From Manual CPC to Value-Based Bidding: The Evolution of Google Ads Bidding Strategies

Google Ads (formerly AdWords) has transformed dramatically over the past two decades, especially in how advertisers bid for traffic. What began as a simple manual bidding system has evolved into a sophisticated, AI-driven auction where marketers focus less on clicks and more on outcomes. This evolution is crucial for performance marketers, senior media buyers, and heads of paid search to understand – not just as a history lesson, but as a guide for transitioning into today’s automated, value-driven bidding era. In this article, we’ll trace the major milestones: from the early days of manual keyword-level CPC bidding, through the rise of automated “Smart Bidding” (e.g. Enhanced CPC, Target CPA, Target ROAS), and into the current era of value-based bidding strategies like Target ROAS and Maximize Conversion Value. Along the way, we’ll highlight how the Google Ads auction mechanics have changed and how the marketer’s role has shifted – from hands-on bid tweaking to strategic oversight, from buying clicks to buying value.

Manual CPC Bidding: Buying Clicks in the Early Days (2000s)

When Google AdWords first launched in 2000, bidding was entirely manual – advertisers set maximum cost-per-click (max CPC) bids for each keyword and managed budgets largely at the campaign level . In this era, success in paid search meant carefully tuning bids by hand. Marketers would analyze performance keyword by keyword, raising or lowering bids to control ad positions and cost. If you wanted more traffic, you’d increase the bid; if a keyword wasn’t profitable, you’d lower the bid or pause it. This hand-crafted approach gave advertisers direct control, but it was labor-intensive and required constant attention.

Early on, the Google Ads auction was a straightforward second-price auction: if you won the auction, you typically paid just $0.01 more than the next highest bid. However, Google quickly introduced mechanisms to ensure that winning wasn’t just about who bid the most. A major innovation was the introduction of Quality Score in 2005, a metric of ad relevance and quality . Quality Score meant that ads were ranked by more than just bid; an ad with a higher Quality Score (due to relevant keywords, ad copy, and landing page experience) could outrank a higher bidder. In practice, Ad Rank became a function of bid and Quality Score, so advertisers had to optimize creative and relevance, not merely spend more. Still, in the manual era the marketer’s role was very much about managing bids and budgets directly. You set individual keyword bids, observed performance, and adjusted accordingly – often using spreadsheets or rules of thumb. Marketers prided themselves on their “bid instincts” and ability to find the right CPC that would drive traffic yet maintain a target cost per acquisition. It was a time of granular control, but also one where you were essentially buying clicks and hoping those clicks would turn into conversions down the line.

Historical milestone: Google AdWords launched in 2000 as a self-service platform for businesses, with manual CPC bidding as the default . Quality Score was introduced in 2005 to increase ad relevance in the auction . Throughout the mid-2000s, manual bidding remained the norm. Marketers set daily budgets (per campaign) and max CPCs for each keyword, and used bid adjustments (e.g. by device or time of day) once those features became available. Every bid change was explicitly made by a human. The Google Ads auction in this period rewarded those who could dedicate time and expertise to tweaking bids and keyword lists. The mantra could be summed up as: you bid, you get clicks. The focus was on driving traffic, with conversion optimization happening outside the platform (e.g. on the website or via offline analysis).

From a role perspective, the marketer was a hands-on bid manager. You might log in daily (or even multiple times a day) to adjust bids based on performance reports. Third-party bid management tools emerged to help, but fundamentally it was the marketer’s skill and manual effort that determined how efficiently a budget was spent. This era laid the groundwork for what would come next – because as campaigns and keyword lists grew larger, the complexity of manual bidding at scale became a pain point. Google started to explore ways to automate and optimize bidding using the growing troves of data and the nascent power of machine learning.

