OPTIMIZING GOOGLE SHOPPING: STRUCTURE Taking a closer look at optimizing Google Shopping and how it is structured
ABOUT THE AUTHORS PART OF THE QUANTADS PPC TEAM THOMAS BYSKOV MADSEN DIGITAL MARKETING SPECIALIST Experienced SEM/PPC specialist with a particular focus on Google Shopping and feed solutions. Thomas works within these areas for several e-commerce clients, and is continuously testing out new ways to improve their setups. LASSE RASMUSSEN CEO Co-founder of QuantAds in 2010 and architect behind numerous methodologies used in QuantAds today within PPC, Analytics and Digital Strategy. Lasse drives many of our client relationships where PPC is a major focus area.
INDEX IN THIS ISSUE Introduction... 04 QuantAds methodology for optimizing digital paid marketing... 07 Google Shopping Optimization Part 1: Structure... 08 Cross-Campaign Traffic Segmentation... 09 In-Campaign Traffic Segmentation... 12 Conclusion... 17 SHARE THE KNOWLEDGE 3
INTRODUCTION IN THIS ISSUE For online retailers hoping to grab a major share of search clicks and sales, Google Shopping has become increasingly important, and as competition for those valuable clicks becomes ever stronger, knowing how to get the most out of your Google Shopping campaigns is crucial to succeeding in today s ecommerce world. This white paper, the first in a series of three, will guide you through detailed optimization of Google Shopping performance, specifically in relation to structuring campaigns and ad groups for optimal impact. Parts two and three will take you through data-driven feed optimization and bid management respectively. This first part will offer solutions to the following key points: 1 HOW TO STRUCTURE YOUR GOOGLE SHOPPING CAMPAIGNS In order to segment different traffic types of differing value - enabling the option of bid differentiation 2 HOW TO STRUCTURE AD GROUPS, PRODUCT GROUPS AND PRODUCTS So that search term data is segmented to the individual products thereby enabling you to make much more informed decisions 3 HOW TO DETERMINE WHICH SEARCH TERMS DISPLAY WHICH PRODUCTS Giving you detailed information about each products performance 4
GOOGLE SHOPPING IS GROWING RAPIDLY Google Shopping is developing rapidly as a source of paid clicks for ecommerce retailers. This is the result of an expansion both in the breadth (places where shopping ads are displayed) and in the depth (search queries that triggers shopping ads), but also because advertisers are increasingly allocating more of their advertising spend towards Google Shopping. Data from Search Engine Land suggests that Google Shopping s share of non-brand paid search clicks in the U.S. retail industry grew from around 45% of total clicks in Q4 2013 to 70% in Q1 2016 (+55%). We see a similar development in Europe and the Nordic region in particular. 1 PLA SHARE OF GOOGLE PAID SEARCH CLICKS AGGREGATED RESULTS U.S. RETAIL OVERALL NON-BRAND 70% 70% 43% 2014 2015 2016 1 http://searchengineland.com/google-shopping-ads-so-hot-right-now-the-meteoric-rise-of-plas-248055 5
Data from QuantAds own client portfolio, segmented on selected important industries, also shows a significant growth in Google Shopping s share of total paid search clicks. Whereas Beauty & Health has always led this trend, presumably due to searches being very brand-centric and product-specific, most other industries have been catching up in 2016. 40% BEAUTY & HEALTH MIXED RETAIL BOOKS BATHROOM & KITCHEN HARDWARE FASHION 2015 2016 Clicks from Google Shopping, as a percentage of all paid search clicks, has increased on multiple industries especially since late 2015 GOOGLE SHOPPING VS. STANDARD SEARCH ADVERTISING Google Shopping differs substantially from standard search advertising. The latter is about identifying keywords that will trigger on relevant search queries, matching these search queries with relevant ads, and then directing the people who click on the ads to relevant landing pages. In Google Shopping there are no keywords, no (directly) editable ads, and no landing pages to change. Instead there are products, product listing ads and individual product pages on your site. The lack of keywords means that the individual products (or product groups) are what you bid on when determining what a click is worth. Furthermore, matching search queries with product listing ads is done based on the content of the product feed. A product feed is basically a list of all the products you are selling and contains various information on the products, such as titles, descriptions, prices, images, colours, sizes etc. And although Shopping ads are not directly editable in the same way text ads are, changing the information in the product feed will not only affect which search queries trigger your Shopping ads but also how these ads will appear. 6
QUANTADS METHODOLOGY FOR OPTIMIZING DIGITAL PAID MARKETING It is clear that an increased focus on prioritizing and optimizing Google Shopping campaign is crucial for everyone working in the online retail sector. What is not so clear is the correct way to go about this. At QuantAds we work with optimization techniques in three main areas. We consider these to be relevant, not only for Google Shopping, but for all areas of Paid Digital Marketing. OPTIMIZATION AREAS WHEN LOOKING AT GOOGLE SHOPPING, THESE AREAS TRANSLATE TO: 1 STRUCTURE OF ACCOUNTS, CAMPAIGNS, AD GROUPS ETC. STRUCTURE TRAFFIC SEGMENTATION 2 QUALITY OF ADS, KEYWORDS ETC. QUALITY PRODUCT FEED OPTIMIZATION 3 FINANCIAL PERFOMANCE BID MANAGEMENT FINANCIAL PERFORMANCE PRODUCT AND CATEGORY BID MANAGEMENT This white paper will provide a detailed look at the first optimization area. 7
