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RKG is a digital marketing agency providing data-driven solutions to online marketing challenges.


Why Google Shopping Yields Smaller Orders, and Why That Might Be Okay

10 Apr 2017 10:40:57

This article appears in the Merkle Dossier 8.1, which includes actionable insights on paid search, SEO, loyalty services, co-op marketing, and more. Download Dossier 8.1 here. Online retailers should know well by now the importance of Google Shopping, also known as Product Listing Ads (PLAs), in paid search. As of Q4 2016, these image-based ad units accounted for 48 percent of all Google paid search traffic for retailers, according to the Merkle Digital Marketing Report. With text ads and PLAs now accounting for roughly equal shares of their total Google search ad traffic, many retailers look to compare metrics between these two formats to find opportunities for optimizations. One key difference that advertisers have consistently seen over the years is that the average order value (AOV) tends to be smaller for PLAs. Here we’ll examine the potential causes for such a difference, and explain why it might not make sense for advertisers to try to force higher AOVs from their PLAs. Google Shopping AOV Lower than Text Ads across Device Types Comparing AOV for PLAs vs text ads by device type for January 2017, PLA average order value was 12 percent lower than that of text ads on desktop and tablets and 17 percent lower on phones. These differences can also be much larger, as about 20 percent of the advertisers studied find PLA average order value more than 30 percent below that of text ads for any given device type. Only about 15 percent of advertisers had a higher AOV for Google Shopping than text ads for any given device type. Importantly, AOV is lower for PLAs because of differences in both the average number of items purchased per order and the average price of the items purchased. Do Product Listing Ad Clickers Buy Fewer Items? While PLAs have steadily expanded to show for a wide range of queries, including very general searches for broad product categories such as “men’s shoes,” PLAs are still more likely to show for queries that indicate the intent to purchase a specific product (for example, “Nike free 5.0 men’s”). For these specific searches, the query indicates that the searcher is looking for a single item, and logically it makes sense that searchers are likely to click on a PLA that displays an image of the precise product they’re looking for. This is to say that searchers looking for a specific product might be more likely to be presented with and then click on a PLA unit, while searchers conducting more general inquiries might be more likely to be presented with and end up clicking on a text ad. Very specific searches in turn probably yield smaller shopping carts than more general searches, since these queries show the intent to find a single product rather than to browse through multiple products. Looking at basket size for PLAs versus text ads in January 2017, this theory appears to hold up as the median PLA advertiser (Merkle clients) found that PLA-driven orders had fewer total items per conversion than text ads for every device type. The difference in the number of items per order is greatest on phones, which also produce the largest gap in AOV overall. However, the difference in AOV is greater for desktop and phone than the difference in the number of items purchased. So this data doesn’t quite tell the whole story. Do Product Listing Ad Orders Include Cheaper Products? Looking at the median price per item purchased of PLA orders versus text ad orders, we find that items in PLA orders are slightly cheaper across every device type. Product price is clearly displayed in each PLA unit, and searchers can deliberately choose to click on the cheapest possible option from among the products listed. Text ads, on the other hand, are not required to include price in ad copy, and very few searches return text ads that all have price clearly listed. Further, searchers have the option to click into Google Shopping and sort all relevant products by price, in true comparison shopping fashion. Google providing searchers with additional information and capabilities with PLAs likely le[...]

