Mobile vs. Desktop Search - Hatchimals and Electronics Searches Lead to the Most Sales During Cyber Week

Area Vice President, Consumer Insights

Surrounding the Black Friday and Cyber Monday hype, 1010data analyzed the online shopper’s journey (path to purchase) and took a detailed look into the top 50 search terms used by consumers during CyberWeek. Boring right?

 

No. No. Hear me out.

 

The search terms we analyzed were search terms that ACTUALLY LED TO A PURCHASE.

 

Wait. It gets better. Really.

 

Not only did we analyze search terms that led to a purchase from an online retailer, we also split the view to see the differences between mobile search and desktop search.

 

 

Here’s what we learned:

  • On mobile devices, 78% of the searches were branded searches. This means that mobile shoppers are searching for a specific brand, such as Pokemon or PS4.
  • Desktop users were more likely to search for something non-branded like “laptops” or “TV.” 28% percent of desktop searches were for non-branded items.
  • “Hatchimals” was the #1 search term that led to a purchase for both mobile and desktop.
  • 48% of searches on mobile were for consumer electronics, compared to 56% for desktop.
  • Searches for the gaming category were consistent among device type, with 20% of mobile and 22% of desktop searches for consoles or games.

 

 

Why does this matter?

Having the knowledge of which search terms lead to an actual purchase can help any brand. Brands can make incremental sales by strategically bidding on the most important branded and non-branded keywords that lead to a purchase in a specific category.

Further, having access to searches for both mobile and desktop can help you take a different approach to mobile search compared to your strategy for desktop search.

Methodology

1010data utilizes a number of sources of consumer spending data representing millions of consumers to provide an accurate assessment of online and offline retail sales, market share, and more. Our data enables clients to track consumer behavior using high-quality, granular datasets that are often difficult to source, cleanse, and consolidate.

 

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