1

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2

3 Executive Summary

4 Detailed Findings

4 Introduction

5 Hypotheses

6 Methodology

6 Findings

6 Hypothesis Evaluations

8 PLAs Behavior Varies By Business Type

15 PLAs Are More Effective Than NTM ads

17 The Law of Diminishing Returns Applies to PLAs

20 PLA “Sales & Profit Zone”

25 Strategic Implications

25 Strategic Questions To Ask

26 Role of PLAs in Marketing Strategy

27 For More Information

28 Appendix

28 Appendix A – Glossary

29 Appendix B PLA Performance ROAS vs Cost %

3

After our recent study on maximizing the value of your SERP results, we were still curious in

particular about the impact that product feed-driven ads have on online retailers’ businesses.

Given the share of space that Google and Bing allot to PLAs, most advertisers feel like they have

to play in this space, or they might miss out on a huge chunk of their audience. However, we

wanted to find out how much businesses really should be investing in Google Shopping to see a

good return for their money, and if there is a “sweet spot” to invest for optimal PLA performance

and ROI.

To find out, we first came up with our hypotheses on Google Shopping, based on anecdotal

evidence as well as common perceptions from our clients. After that, we then combined and

analyzed 2 years’ worth (CY 2013 and CY 2014) of AdWords performance data to evaluate

our hypotheses

As you will read further in our study, you’ll see that almost all of our hypotheses were confirmed.

So, what does all of this mean for advertisers? As the data in our study shows, PLAs contribution

can be exceptional in some cases-- sometimes even outperforming NTM campaigns by 500%.

Our study will show how PLA revenue and spend contribution have grown to impactful levels, and

that they demand a strategic focus to deliver the results they’re capable of bringing your business.

We’ll wrap up our study with a series of questions to help guide your own PLA strategies to

determine if you are in your “PLA Sales & Profit Zone”!

4

After our recent study on maximizing the value of your SERP results, we were still curious in

particular about the impact that product feed-driven ads have on online retailers’ businesses.

Given the share of space that Google and Bing allot to PLAs, most advertisers feel like they have

to play in this space, or they might miss out on a huge chunk of their audience. However, we

wanted to find out how much businesses really should be investing in Google Shopping to see a

good return for their money, and if there

Following up on our recent study on maximizing the value of your SERP results, we were curious

about the impact that product feed-driven ads (Product Listing Ads and then Shopping Campaigns,

which will be referred to as PLAs heretofore) have had on online retailers’ businesses as well as to

see if there is a “sweet spot” of spend for the optimal PLA performance. You may recall that our

SERP study showed that PLAs (which are relatively new in Google, as they became a paid

medium in August, 2012) can take as much as 33% of the SERP real estate that appears above

the fold is allotted.

Figure 1: Typical SERP from Google for a product-oriented query

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Figure 2: Bing SERP with Product Ads taking up 29.2% of above the fold viewable space

To find out more, here at NetElixir University we decided to take a look at PLA trends from 2014.

To start out study, we first formulated a set of hypotheses that were based on common perceptions

among our clients, as well as anecdotal evidence.

1. All online retailers and etailers should be using PLAs in their online marketing mix

2. PLAs behavior varies dramatically by type of business

3. PLAs spend and revenue varies by month within market segments

4. PLA share of paid search spend and revenue is growing

5. PLAs are more effective than non-trademark ads, but not as effective as trademark ads

6. PLAs AOV and basket sizes are smaller

7. PLAs conversion rates are higher than non-trademark (NTM ) campaigns’ conversion rates

8. PLAs CPCs are higher

9. PLAs CPAs are lower vs NTM

10. PLA impression share (out of total impressions in the account) is growing

11. The law of diminishing returns applies to PLAs

6

12. Migration to Google Shopping improves PLA performance

13. PLAs produce incremental growth

We aggregated 2 years’ worth (CY 2013 and CY 2014) of AdWords performance data that covered

more than 50% of our client base of online retailers and pure etailers. Next, we segmented this

data into 6 vertical segments:

1. B2B

2. Apparel

3. Gifts

4. Hobby

5. Health & Beauty

6. Home Furnishings

Our dataset, pulled from Google Analytics, covered over 1B impressions and over 15MM paid

search (medium=cpc user sessions. The total transaction revenue from this dataset exceeded

$60MM.

