Search Engine Marketing is a complex strategy that will only grow more challenging in the future. Businesses today use a variety of approaches to SEM that each come with their own benefits and pains, but they all have one priority in common: to create the most effective SEM strategy while minimizing the effort and resources needed to achieve it. In this article, weâ€™ll explain the key advantages of employing bid optimization technology, and why itâ€™s the best option to achieve this goal.
The Challenges of Manual Bidding
Many PPC managers today prefer to use manual bidding because itâ€™s highly customizable. In theory, youâ€™re in full control of bidding so you can make necessary changes quickly based on your latest data insights. This approach, though, comes with some major drawbacks that make it difficult for SEM programs to scale beyond their initial capacity.
Dealing with Averages
The practical complexities of comprehensive bid optimization can quickly become overwhelming. Say you have a program comprised of 3,000 target keywords and seven retargeting lists: youâ€™ll need to make bid adjustments for each of these keywords based on a number of factors including time of day,
Ignoring Deep Funnel Data
Experienced PPC practitioners today are well aware that there are a wide variety of important data sources that can impact bidding decisions and campaign performance, but they simply ignore them because they donâ€™t have the time, resources, or technology to take full advantage of their potential. Some examples of valuable data sources that often go under-utilized are:
- All publisher data regarding costs, Campaigns, Ad Groups, Match Type, Keyword Type, etc.
- Years of historical revenue, click and conversion data
- Third party analytics or tracking data
- Deep funnel or offline revenue data
- Lifetime value data or other internal metrics that are custom collected
Due to capacity and scalability concerns, itâ€™s simply impossible for digital marketers to consider all of these important data points when making manual bidding decisions.
Dealing with Data Scarcity
SEM professionals are constantly faced with the challenge of data scarcity when making bidding decisions. There are often a large number of highly relevant keywords to target that
The fact is, thereâ€™s always more that can be done to optimize a bidding strategy, but the main blocker is scalability. Consider these optimization opportunities:
- Location – bidding the ideal amount based on the city a user is searching from. Making necessary adjustments based on daily trends within each geo.
- Day Parting – considering both time-of-day and day-of-week in bidding. Adjusting bids based on day parting performance at scale.
- Device – targeting specific devices versus bidding differentially for desktop, tablet, and mobile.
- Audiences – Monitor campaign performance with each audience and adjust bids accordingly. Updating targets based on performance and seasonal trends in a scalable way.
There are many nuanced micro-changes that individually make a small impact on performance but can collectively drive increased ROI and reduce wasted ad spend.
The Power of AI and Machine Learning
The challenges of manual bidding make campaign optimization more difficult and time consuming. As a result, SEM programs that rely on manual optimization find themselves at-scale very quickly. They essentially spend more time and more money to get results. Automated bidding technology is the one solution that addresses these issues, freeing up time and budgets for increased growth. The most advanced automated bidding solutions use artificial intelligence (AI) and Machine Learning to make informed bid changes faster and more accurately than any individual PPC manager ever could.
Google Ads provides an AI-driven bid optimization solution for advertisers to use, utilizing goal-based algorithms and current market data to address many of the issues manual bidding poses to SEM teams. There are also advanced third-party automated bidding solutions that offer even greater opportunities for at-scale SEM programs to optimize and grow. The Google Ads technology isnâ€™t equipped to utilize all the deep funnel data that marketers need to leverage when making bidding decisions.
With QuanticMindâ€™s automated bidding solution, for example, itâ€™s possible to upload and factor in any relevant business data into bid calculations. The platformâ€™s machine learning algorithms take into consideration your historical performance data to inform current tactics. Using predictive analytics, the platform can help PPC managers effectively see around corners, informing strategies for growth.
Predictive modeling is a statistical strategy that has been around for a long time, but with machine learning, itâ€™s possible to create non-linear models using supervised learning. The most sophisticated models can incorporate millions of data points and interactions to understand their significance and impact on one another. When applied to advertising, AI and machine learning can help you make changes to grow strategies that are already at-scale. This is possible by:
- Improving campaign efficiency – PPC managers use bid adjustments to allocate ad spend more precisely, but many bid adjustments are only beneficial given certain market conditions. Leaving these bid adjustments unaltered after the market changes can hurt campaign performance. Using an automated bidding solution that factors in all your important market data ensures your bids are always optimized.
- Reducing wasted ad spend – You only want to spend as much as you need bidding on keywords in order to reach your advertising goals. Bid optimization technology can analyze the value of individual keywords, ensuring you only bid what you need where you need to.
- Driving more revenue – Improving campaign efficiency and performance leads to better ad position and more opportunities to drive conversions. When you also reallocate the budget saved from efficient ad spend, there are more opportunities for growth and revenue.