AdWords Price Extensions Might Not Be as Awesome as You Think

5 Apr 2017 8:35:49

Price extensions, released out of beta testing in mid-2016, feature products or services along with pricing information below traditional text ad copy, significantly expanding the size of text ad units. Along with the additional real estate, these extensions also provide users with more information about product/service pricing, which might help lead to more informed clicks and thus higher conversion rates. While several price extension case studies published show that ads with price extensions have higher click-through-rate than those that don’t, it’s important to account for variables such as keyword mix, device, and the fact that price extensions tend to show in higher average positions than ads that don’t trigger these extensions. Here we assess the performance impact for one advertiser currently using price extensions, and also highlight that Google appears to be showing these extensions in unexpected places on the SERP. Price Extensions tied to Higher CTR and CPC With such a substantial addition to the area taken up by a text ad, price extensions should naturally draw more clicks than ads that don’t feature this additional real estate. Indeed, looking at relative performance for the median exact match keyword when price extensions are featured versus when they are not featured, CTR is higher across all three device types when price extensions are triggered. Relative CTR is highest on phones, which also produce the vast majority of price extension impressions. For the keywords studied, phones accounted for 69% of all impressions when price extensions were triggered, compared to just 34% when price extensions were not triggered. This might speak to how likely Google is to feature these extensions on different device types. It also highlights the need to segment by device when comparing performance since device traffic share isn’t the same for ads that feature price extensions as for those that do not. As you can also see in the above chart, average cost-per-click is also higher for the median keyword in situations when price extensions show versus when they don’t. Are the extensions forcing advertisers to pay more? Probably not, and the difference likely has to do with Google’s propensity to show price extensions for ads in higher positions on the page. Price Extensions Triggered When Ads Appear Higher on the Page Looking at the same set of exact match keywords, we find that ads that feature price extensions have a higher average position than when the same ads are featured without price extensions. In average position, a lower value means an ad is featured higher on the page – hence the flipped axis in this chart. As you can see, while average position was just 0.1 positions higher on the page for desktop computers, that figure is 0.4 for both phones and tablets. Higher position likely explains why CPC is higher, given that clicks in higher positions typically cost more than those in lower positions as they require more competitive bids. While a 0.4 position difference might not seem like a lot, it can have a significant impact on the likelihood of searchers clicking on an ad. Particularly on phones, where the top ad listing might take up the entire screen above the fold, moving up a single position can have massive effects on CTR. As such, it’s fair to attribute at least some of the increase in CTR observed for ads that feature price extensions to the simple fact that these ads are typically in higher positions than those that do not feature price extensions for the same keywords. But what about the value of these extensions in driving more orders for advertisers? Users are getting more information about potential pricing, which should lead to more informed clicks and potentially higher conversion rate. However, the data shows no such effect. Conversion Rate Lower When Price Extensions Show Across all three device types, the median exact match keyword has a lower conversion rate when price extensions are featured compared to when they are not. Thus,[...]

Baidu Adds New Spider with Rendering Job

28 Mar 2017 14:37:27

About a month ago, I was informed by Baidu that they would be releasing a game-changer to rebuild the eco-system of the web in China. On March 24, in alignment with their previously published Web Ads Guideline (guidelines are in Chinese) Baidu Webmaster Tools announced that they have added a new crawling spider.

This new spider will not only crawl the HTML of the web pages but also render the page with other elements including CSS, JS, and images to help Baidu better understand the content of the page and provide more meaningful results. Baidu started the test on the 23rd and they are rolling it out now. The user agent of spider is marked as:

Mozilla/5.0 (compatible; Baiduspider-render/2.0; + (Desktop)

Mozilla/5.0 (iPhone; CPU iPhone OS 9_1 like Mac OS X) AppleWebKit/601.1.46 (KHTML, like Gecko) Version/9.0 Mobile/13B143 Safari/601.1 (compatible; Baiduspider-render/2.0; + (Mobile)

Baidu said this change is adding some pressure for sites, as it costs extra resources, but is advising webmasters not to block those spiders.

This change makes Baidu more like other advanced search engines and signals an elevated investment on the user experience and safety of the web. During the rollout, we expect the results rankings on Baidu to experience increased fluctuation. For technical SEO, Merkle is updating its Fetch & Render Tool to reflect this major change.

In 2015, Merkle became Baidu’s first US-headquartered reseller. For more on our partnership with Baidu and how this can benefit your China program, visit our Baidu page.


