How to make meaningful Search optimizations for eCommerce brands


In previous posts, I’ve discussed many of the paid search challenges that eCommerce brands selling thousands of products face. One additional challenge that is sometimes overlooked is the difficulty in accumulating adequate performance data at the keyword level to make meaningful optimizations. For some brands in certain industries – insurance for example – there are a smaller number of products and relatively common ways to search for them. Life insurance, homeowner insurance, car insurance, and variations of those phrases likely make up the bulk of converted search volume. For eCommerce companies with massive SKU catalogs, converting product searches are often specific, with very little traffic associated to each search. In some cases, certain keywords or products may only have one or two conversions in a year. Now, you might think, “why bother with those long-tail keywords that seem to be exceptions?” For brands that sell a lot of different yet specific products, these long tail searches can account for 20-50% of search revenue cumulatively.
 
So, how should marketers optimize keywords and PLA placements when there is not a critical mass of data to make bid decisions at the keyword level? In this case, the best strategy is to roll-up and aggregate individual keywords and products into categories or sub-categories until there is a significant volume of data to make decisions. This strategy differs from the keyword-level optimization approach that we often recommend. However, it is necessary for eCommerce brands with this challenge to think differently. The goal is to aggregate long-tail keyword data into sub-categories that are as granular and specific as possible, while still having the volume of performance data to make decisions.
 
In order to execute this strategy effectively, there are a couple of critical elements:

1. A reporting infrastructure that allows marketers to analyze the aggregated performance data at the category or sub-category level.

2. A bidding framework and strategy that supports bid changes at the sub-category level.

If you would like to learn more about how to apply these strategies to your search campaign, fill out the form today.

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