The Rise of Automated Bidding and Smart Bidding (2010s)

By the late 2000s and early 2010s, two important trends were underway: advertisers were increasingly tracking conversions (sales, leads, etc.), and Google’s technology was advancing to allow more automation in bidding. Google introduced its first major foray into automated bidding with a feature called Conversion Optimizer in 2007 . Conversion Optimizer allowed advertisers who had enabled conversion tracking to let Google adjust bids per auction to hit a desired cost-per-acquisition. In essence, it was an early Target CPA system – advertisers could specify a target cost per conversion, and Google would use historical conversion data to automatically bid on keywords in a way that aimed to achieve that CPA target. At the time, Conversion Optimizer required at least 15 conversions in the last 30 days to activate, since it needed data to work . This was a significant shift: instead of buying clicks, advertisers could now start buying conversions, handing over bidding control to Google’s algorithms to get more bang for their buck.

Another milestone in this period was the launch of Enhanced CPC (ECPC) in 2010, which was one of Google’s first “smart” bidding features integrated into standard campaigns. Enhanced CPC was a hybrid between manual and automated bidding – you still set a base max CPC for each keyword, but Google was allowed to adjust your bid in real time (up or down, originally by up to 30%) if its system detected that a particular search was more or less likely to convert . For example, if a user’s query and context had a high predicted chance of leading to a sale, ECPC might bid a bit higher than your max CPC; if the likelihood was low, it might bid lower. Enhanced CPC thus eased advertisers into automation by retaining manual bids as a starting point but using data signals to tweak those bids for better conversion outcomes. (Notably, ECPC itself was so widely adopted that it eventually became the default bidding mode for new campaigns in later years .) This period also saw Google gradually increase the flexibility of ECPC – eventually lifting that strict ±30% cap – as confidence in the algorithm grew .

By 2010, Google officially introduced the Target CPA bidding strategy as an option in AdWords (as an evolution of Conversion Optimizer) . Instead of setting keyword bids, the advertiser would set a target cost-per-action, and Google’s system would handle all the bidding decisions to try to achieve that average CPA. Many performance marketers at first approached these tools cautiously – after all, handing the steering wheel to Google’s automation felt like ceding control. Early results were mixed; while some campaigns saw improved conversion volume at the target CPA, experienced PPC managers could often still outperform the algorithm by applying their own insights and adjustments. It’s fair to say that in the early 2010s, automation was in its infancy and human expertise often beat the machine in head-to-head comparisons . Agencies would frequently test automated bidding against their tried-and-true manual optimizations, and not always be convinced – especially if the algorithms didn’t have much data to learn from.

However, Google continued to invest heavily in improving automated bidding. Target ROAS (Return On Ad Spend) was introduced around 2013 as another automated strategy, aimed particularly at advertisers who cared about revenue value, not just conversion volume . With Target ROAS bidding, instead of a CPA goal, the advertiser sets a desired revenue-to-ad-cost ratio (for example, a 500% ROAS goal means $5 of revenue for every $1 spent). The system then bids higher or lower in each auction based on the expected conversion value of a click. This was a big conceptual step towards value-based bidding (more on that in the next section). In practice, Target ROAS required advertisers to be tracking the monetary value of each conversion (e.g. the sale amount for each e-commerce transaction) and passing that data to Google Ads. If you weren’t tracking revenue or value, you couldn’t use Target ROAS effectively. Many advertisers in 2013–2015 were still catching up on implementing robust conversion tracking, so Target ROAS initially had a more niche adoption compared to the more straightforward Target CPA. Nonetheless, it signaled Google’s direction: bids should ultimately be optimized not just for getting a conversion, but for getting the most valuable conversions.

