In your eCommerce garden, ad campaigns are the seeds of success. As a skilled digital gardener, you know the sense of accomplishment watching your return on ad spend (ROAS) go up — and the challenge of pruning your campaigns to cultivate healthy growth.

Today, as global eCommerce surpasses 6.3 trillion, mastering the art of advertising isn’t just an advantage. It’s survival.

The million-dollar question is: How do you make sure your ad spend is bolstering your bottom line, and not just Amazon’s?

In this guide, we’ll dive into the nuances of artificial intelligence and rule-based bidding to help you maximize ROAS and keep profits where they belong — in your pocket.

AI vs. Rule-Based Bidding

Whether you’re running Amazon PPC campaigns or focusing on dominating a different marketplace, understanding the differences between these two bidding strategies is crucial to maximizing your ROAS.

What is AI bidding?

Also known as: Dynamic bidding, algorithmic bidding

How it works: AI-based bidding uses machine learning to analyze vast amounts of data and make real-time bidding decisions. These algorithms incorporate information like:

• Historical ad performance
• Ad budget
• Click-through rates
• Conversion rates
• Market trends
• Consumer behavior

Dynamic bidding tools use this data to automatically adjust bids for each ad placement.

What is rule-based bidding?

Also known as: Manual bidding, conditional bidding

How it works: Rule-based bidding is based on predetermined rules set by advertisers. Preset if-then statements adjust bids based on specific conditions or performance metrics. For example, you could set a rule to maintain a specific ROAS. The marketplace will then adjust your bids based on the likelihood to achieve the guardrails you set.

Both bidding approaches seek to optimize campaign performance, but they differ in their level of automation and adaptability. Let’s take a look at a high-level comparison of these two strategies, then dive into specific use cases for each.

Comparing AI and Rule-Based Bidding

When is it best to use AI bidding, and when should you consider creating rules manually? Ultimately, it depends on your goals and priorities for every campaign.

We’ll get into the details, but first, here’s a quick comparison of each bidding strategy’s strengths and weaknesses across key areas. In the table below, we’ve indicated which system excels in each category.

Both bidding approaches have their unique advantages. The best route for your brand depends on your goals, resources, and the complexity of your ad campaigns.

Now that we’ve covered the high-level pros and cons, let’s dive into the top use cases for each bidding strategy.

When to Use AI Bidding

Since AI bidding uses machine learning algorithms, it shines in situations that require quick adjustments and complex data analysis. Here are some key scenarios where dynamic bidding can give you an edge.

☑️ Bidding for high-demand products

If you’re competing in product spaces with high competition and fluctuating demand, dynamic bidding can help advertisers quickly adjust bids to maintain visibility without overspending.

☑️ During peak shopping seasons

During high-traffic periods like Black Friday or holiday seasons, even the most savvy advertiser would struggle to keep up with manual bidding adjustments. AI bidding can instantly respond to changes in the marketplace during peak sales seasons.

☑️ Optimizing bids for short-tail keywords with high search volume

AI recognizes key patterns in vast amounts of data. The algorithms in your dynamic bidding tools can quickly identify the highest-converting keywords and focus there, while balancing ad spend with sales potential.

☑️ Optimizing bids for long-tail keywords

Long-tail keyword optimization can be challenging with rule-based automation. Advanced AI bidding systems can effectively manage these specific, high-intent keywords so you don’t miss out on valuable conversion opportunities.

Dynamic Bidding: Benefits and Drawbacks

AI bidding has a lot of benefits and a few drawbacks.

Benefits:

• Fast adaptation to market changes
• Uses complex data analysis
• Automation saves time and scales easily
• Potential to improve ROAS with precise bidding

Drawbacks:

• High initial costs as the algorithm learns
• Less direct control over bidding
• Relies on quality data inputs (garbage in, garbage out)

In complex or rapidly changing conditions, like promoting products with fluctuating demand, many advertisers rely on AI bidding to optimize ad spend.

When to Use Rule-Based Bidding

While AI bidding offers powerful automation in a wide range of scenarios, many advertisers still find rule-based systems valuable. Here are a few such cases.

☑️ When you have a specific ROAS goal

If you live and breathe ROAS, rule-based bidding might be for you. It lets you set precise rules to adjust bids based on your ROAS goal, giving you tight control over ad spend and profitability.

