Bid management software

Bid Management is software used for the automatic controlling of bids in Search Engine Marketing (SEM). With a bid management tool, any number of search terms (keywords) can be managed via various paid search providers (e.g. Google AdWords or Yahoo). The system automatically transmits changes to bids to the relevant channels, e.g. Google AdWords, via API. The role of bid management here is to determine the relevant optimum bid for each keyword and to continuously adapt it. With most systems, varyingly complicated algorithms are used for this purpose. For the user, the automatic bidding results in time savings and usually an increase in performance. Bid management tools offer additional functions for easier administration of SEM campaigns. These include, amongst others, the areas of keyword expansion, search query analysis and advertising copy suggestions, as well as controlling of the landing page or trademark protection. Often, bid management solutions are enhanced by tools for campaign management. The customer thus gets the opportunity to conduct intensive analyses via a login area and to transmit the results directly to campaigns and search networks, for example by pausing so-called ad groups in Google AdWords. The trend with current systems is towards the enhancement of tools in the tracking area. More and more tools can now measure not only the clicks that took place directly before a conversion, but also the funnel preceding it, as well as other channels such as display and affiliates. As the channels occasionally influence each other greatly when it comes to performance, more transparency as regards the evaluation of the performance of individual advertising channels is achieved.

Benefits

The success of advertising in search engines depends significantly on the price a company will or must pay the keyword marketer for a click on their ad. That is why this billing model is called pay-per-click. The cost-per-click prices are determined by a bidding system per search keyword. The more extensive SEM campaigns are (they can contain hundreds of thousands of search terms), the more difficult it becomes to optimize bids in such a way so as to meet target figures. Target figures can be the net profit or also the cost of a purchase, i.e. cost-per-order (CPO), or the cost of a new customer contact, i.e. cost-per-lead (CPL). Bid management tools help to find statistically secured optimum bids based on automatic analyses.

Goals of Bid Management – Bidding Strategies

Bid management can have different goals, which can be defined by the user in the system:

Click Maximization for a Specified Budget

Here, the system tries to buy bids for the lowest possible price. This happens through the redistribution of the keywords via which the clicks are obtained. For expensive keywords, the bids are lowered and for low-priced keywords, it attempts to generate even more clicks via a higher position. This strategy is suitable for all those who do not want to or cannot take into account any quality differences between different keywords. As a rule, these are smaller businesses and online shops who simply want to generate traffic for their website. The conversion behavior, e.g. whether a purchase is made, is ignored with this strategy.

Conversion maximization

In conversion maximization not only the cost of the clicks is taken into account but also their quality. This must be measured using conversion tracking. Tracking is offered both by paid search providers themselves, as well as by bid management tools. The integration of the conversion tracking code can be carried out by the webmaster. The key figure for assessing quality is cost-per-order (CPO) for this strategy. It reveals what costs arise for a conversion (sale, download, registration, etc.). Therefore, the keywords with the lowest CPO are most attractive. Bid management thus attempts to buy as much traffic as possible via these keywords. The optimization process takes longer here, as conversions must now also be taken into account along with the clicks and CPCs, and these are usually significantly fewer in number than the clicks. As a result, the system has to wait longer for data.

Profit maximization

This strategy is also based on conversion measuring. However, the parameter for measuring quality is not the number of conversions here, but their value. This can be included in all current conversion tracking systems. Some tools also allow for alignment with the customer’s inventory control system so that an exact profit contribution per order or conversion can be determined. For customers, this enables the most precise form of optimization, provided they know the customer lifetime value. However, the volatility of the data is highest here, particularly when order values per keyword vary greatly. Customers with little conversion data therefore tend to fall back on conversion maximization.

Secondary conditions

In addition to these indirect goals, virtually all systems also allow for the setting of direct goals. These are, amongst others, preferred position in the relevant search network, highest or lowest bid, compliance with first page CPCs and ad scheduling.

Functionality

Rule-based bid management systems

Rule-based systems make decisions on changing a bid based on strict rules. These rules can be predefined by the manufacturer or determined and expanded by the user. A combination of different rules regarding the individual target figures such as CPO, CPC, position, conversion rate, time of day, etc., allow for a degree of adaptation to individual requirements. However, the changes in the pay-per-click channels are very high. Therefore, the rules must always be controlled and frequently adapted. For this reason, one often refers to “semi” bid management tools, as it is one of the tasks of modern systems to find these rules independently and to improve them continuously. The problem of data shortage is very high here because, for example in the long tail, certain rules cannot be applied in cases of missing conversion or lacking historical conversion rates. Well-known systems with a rule-based approach are Aquisio and DC Storm, for example.

Portfolio-based bid management systems

Portfolio-based systems try to increase the degree of optimization using theoretical models, whereby not every keyword is subject to rigid boundaries or rules, but the impact of the overall performance of the portfolio is what is decisive for the regulation of each keyword. This way, individual keywords can exceed certain thresholds, which can lead to increased performance of the entire portfolio if other keywords are accordingly adjusted. The basis of this is Markowitz’s model for portfolio optimization, which is very widespread particularly in the financial world and has significant influence on the selection of shares in funds (see portfolio theory). It makes sense to translate Markowitz’s ideas to the PPC world in order to achieve increased performance. However, this model does require a lot of information or data per keyword in order to function correctly. In practice, these are usually not available and if they are, they are subject to constant change. The points of criticism mentioned in the article on portfolio theory (Portfoliotheorie) therefore also apply to bid management of this kind: sometimes inaccurate prognoses, as they only refer to historical data and some fundamental assumptions are not practice-based. Well-known systems with a portfolio-based approach are Marin Software, Efficient Frontier and Kenshoo, for example.

