Data Dudes March 14 2024
[00:00:00] Will: Welcome to this episode of Amazon Data Dudes. I'm your host, Will Christensen, and I've got Brandon Chekits and Nandeen here with me. Hello. All right. So, you may have watched our parent child ASIN episode. we're actually going to dive in on something here.
[00:00:13] Will: we have, what you might call a Rosetta Stone here. to translate back and forth between these two and other data points. Why as a seller is it interesting or as an agency is it interesting to connect parent and child ASINs and other data points that way?
[00:00:34] Nhan: Well, the challenge with Amazon is they have architecturally different things.
[00:00:39] Nhan: For example, an ASIN can have multiple SKUs, but a SKU cannot have multiple ASINs, or FUNSKU, FNSKU is what we call a FUNSKU, is another, just another alias for a SKU or a parent ASIN. So the mapping is, you got these different hierarchical mapping that you got to keep track of. that's the challenging part is knowing the terminology, knowing how they relate to one another is a challenge.
[00:01:04] Nhan: And getting to the data is even bigger challenge too.
[00:01:07] Brandon: depending on what you're looking at on Amazon, sometimes it has SKU, sometimes it has parent ASIN, sometimes it has FNSKU. The exact report you're looking on might have one, but not the one you want to join on somewhere else.
[00:01:17] Nhan: Yeah, and also the parent child is not in any of the reports.
[00:01:21] Brandon: only available from an API endpoint, so. Not a report anywhere. So fortunately we built one of those.
[00:01:27] Will: and we're gonna sneak this in here and we can probably do an entire episode on AI and how we are using AI, but we have an AI query builder, which is basically tool that sits on top of GPT 3.
[00:01:40] Will: 5 and our architecture hierarchy and can basically spit out some results from inside of what's going on. You're going to show us. how we can put, these, parent and child license next to each other, along with other data to just make that rapid connection between the data
[00:01:58] Nhan: points. Yeah, so let me start with the problem.
[00:02:00] Nhan: The problem we were running to is like we have this data stored in a SQL database in tables. Just think of a bunch of tabs on Excel sheets of data and like how to tie that together and how to get access to the user easily. that's been a big challenge for us to make that data available before we had sheets and you may have to mess with connections and tokens and things like that.
[00:02:23] Nhan: We just wanted to make it seamless where you log in and you can get to your data. And not only that, like in order to tie the data together, oftentimes you have to know SQL or some other sheets formulas that is hard to learn. So we wanted to put a layer on top. so an AI layer on top of my SQL database.
[00:02:42] Nhan: So we built this sheets query. For example, if we wanted to get the. The, SKU, parent ASIN, ASIN, and FN SKU table with all those identifies mapped. where would we find that? So we would go to the AI Query Builder, it's in our listings table, and I'll just do select all, so that way it's easy. And I just run this.
[00:03:03] Nhan: And what that does is it generates the chat to be generates the SQL query and then our system run the SQL query and spits back the data for you. So as you can see on here, it has which venue you're on, which is like marketplace. Um, and then listening to you. Asyn, parent Asyn. sales price, brand, and a bunch of other data, and then FNSKU here, and your custom, product group.
[00:03:27] Nhan: But our ability to get to the data, and you can filter down, you can delete some of the columns, and unselect a bunch of them, was this, we call the AI query builder superpower, which allows you to get to, the data quickly, and you can filter it down by SKU, and you can put it into Excel, and do a bunch of search, but like, The power of this is to get to the data quickly, and I use this quite a bit.
[00:03:49] Nhan: So
[00:03:50] Will: it really gives you the ability to sort of plug and play, explore the database.
[00:03:55] Nhan: Exactly. Exactly. let me demonstrate here. Like get to the data. So once we have the data, you just hit this copy clipboard. And then a lot of people are familiar with Excel or Google Sheets. And then you can just filter it down and, and see like, okay, I just want to see a list of search for the parent nation and then cost of goods.
[00:04:16] Nhan: And like that data is available without having to. Connect to an API without having to mess with sheets token or anything like that. It's just you log into seller labs, pull up the app and you got access to the data.
[00:04:31] Will: Beautiful, fantastic way to explore things and get to know a little bit more about what's there.