☑️ Bidding for specific product categories

Some niche product categories still require a human touch. If you’re promoting products that require distinct bidding strategies, you can create rules for each category. These could incorporate factors like profit margins, seasonality, and inventory levels.

☑️ Keyword sets with consistent performance

For keywords with stable performance, rule-based bidding can be more straightforward and predictable than AI bidding. It can work well in slower-paced product categories and industries that rarely require a quick response.

☑️ Competitor bidding

Setting out specifically to outbid competitors? Whether you’re looking to force a certain competitor out of the market or disrupt an established space, manual bidding lets you be as aggressive as you want.

☑️ When prioritizing rank over sales

Sometimes, keeping a top ranking position is more important than immediate sales. Rule-based bidding lets you set rules to prioritize rank for a keyword, even if it means accepting a lower ROAS or just breaking even.

☑️ Targeting an entire funnel with a wide range of keywords

Rule-based bidding works well with a full-funnel targeting strategy. Creating distinct rules for awareness, consideration, and conversion stage keywords lets you capture more demand and create a cohesive customer journey. For example, a full-funnel approach would target short-tail, awareness stage keywords like “soft toothbrush” as well as long-tail, intent stage terms like “extra soft toothbrush with tongue cleaner 4 pack.”

Rule-Based Bidding: Benefits and Drawbacks

Here’s the rundown on the pros and cons of rule-based bidding.

Benefits:

• Greater control and flexibility
• Transparency in decision-making process
• Ability to set complex, multi-factor bidding rules
• Opportunity to incorporate advertiser’s expertise

Drawbacks:

• Time-intensive campaign management is difficult to scale
• Less responsive to rapid market changes
• Potential for human error
• Limited ability to incorporate large amounts of data

Rule-based bidding offers precise control and customization. It’s ideal for seasoned advertisers with specific bidding strategies in mind.

The Best of Both Worlds: Hybrid Approaches

When it comes to bidding strategies, you don’t have to stick to one lane. Many advertisers use both AI and rule-based bidding to maximize their ROAS.

For example, it might make sense to use AI bidding for highly competitive keywords where quick adjustments are crucial, but set manual bidding rules for a few of your more niche product categories.

During peak seasons, you might rely on AI systems to handle the bulk of your campaigns, while keeping manual control over brand-specific terms or limited-time promotions.

The key is choosing the right technology. Advanced advertising platforms let you integrate both approaches to customize your advertising strategy to your specific brand and product portfolio.

Measuring the Success of your Bidding Strategy

Whether you use AI bidding, rule-based bidding, or a hybrid system, measuring the success of your strategy is crucial. 

Here are some key metrics to evaluate your bidding performance:

• Return on Ad Spend: ROAS remains your north star metric. Track how ROAS changes as you try different bidding strategies.
• Conversion rate: A successful bidding strategy should lead to improved conversion rates as you target the right audience more effectively.
• Cost per click (CPC): Effective bidding will help optimize CPC over time while maintaining or improving performance in other areas.
• Impression share: This metric helps you understand how often your ads are shown compared to the available impressions in your market.

To measure your success, consider which metrics are most important and set clear benchmarks before changing your bidding strategy. As you analyze the data, don’t forget to account for seasonal trends and other external factors that might influence your ad performance.

Remember, success looks different for every brand. While ROAS is important, it’s not the only measure. Consider how your bidding approach impacts broader business goals like market share growth or product launches.

Master your Bidding Strategy with Trellis

Both AI and rule-based bidding have their place in eCommerce advertising. Dynamic bidding excels in handling complex, high-volume scenarios where speed, adaptability, and scale are crucial.

On the other hand, manual bidding offers precise control and transparency when you just can’t trust AI to get it right. The key to maximizing your ROAS is understanding when to use each approach and how to combine them effectively.

The right tools are crucial for implementing a hybrid strategy that’s tailored to your goals. With Trellis’s Advertising Platform, you can create Amazon advertising campaigns in seconds. Advanced automation tools help you secure the lowest auction price through daily bid optimizations informed by your goals and objectives.

By combining campaign automation and dynamic bidding with the right ad types, Trellis helps you achieve your ROAS goals. To discover how it works, book a demo today.