3 Tips for Effective Paid Search Audience Management

9 Mar 2017 16:12:56

Audience targeting is one of the most powerful levers paid search marketers have at their disposal. With limited budgets, complicated service/product offerings, and aggressive goals, harnessing the power to hone in on subsets of searchers is a must for a successful program. The challenge with audience targeting is that paid search advertisers can fall prey to pitfalls such as over-complication: too many segments can quickly become hard to manage and make remarketing efforts messy. By following a few guidelines, you can avoid common pitfalls and set your program up for success. 1) Ensure there is a business reason behind each specific audience you're targeting. There are two main reasons to isolate audiences from the rest of your traffic: to serve a unique experience or to bid differently based on performance. If your use case doesn't fall into one of these two scenarios, consider that it may be more work to tactically break out the subset of people than it is worth in reward. However, there are exceptions. For instance, you may need the ability to report on an audience separately using metrics not available in the user interface (i.e. 3rd party conversions). But if that's not the case, plan carefully so you don't inadvertently overcomplicate your account. 2) Prioritize audiences based on client objective. In most cases, it's possible for searchers to meet the criteria of multiple AdWords remarketing and Customer Match audiences. Because users can fit criteria for multiple lists, it's important to develop an audience hierarchy to prioritize them. For example, if shopping cart abandoners of high value products are worth more to an advertiser than page visitors of those high value products, the more important audience should be prioritized and trump other audiences. An audience hierarchy can be enforced either via audience negatives (if using AdWords' Target & Bid setting for audiences) or by bid strategy (if using the Bid Only setting). In the example below, if using the Target & Bid setting, campaigns targeting the “Cart Abandoners: High Value Product” audience wouldn’t have any negative audiences. This ensures that any searcher falling into that audience (regardless of what other audiences he/she falls into) is served the experience for the highest value product. For campaigns targeting the lowest priority audience, (“Page Visitors: Low Value Product” in this example), add all higher priority audiences as negatives to the lower priority campaigns. If using the Bid Only setting, simply bid most aggressively for top priority audiences and less for lower priority audiences, as search engines respect the audience with the highest modifier. Be methodical about which buckets searchers can fall into and ensure you're using audience negatives, which prevent ads from showing to members of a particular audience, to properly shepherd traffic to the correct audience. Keep in mind that if you are targeting Customer Match & remarketing audiences separately, overlap must be taken care of at the campaign or ad group negative list level but cannot be addressed within custom combination lists. Keep up with this! Negative audiences should be evaluated and possibly updated every time you update your lists, add new lists or update targeting in campaigns and ad groups. Do yourself a favor and save a running document to keep track of changes. It's difficult to audit and clean up later if you aren't diligent, so keep current and make this an ongoing effort. If you let it slip, you could jeopardize the efficacy of your program! 3) Always keep your focus on the areas of greatest return. It's easy to get bogged down by all of the possible segments! Keep your focus on what moves the needle for your account and don't let the tiny audience that doesn't perform cause distractions. Prioritize testing based on audience size and performance: launching experience tests across 50 audiences at once might sound like a good idea, but can quic[...]

Creating a Local Paid Search Strategy to Drive Customers to Stores

8 Mar 2017 17:13:04

Hi, I'm Laura Stiles. I'm a senior analyst here at Merkle, and today I want to talk about how to create a paid search strategy to drive customers into stores.

Now, more than ever, we're seeing that customers are blurring the lines between online and offline. So they're researching new brands and products on their phones before going in to stores to make a purchase. For paid search, we want to create a strategy that follows a similar line of thought.

So, the first thing that we want to think about is how we're going to measure our in-store campaigns. We need a way to capture the user's store visits or their in-store transactions. Google offers several tools to help measure both of these things. This part is really important because it's going to help us to discover parts of our current program that are really effective at driving customers into stores.

The next step is going to be creating local campaigns. We want to target keywords that capture the user's intent as they go into stores. Think of keywords like, "shoes near me," or, "rain boots in Charlottesville, Virginia," as examples of the queries they might use. 

Next we want to geo-target these campaigns so that we're only serving ads to users that fall within a reasonable distance of the store. That way, we're not wasting ad spend on users that couldn't reasonably drive to your physical store. We want to have ad copy that reinforces the user search. We also want to make sure that we're focused on in-store value propositions as opposed to online value props. As an example, where you would normally focus on free shipping, maybe instead you want to focus on something instead like door busters.

Next we need to figure out what landing page to send the users to. Typically the store locator page is going to be your best bet. Ideally, have the store locator already targeted towards your user's immediate area.

