# Achieving the Gold Standard in Paid-Search Bid Optimization

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1 MORE MATH. BETTER MATH. BEST ROI. Achieving the Gold Standard in Paid-Search Bid Optimization Understanding the benefits of global keyword-level modeling white paper: The Gold Standard

2 Achieving the Gold Standard in Paid-Search Bid Optimization is a detailed look at the four primary methods of optimizing keyword bidding: rules-based optimization and three different approaches to modelbased optimization (global keyword-level modeling, global cluster-level modeling, and local optimization). This page provides a brief summary of each method presented in order from highest to lowest performance. Read on for a more detailed look at each of these different optimization approaches. Model-Based Optimization Global optimization with keyword-level modeling: The customer selects the financial goal to maximize, e.g., revenue, profit or return on ad spend. Every keyword is individually modeled using the customer s cost and conversion history, an array of models, and a large number of variables to identify the specific models and variables that best predict performance for each keyword. The bid price for a given keyword is set to best maximize the desired global financial outcome. The models are re-trained frequently with the newest cost and conversion data, and the models and variables best predicting keyword performance might change accordingly. Even those keywords with low conversion rates are individually modeled and each keyword is treated as its own unique market, all aimed at maximizing the overall paid-search financial performance. This approach must be fully automated, given the millions of computations to determine the best treatment for each keyword. The result is a fully transparent process, individually modeled keywords, and excellent global financial performance. Global cluster-level modeling: Global optimization is the overall goal, but keywords with lower conversion rates are clustered to create sufficient data for modeling. Net, a hybrid approach is typically used with the keyword-level global approach used for head terms and clustering used for tail terms. Global cluster-level bidding tends to be stable, but what is gained in stability is lost in performance and automation. Performance suffers as clusters ignore the fact that every keyword is unique and the value of the aggregated data is outweighed by the loss of that uniqueness. A variety of factors such as seasonal changes, expanded keyword lists, and changes in product offerings can render clusters obsolete. When obsolescence occurs, statisticians are typically needed to tune the models manually, so cluster-based solutions are rarely pure software applications with the loss of automation and transparency. Local optimization: Local optimization simply sets bids so that each keyword meets the selected financial goal. For example, if the target ROAS is 200 percent, then each keyword is bid to obtain an individual ROAS of 200 percent. There is no trade-off between keywords. In another application, the bid price of each keyword is set based on the historical average value. Again, no trade-off or distinction is made between keywords. Either approach is simple but performance is lower as keywords are not assessed based on their relative impact on maximizing overall performance. RULES-Based Optimization Rules-based optimization is touted for being simple and easy to understand. For example, a rule might state, If ROAS is less than 200 percent, then lower bids by 10 percent. However, if rules are layered upon rules, simplicity and understandability are quickly negated, making it difficult to understand what will happen to the bids. The significant issue with rulesbased optimization is performance. Rules-based systems are reactive, with pre-defined responses to certain situations. In a rules-based system, historical data is not considered because the situation drives the reaction. Because of their reactive nature, rulesbased optimization systems can be very good at protecting position, but playing defense rarely leads to optimal results. In general, models-based systems are predictive, while rules-based systems are reactive.

4 transparent and meeting critical business constraints for example, maximizing profit while providing some minimum order volume, or maximizing revenue while maintaining a minimum return on ad spend (ROAS). For advertisers managing a large number of keywords, some form of model-based bid optimization technique is virtually essential. But success in using bid optimization software requires understanding the advantages and limitations of the different modelbased techniques and the recognition that there are no set-and-forget solutions. And bid optimization still requires some degree of human intervention from analysts with domain knowledge and an understanding of the business. BID OPTIMIZATION BASICS For large, complex campaigns, model-based optimization is superior to rules-based in every way, but it s not a simple choice between A and B. Of the three model-based methods, only one global keyword-level modeling delivers consistently superior performance improvement. Rules vs. Models The benefits of modeling exceed those of rulesbased optimization, which reacts to situations rather than predicting and adjusting by learning historical conversion data. As shown in Figure 1, there are two main approaches to bid optimization: rule-based versus model-based. A model-based system uses historical performance data to train statistical models to predict future performance. For example, a model-based system could predict the bids necessary to achieve a 200 percent ROAS tomorrow. They contrast with rulesbased systems, which use a pre-defined set of reactions to certain situations. For example if ROAS is less than 200 percent, then lower bids by 10 percent. In general, models-based systems are predictive while rules-based systems are reactive. CLUSTER LEVEL KEYWORD LEVEL LOCAL OPTIMIZATION GLOBAL OPTIMIZATION RULES BASED MODEL BASED FIGURE 1 BID OPTIMIZATION