All online retailers and etailers should be using PLAs in their online marketing mix

If this was an episode of Mythbusters, we would rate this hypothesis as “plausible”.

Given the share of space that Google and Bing are allotting to PLAs, it’s easy to see why

advertisers might say to themselves “I have to play in this space, otherwise I am missing a large

chunk of the audience for a given auction”.

We found that PLAs can produce a significant chunk of your paid search revenue. In our study,

the % of paid search revenue from PLAs in 2014 ranged from 1% to 49%, with an average for the

entire year of 13%. Now, if you assume that PLAs are almost 100% incremental (more on this

later), you could look at that average and think to yourself “Hmmm, double-digit growth in my paid

search account if I turn on PLAs…not bad”.

We also found that revenue from PLAs is growing YOY at a healthy rate of 38.4% in 2014. As we

will see a bit later, we also found that PLAs outperform NTM campaigns in a number of key

metrics.

7

So, where’s the catch? Why not rate this hypothesis as “Confirmed”? While we do think everyone

should be using PLAs, you still need to be strategic about your investment in PLAs. Why do we

say that? Profitability. PLAs ROAS for 2014 was 2.4. As you can see in the graph below, a

ROAS of 2.4 requires a gross margin slightly over 40% to break even on the transaction. This

means that if your gross margins are below 40%, you might want to think a bit more about your

PLA strategy.

Figure 3: Break-even Gross Margin Vs. R/C

For example, another question to consider is the lifetime value (LTV) of your customers. If you are

in a below 40% gross margin business that gets most of its LTV from the first transaction,( e.g. you

sell non-consumable, non-perishable items for which most consumers have no need for multiple

versions and therefore repeat purchases are rare), you need to be smart about investing in a

medium where transaction-level profitability might be a challenge. On the other hand, if your

gross margins are above 50%, or you know that your average customer buys 3.6 times again from

you and spends 4.5 times as much as the first order over their lifetime, the decision to invest in

PLAs is a bit easier to make.

There are a number of strategies an advertiser can use in order to achieve profitability in their

PLAs. We’d recommend trying:

1. Only including higher margin items in your product feed

2. Adjusting your bid strategy based on the item’s gross margin, average selling price, and

conversion rate. For example, in NetElixir’s LXR platform, the custom bid management

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feature allows you to set a target ROAS level and target CPA level for each individual ad

group. If you know your AOV trends by ad group, you can then set targets for ROAS and

CPA that ensure your profitability for transactions generated by that ad group.

3. Using day-parting, geo-targeting, and device-level bid adjustments to control when and

where your PLAs appear, to ensure you are exposing them to profitable audiences only.

This hypothesis is true. As indicated above, the % of paid search revenue generated through

PLAs varied dramatically across our dataset from 1% to 49%. As you can see in the below table,

key metrics for PLAs varied widely across our dataset.

Table 1: PLA Key Metric Performance Ranges

% Paid Search Rev 13% 1.0% 49%

% Paid Search Spend 23% 2% 68%

ROAS 2.4 .56 5.14

Basket Size 1.42 1.12 3.7

AOV $72 $35 $198

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As you can see in the following graphs, we found that PLA campaigns’ performance varies

significantly across market segments, as well as within those segments by time of year. Figure 4

shows the aggregated revenue and spend % for the PLA campaigns in our dataset. Because our

dataset is from predominantly B2C businesses, you can see an upward trend in the revenue

contribution during Q4. Overall for Q4, PLAs were 15% of paid search revenue compared to

11.7% for the first 3 quarters.

Figure 4: 2014 PLA Rev % & Spend % By Month

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Below, Figure 5 compares the PLA revenue and cost contribution, as well as the ROAS as

measured by R/C for the 6 segments we used in this study. The size of the bubbles indicates the

relative size of the R/C value for that segment.

Figure 5: PLA Rev vs Cost vs R/C by Segment

B2B Apparel Home

Furnishings

Gifts Hobby Health & Beauty

Because our dataset included a mix of retailers and manufacturers that are selling directly to

consumers via their ecommerce sites, we also compared performance for those 2 segments

relative to the overall performance for 2014, shown below in Figure 6.