STRUCTURE GOOGLE SHOPPING OPTIMIZATION PART 1 Specifically, optimizing Google Shopping campaigns through structure is about segmenting the search traffic. Before diving into how this can be done, we must first establish the difference between two distinct types of traffic segmentation. We refer to these as: CROSS-CAMPAIGN TRAFFIC SEGMENTATION Cross-Campaign Traffic Segmentation refers to the way different search traffic types can be segmented between two or more campaigns. For example, it is common practice in standard search campaigns to divide search traffic into generic and brand searches. This can also be done in shopping, which offers bid management advantages. IN-CAMPAIGN TRAFFIC SEGMENTATION In-Campaign Traffic Segmentation refers to the breakdown of search term data into individual products within a single campaign, as opposed to the more usual scenario whereby a search term s data is the aggregation of multiple products. For example, the search term red t-shirt may trigger several different red t-shirts in a product feed. As these will vary in fit, design, price and so on, so will people s desire to buy them. Therefore, it is very unlikely that the search term red t-shirt will perform identically on all the varieties of t-shirts it triggers - so knowing exactly how this search term performs for each product is valuable information. 8
CROSS-CAMPAIGN TRAFFIC SEGMENTATION In standard search campaigns (with keywords), segmenting different traffic types goes without saying. The reasons for doing so include: ad messaging, bid management, reporting and providing the account specialist with a better general overview. In Shopping campaigns, the main reason for segmenting traffic is that different traffic types have a higher expected value than others, which should ideally mean different bids. As mentioned, the most common segmentation is to split brand and non-brand/generic search terms into different campaigns. In Keyword campaigns, this segmentation is easily done by adding the brand(s) as part of the keywords in the brand campaign. However, this has to be done quite differently in Shopping campaigns, given that there are no keywords. USING THE CAMPAIGN PRIORITY SETTING Rather than using positive keywords, a combination of negative keywords and the Shopping-specific setting: Campaign Priority can be used. This can be set at three different levels: MEDIUM HIGH LOW What is unique about this setting is that it is the sole factor for determining which one of multiple campaigns will display an ad (assuming that they are all eligible otherwise to display ads for a search term). This is the opposite to keyword campaigns, where the mix of Max Cost per Click and Quality Score are the deciding factors. This means that, all other things being equal, in a scenario involving two Shopping campaigns that are both entitled to display an ad for a specific search term, the campaign set to the highest Campaign Priority level will always be the one that displays the ad. Note however that Campaign Priority has no effect in relation to how one company s ads are shown against the competitors. So how can we use this priority setting to segment certain traffic types? 9
BRAND VS NON-BRAND SEGMENTATION Let s assume that we want to set up the common traffic segmentation of brand traffic vs. nonbrand/generic traffic. In the traffic flow chart below we have two different search scenarios representing the two traffic types one where the search term contains the brand, and one with a generic search term. TRAFFIC TYPE PRIORITY CAMPAIGN NEGATIVES BIDS Low Brand None High Brand Search Generic Search High Generic Brand Phrased Low BRAND SEARCH GENERIC SEARCH With a parallel campaign structure and the use of campaing priority and negative keywords, it is possible to segment the traffic into generic and brand searches. This enables bid differentiation bewteen these two traffic types which typically varies on conversion rate As priority comes before bids, the high-priority campaign will attempt to trigger its ad for both search terms. For the generic search term, it is this high-priority campaign that ends up displaying an ad (ignoring external factors such as competitors). For the branded search term however, a conflict arises as the brand is negated, thus preventing the ad from triggering. This allows the low-priority campaign to come into play; here, no conflict arises, and the ad is triggered. With this method, it becomes possible to segment brand and non-brand traffic, and consequently to differentiate bids between these two types. Note that if product bids in the two campaigns are managed from a CPA or ROI-based perspective, the generic search terms will end up with a (relatively) lower bid than the branded search terms, as a result of the expected difference in conversion rates from these two types of searches. 10
