How Performance Max Is Shaping Paid Advertising.pdf
4 Ways Advertisers Can Win at Google Shopping
1. Google Shopping Campaigns have rocketed to the top of many advertisers’ budget
priorities in the past few years. Distinct from traditional, text-based ads, Google
Shopping Campaigns are being used more and more by e-commerce leaders to
attract and win customers. As the competition for online customers grows ever more
heated, these advertisers are using Predictive Search Bidding to better manage the
performance objectives of their Google Shopping Campaigns
Predictive Search Bidding uses a combination of predictive analytics and high-
frequency bidding to accurately map an advertiser’s bids to changes in click values
throughout the day. Many advertisers are already using the technology to secure
a competitive advantage and gain maximum efficiency and return on ad spend for
their paid search campaigns. Now, they are applying that same technology to win the
Google Shopping race.
4 Ways Advertisers Can Win
at Google Shopping
2. Google’s first major release of paid ads that included
images, descriptions, and prices started with the
introduction of Product Listing Ad Campaigns (PLAs)
in 2011. Part of the motivation for this format was to
deliver more relevant content to searchers and a way
to view multiple retailers on a single search query. In
2015 Google updated the way advertisers manage
PLA campaigns in AdWords by implementing Google
Shopping Campaigns.
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Google Shopping Campaigns are an increasingly critical source of
revenue for e-commerce leaders. When a consumer types in
“running shoes,” for example, images, along with pricing, product
ratings, location detail, inventory status, and a link to the product on
the advertiser’s site matching the search are prominently displayed.
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Google’s top 20 paid
search advertisers have
embraced this format,
allocating 63 percent
of their marketing
budgets to Google PLA
campaigns in 2014
(Leichenko, 2014). As
more and more retailers
have recognized the
opportunity that lies in
implementing a successful Google Shopping program, the competition to place
prominently in these results is growing.
Retailers keep their physical goods in a warehouse, but how does Google know
which product to serve when a search query is submitted? Every retailer that
utilizes Google Shopping has a Google Merchant Center account that contains a
product feed. Retailers include product information such as Brands, Conditions,
Prices, SKUs, Image URLs and other vital details. The more organized and robust
the data is in a product feed, the easier Google will be able to serve the right
product based on the query. Having an organized and compliant feed is only half
the story.
While each advertiser’s product feeds are unique based on their own inventory,
they all are subject to the same auction managed through Google’s proprietary
algorithms. The vast amounts of product data, complex campaign structures,
and ever changing bidding landscape make succeeding in this channel even
more challenging for marketers. Conventional bidding processes, often involving
manual bids placed once a day or less, are woefully inadequate as the bid
decision landscape grows faster, more demanding and more complex. As a
result, advertisers often find they are overbidding for clicks when customers aren’t
purchasing, or under bidding in the auction and losing the opportunity to win a
customer entirely.
This white paper focuses on how retailers can effectively manage their Google
Shopping campaigns using Predictive Search Bidding. Using this next-generation
bidding technology, retailers can now calculate values, assign bids, and reiterate
multiple times per hour to surpass performance goals and reduce inefficient spend.
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Google Shopping Overview
Google Shopping campaigns are a powerful and effective Search Engine
Marketing tool that delivers multiple product choices to searchers from a single
query. Setting up campaigns requires both a Google AdWords (Shopping
Campaigns) and a Google Merchant Center (inventory data) account.
Within Google AdWords, marketers:
• Set specific budgets at the Campaign level;
• Assign Shopping Campaign priorities(High/Medium/Low);
• Divide products into Ad Groups;
• Segment their inventory further into Product Groups, and
• Assign bids by each sub-division of their Product Groups.
Shopping Campaigns in AdWords are connected to a Merchant Center account
and use the product feed as a framework to construct Campaigns, Ad Groups and
Product Groups.
One of the primary differences between traditional Search Campaigns and
Shopping Campaigns are the absence of Quality Score and Average Position
metrics. It is widely believed that Google’s algorithms assess the quality of data by
individual product in the feed and the corresponding bid in AdWords to decide what
products are served after a query is submitted through the Search Engine.