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Figure 6: Mfr vs Retailer 2014 PLA Performance

Manufacturers Entire Dataset Retailers

Figures 7 through 12 chart the PLA revenue and spend contribution by month for 2014. One

common theme in these charts is that the spend contribution percentage is almost always greater

than the revenue contribution percentage. The only exception is in the Gifts segment, where you

can see in Figure 12 the impact of the Christmas selling season, as the revenue contribution in

December jumps above the spend contribution.

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Figure 7: 2014 B2B PLA Rev % & Spend % By Month

Figure 8: 2014 Home Furnishings PLA Rev % & Spend % By Month

13

Figure 9: 2014 Apparel PLA Rev % & Spend % By Month

Figure 10: 2014 Hobby PLA Rev % & Spend % By Month

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Figure 11: 2014 Health & Beauty PLA Rev % & Spend % By Month

Figure 12: 2014 Gift PLA Rev % & Spend % By Month

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In Figure 4 above, you can see that in 2014, PLAs’ share of revenue and spend did grow from the

beginning of the year to the end of the year. That being said, we didn’t see this as a trend that

holds for all 6 segments. For example, Figures 9 & 10 tell a different story for the Health & Beauty

and Gifts segment. Health & Beauty clearly shows a downward trend in both metrics, while the

Gifts sector shows growth in revenue contribution, while the spend contribution drops over the

course of the year.

There were a number of hypotheses we had regarding the relative performance of PLAs vs NTM

campaigns. We hypothesized that:

1. PLAs AOV and basket sizes are smaller

2. PLA conversion rates are higher than non-trademark (NTM ) campaigns’ conversion rates

3. PLA CPCs are higher

4. PLA CPAs are lower vs NTM

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Table 2 compares these key metrics across the 3 campaign types. The only hypothesis we found

to be not true in 2014 was that PLAs CPCs are higher.

Table 2: PLA vs Trademark vs Non-Trademark 2014 Performance

% Rev 13% 18% 69%

% Cost 23% 58% 13%

ROAS 2.37 1.3 22.0

Conv Rate 2.4% 2.2% 4.7%

AOV $72 $85 $122

Basket Size 1.42 2.74 2.68

CPA $31 $66 $6

CPC $.61 $1.18 $.33

The AOV and basket size findings might surprise some readers! We believe this characteristic is

related to the purchase decision funnel, and where PLAs play in that process. PLAs are matched

based on the product name and description in the advertiser’s product data feed, and therefore

tend to appear on narrower, bottom of the funnel type queries, where the user is looking for a

specific product. PLAs are also probably used more often by consumers executing repeat

purchases of consumable products they use. The last factor we believe that affected AOV and

basket size is the landing page. PLAs by definition have to land the user on a product detail page.

NTM campaigns usually include broader query types that bring consumers to less narrow landing

pages where there is more opportunity to cross-sell or make consumers aware of other products.

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Figure 11 compares PLA campaigns’ share of total AdWords impressions for 2013 and 2014. With

a whopping 71% YOY growth, we’d definitely conclude that this hypothesis is true.

71%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Impression Share

2013

2014

YOY

Figure 13: PLA Impression Share

The law of diminishing returns means that at some point, an incremental investment will reach a

point where the return on that incremental amount fails to perform as well as the investments

preceding it.

A common question we hear from clients and prospects is “how much of my budget should I spend

on PLAs?” In order to assess whether diminishing returns applies to PLAs, we compared PLAs

revenue share to spend share over the 2 year study period. Figure 12 represents that data for our

entire data set over the 2 year period. The datapoints are monthly calculations of revenue and cost

contribution.

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Figure 14: PLA Revenue % vs Cost % 2013-2014

The shape of the trendline seems to indicate that we may not have actually hit the diminishing

returns point yet for PLAs! You can see that there are a few outliers in this dataset. Figure 13

shows this relationship when we removed the 3 most extreme datapoints. The trendline looks quite

different.

Figure 15: PLA Revenue % vs Cost % Outliers Removed

Because of the marked differences we saw in PLAs performance by market segment, we plotted

the revenue % vs cost % for each of our 6 segments to get more precise data for each vertical.

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You can see that in B2B and Hobby markets, the diminishing return concept appears to apply,

while the other 4 segments don’t seem to have hit that point yet, as their curves tend to move up

and to the right. So, what does this mean? We’d recommend that you monitor your own specific ad

spend portfolios to see if for your business PLAs have hit the point of diminishing returns yet.