Evolutionary bid management systems

Through constant self-adaptation of the algorithm and the internal rules, it is attempted here to adapt the system to the constantly changing framework conditions and, as a result, to continually increase performance. The knowledge gained in this way flows into a combined portfolio optimization and rule-based approach. On the basis of an evolutionary algorithm, a selection of good rules and settings is made and approaches that don’t work are suppressed. Through the combination with portfolio optimization, the advantages for global optimization of the account are seized while at the same time too much reliance on dated, historic data is prevented. Continuous back testing to verify the results is crucial here, as is also the established method in the financial sector. The interplay of bid, position, CPC and clicks can thus be adapted continuously to the changing competitive situation and click behavior. On this basis, systems of this kind attempt to map the entire bidding landscape per keyword and to identify the optimum bid.

Criticism

Use of a bid management system is often called into question. The task of optimizing bids just seems too difficult and too complex, particularly in light of the fact that, in practice, far more than just pure data must flow into the optimization of the bids. Rather, it is a matter of anticipating which keywords will be the top performers in the account in the short to long tail. An SEM manager can provide keywords to the preferred position before a certain event, for example. It can act at any time, while bid management systems simply react – and their reaction is only as good as the data that forms the basis of the regulations. If the data is limited or subject to fluctuation, bid management can even be counterproductive, depending on the quality of the algorithm. Because where the SEM manager can reconsider assumptions, the system works strictly according to a predefined pattern. If an article has sold out, for example, the conversion rate will collapse. In the ideal case, the SEM manager knows this instantly, while bid management first needs to “experience” this based on the data. Its use in the long tail is also disputed. By definition, there is not enough data per keyword available here to make a statistically significant statement in a sufficiently short time. The same is true for the SEM manager, however. In practice, the bid management system algorithms should have an advantage here thanks to their deep calculations. However, the SEM manager can also improve its results through clever combinations. But this costs it a lot of time in the long tail, which will cause the short tail performance to suffer. Against this backdrop, it makes sense to assume that an optimized account needs both: bid management with good algorithms and an SEM manager that always maintains control and an overview.

Trends

More and more bid management providers integrate their own conversion tracking systems into their own systems. This increases the data volume and thus the security with which the right bidding decisions are made. The provider intelliAd enhances data, for example by an in-house onsite tracking in order to allow the bounce rate to flow into the decision-making process on keyword performance. Information from telephony, too, such as the number of calls created per keyword, can be taken into consideration using a connected telephone tracking system. Various providers, such as Efficient Frontier, Kenshoo, and Marin Software, enhance their systems to include social media and display advertising. Classic bid management tools thus influence an ever expanding area of both the online and offline marketing landscape.

References

• Ashish Agarwal, Kartik Hosanagar, Michael D. Smith: Location, location, and location: An Analysis of Profitability of Position in Online Advertising Markets., 2008 Abstract online
• Animesh Animesh, Vandana Ramachandran, Siva Viswanathan: Research Note--Quality Uncertainty and the Performance of Online Sponsored Search Markets. An Empirical Investigation.In: Robert H. Smith School Research Paper, Nr. RHS 06-019, NET Institute Working Paper, Nr. 05-27, 2009 Abstract online
• Google, Anne Beuttenmüller, Thomas Bindl: 2009 Refined Labs case study – long-tail keywords, 2009 PDF, 1032 KB
• Animesh Animesh, Siva Viswanathan, Ritu Agarwal: Competing 'Creatively' in Online Markets: Evidence from Sponsored Search. In: Robert H. Smith School Research Paper, Nr. RHS 06-064, 2010
• Eva Gerstmeier, Tanja Stephanchuk, Bernd Skiera: An analysis of the profitability of different bidding heuristics in search engine marketing., 2009
• Anindya Ghose, Sha Yang: An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets., NET Institute Working Paper, 2009 Abstract online
• Anindya Ghose, Sha Yang: Modeling Cross-Category Purchases in Sponsored Search Advertising., 2010 Abstract online
• Avi Goldfarb, Catherine E. Tucker: Search Engine Advertising: Pricing Ads to Context., NET Institute Working Paper, Nr. 07-23, 2009
• iProspect: 2009 Search Engine Marketing and Online Display Advertising Integration Study., 2009 PDF, 238 KB
• Kinshuk Jerath, Liye Ma, Young-Hoon Park, Kannan Srinivasan: A 'Position Paradox' in Sponsored Search Auctions. In: Johnson School Research Paper Series, Nr. 36-09, 2010 Abstract online
• Zsolt Katona, Miklos Sarvary: The race for sponsored links: A model of competition for search advertising., 2008 PDF, 322 KB
• Oliver J. Ruth; Randolph E. Bucklin: A Model of Individual Keyword Performance in Paid Search Advertising., 2007 Abstract online
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• Hal R. Varian: Position Auctions., In: International Journal of Industrial Organization., Bd. 25, Nr. 6, 2007, S.1163-1178 PDF, 344 KB
• Sha Yang, Anindya Ghose: Analyzing the Relationship between Organic and Sponsored Search Advertising: Positive, Negative or Zero Interdependence?, 2009 Abstract online

Individual sources

de:Bid-Management

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