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Facebook and Google have been predicted to control an estimated 59.3 percent of all digital ad spend in the U.S., according to recent reports. Part of the appeal of both platforms is the ability to manually create precise targeting segments and optimize based on a variety of performance metrics. But as Google and Facebook continue to expand their advertising offerings, the role of the traditional media planner is evolving to adapt for less hands-on control of their campaigns.
Both platforms have launched a series of capabilities to bring more automation into campaign management. Fundamentally, Facebook and Google’s updates are building machine learning into campaign strategy at a level not seen before from either company. These tools are primarily aimed to assist small businesses by simplifying the platforms’ algorithms. However, these updates represent a tectonic shift for the major walled gardens, and agencies are having to adapt how they plan, buy, and optimize their digital media. So while these changes make the platforms more accessible, the trade-off is less visibility in how and why the systems are spending.
Historically, advertisers were able to manually control budget using a combination of targeting, placement, audience signals and affinities, bidding strategies, and keywords. This approach allows advertisers to manually control spend toward different audience segments – for example, advertisers could assign spend to prioritize new customer acquisition while also allocating spend for brand loyalty initiatives.
However, Google and Facebook have been moving away from this traditional model with the release of new ad formats that simultaneously start to minimize day-to-day management operations from agencies and push advertising dollars toward historically underutilized ad inventory in their respective ecosystems. Product releases such as Google Local Ads and Facebook’s automatic placements automate where ads are shown across their available inventory while limiting transparency into where ads are shown. In addition to automatic placements, Google’s Smart Bidding and Facebook’s Campaign Budget Optimization go a step further and implement automated budget management strategies across all campaigns. It must be stated that by and large these tools remain opt-in as incremental capabilities rather than standard practice for both Google and Facebook; however, the consistent expansion of capabilities following this pattern and the urgency with which the platforms advocate their use suggests this trend will only continue to grow.
While Google and Facebook claim these changes were made to increase performance and provide media planners with more time for high-level strategy, there could be a fear that this lack of control will be accompanied by a misunderstanding that the role of agencies is no longer needed.
But you can rest easy. We are here to quell those fears.
Introduced in 2019, the Google Local Ads format is designed for one purpose: to drive brick-and-mortar store visitation. Google Local Ads use a combination of display, text, and video assets across Google search, Google Maps, Google Display Network, Google My Business, and YouTube to optimize across platforms with the goal of driving brick-and-mortar store visitation.
As you may have guessed, the catch here is a lack of transparency. Advertisers are unable to select target audiences or even keywords they want to target. Campaign reporting is no different, as advertisers lose visibility as to where their ads were shown or even which Google channel received impressions.
So how does it work? Google Local Ads use a “black box” targeting/bidding strategy that ingests several million data points to identify their target audience and optimize toward users most likely to visit an advertiser’s brick-and-mortar location. Google collects data on their more than one billion active users across three million websites and apps to identify users’ demographics, behavior, location, and intent. They then use machine learning technology to identify consumer patterns to optimize budgets toward users most likely to visit a store location.
Even with the lack of visibility, Google Local Ads have proven to be a valuable addition to several of The Richards Group’s clientele. This is where the importance of strong media planning comes into play – the ability to identify when sacrificing transparency and control is warranted to achieve campaign goals. To ensure brand safety, The Richards Group developed a proprietary exclusion list that limits where ads can be shown across YouTube and Google Display Network while not sacrificing performance.
When Facebook initially launched their ad platform in 2007, advertisers could only showcase ad units on the right-hand side of the news feed. Since then, similar to Google, Facebook includes a laundry list of inventory where advertisers can purchase media, including Facebook, Instagram, and Facebook’s related mobile apps, mobile websites, Instant Articles, and videos. In June 2019, the platform slowly started to introduce Instagram Explore as an additional placement for advertisers to display creative messaging. By October 2019, Facebook Search will also be available to all advertisers.
Despite the platform increasing their available inventory, the feed still continues to be Facebook’s primary source of advertiser revenue and to drive bottom-line results. At The Richards Group, we’ve tested bidding on placements in addition to the feed across dozens of in-house clients. Some campaigns have allocated 98 percent of total media spend toward feed placements and up to 95 percent of total trackable conversions.