Without an explicit Quality Score and Average Position, it makes it even more
challenging for marketers to understand how Google ranks competing retailers’
Shopping ads. This leads to the question of how to calculate bids appropriately in
relation to the value that each click represents.
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Competition Creates Obstacles for Advertisers
Google Shopping has become increasingly popular since the concept initially
launched and has naturally created obstacles for advertisers. The rising
participation in the channel has sent branded and non-branded CPC rates
soaring as advertisers realized the value of product ads in comparison to text ads.
Additionally, maintaining a high-quality product feed is no longer an option, but a
requirement.
The good news is that the market isn’t saturated yet for advertisers wanting to tap
into Google Shopping (Hoff, 2015). However, retailers will need every advantage
they can get in order to cut through the growing field of equally keen participants
and manage their Google Shopping programs at scale.
Miscalculated or stale bids can potentially cause advertisers to overpay for
clicks during times when success metrics are not at their peak or miss out on
opportunities because bids are too low to be considered in an auction. Manually
responding to changes can help advertisers be more proactive, but it isn’t the most
efficient or effective option.
Predictive Search Bidding offers a solution for online retailers looking to improve
their Google Shopping performance by efficiently managing spend and exposing
conversion opportunities when they are present.
BID
Challenges of Google Shopping
Advertisers have been dedicating a larger share of their budget to Google
Shopping campaigns as compared to traditional text ads, but at what cost? In the
early days, retailers were not as fierce when it came to capturing a larger portion
of the market. If you had an organized inventory feed and a decent budget, you
hadn’t a worry. Now, managing all aspects within Google AdWords (i.e. campaign
structure, campaign priority, mobile/geographic/audience bidding adjustments,
competitive bids) and Google Merchant Center are necessary for success.
Calculating and assigning bids based on daily or weekly averages can reduce
management time, but this strategy can be detrimental in a highly competitive
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channel such as Google Shopping. One of the issues with daily or weekly bid
placements is not accounting for intra-day market fluctuations. Retailers that aren’t
aggressively bidding around the clock aren’t maximizing their budgets, as this
approach assumes a relatively static auction landscape and steady conversions
throughout the day.
Customer intent can vary when considering the time of day and day of week,
especially during seasonal periods. This demand can cause conversion rates
during specific periods of time to vary as much as 20 percent compared to
daily averages. Fluctuating demand on an hourly basis can impact ROI, forcing
advertisers to spend inefficiently during poorer performing hours.
Retailers can maximize their performance during peak hours by being more
responsive to changes in the auction by adjusting bids quickly and appropriately.
However, manually adjusting hundreds, or even thousands, of bids every few
minutes is virtually impossible for even the best Search Marketing professional.
Predictive Search Bidding is both a solution and strategy for placing the best
bids throughout the day, so retailers can spend less time worrying about their bid
decisions and more time developing strategies to their further their programs.
Tapping into Intra-day Opportunities
All advertisers expect Google Shopping campaigns to maximize a specific KPI
such as Margin, ROI or Cost/Order. Luckily, Predictive Search Bidding gives
retailers a competitive edge in an increasingly volatile auction landscape. Shopping
Campaigns that have numerous subdivisions of their product feed need to take
action on a massive scale to achieve their goals. However, there’s a lot more to
Predictive Search Bidding than simply setting bids. The best Predictive Search
Bidding platforms interpret large sets of data, calculate values and place bids
quickly, make decisions on limited data, and are scalable so advertisers can run the
most efficient campaigns possible.
With the various challenges related to bidding and campaign management,
there are also solutions to those problems to streamline the process and exceed
performance goals.
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4 KEY WAYS ADVERTISERS CAN WIN AT
GOOGLE SHOPPING CAMPAIGNS
Multi-Dimensional, Predictive Data Modeling
It’s surprising to think how much value a single click can add to a company’s
bottom line. Tracking clicks comes with the territory of any pay-per-click campaign,
from Google AdWords text ads to Google Shopping ads. However, accurately
calculating a click’s future value is another monumental task. Determining the
best bids around the clock starts by parsing extremely large volumes of data that
consider the hour of day, day of week, device, and other available dimensions.