Finally, let's talk about the bidding piece. We want to be more aggressive on mobile devices. In this case, we want to capture users that are on the go, so mobile is going to be a great place for you to aggressively bid. We want to set different goals for our offline campaigns compared to our online campaigns. We want to make sure that we're judging these campaigns by their ability to drive users into stores as opposed to drive online conversions.

From all of this, there's a lot that can be gained. We're going to be able to drive users in to the store more effectively than we ever were before. Overall, that's how we can capture users searching online before making purchases in stores.



Unlock the Value of Audience Lists for Search

2 Mar 2017 17:22:54

The introduction of Google Customer Match in September 2015 unveiled a wealth of audience targeting options for search marketing via first-party lists and email addresses.

Since the launch of Customer Match, many brands have excitedly begun testing its capabilities, with a varying return as a result of typically limited traffic volume. Mark Ballard, senior director of research at Merkle, commented at that time, “Customer Match may not be a game changer for many brands, but it should provide another leg up on the competition for sites that use it in smart ways.”

In the following year and a half since the Customer Match announcement, there have been many theories regarding its targeting potential and the value that audiences brings to search compared to other marketing channels.

Given these unanswered questions, we put together the below infographic to help level set the expectations of the value of audiences, and to help advertisers most effectively unlock the potential of audiences for search. Click or tap on the image to download a PDF version of the infographic.



Advertisers Struggling to Bid Effectively on Connexity

1 Mar 2017 8:32:44

The Merkle Q4 Digital Marketing Report (DMR) was released earlier this quarter to highlight trends our client base observed from October through December of 2016. In the Comparison Shopping Engine (CSE) section there was one major theme that deserves a deeper look: the decline of Connexity from both a spend share and revenue growth perspective. The Cold, Hard Facts As a refresher, the DMR called out the below performance trends for Connexity:  Connexity has struggled to maintain spend share since accounting for 50% in Q1 2016. They are currently holding steady at 29%. Revenue from Connexity ads declined by 30% YOY, while revenue driven by ads on eBay Commerce Network rose 30% YOY. eBay Commerce Network significantly outperforms Connexity on conversion rate across most product categories. Our Q4 data very clearly illustrates that, between the two major Comparison Shopping Engines, ECN is substantially outperforming Connexity. What Is Driving Connexity’s Decline? The overall reason for less investment in Connexity is that retailers aren’t seeing efficient returns from the engine. We are usually able to take a client’s goals regarding ad spend to cost ratios and adjust bids or product mix on a given platform to maximize revenue. If a product, keyword, or product group is performing inefficiently, we look to reduce bids to a level that works with our expected sales-per-click (SPC). Reduced bids should lead to lower cost-per-click (CPCs), lower click volume and, as a result, lower overall ad spend. Connexity’s platform bucks this trend in that lower CPCs do not necessarily mean lower costs or better efficiency. The data below illustrates the correlation between weekly CPCs and efficiency for 19 Merkle advertisers during Q4 2016. A value of 1 indicates perfect positive correlation, meaning CPCs and ad-spend-to-cost (A/S) increase together in a linear fashion. A value of -1 indicates negative correlation, or that A/S decreases (efficiency improves) linearly when CPCs rise. Overall we’d expect to see a largely positive correlation, with more aggressive bids and CPCs leading to a higher A/S, meaning less efficient performance. Exceptions might be if a retailer is already showing very prominently, in which case higher bids won’t add much incremental traffic, or if their bids are so low that increases won’t get them anywhere close to the first few pages of results. In our client sample of 19 retailers, correlation figures were all over the place with eight positive, nine negative, and two right around 0. For comparison’s sake, here is what the correlation data looked like for eBay Commerce Network when looking at the clients active on both platforms. All of the values are positive, with just over half at .5 or higher. The results for Connexity are surprising given what we see across other platforms. One reason we believe this occurs on Connexity is that lower bids can result in ads no longer qualifying to appear for higher value searches on higher value domains in the Connexity network, which require higher bids. Thus, by decreasing the bid for a product, advertisers end up reducing the traffic that drives the most value, and are left with less valuable traffic requiring even lower bids. This situation creates a downward spiral effect for many programs. Advertisers decrease bids to get more efficient, but that chops off the best performing traffic and forces greater pullbacks. Eventually one is left with just a handful of products at low bids in order to keep the program afloat. In many cases, the only viable options are to 1) reduce bids so low that even a substantial increase in traffic would still lead to lower overall ad spend or 2) pause the program. As such, many of our clients are steadily reducing Connexity investment, and some are walking away from the engine entirely. Ways to Right the Ship There are several improvements Con[...]

