---
title: Why you should not split your catalog into 1 strategy per ASIN?
description: To set up your first strategy, you’ll need to define a Product Group, which is a list of ASINs. We recommend breaking down your product portfolio according to one of these three dimensions:
---
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# Why you should not split your catalog into 1 strategy per ASIN?
### How to group ASINs?
To set up your first strategy, you’ll need to define a Product Group, which is a list of ASINs. We recommend breaking down your product portfolio according to one of these three dimensions:
- Products categories,
- The life cycle of your products (new launches, mature products, products reaching end of life),
- Profitability per product or per group of products.
Based on our experience, we recommend using a mix of these dimensions to start building up your first set of strategies. You should keep the newly launched products isolated in one specific strategy in Monthly Budget or Force Product Visibility algo mode. We suggest breaking down the rest of your product portfolio by categories and group them according to similar ACOS targets.
### What are the risk of creating one strategy per ASIN?
The main risks are:
- The underperformance of m19 campaigns
- Overspending
Limit the number of strategies you are running. It is always easier to start with a simple structure. Rule of thumb: a strategy should spend $500/month (€500/month) or more for you to get the most of our optimisation capabilities.
### What is the benefit of grouping ASINs together?
The general rule is that any AI algorithm is always better when fed with more data. With your current setup, you are running "multiple different instances" of the AI algorithm, each one trying to optimise for one ACOS target.
By grouping the products together, you will run "one unique instance" of the AI algorithm, optimising for one single ACOS target. You are feeding the AI with more conversion signals, which increases its predictive accuracy.
For example, let's say you have two ASINs, A and B, that are similar in features but convert differently between each other. If you use the A+B group for training the AI, then the predictions will be much more accurate since the AI takes advantage of these conversion signals to boost its predictive accuracy.
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