[00:04:36] Will: And, I'll mention it. we have that, AI query builder built into the sheets extension as well. So if you're already using the sheets extension, you do have the capability to, play with the AI features there. So anything else that either of you want to add to, our advancement and connections between parent and child ASIN and other data exploration?
[00:04:53] Nhan: I do want to say that like for more advanced users who know, how do you leverage SQL? So this is just a basic SQL query that pulls the data back from one. Like you can join the different tables and get as complex as you want, once you know. A little bit about SQL and leverage chat GPT to write it for you.
[00:05:12] Nhan: You can write quite a bit of complex queries in it using our data that we've made available for you. so any of like, you can add advertising data, inventory data into a report and pull that into sheets. so that's the power of getting data, into customers hands with a, AI layer on top that guides them.
[00:05:32] Nhan: The
[00:05:32] Brandon: I would say is a lot of those tables, we've already kind of joined some of those for you. So for example, it's like some of the Amazon FBA inventory reports, they just have FNSKU on it joined those tables on the backend. So it has FNSKU and ASIN on those or something.
[00:05:46] Brandon: so a lot of our reports are a little bit better than what Amazon provides because we've already done some of that simplification. but you can still obviously use the AI to join those in any way that you want. Now, the other thing that I was going to say is it's not just about parent ASIN and ASIN.
[00:05:57] Brandon: Also, a lot of times it's about translating a SKU to an ASIN, which is not also trivial in other places too. So sometimes, a certain report might give you a, SKU, but you want to group it by parent ASIN or something like that. So with that simple query that you saw that non demonstrated there on the listings table, you can see how it has all those columns available for you to, to look up and translate from one to the other.
[00:06:18] Brandon: That's, I think you called it a Rosetta stone earlier. Well, and it's kind of like that. If you, know an ASIN, but you need the parent SKU, like we can do that. Or if you know, the parent SKU needed all the child ASINs, we can do that too, all those things are possible with it.
[00:06:30] Will: Yeah.
[00:06:30] Will: Beautiful. Well, thank you both for joining us. That's it for our episode today around the Rosetta stone. That is the database that Amazon, makes a little difficult get into and seller labs, add some simplification and layers too, so that you can get what you need out of it. Thanks everybody. See you next time.
[00:00:00] Will: Welcome to this episode of Amazon Data Dudes. I'm your host, Will Christensen, and I've got Brandon Chekits and Nandeen here with me. Hello. All right. So, you may have watched our parent child ASIN episode. we're actually going to dive in on something here.
[00:00:13] Will: we have, what you might call a Rosetta Stone here. to translate back and forth between these two and other data points. Why as a seller is it interesting or as an agency is it interesting to connect parent and child ASINs and other data points that way?
[00:00:34] Nhan: Well, the challenge with Amazon is they have architecturally different things.
[00:00:39] Nhan: For example, an ASIN can have multiple SKUs, but a SKU cannot have multiple ASINs, or FUNSKU, FNSKU is what we call a FUNSKU, is another, just another alias for a SKU or a parent ASIN. So the mapping is, you got these different hierarchical mapping that you got to keep track of. that's the challenging part is knowing the terminology, knowing how they relate to one another is a challenge.
[00:01:04] Nhan: And getting to the data is even bigger challenge too.
[00:01:07] Brandon: depending on what you're looking at on Amazon, sometimes it has SKU, sometimes it has parent ASIN, sometimes it has FNSKU. The exact report you're looking on might have one, but not the one you want to join on somewhere else.
[00:01:17] Nhan: Yeah, and also the parent child is not in any of the reports.
[00:01:21] Brandon: only available from an API endpoint, so. Not a report anywhere. So fortunately we built one of those.
[00:01:27] Will: and we're gonna sneak this in here and we can probably do an entire episode on AI and how we are using AI, but we have an AI query builder, which is basically tool that sits on top of GPT 3.
[00:01:40] Will: 5 and our architecture hierarchy and can basically spit out some results from inside of what's going on. You're going to show us. how we can put, these, parent and child license next to each other, along with other data to just make that rapid connection between the data
[00:01:58] Nhan: points. Yeah, so let me start with the problem.
[00:02:00] Nhan: The problem we were running to is like we have this data stored in a SQL database in tables. Just think of a bunch of tabs on Excel sheets of data and like how to tie that together and how to get access to the user easily. that's been a big challenge for us to make that data available before we had sheets and you may have to mess with connections and tokens and things like that.