Brand Building and Reaching China's Connected Consumer

14 Feb 2017 11:55:53

Hi, I’m Dalton Dorné, VP of Marketing here at Merkle. Today we’re going to talk about how advertisers can navigate China’s digital ecosystem, and ultimately, build their brand in China. We all know that China is one of the world’s most exciting markets, and it’s no secret the opportunity for marketers is huge. However, China is also a complex market, and Chinese consumers are among the world’s most brand-conscious buyers. In fact, they’re willing to pay more for branded products compared to their price-driven peers in other markets. This trend is also reflected in Chinese search behavior. In the US, searchers are more likely to search non-brand products and look for price comparisons amongst different retailers and brands. Chinese consumers are more likely to use search to research and validate brands beliefs, which, ultimately, may inform a higher-priced purchase. As such, brand value is extremely important in China. Sounds like a brand manager's dream. But it's exceptionally important for digital marketers too. It matters because China is the world’s largest mobile market, its rising middle class is extremely comfortable purchasing across digital devices, not to mention, nearly 90% of Chinese consumers use Baidu for search. Luckily, Baidu offers some pretty unique opportunities for brand building. As Merkle is Baidu’s first US headquartered reseller, we’re going to explore how marketers can take advantage of some of these unique offerings. Probably the most exciting brand opportunity on Baidu is Baidu’s Brand Zone. Brand Zone allows advertisers to own a sizable amount of the SERP with brand content, which is similar to a SERP takeover experience as advertisers essentially own all the space above the fold. This is completely unique to Baidu and not available on other search engines. There are a variety of formats and options within Brand Zone, and the experience is very customizable based on content, assets and consumer value-adds that the brand can provide. Not surprisingly, Brand Zone works for exact match brand searches only. Baidu believes that for exact brand searches, the consumer is truly looking for a specific brand and therefore enjoys the rich brand experience that Brand Zone delivers. The results back up this belief. Engagement with Brand Zone is high, with an average CTR of over 50%. By comparison, data from our quarterly digital marketing report shows that advertisers see closer to a 25% CTR for brand searches on Google. Beyond Search While Baidu is China’s largest digital platform and the world’s fourth most trafficked site, there’s much more to Baidu than the search engine alone. Baidu has numerous successful and important properties, including Baidu Knows (community forums), Baidu Baike (wiki-like encyclopedia), Baidu Music, Baidu Map, Baidu Cloud, Baidu Space (social network) and more. For enterprise brands looking to make a big splash in China, Baidu offers Brand Zone Matrix – which is the ability to have Brand Zone on up to six Baidu platforms - ensuring strong brand visibility and performance, at massive scale. Brand Protection While these are great opportunities for building your brand in China’s digital ecosystem, it’s equally important that you take steps to protect your brand as well. Designing and implementing a brand protection strategy is one of the first things Merkle does when onboarding new clients in China, and is a form of trademarking your brand keywords on Baidu. Let’s quickly look at this brand search for Topshop. Without brand protection, Topshop is not able to secure the top placement on the Baidu SERP, with competitors like GAP and shopbop appearing above them using their brand name. It’s an understatement, but ouch. If you’re interested in learning more about unique ad formats on Baidu, check out my recent Dossier article “Brand Build[...]