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Figure 16 highlights what we call the PLA “Sales & Profit Zone” for the Health & Beauty segment.

You’ll notice in the area highlighted by the green bar, the relationship between the investment level

and the return level is almost linear. So, what does this mean for Health & Beauty retailers, or

retailers whose chart looks similar? It means there is untapped sales and profit potential if you

increase your level of investment in PLAs.

Still looking at the Health & Beauty segment example, the dashed lines shows that if an advertiser

was spending 20% of their budget on PLAs, the revenue they get from those ads would be just

under 20% of their paid campaigns’ revenue. In theory, that same company could increase the

percentage up to almost 45% and expect to get a proportional increase in revenue. You can see

the highlighted Sales & Profit Zones for each of our 6 studied segments in Table 3.

Our recommendation for advertisers is to do your own analysis of your PLA/Shopping Campaign

investment and return and determine if the law of diminishing returns applies for your business,

and if you too have untapped sales and profit potential with those campaigns.

Figure 16 PLA Sales & Profit Zone

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Table 3 PLA Sales & Profit Zones by Segment

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We also examined correlation factors in PLAs performance by segment. We examined the

correlation of:

1. The paid search revenue contribution % to paid search cost contribution %

2. ROAS as measured by R/C to the relative investment level as measured by cost

contribution %

3. ROAS as measured by R/C to the absolute investment level as measured in actual $.

Table 4 contains the results of these correlation calculations. Given the shapes of the curves

above, it was not surprising to see Apparel, Home Furnishings and Health & Beauty having very

strong positive correlations between their investment level and revenue contribution. Interestingly,

the B2B sector had a negative correlation, another indicator that B2B firms should keep a close

eye on PLAs performance for diminishing return levels. Also interesting is that we found that there

was only 1 strong positive correlation between the return on spend and the 2 measures of spend

we used, (the cost contribution and the actual amounts invested.) The only positive relationship

we found was in the Health &B segment, between the return metric and the cost contribution. In

that same segment, the correlation between return and actual spend amount was also positive,

although not by such a large margin.

Table 4: PLA Correlation Factors

Correlation Factors gifts apparel hobby home furn h & b B2B

correl rev% to cost % 0.47 0.82 0.43 0.83 0.93 (0.04)

correl r/c to cost % 0.10 (0.46) (0.30) (0.55) 0.60 (0.91)

correl r/c vs spend $ 0.19 (0.45) (0.05) (0.41) 0.31 (0.20)

One hypothesis that we formulated from our data that was not part of our initial predictions is that

perhaps PLA performance is related to the level of engagement customers have with the type of

product being sold. Take another look at the correlation factors of revenue % to cost % in Table 4.

We theorized that product and/or brand affinity can play a huge role in PLA performance since the

3 highest correlation factors are in Apparel, Home Furnishings, and the Health & Beauty, industries

known to have strong product and brand affinities.

Another insight this behavior might show is that highly-considered purchase categories can

perform well as PLAs. Watching my daughter browse hundreds upon hundreds of dress designs

for her prom dress gives me an appreciation for how engaged some apparel shoppers can be!

When it comes to decorating or adding items to your home, that same logic applies. When it

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comes to your health or to your appearance, there are a number of reasons why a customer might

have an affinity for a specific brand or type of product. For example, the “natural” aspect of

mineral-based makeup appeals to users with skin conditions or sensitive skin.

However, the data doesn’t back this theory completely, as one can make the argument that gift

items and hobbies are also high engagement, highly considered purchases, and the correlation in

those sectors, albeit positive, is not quite as strong as the other 3 cited. However, we feel that as

the correlation is still a relatively strong positive one, this is still enough reason for advertisers

selling highly engaged brands or products to think seriously about PLAs as part of their marketing

mix.

One can also view the lack of correlation for B2B businesses as more support for this theory. Most

B2B purchase decisions are made more rationally than B2C decisions, especially when significant

investments are involved. This could mean that the less emotional connection consumers have to

specific brands or products, the lower performance will be for PLA type ads.

One of the most surprising findings in our study was the change in performance that occurred after

the PLA campaign migration to Shopping Campaigns in August 2014. We compared the

performance in our segments from January to July and then from September to December. Table

5 below shows the changes by the key metrics.