The reality is that manually monitoring every click 24 hours a day isn’t realistic or
efficient. Not to mention, human error can result in inaccurate valuations resulting
in overpaying for clicks or missing out on conversion opportunities. Predictive
Search Bidding utilizes sophisticated algorithms and Bayesian statistics to
determine a click’s value and what bid should be placed at specific moments in
time, even with sparse data.
Evaluating the value of a click using the right mix of recent data in conjunction
with historical data will give advertisers the best guidance on their current bids.
Constant re-evaluation of these values enable advertisers to adapt to changes in
the landscape faster than others, providing another huge advantage compared to
less sophisticated methods of bid management.
Automated, High-Frequency Bidding
Advertisers who aren’t using Predictive Search Bidding typically place bids once
a day using daily or weekly averages to make their decisions. Not surprisingly,
this basic approach results in missed opportunities due to changes in the auction
landscape.
High-frequency bidding is a core component of Predictive Search Bidding that
enables advertisers to automatically adjust bids throughout the day. Calculating
and placing the correct bid at a specific time of the day results in advertisers paying
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the right price for clicks commensurate with their value. High-frequency bidding
is necessary for advertisers that require faster responsiveness to changes in the
market and placement of smarter bids more frequently.
Advertisers that employ high-frequency bidding typically realize a 10-20 percent
uplift in their campaign performance ([24]7, 2015). These results are not achieved
by just bidding more frequently, but by leveraging all available data to discover
the optimal bid levels based on specific time of day and day of week segments.
This ability enhances Google Shopping campaigns across every vertical, as
high-frequency bidding can inform retailers when it’s time to invest or to be
conservative. This way, advertisers can avoid squandering budgets during non-
peak hours, or overpaying during the best bidding times of the day.
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Long Tail Optimization
Advertisers have been given more flexibility in how they structure their campaigns
with the evolution from the now retired Product Listing Ad campaigns framework.
With these new capabilities come new challenges in how campaigns are structured
and how to effectively value and place bids on sparse data.
Understanding that advertisers can have unique configurations to better serve
searchers is key in developing strategies using Predictive Search Bidding. Similar
to traditional keywords that do not consistently receive clicks or conversions,
opportunities can be hidden within granular subdivisions of Shopping Campaigns
that do not consistently receive clicks or conversions. The ability to capture data,
assign a value, and retest in shorter durations of time helps to unlock the potential
of the long tail within Shopping Campaigns. Advertisers that tap into these long tail
opportunities can expose deficiencies among their competitors and as a result be
more efficient in capturing once unrealized conversions.
Modern, Highly Scalable Architecture
Although these individual solutions can put advertisers on the fast track to
successful campaigns, integrating all of these features can be a daunting task.
The overall solution lies within a highly modernized and scalable platform that has
the capability to process millions of bids around the clock. With that said, many
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advertisers are still using legacy bidding tools that were built when placing a single
bid a day was enough.
While these tools can be useful for some advertisers, they do not have the capacity
to calculate and assign bids with the high level of responsiveness required for
intraday changes in auction landscapes.
Being responsive to changes in the competitive landscape by creating accurate
predictions to bid the right amount at the right time will be necessary in managing
successful Google Shopping campaigns.
Learn More About Predictive Search Bidding
The Google Shopping landscape is constantly evolving, and it’s critical to leverage
every element possible to beat the competition. To learn more about Predictive
Search Bidding and how it can help your program achieve its goals, contact [24]7
today.
References
]24]7. (2015, January 27). High-Frequency Bidding Explained. http://www.slideshare.net/campanja/
highfrequency-bidding-explained.
Google Shopping Ads Score Big With Retailers. (2015, January 27). http://www.forbes.com/sites/
roberthof/2015/01/27/google-shopping-ads-score-big-with-retailers/#2715e4857a0b7a344ed41018.
Leichenko, J. (2014, August 11). Paid Search Advertisers Spent 63 percent of Budget on Product
Listing Ads: http://www.adgooroo.com/resources/blog/top-paid-search-advertisers-spent-63-of-
budget-on-product-listing-ads/#sthash.X2xA8SXm.dpuf.
