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Why Google Shopping Yields Smaller Orders, and Why That Might Be Okay

This article appears in the Merkle Dossier 8.1, which includes actionable insights on paid search, SEO, loyalty services, co-op marketing, and more. Download Dossier 8.1 here.

Online retailers should know well by now the importance of Google Shopping, also known as Product Listing Ads (PLAs), in paid search. As of Q4 2016, these image-based ad units accounted for 48 percent of all Google paid search traffic for retailers, according to the Merkle Digital Marketing Report.

With text ads and PLAs now accounting for roughly equal shares of their total Google search ad traffic, many retailers look to compare metrics between these two formats to find opportunities for optimizations. One key difference that advertisers have consistently seen over the years is that the average order value (AOV) tends to be smaller for PLAs.

Here we’ll examine the potential causes for such a difference, and explain why it might not make sense for advertisers to try to force higher AOVs from their PLAs.

Google Shopping AOV Lower than Text Ads across Device Types

Comparing AOV for PLAs vs text ads by device type for January 2017, PLA average order value was 12 percent lower than that of text ads on desktop and tablets and 17 percent lower on phones.

These differences can also be much larger, as about 20 percent of the advertisers studied find PLA average order value more than 30 percent below that of text ads for any given device type. Only about 15 percent of advertisers had a higher AOV for Google Shopping than text ads for any given device type.

Importantly, AOV is lower for PLAs because of differences in both the average number of items purchased per order and the average price of the items purchased.

Do Product Listing Ad Clickers Buy Fewer Items?

While PLAs have steadily expanded to show for a wide range of queries, including very general searches for broad product categories such as “men’s shoes,” PLAs are still more likely to show for queries that indicate the intent to purchase a specific product (for example, “Nike free 5.0 men’s”).

For these specific searches, the query indicates that the searcher is looking for a single item, and logically it makes sense that searchers are likely to click on a PLA that displays an image of the precise product they’re looking for.

This is to say that searchers looking for a specific product might be more likely to be presented with and then click on a PLA unit, while searchers conducting more general inquiries might be more likely to be presented with and end up clicking on a text ad. Very specific searches in turn probably yield smaller shopping carts than more general searches, since these queries show the intent to find a single product rather than to browse through multiple products.

Looking at basket size for PLAs versus text ads in January 2017, this theory appears to hold up as the median PLA advertiser (Merkle clients) found that PLA-driven orders had fewer total items per conversion than text ads for every device type. The difference in the number of items per order is greatest on phones, which also produce the largest gap in AOV overall.

However, the difference in AOV is greater for desktop and phone than the difference in the number of items purchased. So this data doesn’t quite tell the whole story.

Do Product Listing Ad Orders Include Cheaper Products?

Looking at the median price per item purchased of PLA orders versus text ad orders, we find that items in PLA orders are slightly cheaper across every device type.

Product price is clearly displayed in each PLA unit, and searchers can deliberately choose to click on the cheapest possible option from among the products listed. Text ads, on the other hand, are not required to include price in ad copy, and very few searches return text ads that all have price clearly listed.

Further, searchers have the option to click into Google Shopping and sort all relevant products by price, in true comparison shopping fashion.

Google providing searchers with additional information and capabilities with PLAs likely leads consumers to products with lower prices than text ads. Given that searchers are probably more likely to click on cheaper products for many queries, this likely influences Google’s measure of the relevancy – and therefore the quality of specific products in searches. Its goal is to show ads that are most likely to appeal to searchers. If they signal that cheaper options are more likely to appeal to them with their click behavior, it follows that the auction might favor cheaper products.

While the median advertiser sees only slightly lower sales per item for PLAs than text ads, brands that sell higher ticket items in general see a greater gap in sales per item. This may indicate that user tendencies to seek out the lowest-priced option might be stronger for more expensive products, and/or that Google’s PLA algorithm gives more of an advantage to price-competitive products in auctions featuring higher ticket items than auctions featuring lower ticket items.

Overall, item price is much less to blame for lower AOV in PLAs than the number of items purchased for most brands. However, some advertisers in price-competitive industries find item price can differ significantly between PLAs and text ads.

Should Advertisers Try to Force Higher PLA Average Order Value?

Given that these causes seem to come from natural human tendencies which would lead to smaller order values for PLAs, trying to combat smaller Google Shopping cart value might be a losing battle for advertisers.

The most common reason that PLA average order value lags that of text ads is that PLA clickers just don’t buy as many items per order, so encouraging consumers to add more items should seemingly be the starting place of any brand looking to close the AOV gap.

However, aside from making the site as easily navigable as possible, and clearly displaying complementary products on landing pages, there’s only so much brands can do to convince users to purchase multiple products when they are more likely to want only a single product.

Discount offers which only take effect when customers reach particular order minimums can help incentivize larger carts, but they also sacrifice margin per item for volume.

Brands could also try to adjust the products featured in PLAs to shift traffic towards higher ticket items, either by omitting cheaper products from feeds or using bids to push more expensive products to show more often.

However, if searchers are looking for the best deal when checking the prices featured on PLAs, they’re probably less likely to click on higher-priced options. And again, Google is taking users’ likelihood to click on ads into account when determining which PLAs to show, so more expensive products with lower CTRs might not be featured in as many search results as would lower-priced items with higher CTR.

Don’t Sweat Google Shopping AOV, Do Optimize for Bidding

It’s very common that Product Listing Ads drive smaller orders than text ads, and there are some intuitive reasons for why this is the case. Advertisers can try to force bigger orders out of PLAs, but are likely better served by doubling down on PLA optimization best practices.

Rather than push more expensive items, brands should focus on setting effective bids for each product based on the expected value of traffic. If a lower-priced shirt is driving a higher sales margin per impression/click than a higher-priced shirt, advertisers should be bidding more aggressively for the less expensive product in PLAs.

Setting effective bids requires that advertisers structure Google Shopping campaigns in such a way that bids are placed as granularly as possible. Further, advertisers should be filtering out any irrelevant traffic by using negative keywords. They should be combining negatives with campaign priority settings to funnel important segments of traffic, such as those containing brand keywords, to the correct product targets.

Obviously, advertisers want higher order values, but in the case of PLAs it seems there are some natural consumer behaviors and auction variables that lead to AOV trending below that of text ads for many advertisers. As such, brands shouldn’t place too much importance on AOV parity between the two formats.