Using Automation for Product Listing Ads
Product Listing Ads (PLA) are a type of ad unit used in the Google Ads network. The ad is based off of product data rather than free-text headlines and descriptions, and gives midsize online retailers the ability to showcase their products among bigger e-commerce companies.
How Did PLAs Get Started?
In ~2010 Google was allowing websites to upload a product data feed that would then show in Google search. At its very beginning, the service was completely free and operated similar to an organic listing (SEO). Once the company realized they could monetize the service and base it off of the same rules as pay-per-click advertising, it became one of Google’s key advertising channels.
Now, in 2019, product listing ads (also known as Google Shopping Campaigns) take up nearly 50% of online advertising budgets. Retailers have realized the incredible value of the channel and use it to grow traffic, revenue and ROAS.
Managing product listing ads begins with a data feed. A product data feed consists of all of the attributes that would go into describing a product — color, size, shape, material, name, brand, manufacturer part number, etc. For some retailers, the product data feed can be incredibly large and contain over 500,000 SKUs. And even for smaller retailers, a typical product data feed is at least 10,000 SKUs. That’s a lot of data to work with.
Over the last few years, several digital marketing agencies have tried to create automated rules to best manage the campaigns using these large data feeds. Companies have created their own rules, leverage scripts in Google Ads or harnessed the power of 3rd party software. Automation has delivered exceptional results and caught the attention of Google developers as well.
Knowing the power of making automated bids, Google began offering machine learning to customers at the beginning of 2018. Bid management using machine-learning consists of target cost per acquisition (tCPA), target return on ad spend (tROAS), enhanced CPC and maximize conversions. These methods take into account the advertisers most important KPI to then acquire the traffic that is most likely to improve that KPI. Automation takes the daily tactical work out of the hands of the advertiser and allows them to focus on the overall strategy and directional trends of the shopping campaigns.
The Proof in the Pudding
In 2018, I had the privilege to launch a shopping campaign account on tROAS for a midsized retailer of modern lighting and home decor. The set up was relatively simple as Google makes it as easy as possible to get started; the quicker an ad can go live, the quicker Google makes money.
After setting up my campaign structure for brand vs. non-brand terms and including a few negative lists of searches I knew the company did not want to be a part of, I then established the ROAS goals based on the company’s profitability targets. Within Google Ads, advertisers are able to input their exact ROAS target for each campaign or ad group. This gives advertisers unmatched flexibility you simply cannot find in scripts or through other software. I set up the ROAS targets (350%) to get started and launched the campaigns.
Within 2 weeks, the campaigns were delivering high-quality traffic, sales and meeting the ROAS targets I established. This form of automation allowed me to take a step back to assess the overall PLA strategy, improve the data feed and make adjustments I would otherwise not have time to make.
Getting Started with Automated Bidding
For any small-to-midsize e-commerce business having trouble getting their footing with shopping campaigns, automation can really help out and allow the monotonous decisions to be made purely through data.
However, there are a lot of things to consider and it may be in your best interest to work with a paid media consulting firm to dial everything in correctly.
Please feel free to reach out if you have any questions!