ADDITIONAL CROSS-CAMPAIGN SEGMENTATIONS Besides the generic versus branded segmentation, which in the majority of cases will be the most relevant, it is also possible to segment traffic in other ways, for example: GENERIC VS. PARENT BRAND VS. CHILD/SUB BRANDS (RELEVANT TO A RETAILER) GENERIC VS. BRAND VS. PRODUCT SEARCHES (E.G. USING THE PRODUCT S ID OR NAME ETC.) SEGMENTATION OF TRAFFIC CONTAINING CERTAIN ADJECTIVES OR VERBS E.G. CHEAP, BUY, ONLINE, USED ETC. CONDITIONS FOR A WORKING SETUP One important condition in order for the above structure to work is shared (non-capped) budgets across the campaigns. It is very important to note that if your high priority campaign gets budget-capped, the low-priority campaign will have no restrictions in terms of triggering its ads for all search term types. Your search term data will then get mixed up and more importantly, the bid differentiations made between traffic types will discontinue. 11
IN-CAMPAIGN TRAFFIC SEGMENTATION Besides segmenting traffic types, it is also possible to segment identical search terms based on the individual products that they trigger. This is done via a method we at QuantAds call the Single Product Ad Group Structure or simply SPAGs. THE SPAG STRUCTURE As the name indicates, the SPAG Structure means that every single product in the feed will have its own ad group. This is in contrast to having all products placed within one ad group or creating ad groups based on product categories or product popularity, which are some of the typical approaches used in Google Shopping. COMPARISON OF GOOGLE SHOPPING CAMPAIGN STUCTURES ADVANTAGES DISADVANTAGES SPAG STRUCTURE Product-segmented search term data Option of not advertising certain products for certain search queries Key metrics like Benchmark CPC and CTR for every product Added complexity Hard to manage manually Requires regular updating as the feed changes Many ad groups with little data information to bit on Only 20K product ad groups are allowed per campaign MULTIPLE OR ALL PRODUCTS IN ONE ADGROUP Less complex Easier to manage manually No information on individual products vs. search terms Loss of key metrics for specific products Negative keywords will affect all eligible products 12
The biggest advantage of the SPAG Structure versus other approaches is that it enables us to evaluate search term data for every single product. As search term reports are only available at ad group level, this is not possible in ad group structures where multiple products are placed in the same ad group, as data is then aggregated. In the example below, reports for a particular search term are shown according to the two different setups. With all products in one ad group, each unique search term only appears once, and is aggregated into the single ad group. In the SPAG Structure however, the same search term can be assessed in many different ad groups, all representing a unique product. With the SPAG structure it becomes possible to evaluate the same search term s performance on multiple products. With all other structures, search term data will be an aggregation of more that one product, providing that multiple products can be triggered by the same search term 13
This segmentation enables us to determine how a specific search term performs in relation to the different individual products. With this information, we can choose to exclude the search term (via negative keywords) for one product, but keep the search term triggering ads on other products, depending on performance. The SPAG Structure thus provides us with an indirect financial advantage, as we can limit the amount of products we want to have shown on certain search terms, thereby saving money if they are not performing as desired. The primary drawback of the SPAG Structure is that it increases the complexity of the Shopping campaigns, making them harder to manage manually. Furthermore, there will always be a part of the product portfolio that represents a very limited dataset. Here, bid management has to be done in a different way, as there won t be an Everything else product group like in most Shopping campaigns, where one bid can be set based on the aggregated data of all the products with limited data. Thus, in the SPAG Structure each product has to be given a specific bid. This challenge can be dealt with in two ways: 1. Setting the same bid for all the product groups with limited data, based on either 1) an aggregation of products with data below a certain threshold, or 2) data from the entire campaign. These bids will need to be updated manually, and as product groups begin to get sufficient data, they should then be given an individual bid based on performance. 2. Using automated bid engine software that as in scenario 1 calculates bids based on either all products with limited data, or all products in total. The bid engine should then update bids individually on each product group, as they begin to accumulate sufficient data. How to handle bid management for Shopping campaigns will be treated in depth in the third part of this white paper series. 14
