the information or filtering a lot of the information using this technology, or we sort of look at gain introduction of bot at some level in the journey, which can filter the generic queries that come our way on the post-sales service queues.
So that’s one area that is work on, which will clearly give us more efficiencies. And of course, we’ll also enhance the customer experience for the post-sales queries, et cetera. And on the other hand, I think there’s going to be some low-hanging fruits also on the software development side, on the programming side for the engineers, where this technology, again, can be leveraged to look at the basic, the first level of programming queries, just to make life easier and efficient for the engineers at the sort of first level, which would help us get some productivity gains on that front as well. I would just say that the work on our side is actually on, on all fronts, but it is going to be a journey, and we will keep sort of working on use cases on one side, which will impact the customer.
And on the other side, we will continue to keep focusing on wherever we can get some productivity gains internally.
Gaurav Rateria: Rajesh, just a clarification here in terms of what’s our dependence on paid traffic? And do you think that dependence also can come down with this use of this technology?
Rajesh Magow: Right, that’s an interesting one. Actually, we are already, Gaurav — lot of our traffic is direct. So our dependence on paid traffic is relatively speaking low and on the back of brand that has been established, all our brands actually MakeMyTrip, redBus and Goibibo even from an OTA overall brand standpoint as compared to the rest of the market. And dependence is already low. There are areas that they’re also — specifically on search engine optimization, we’ve already seen some gains that we have — I should have actually mentioned about that as well that we have a — we have leveraged — we have started to leverage this technology there as well. And that would help more conversion because SEO traffic is not necessarily a paid traffic.
And we’ll keep sort of learning more and keep looking at it and exploring more areas to see if there are any further gains even on this side can come from here, from leveraging this technology. But like I said, the dependence is already relative quite low, and it’s an underpenetrated market overall, and we still need to have a healthy mix of new users coming in. And so therefore, do I really see immediate, immediate gains that — material immediate gains that will come from this area? Perhaps not. Maybe it’ll get balanced out because we will also continue to keep investing in getting some new users in.
Vipul Garg: The next question is from the line of Ashwin Mehta of AMBIT Capital.
Ashwin Mehta: Yes, Vipul, can you hear me?
Vipul Garg: Yes, we can.
Ashwin Mehta: Yes. Congrats on a good set of numbers. I had a question on the take rate. So we’ve seen bump-ups in terms of take rates in both air as well as hotels. And also in bus ticketing, we’ve seen stability on take rates at elevated levels versus historical. So what are the drivers for these take rate improvements? And how sustainable near term do you see these take rates to be?
