Today we're going to hear from Helium 10's CTO Boyon, who's going to tell us everything that we wanted to know that goes into sales estimation tools, such as that Helium 10's X-ray Chrome extension has.

We're also going to find out what upcoming features we have in store for follow-up.

How's it going, everybody? Welcome to another episode of this serious sellers podcast. Now, this is our third edition of Tech Talk with Boyon. Boyan is our CTO here at Helium 10. And he always brings us some great insight from the development side. And today, I definitely wanted to talk about something that's been on my mind. I actually just did an AMA about it. It's about sales estimates. You know, a lot of different tools are out there, including Helium 10 that gives sales estimates as far as, you know, what sellers are doing on Amazon. And I think there's a mind shift, boy, you know, you and I have talked about this that kind of needs to happen. And it goes back to one of your things that you've always tried to teach people in different aspects is ask yourself why or what is your goal. You know, what are you trying to do? And a lot of times the questions that people are making is going to change if they have this mindset difference or a shift, right? So can you talk about that a little bit, first of all? You know, we see people coming out with different tools. They're coming out with different case studies, you know, for example, saying, oh, X, X, X, tool is X percent accurate? Now, here's Helium 10. Is this accurate? Now, you know, we've had X-ray for quite a while, but helium 10, as far as I know, has never come out with some kind

of case study or some kind of comparison chart with other tools as far as accuracy of sales

estimates. Now, why is that? Other tools, and people look at the problem space and it can be

deceptively simple. So making that the sales estimate is essentially attached to bestseller rank, BSR number.

Since BSR number is exposed by Amazon, when you look at the BSR, when you look at your own sales

in relation to that BSR, you feel as a seller, you might feel that it's relatively easy to make that estimate,

like how BSR translates to a number of sales. So BSR number is exposed. And if you look into it,

the way Amazon calculates, BSR is documented publicly. It's not on Amazon.com, but if you know where to look,

you can see, you can find more details about how BSR is calculated. So on the surface, it sounds like

a serial problem. And now more people are getting into data analysis and data science and

statistical methods are getting more evolved and the

computational power is cheaper. So it seems like something that's really easy to achieve and

it can be very deceptive. You look at your numbers, you look at BISR, say, okay, it's so easy.

I'm going to build a model that's very successful and has a high accuracy.

The problem with sales estimation is not the model.

The model that the science around that is pretty settled.

So that's the trivial part.

The tricky part is actually defining that problem space and recognizing what needs to be estimated.

So let's say you have two products that is fairly stable, BSR and you know sales for those

two products.

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