From Store to Door: Nextuple's Omnichannel Order Management System - with Das Pattathil
Das Pattathil, Co-Founder & Head of Product, Nextuple
In episode 99 of The Payments Show Podcast, I spoke to Das Pattathil who is the Co-Founder & Head of Product at Nextuple.
Nextuple helps retailers transform their legacy Omnichannel Order Management systems using microservices.
AUDIO VERSION: thepayments.show
Episode Highlights:
Quicker Delivery Dates Directly Impact Sales Conversion
With a day shaved off the order to delivery lead time, we saw that there is 3% - 5% improvement in conversion. This varied by certain categories like electronics where it was much higher, but with certain other categories it was smaller. But on average it was 5%. This was eye opening for me. What Nextuple is doing is to think about order promising as a micro service and help retailers really get dialled down into knowing what their network can do.
Stores as Fulfilment Centres and Local Fulfilment
Store real estate is increasingly being converted into fulfilment space. Bigger back rooms, redesign of stores where your stores can function as mini fulfilment centres. There is likely to be more focus on local fulfilment, meaning if you have a group of stores in the market. How do you really think about that as a market, not as a single store: transferring inventory from one store to another for click & collect, or a hub and spoke model for you to pull inventory and ship to your customers. Some of the top retailers are already doing it.
Improving Order Promises
Retailers or other brands don't give a precise delivery promise because they don't trust their own network. They don't trust the capacity they have to ship something. They don't trust the carriers to deliver something in the time they promise. Lots of buffers get added in so the delivery date becomes very loose and conservative. If you're able to actually understand your network fully and capture that into your order promising service, you can get much better in terms of when can I get something from point A to point B, and what's the risk that I need to add into that. That's precisely what we're trying to do. We've added a layer on top of it where we can look at predictions because it's much easier to look at your own network, your own stores, and your own fulfilment centres.