MANUALLY SELECTING WHICH PRODUCTS TO DISPLAY (AND WHY IT IS A RISKY STRATEGY) An interesting option that comes with the SPAG Structure is that it enables you to manually select which products to show for specific search terms. However, whether this should be done or not is a frequently debated question. To shed light on it, let s return to the red t-shirt example and assume that we sell anywhere between 1-10 different red t-shirts, depending on our stock. Now assume that three of these red t-shirts sell significantly better than the remaining seven. Should we then negate red t-shirt on the seven low-performing products, thereby ensuring that we will only show our top sellers for this search term? One obvious reason for doing this is the expected increase in conversion rate gained by only showing the most popular products. However, there are also several compelling arguments for why we should not be doing this. These include: WHAT IF ONE OR MORE OF OUR TOP SELLERS ARE SOLD OUT? OUR LOW-SELLING PRODUCTS CANNOT TAKE OVER IF THEY HAVE BEEN EXCLUDED WHAT IF THE PRICES FOR OUR PRODUCTS CHANGE? HOW WILL THIS AFFECT THE PRODUCT S POPULARITY WHAT IF PEOPLE CLICK ON THE SHOPPING OR IMAGE TAB RESULTS, WHERE THERE ARE FAR MORE AD RESULTS? OUR SHARE OF ADS IN THESE SEARCH RE- SULTS WOULD THEN BE FAR LESS THAN IT COULD HAVE BEEN WHAT IF WE WERE OTHERWISE ELIGIBLE TO HAVE FIVE ADS DISPLAYED ON A GIV- EN SEARCH? WE WOULD THEN LOSE TWO AD IMPRES- SION DUE TO OUR KEYWORD NEGATION GIVING ROOM FOR OUR COMPETITORS 15
These risks might seem manageable on a small scale with 10 red t-shirts. But assume that you are running Google Shopping on a webshop with 10,000 or even 100,000 products. Keeping track of which search terms you have negated for which products can then become overwhelming. Furthermore, as a Shopping campaign matures and accumulates a significant amount of transaction data on individual products, top-selling products will start to stand out naturally. Through bid management, maximum click prices on these products is likely to increase, and this, combined with a CTR-level that will probably be higher than on other products due to popularity differences, will ensure that top-selling products are displayed most often. Given the risks above, and the fact that bid management provides an effective solution in the long run, we at QuantAds do not recommend following this strategy - unless you have a very small, easily manageable product selection. In general, negative keywords should only be used for traffic segmentation and excluding search terms that are generally irrelevant or consistently underperforming. 16
CONCLUSION Just as in any other area of Paid Search Marketing, getting the full potential out of Google Shopping campaigns requires a well structured setup. This comes before anything else. Furthermore, structure has to be considered and implemented on all levels of an AdWords account: at campaign-, adgroup, and product group-level. The first part of this three-part white paper series on Google Shopping optimization suggests that different traffic types, varying on expected performance, should be segmented into different campaigns. This enables us to bid differently on them, and thereby get the most value out of each traffic type. Furthermore, the white paper explains in detail how to implement this segmentation. The white paper also suggests using the SPAG Structure (creating one ad group for each product in the feed), with the added advantage of search term data being segmented on individual products, providing increased insight for the account specialist and potential cost savings. Lastly, the white paper discusses the question of whether or not to utilize the option of actively influencing which products should be displayed for specific search terms, an option that is made available by the SPAG Structure. It recommends not making use of this, not only due to the number of risk factors associated with it, but also because proactive bid management will ensure, in the long run, that the most popular products are displayed on their relevant search terms. The next part of this white paper series will explain how the quality of the product feed can be increased based on a data driven and dynamic approach. 17
ABOUT QUANTADS Fast-growing, international digital marketing agency We help e-commerce companies formulate and execute digital strategies across all major traffic channels (eg. Search, CRM, Social, Display). We specialize in delivering high-quality support and believe that a data-driven and documented approach leads to better decisions and outcome. Our digital specialists are supported by an in-house software team that provides tooling, technical help and web development when needed. QuantAds was founded in 2010 and currently has offices in both Amsterdam, Netherlands & Copenhagen, Denmark 45+ dedicated people who speak 12+ languages natively International e-commerce We believe international execution is much more than language support, it is also providing local market knowledge and understanding how local customers research, decide and purchase online products and services. International online businesses often face fierce competition from local champions and winning that game requires a lot more than a translated website and properly spelled ad-texts. #1 fastest growing agency in Denmark in 2015 Serve 50+ clients across all online industries about half are international e-commerce companies Business oriented approach to digital marketing The digital world develops so rapidly with new marketing platforms, targeting methods, and tracking technology that staying at the forefront requires a structured, factbased approach. We have therefore built our team by attracting and developing people with a strong business mindset, who love data and analysis and thrive in creating simple solutions for complex problems. SHARE THE KNOWLEDGE 18
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