New Products Strategy

New products added to your product feed present a specific challenge for any performance-based segmentation system. Because they have no history of performance data (such as impressions, clicks, conversions, ROAS, etc.), their placement purely based on current (zero) values would be misleading. For example, a new, potentially very successful iPhone would end up in a "non-performing" cluster if its initial zero performance was taken as final.

Karsa Labelizer offers several strategies for intelligently handling these new products to give them a fair chance to prove their potential. In the Cluster Designer, you can choose one of the following options for Select the behaviour of newly added products:

1. Automatic derivation of values

• Principle: For new products, Karsa Labelizer will try to derive (approximate) their potential performance values based on similar, already existing products in your catalog. For this derivation, attributes such as:

  • Manufacturer (brand)

  • Product category

  • Price level are typically taken into account.

• Placement: Based on these derived values, the new product is immediately assigned to the cluster that best corresponds to its estimated potential.

• Ongoing update: These derived values for the new product can be updated daily based on the broader context, and at the same time, the system begins to collect real performance data for this specific product. Once the product gains sufficient history of its own, its placement is primarily governed by its actual performance and it can be moved to another cluster (if product movement is allowed).

• Advantages:

  • The product is from the beginning placed in a potentially relevant (high-performing) cluster.

  • It can more quickly start generating data in an environment with corresponding budget and targeting.

  • The risk of the product remaining "lost" in a general or non-performing segment is minimized.

• Possible disadvantages:

  • The accuracy of derivation depends on the quality of data and the existence of truly similar products.

  • The initial estimate may differ from the real future performance.

2. Add to category for new products

• Principle: All newly added products are automatically placed in a special, dedicated category (and thus also campaign in Google Ads).

• Duration of stay: The product remains in this "incubation" campaign for a defined period of 30 days, during which it collects real performance data.

• Subsequent placement: After this period has elapsed and sufficient history has been collected, the product is automatically moved from this special category to the cluster that best corresponds to its actual performance.

• Advantages:

  • Full control over new products: You have all new products together in one dedicated campaign.

  • Specific budget and ROAS target: For this "incubation" campaign, you can set your own, for example looser, budget and target ROAS to give products space to "look around" and collect data without too strict limitations.

  • Clearly separated phase of data collection from the phase of optimization based on performance.

• Possible disadvantages:

  • If the "incubation" campaign is not properly set up and managed, products in it may not gain sufficient visibility.

  • High-potential products may be in a less optimized environment for a certain period than they could be with value derivation.

3. The product is placed with zero values

• Principle: The new product is assigned to one of the existing clusters based on how the algorithm evaluates "zero" or very low initial values of its performance metrics.

• Placement: This often means that the product ends up in a cluster designated for low-performing or "other" products.

• Gradual data acquisition: The product gradually acquires real performance data and based on this can later be moved to a more suitable cluster (if product movement is allowed).
• Advantages:

  • Simplicity of implementation – does not require special derivation logic or a dedicated campaign.

• Possible disadvantages:

  • Least optimal strategy for potentially high-performing products: The new product is initially "penalized" for the absence of history and may be placed in a campaign with a low budget or low priority, where it doesn't get a chance to quickly prove its true potential.

  • It may take longer for the product to get into the right, high-performing cluster.

Which Strategy to Choose?

The choice of optimal strategy depends on the specifics of your assortment and your goals:

• Automatic derivation of values is often a good choice if you have enough similar products in your catalog and believe in the system's ability to estimate the potential of new items. It is more aggressive in trying to quickly place the product correctly.

• Add to category for new products provides the most control and is suitable if you want to have a specific regime (budget, ROAS) for new products and a clearly separated data collection phase. It is a safer, more conservative approach.

• The product is placed with zero values is generally recommended the least if you care about a quick and efficient start for new products.

Proper handling of new products is an important part of maintaining dynamic and effective segmentation. After setting this strategy and rules for movements, you can proceed to the final Launch and Monitoring of clusterization.