SEM-Like Control For Successful PLA Campaigns

There are notable similarities between keyword and PLA campaigns that, when leveraged properly, will give retailers greater control of their campaign management. Let’s evaluate how retailers can leverage best practices from their keyword campaigns and apply them to their PLA campaigns for maximum efficiency and control over their budgets.

In SEM, search engines decide which ads are shown for which queries based on keywords; but, advertisers don’t have to yield control of their spend to the search engine. Instead, they can institute a proper campaign setup that allows for control at a granular level.

Control Over Your PLA Campaigns

In order to achieve this level of control, it’s best to split your keywords into ad groups by unique match type. Small, tightly knit ad groups allow you to write ads specifically tailored to the match type you are targeting.

If you are targeting a broad query, you can write an ad that applies in a broad sense to the consumer you are targeting. If, on the other hand, you are targeting a very specific exact match query, you will have the ability to specify this level of detail within your ad copy.

Splitting match types into their own ad groups isn’t the only option for maintaining granular control. Another option is to split match types into their own campaigns. If you are an advertiser who requires management of your budget at the match type level, this is the right approach for you.

Marketing your PLA campaigns demands a similar approach to campaign structure. For PLA campaigns, Google maps user queries to individual product targets that retailers set up in their AdWords campaigns.

Setting Up PLA Campaigns

There are several approaches to setting up your PLA campaigns in Google. The easiest is to set up an All Products target, which will market a retailer’s entire inventory in their data feed. This setup is simple and only takes about five minutes to get going, but limits the ability to be relevant at scale.

Because ads are assigned to ad groups, and there’s only one ad group that covers all products, you can’t target different ads to specific products. Furthermore, the same bid is applied to all products because there is only one ad group. If all products are in the same product target, you cannot apply different rules to different performing product types. Therefore, you can’t have different bids for your high-margin or best sellers versus your generic, low-margin targets.

If you apply the same principles of granular keyword targeting (ad groups by match type) to your PLA campaigns, you can overcome the limitations of All Products targeting.

The ideal situation for retailers is to place individual SKUs into unique ad groups. Benefits of this setup include writing specific ad copy for individual products and making extremely powerful bids (details covered a bit later in the post).

It’s important to note that campaigns can only hold 20,000 ad groups, so you’ll need more than one campaign if you’ve got more than 20,000 products in your merchant feed.

Below is an example of how you can split a broad product target out into multiple product targets to make it more granular:

Example 1: Broad Targeting

Ad group – pants

Target a product_type =jeans

Target a product_type=slacks

Target a product_type=trousers

Target a product_type=shorts

Example 2: Granular Targeting

Ad group – levi strauss mens jeans

Target a product_type=jeans AND brand=levi strauss AND gender=mens

Target a product_type=jeans AND brand=levis AND gender=mens

Example 3: SKU-Level Targeting

Ad group – levi strauss mens stonewashed bootcut jeans size 32×24 id1234

Target a product_type=jeans AND brand=levi strauss AND gender=mens AND style=stonewashed AND type=bootcut AND size=32×34 AND condition=new AND material=denim AND demographic=adult AND color=denim blue AND product_ID=1234

 Example 2 has a substantially higher degree of granularity than Example 1 and allows you to write specific, targeted ad copy directly related to the brand, gender and product type. Example 3 actually takes into consideration all attributes and features of the product and gives you the most control of your ad copy development as well as the way in which you bid. At this level, you are able to bid more aggressively for your higher-margin items and best selling items.

Granular Control of Ad Copy Message

When you group keywords by match type, you’re able to offer consumers ads targeted to the broadness or exactness of the query.  When it comes to PLAs, the same principles apply. Therefore, there is no better option than creating ad groups based upon SKU.

In Example 1, you cannot write ads with any degree of specificity past the general idea of “pants.” If your store has jeans, slacks, capris and other kinds of pants, you’ll have to write one ad that covers all different types of pants and you cannot target each individual pant type your store covers.

Since there are no keywords to bid on in PLAs, creating ad copy carries a significant impact in helping determine relevance. Creating ad groups based upon SKU gives retailers the flexibility to target each product in their inventory with a greater degree of specificity. Also, you can leverage your feed to associate real world promotions with their products in a powerful and dynamic manner.

Not only can each ad copy contain detailed features of specific products found within inventory, but your ad copy can reflect real time promotions by enriching the data feed to include promotional details at the product level.

We’ve heard some retailers argue that ad copy for PLAs isn’t as important as it is for keywords because the ad often doesn’t even show unless you hover over the image. However, experienced search marketers will agree that relevance from query to ad is key to any successful PPC campaign.

With keyword campaigns, it is evident that the relevance from query to ad impacts quality score. Currently, the quality score associated with PLA campaigns exists, but it’s not yet visible to advertisers. Is anyone else expecting Google to weigh relevance of query to ad for PLAs when determining quality score? If so, you can be sure that the more granular the structure of your campaign, the more ability you will have to make your ads relevant at scale.

Granular Bidding At The Product Level

For retailers, bidding should be about driving bottom line performance for keywords and PLAs: different lines of products require different bidding strategies.

If you sell prom dresses, you focus your efforts on generating traffic because most consumers interested will try the dress on before buying, so they’ll likely research online and then buy in-store. For cheap watches, you will want to drive performance based on ROAS; but, luxury/expensive watches will demand you drive performance toward gross margin % or CPA.

ROAS may not be an effective measure for high-ticket items due to the proportion differences between the CPCs and AOV, causing too much volatility for your bidding algorithm to optimize against. Only granularly structured campaigns will enable a strategy-based approach that is applicable across lines of business, categories and products.

Optimal Product Target Strategy

Control Your Budget With Negatives

By splitting up campaigns/ad groups into match types, SEMs can better control their ability to show the ads they desire instead of letting the search engine decide on their behalf.

For example, let’s say a retailer is bidding on broad, exact and phrase match keyword [little black dress], and they split up each match type into unique ad groups. In this case, they can add exact match and phrase match negative to the broad match ad group and the exact match negative to the phrase match ad group.

By doing so, the retailer is given complete control of their bidding approach. Without adding match type silos, the search engine could serve the ad for any of the match types for the query [little black dress] and the retailer loses control of how ads are targeted.

Negative keywords for PLAs can behave in a similar way for retailers, ensuring they are retaining the control they need to spend their marketing dollars in the most efficient way possible. Make sure to negative out your SKUS from each other. By taking the various feature differences between your products and adding the opposing features as negatives, you can ensure Google picks the right product for the right user query.

An example of this is by negativing out [large] and [3.2 oz] from a 1.1 oz Burberry cologne. When using these negatives, you are preventing the 3.2 oz version from showing when the 1.1 oz version is the best candidate to be displayed within the SERPs. If you choose not to negative out the 3.2 oz version, you risk this bottle appearing with a significantly higher price near the competing 1.1 oz listings.

Conclusion

PLAs can save SEMs a lot of time — they don’t have to pick keywords and match types, or organize their keywords into ad groups. At the same time, many of the same principles in SEM apply to PLA campaigns.  If you want to have SEM-like control over your PLA campaigns, you need to deploy a highly granular campaign structure.  The more granular your campaign structure, the more control you’ll have over bids, budgets, ads, and keyword negatives — and the more successful your PLA campaign will be.