To understand the value that bid optimization technology has for at-scale SEM programs, itâ€™s best to look at an example. Luxury cruise liner Windstar Cruises took advantage of QuanticMindâ€™s AI-powered bid management solution, using large data sets to generate insights to improve conversions, reduce CPC, and increase overall sales. As a result, they were able to lift goal conversions by 16 percent year-over-year, all while saving a massive $700,000 on their SEM budget.
How Advanced Bid Optimization Technology Works
The world of bid optimization technology is certainly complex. In essence, it does much of what PPC managers wish they could effectively analyze and adjust, at a scale beyond what even the largest team of data scientists is capable of. Hereâ€™s how advanced bid optimization technology works to improve SEM performance while minimizing the time and resources needed to achieve it:
Comprehensive Data Inputs
Such technology doesnâ€™t limit businesses to interpreting pre-set data sources. You can choose whatever data is important to your business and factor it into bid optimization calculations. Using third-party integrations or manual data uploads, you can apply any and all important data to the algorithms for more precise calculations.
Keyword Analysis and Valuation
QuanticMind calculates revenue-per-click, or RPC, for each keyword, using machine learning algorithms and any relevant business data you provide. The predictive model considers all cost and revenue factors to determine the dollar value of each keyword. The platform can also help solve the issue of data scarcity: if you have low volume or long-tail keywords with little data, QuanticMind determines their value using deep learning text-recognition and Natural Language Processing, or NLP. It does this by matching low-data keywords with other semantically similar keywords that have good clicks, conversions, and data. Where PPC managers would have to rely on guesswork to estimate the value of low-data keywords, QuanticMind can make an informed valuation and adjust it as more performance data comes in over time.
QuanticMind creates scenario models to understand how potential changes in CPC bids can impact clicks and costs. This is done using Bid Landscape Data from Google and other custom models. The result is an array of potential CPC bids that the decision engine uses to select an optimal bid.
Determining Optimal CPC for Business Goals
The next step in the process is determining the ideal bid for each keyword given your unique business goals. Now that the machine understands how different bids can impact clicks and costs, itâ€™s possible to select the optimal CPC to achieve desired results. The calculation considers your prioritized goals, such as maximizing profit margin or meeting a set ROAS, then it calculates individual bids to meet that goal in aggregate across the bid policy. Â
The system also includes a Bias Correction mechanism to quickly make adjustments to the CPC calculations based on performance from the previous day. Normally a PPC manager needs to manually monitor anomalies and make this kind of adjustment. With QuanticMind, itâ€™s another automated task.
Calculating Bid Modifiers
Most legacy bid optimization solutions automate adjustments to bid modifiers. QuanticMind uses data science and similar models to understand which bid modifiers can improve efficiency and performance. Currently, it can automatically calculate Location and Device bid modifiers. Automated Audience Bid Modifiers will soon be a part of the process.
One potential concern PPC managers have about automated bidding is that a tool could make poor bidding decisions when presented with inaccurate data. Those practitioners who stay involved in the optimization process are in a position to catch these issues and fix them before they harm campaign performance. Machine Learning isnâ€™t just a series of optimized algorithms, though. It can detect unexpected performance changes and identify the presence of inaccurate or deviant data as the root cause. QuanticMind has several anomaly detection techniques in place to detect data issues on a daily basis. When necessary, it will also halt bidding until the issue is resolved.
Automated Bid Optimization
Once your ad campaign information is pushed through all that infrastructure and all those algorithms, the bid optimization technology applies the final bids to Search Engine publishers. Using fast data analysis and performance modeling, QuanticMind is able to make quick changes to ensure the best possible bids are always applied.
New Opportunities for At-Scale SEM Programs
When you handle bid optimization manually, there is little time for much else. You have to group keywords, calculate CPC, select bid modifiers, monitor performance, and more. With so many important success factors to focus on, SEM programs can find themselves at-scale with even the most modest campaigns. So what happens when these tasks are automated? Team members become free to pursue other opportunities and your once at-scale SEM program is able to grow, without any additional budget or labor.
Bid optimization technology offers more precision and efficiency than what PPC managers can. It achieves necessary goals using less budget, giving you the ability to reallocate ad spend in new areas. And since your SEM team is no longer bogged down by endless manual adjustments, they have time to focus on finding new keyword opportunities, improving your advertising message, and more.
The Bottom Line
Some of the tasks advanced bid optimization technology perform would take an age for businesses to accomplish with manual bidding. Other tasks stretch far beyond what a human could possibly process and implement. Bid optimization is a complex, open-ended goal with millions of relevant data points and calculations to consider. Bid optimization technology is the only solution equipped to maximize performance given the data and techniques available today.