Advertisers are strongly encouraged to bid on automatic placements to achieve more efficient campaigns by selecting all available inventory. In 2019, 68 percent of competitor agencies have started adopting automatic placements or using at least four placements. Facebook’s bidding system leverages machine learning to help automate where ads are seen to maximize results. The trade-off, however, is limited visibility in where the platform is prioritizing spend.
The bidding system is designed to get the best results at the lowest cost on the campaign level, not necessarily by individual placements – this can be misleading when we look at more granular-level reports. For example, let’s say an advertiser opted in to automatic placements through Facebook and Instagram. The platform will look across all placements and predict where the opportunities are.
In this example, even though Facebook and Audience Network placements look more efficient on average, the system predicted that the cost of Audience Network placements was going to rise and shifted budget.
The lack of visibility in automatic placements affects how agencies should approach creative storytelling. When bidding across channels, agencies need to be smarter about how to develop creative that resonates in a variety of placement experiences.
Local Ads aren’t the only product that Google is moving into “black box” territory. From bidding strategies to search ad copy, Google continues to move more and more pieces of their advertising business into automation.
Google’s Smart Bidding algorithms differ from traditional cost-per-click bids as they are automated to optimize toward specific goals. Whether advertisers want to drive website conversions, maximize traffic to their website, target a specific return on ad spend (ROAS), or target a goal impression share, these automated bidding strategies take keyword-by-keyword optimization management out of the hands of agencies and put them under the trust of Google’s algorithms.
Conversely, Google’s responsive search ads use a variety of headline and description variants to create ad copy optimized for performance. While advertisers can see how each piece of ad copy is performing at a high level via a “best/good” rating, they do not have any control over which pieces are served nor are they able to get impression totals for each line of copy.
While these solutions may work for a large majority of advertisers, it’s important for agencies to recognize that each campaign is nuanced and these automated bidding strategies may not work across the board. For example, Smart Bidding strategies such as maximize conversions may work to generate certain website actions, but they cannot accurately put weight behind multiple conversion actions happening on a page. This may cause lower-value conversions to generate higher volume than other actions that advertisers deem to be of higher value.
The role of agencies and media planners continues to be a critical piece to an effective Google strategy. From having a deep understanding of the client’s goals to having the ability to implement a nuanced strategy that can achieve these goals, agencies bring experience and expertise to advertisers’ Google campaigns that automation simply cannot match.
Campaign Budget Optimization was introduced in November 2017 as an alternative bidding strategy. Instead of controlling spending by individual targeting, Campaign Budget Optimization looks at the campaign holistically across all audiences and placements within the same campaign to determine where the lowest cost per action or the highest return on ad spend is projected to be. This update was especially useful for small businesses – Campaign Budget Optimization became the solution to simplify difficult budgeting questions and inform daily spending. Simplifying daily spending decisions would also free up valuable time: Advertisers could spend less time establishing daily budgets and instead focus on higher-level strategy and optimizations.
Starting in September 2019, Campaign Budget Optimization became a fixed default across all accounts. While beneficial in creating efficient campaigns, it will impact the way advertisers build and strategize for campaigns.
One of the main drawbacks in simplifying the bidding process is less flexibility and limited optimization in how the platform prioritizes spend. For example, let’s say that an advertiser’s end goal is to raise online donations for a nonprofit brand. The advertiser would likely buy and plan for media around this goal. In this example, let’s say they launched the following audience segments:
Audience that’s interested in competitor nonprofits: $1.26 ROAS
Audience that’s most similar to historical donors: $2.00 ROAS
Campaign Budget Optimization would likely allocate the majority of budget to the audience that’s most similar to users that have historically donated because they have a more profitable ROAS. But what if the first audience had more recurring donations compared to the second? Even though the first-time donation looks less profitable for this audience segment, the projected lifetime value might be worth paying the extra premium. Campaign Budget Optimization wouldn’t be able to differentiate this nuance.
As both platforms place an emphasis in automating their advertising platforms, the role of media planners and subject matter experts will likely shift to a strategic focus. In this new automated landscape, it is important that brands have clearly defined goals and an experienced agency that understands what mixture of each channel best achieves these goals and how to maximize return from each.
Platforms become more accessible for small businesses by automating day-to-day responsibilities.
Signals a trend to automate all digital campaigns within Google and Facebook, with more data signals to optimize what would be difficult to do manually (smarter campaigns).
Demand for experienced agencies to assist with technical setup and optimizations given lack of flexibility.
Think about creative messaging across inventory and multiple placements.
Categories: 2020 Digital Trends