Table 5: Performance Change After Shopping Campaign Migration

All Home Furn Apparel Hobby Health Gift b2b

R/C -7% -30% -50% 64% -36% 34% -5%

CPA 11% 64% 125% -39% 66% -25% -7%

CPC 36% 80% 86% -23% 50% 62% 16%

CTR -13% -17% -14% -26% 0% -5% -41%

AOV 3% 15% 13% 0% 7% 0% -12%

Basket Size -6% -7% -6% 25% 6% 4% 12%

To be fair to our friends at Google, the above comparison is not 100% unbiased. Q4 seasonality

and increased competition in PLA auctions in general are probably factors in this dataset. For

example, we have seen what we call “CPC inflation” from increased competition in many of the

markets our clients participate in.

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One of the most frequent questions we hear from advertisers is about determining how incremental

the results are of their marketing investments, especially PLAs. Figures 1 and 2 show that PLAs

often appear on SERPs that have paid text ads as well, so this curiosity makes sense. We tried to

answer this question by first isolating the results of clients in our dataset that had comparable

periods in 2013 and 2014 without any kind of PLA campaign running. For example, if a client did

not have PLAs running from January to March in 2013 from January to March in 2014, we used

that data, along with the results from 2014 when they did have PLAs running to compare their

results. We compared the year-over-year results during the periods when PLAs were not present

to those when they were present in order to deconstruct the growth in the latter period to run-rate

growth, growth from the addition of PLAs and any unexplained growth. We found that the impact

of PLAs appears to be mostly incremental.

Table 6 shows the results for impressions. Here is how the figures break down: For the periods in

2013 when there were no PLAs running in both 2013 and 2014, there were 21MM impressions.

The growth rate for impressions in 2014 for those periods was 8%. For the 2014 period when

PLAs were running, we would expect the run-rate baseline projections from the other campaigns in

those accounts to reach the 22.9MM impressions in the explained growth (the run-rate row of the

After PLA column). We found that the After PLA period in 2014 actually had 32MM impressions,

an additional ~10MM impressions over what the run-rate growth would predict. We found that

about 5.9MM of these impressions are from the PLA campaigns, while the remaining 3.2MM are

from unexplained factors.

In theory, if PLAs were 100% cannibalistic, you would expect the unexplained growth figure to be a

negative number.

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When you look at clicks, you see the same pattern with a positive 20k in unexplained click growth.

Table 6: PLA Incremental Impressions

Impressions Before PLA After PLA

explained growth - run rate 21,173,871 22,906,825

unexplained growth 0 3,230,477

PLA actual 0 5,879,371

Total Actual 21,173,871 32,016,673

Table 7: PLA Incremental Clicks

Impressions Before PLA After PLA

explained growth 363,152 330,212

unexplained growth

20,379

PLA actual

87,955

Total Actual 363,152 438,546

From this analysis, it appears that PLAs are producing incremental impression and traffic growth.

So what does all of this data mean for advertisers? Here are the series of questions that you

should be asking about your own PLA campaigns to decide what your next move should be.

What role do PLAs play in my marketing strategy?

What is the LTV of the customers I acquire via Shopping Campaigns?

How much can I afford to pay for Shopping Campaign Conversions?

How varied are the AOVs coming from my Shopping Campaigns?

How does my PLA performance compare to my non-trademark performance?

Is there room to improve my PLA performance? Are we operating in our own PLA Sales &

Profit Zone?

Am I following known best practices for Shopping Campaigns?

Am I a retailer that sells a lot of 3rd party brands with high demand or consumer preference?

Do the items I sell lend themselves better to visual ads vs textual ads?

26

The first decision to make regarding your PLAs (or just if you should start using them) should be on

what role PLAs will play in your marketing strategy mix. For example, are PLAs a new customer

acquisition strategy? If you think yes, then your PLA objectives should consider the LTV impact of

those new customers, i.e. how much additional revenue and margin will those customers deliver

beyond their first transaction. For example, when I used to manage an online ecommerce

business, I knew that repeat purchase rate was over 80% for the company, and that the average

value of the 2nd purchase was almost 3x as high as those first purchases! Knowing this, we

adjusted our CPA and ROAS targets accordingly for campaigns that delivered a high percentage

of new customers. On the other hand, if you find that PLAs tend to bring you mostly repeat

customers, you’ll probably want to ensure each individual transaction meets your profitability

metrics on their own.