5 Global Optimization vs. Local Optimization Global optimization (referred to as a portfolio approach by some vendors) considers all of the Global optimization strives for the success of the whole campaign, while local optimization makes sure every keyword achieves the same goal, regardless of the overall impact. keywords at once, assigning bids so that, on average, the group as a whole maximizes a goal while meeting certain constraints, such as a specific ROAS or minimizing cost per action (CPA). The advantage of global optimization is that it treats each keyword Within model-based approaches, we need to make a distinction between local and global optimization. (See Figure 2). Local optimization simply bids each keyword separately from the other keywords in an SEM program. For example, if your target ROAS is 200 percent, then every keyword is bid to obtain an individual ROAS of 200 percent. A local solution won t trade off a low ROAS on one keyword with a high appropriately with respect to the whole campaign. For example, it might turn out that one keyword can drive significant revenue at a ROAS of 180 percent, while another drives the same amount of revenue at a ROAS of 220 percent. As long as the average ROAS is 200 percent, the global solution will declare success. This approach generally provides higher value from a set of keywords than local optimization. ROAS from another. KEYWORD B Goal: Low Bid Cost: \$120 Orders: 2 Cost per Order: \$60 High Bid Cost: \$350 Orders: 5 Cost per Order: \$70 Maximizing orders Orders Constraint: KEYWORD A Low Bid Cost: \$300 Orders: 10 Cost per Order: \$30 High Bid Cost: \$660 Orders: 11 Cost per Order: \$60 Total Cost: \$420 Orders: 12 Cost per Order: \$35 Total Cost: \$780 Orders: 13 Cost per Order: \$60 Total Cost: \$650 Orders: 15 Cost per Order: \$43.33 Total Cost: \$1010 Orders: 16 Cost per Order: \$63.13 \$60 maximum cost per order Local Optimization Best Solution Global Optimization Best Solution FIGURE 2: In this simple example there are two keywords (A & B) that only have two possible bids each (High & Low), for a total of four possible bidding scenarios. The goal is maximizing orders; the constraint is a \$60 maximum cost per order. With local optimization, all keywords must meet the constraint. With global optimization, all keywords are considered at once and bid individually, leading to a substantially lower cost per order and a dramatically higher ROAS.

6 Global Keyword vs. Global Cluster Modeling general performance characteristics. Simply put, clustering was developed to assemble data sets large Global keyword optimization results in improvements that are significantly better than cluster modeling enough to permit human analysts to apply more traditional statistical techniques to determine bids. because each keyword is treated individually. Cluster modeling creates groups of keywords, treating each member the same as the others a hazardous shortcut for advertisers with large, complex paidsearch campaigns. While clustering may seem like a necessary way to manage keywords that receive very few clicks or impressions, the technique is in many ways suboptimal. On the plus side, using clusters leads to model stability, meaning the results are repeatable, Within model-based global optimization approaches there are two distinct levels of modeling: keywordlevel modeling and cluster-based modeling. which is something statisticians love. But the negative effects of using clustering are substantial. For one, keywords in a cluster might seem similar, but each keyword is actually unique at some level. While Cluster model-based systems were developed to cope with the sparse data problem of tail terms for which there is very little historical data available. As the name implies, clustering aggregates data from several keywords to hundreds and even thousands of keywords, which are assumed to have the same statisticians might value the extra data, individual keywords lose their uniqueness in a cluster. The result is a loss of performance compared to modeling each keyword as a unique entity (see Figure 3), as the advertiser spends too much on some keywords in a cluster and too little on others. SILVER ANKLETS STERLING SILVER ANKLETS Weekly cost Cost per click Clicks Weekly Position Cost Cost per click Day of the month Position Monthly position Weekly impressions FIGURE 3: Clustering is inherently sub-optimal since each keyword loses its uniqueness. The figure above shows two similar, live customer keywords and the variables affecting performance as determined by keyword-level modeling. Note that only one of the top variables is common between the two keywords. If these two keywords had been clustered and forced to use a common set of variables, performance for one or both keywords would be reduced. A second problem with clustering is that it cannot be automated and requires regular human attention to maintain performance. Often customers will achieve an initial improvement in bidding performance followed by declines as the clustering goes out of date. In a complex bidding situation e.g., significant numbers of new keywords, changing campaigns, significant seasonality, etc. clustering is even more difficult to keep current.

7 A second issue is clustering typically requires statisticians to tune the models manually. As a result, cluster-based solutions are rarely pure software applications and thus can t offer the degree of automated bid optimization needed by advertisers managing thousands or millions of keywords in a dynamic bid-based marketplace. A third issue is the time-intensive task of creating clusters, as all statistical models decay over time, making it necessary to periodically retrain the statistical models involved. For these two reasons, cluster models tend to be used far beyond their useful life cycle with a resulting negative impact on performance. Clustering survives due to the belief that keywordlevel modeling, while far superior in performance terms, can t be done on tail terms for which there simply isn t enough data available to build accurate models. That s why most paid-search bid optimization vendors extensively rely on clustering, even though they might not reveal this fact to customers. Why? Clustering has yet to be automated and requires human analysis driving up cost and response times, even as it leads to sup-optimal performance on the vast majority of keywords in a campaign. Fortunately for advertisers, this belief is false. Keyword-level modeling can be done on keywords with as few as 10 conversions a year. What s required is the right balance of the appropriate math, software automation, and transparency into the specific variables that drive individual keyword performance. Global keyword-level modeling is the gold standard of bid management. It regularly improves performance (profit, revenue, ROAS, etc.) by 25 percent or more in controlled tests against global cluster-level, local keyword-level and rules-based technologies. The basic elements driving success for global keyword-level modeling can be summarized as follows. Appropriate math: The mathematical approach to global keyword-level modeling rejects the requirement to cluster in order to create sufficient click or impression data. Clustering might produce an acceptable average for all keywords in the cluster but has little relevance to the future performance of the individual keywords the cluster contains. This accounts for the sub-optimal performance of clustering versus modeling the specific performance of the individual keywords within the cluster, which is achievable for even keywords with small amounts of data. Achievable, that is, using the appropriate mathematical modeling techniques such as structural risk minimization, a technique that trains models to become simpler as data sets become sparser as opposed to complex clustering techniques, which deal with sparse data by building models that are often far more elaborate than the data will support. In one controlled test, using the right math to individually model each keyword drove 216 percent more account sign-ups than a competing cluster-level solution. One issue in this test was the age of clusters used, which hadn t been refreshed and contained keyword groupings that were simply obsolete. Keyword-level modeling techniques identified a handful of good keywords hidden in clusters of bad keywords. Separating those out and bidding them up led to the volume increase.

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