[00:02:23] Nhan: We just wanted to make it seamless where you log in and you can get to your data. And not only that, like in order to tie the data together, oftentimes you have to know SQL or some other sheets formulas that is hard to learn. So we wanted to put a layer on top. so an AI layer on top of my SQL database.
[00:02:42] Nhan: So we built this sheets query. For example, if we wanted to get the. The, SKU, parent ASIN, ASIN, and FN SKU table with all those identifies mapped. where would we find that? So we would go to the AI Query Builder, it's in our listings table, and I'll just do select all, so that way it's easy. And I just run this.
[00:03:03] Nhan: And what that does is it generates the chat to be generates the SQL query and then our system run the SQL query and spits back the data for you. So as you can see on here, it has which venue you're on, which is like marketplace. Um, and then listening to you. Asyn, parent Asyn. sales price, brand, and a bunch of other data, and then FNSKU here, and your custom, product group.
[00:03:27] Nhan: But our ability to get to the data, and you can filter down, you can delete some of the columns, and unselect a bunch of them, was this, we call the AI query builder superpower, which allows you to get to, the data quickly, and you can filter it down by SKU, and you can put it into Excel, and do a bunch of search, but like, The power of this is to get to the data quickly, and I use this quite a bit.
[00:03:49] Nhan: So
[00:03:50] Will: it really gives you the ability to sort of plug and play, explore the database.
[00:03:55] Nhan: Exactly. Exactly. let me demonstrate here. Like get to the data. So once we have the data, you just hit this copy clipboard. And then a lot of people are familiar with Excel or Google Sheets. And then you can just filter it down and, and see like, okay, I just want to see a list of search for the parent nation and then cost of goods.
[00:04:16] Nhan: And like that data is available without having to. Connect to an API without having to mess with sheets token or anything like that. It's just you log into seller labs, pull up the app and you got access to the data.
[00:04:31] Will: Beautiful, fantastic way to explore things and get to know a little bit more about what's there.
[00:04:36] Will: And, I'll mention it. we have that, AI query builder built into the sheets extension as well. So if you're already using the sheets extension, you do have the capability to, play with the AI features there. So anything else that either of you want to add to, our advancement and connections between parent and child ASIN and other data exploration?
[00:04:53] Nhan: I do want to say that like for more advanced users who know, how do you leverage SQL? So this is just a basic SQL query that pulls the data back from one. Like you can join the different tables and get as complex as you want, once you know. A little bit about SQL and leverage chat GPT to write it for you.
[00:05:12] Nhan: You can write quite a bit of complex queries in it using our data that we've made available for you. so any of like, you can add advertising data, inventory data into a report and pull that into sheets. so that's the power of getting data, into customers hands with a, AI layer on top that guides them.
[00:05:32] Nhan: The
[00:05:32] Brandon: I would say is a lot of those tables, we've already kind of joined some of those for you. So for example, it's like some of the Amazon FBA inventory reports, they just have FNSKU on it joined those tables on the backend. So it has FNSKU and ASIN on those or something.
[00:05:46] Brandon: so a lot of our reports are a little bit better than what Amazon provides because we've already done some of that simplification. but you can still obviously use the AI to join those in any way that you want. Now, the other thing that I was going to say is it's not just about parent ASIN and ASIN.
[00:05:57] Brandon: Also, a lot of times it's about translating a SKU to an ASIN, which is not also trivial in other places too. So sometimes, a certain report might give you a, SKU, but you want to group it by parent ASIN or something like that. So with that simple query that you saw that non demonstrated there on the listings table, you can see how it has all those columns available for you to, to look up and translate from one to the other.
[00:06:18] Brandon: That's, I think you called it a Rosetta stone earlier. Well, and it's kind of like that. If you, know an ASIN, but you need the parent SKU, like we can do that. Or if you know, the parent SKU needed all the child ASINs, we can do that too, all those things are possible with it.
[00:06:30] Will: Yeah.
[00:06:30] Will: Beautiful. Well, thank you both for joining us. That's it for our episode today around the Rosetta stone. That is the database that Amazon, makes a little difficult get into and seller labs, add some simplification and layers too, so that you can get what you need out of it. Thanks everybody. See you next time.