Managing marketing budgets is a portfolio management process where you attempt to optimize

your results by managing the relative weights of different investments. Therefore, it is important to

understand how your PLAs performance compares to your other marketing investments, and within

paid search, how it compares to the other elements in your paid search program (such as NTM,

TM, Remarketing, Display and Dynamic ads.) Our study indicates that the balance between PLAs

and NTM campaigns will most likely be the most important aspect of these optimization efforts.

As our examination of the law of diminishing returns for PLAs showed, figuring out how to improve

your PLAs performance depends a lot on the dynamics in your specific industry. To begin solving

this for your business, you should be examining whether or not you have entered your “PLA Sales

& Profit Zone” or not by looking at the relationship between your investment in PLAs and the return

you are getting. We have presented several methods for assessing that question.

In addition to determining whether or not you are in your own PLA Sales & Profit Zone, you should

also ensure that you are following the best practices with your PLA campaigns to improve their

performance.

Some of the key questions to ask yourself with respect to best practices are:

Have we done our SEO for PLAs? Have we optimized our title and description fields in

order to optimize our query matching, click through rates and conversion rates?

27

Have we done the search query analysis to identify negative keywords to ensure our PLAs

are showing up in the highest quality auctions as possible?

Have we set up our negative keywords so that we are steering our auctions based on the

purchase decision funnel position of user queries?

Are we utilizing all of the tools available to optimize our performance? For example, are we

using the benchmark data to optimize our bids? Do you have a promotion feed entry for

every product in your feed?

Are we structuring our campaigns so that we can weight our spend according to the return

expected? Have we adjusted our bid strategies based on the AOV variance we see across

our PLA ad groups?

Are we strategically selecting the products in our product feed?

As the data in our study shows, PLAs contribution can be exceptional in some cases, even 500%

more effective than NTM campaigns! PLA revenue and spend contribution have grown to tangible,

impactful levels, they should be given the strategic focus they deserve.

For More Information

Call NetElixir at 609-356-5112 or visit www.netelixir.com

Follow us on Twitter @NetElixir

For over 10 years, NetElixir has been helping 100’s of online retailers succeed in their search

marketing efforts. Our services cover PPC management, SEO, Product Listing Ads & CSEs,

Mobile Advertising, Social Media Marketing, Web Analytics and Google Analytics Consulting.

NetElixir University is our complimentary educational program that provides businesses with

proven strategies and analytical tools for running successful search marketing campaigns.

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Through webinars, workshops and data-driven whitepapers, our goal is to train 10,000 businesses

on the best practices of search marketing.

© NetElixir 2015 All Rights Reserved

NTM & TM –Non-trademark and trademark. At NetElixir, we delineate between campaigns

targeting queries that contain the advertiser’s trademarked terms vs campaigns targeting queries

containing 3rd party brand name terms that the advertiser sells. For example, if the advertiser was

Wal-Mart, any campaigns targeting queries containing Wal-mart, Walmart.com or variants thereof

would be categorized as a trademark or TM campaign. Wal-mart’s campaigns targeting terms like

Tide, Crest, Colgate, Tropicana or any of the other brands it carries would be categorized as non-

trademark or NTM campaigns. For retailers who are vertically integrated, i.e. they sell their own

brands of products like the UP brand at Target stores, or manufacturers that are selling direct to

consumers such as Lenovo, Dell, HP, etc., in this study we have included campaigns aimed at

product-oriented queries containing their trademarks in the TM segment.

CPC – cost per click

CPA – cost per acquisition

ROAS or R/C – Return on Ad Spend. In this report we have used the simple version of dividing

revenue by cost (Transaction Revenue / Advertising Costs)

AOV – Average order value = Total Transaction Revenue / Total # of Transactions

Basket Size - # of line items or skus per order = Quantity / Unique Purchases

Revenue Contribution – in this paper, we use this term to describe the share of Paid Search

revenue that PLA/Shopping Campaigns produced. Again, since we are only using data from

AdWords, this is the percentage of AdWords driven revenue that PLA/Shopping Campaigns

delivered for the stated period.

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Spend or Cost Contribution - in this paper, we use this term to describe the share of Paid Search

media expenses that PLA/Shopping Campaigns consumed. This is the percentage of AdWords

expenses that PLA/Shopping Campaigns consumed for the stated period.

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