Amazon Focusing on Mobile with Google Product Listing Ads

14 Feb 2017 9:32:10

A collective shudder rolled through the shoulders of the paid search industry at the end of December as news spread of Amazon’s move to begin competing in Google Shopping, also known as Product Listing Ads (PLA). As the largest American online retailer, Amazon is uniquely positioned to drive significant competitive changes simply by entering an ad space, and many brands advertising through PLAs rightly fear that there will be significant negative effects on business as a result of this change. Digging into the Google Auction Insights data, it seems Amazon is much more competitive in phone and tablet Google Shopping results than it is on desktop, a trend not observed for text ads. This may be because phones are the largest and fastest growing segment of PLA traffic, drawing Amazon’s initial focus. It could also be Amazon's response to fading organic search growth on phones driven by Google updates in recent years. Amazon Impression Share Higher on Mobile Devices Marketers can identify when Amazon is directly competing with their brand in Google Shopping through AdWords Auction Insights reports, which detail which competitors are appearing in paid results for the same search queries as an advertiser. While, in December, Amazon’s presence in these reports was limited to a handful of advertisers that focused on home goods products, it rapidly expanded in January and Amazon is now a competitive presence for most brands with any connection to home goods. Brands that now see Amazon competing against them in Google Shopping find that Amazon’s impression share is higher on phones and tablets than desktop – a fact that is true for every single advertiser studied. Below is a table of the median value by week for Amazon’s PLA impression share against individual retailers, showing that the median Amazon competitor consistently finds that Amazon has a higher impression share on phones. Note: In Google’s Auction Insights Report, competitor impression share is populated as ‘<10%’ if the value is less than 10%. Some brands with Amazon as a competitor find that Amazon does not appear in desktop Auction Insights at all. While the median Amazon PLA impression share was <10% for both tablets and desktop for a couple of weeks in the sample, there isn’t a single retailer that found Amazon’s desktop impression share higher than that of tablet devices in any given week. Given that different device types display PLAs in different bundles, it could be possible that device impression share is impacted by the number of impressions available for different devices and what that does to the competitive landscape. If there are fewer possible impressions on one device, the vast majority of brands would have a lower impression share for that device type and vice versa. However, Google Shopping impression share for the Merkle advertisers studied is very similar across all three device types, indicating that the differences by device in Amazon’s impression share are the direct result of its bidding strategy. Taking a look at Amazon’s text ad impression share, we find that its presence on phones is often the weakest of the three device types. However, we find the same is true for Merkle advertisers, which likely reflects that these differences are indeed caused by differences in the number of total ad impressions for each device type rather than bidding strategy. Bottom line, we don’t see the type of higher Amazon impression share on phones with text ads that we find for Google PLAs. Thus, it appears that Amazon is in fact focusing its Google Shopping efforts on phones in a different strategy than it uses for text ads. Why Might Amazon Focus on Mobile Google Shopping? As shown in the Q4 Merkle Digital Marketing Report, phone PLA spend grew 61% Y/Y, driven entirely by click growth, and phones a[...]

Winners and Rookie Callouts from the 2017 Digital Bowl Report

9 Feb 2017 14:40:24

Thanks for joining us for this Merkle Insight video. I'm Dalton Dorné, VP of marketing. I'm joined by George Kamide, our social media and content manager. We're here to talk about the 2017 Digital Bowl.

This year's winner was T-Mobile, which ran an astonishing three ads throughout the big game and ran a multi-platform call to action for user generated content, and multi-narrative campaign across all digital channels. 

Our second-place winner goes to Avocados from Mexico. Avocados from Mexico performed killer across the three categories of SEO, paid search, and social media, getting a perfect score in those three areas. There was some opportunity for them to capitalize on in digital media and display, specifically with social retargeting pixels. If they had done that, they would have been this year's winner. As such, they remain our advertiser to watch in 2018.

And this year, we have some rookie call outs for Airbnb and 84 Lumber. Airbnb jumped in at the last second, and dropped an ad with relatively little ground game in digital, but the ad was so creative and so compelling that it trended for most of the game without any of that digital support. We have 84 Lumber, which is a first-time advertiser, jumping in with an astonishing 90-second ad buy, and which had suffered a setback earlier with a rejected ad, coming full force with a beautifully designed website. And again, they had a small digital ground game, but their compelling creative created a conversation throughout the big game.

That was an interesting one because that had a pure play digital call to action to go to the site. They did crash for a bit, but they were able to recover and get back up and had a lot of positive momentum in social.

For more insights from this year's Digital Bowl, and to deep dive on the various sections that we looked at, including paid search, SEO, social media, display, and digital media, please download our report. We'll see you next time.

See you next year!