A watershed moment came in 2016, when Google introduced the term “Smart Bidding.” Smart Bidding refers to the class of bidding strategies that use machine learning to optimize for conversions or conversion value in every auction, automatically . By 2016, Google’s machine learning algorithms had become far more sophisticated, able to analyze countless signals (device, location, time of day, user demographics, past search behavior, etc.) to predict the likelihood of a conversion (or conversion value) for each individual search query. Smart Bidding encompasses strategies like Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value (as well as Enhanced CPC, which is sometimes included as a semi-automated tool). The key advancement of Smart Bidding was real-time, auction-time bidding: instead of setting one static bid for all auctions a keyword might enter, the system could determine an optimal bid on the fly for each auction based on the context of that search. This was something no human could practically do, as it would require evaluating millions of combinations of signals in milliseconds. With Smart Bidding, Google Ads truly moved from the era of explicit manual control to an era of algorithmic optimization. The auction was now being influenced by predictive models – for example, if the model determined a given search (by a certain user, at a certain location, using a certain device) had a 3x higher chance of converting, it could bid proportionally higher in that auction. Quality Score and Ad Rank still mattered, but now your effective bid could vary dynamically per impression to better align with the likelihood of achieving your goal.

For experienced marketers, this shift required a change in mindset. No longer was success about outbidding competitors with a better max CPC; it became about feeding the right data and goals into Google’s system so that it could bid on your behalf. The marketer’s role started to evolve from micromanaging bids to managing the bidding strategy. That means choosing the right strategy for the job (e.g. Target CPA vs. Target ROAS), setting the appropriate targets, and ensuring conversion tracking was accurate. It also means monitoring performance and giving the algorithms time to “learn.” Google often described Smart Bidding as self-improving: it would learn from each conversion to get better at predicting what traffic was valuable. Indeed, over time the performance of Smart Bidding generally improved, and Google began actively encouraging (even pressuring) advertisers to adopt these automated strategies . By the late 2010s, many of the manual levers (like device-specific bids, ad scheduling bid adjustments, etc.) became less impactful or were even overridden by Smart Bidding, since the algorithm could account for those factors itself.

Historical milestones in this era:

• 2007: Launch of Conversion Optimizer (predecessor to Target CPA) – the first big step to automate bids for conversion goals .
• 2010: Introduction of Enhanced CPC and Target CPA bidding , making conversion-focused automation accessible to advertisers within standard campaigns.
• 2013: Introduction of Target ROAS bidding for value optimization .
• 2016: “Smart Bidding” is announced, leveraging machine learning for auction-time bid adjustments . Google’s algorithms can now optimize bids based on dozens of real-time signals, far beyond what any human could parse.
• 2017–2018: Google rolls out Maximize Conversions and later Maximize Conversion Value as one-click automated strategies. These use your entire budget to maximize the chosen outcome (conversions or conversion value) without requiring a specific CPA or ROAS target. (Maximize Conversions was introduced around 2017, and Maximize Conversion Value in 2019 .)
• Late 2010s: Adoption of Smart Bidding accelerates. Google starts to set many new campaigns to an automated bidding default, and provides tools like portfolio bid strategies to manage targets across multiple campaigns. The algorithms also improved with features like seasonality adjustments and learning period indicators to help advertisers trust the process.

Throughout this period, the Google Ads auction itself evolved to accommodate these smarter bids. While the core auction mechanism (Ad Rank = f(bid, Quality Score, etc.)) remained, the fact that bids could change per query meant the auction became more fluid and personalized. Each query’s auction could have different effective bids for the same advertiser depending on predicted performance. It’s also worth noting that in the broader advertising world, Google’s rivals and partners were also moving to machine-learning-driven auctions (e.g. programmatic display advertising adopted first-price auctions with real-time bidding). Google’s search ads remained a unique blend of old and new – still a ranked auction, but now heavily influenced by AI. The marketer’s role was increasingly to coach the AI: for example, by ensuring that conversion tracking was properly measuring the right actions, by providing budget constraints and reasonable targets, and by using tools like bid simulators or experiments to set the right goals. Instead of manually adjusting bids, marketers began to spend more time on strategy, creative, and analytics – figuring out what to optimize for, letting Google handle the how.

By the end of the 2010s, a conceptual leap had occurred for those embracing automation: we moved from buying clicks to buying conversions. If the manual CPC era was about getting the click and hoping it converts, the Smart Bidding era was about telling Google “get me conversions at X cost” and allowing the system to pursue that outcome. But an even bigger shift was on the horizon, one that would further refine what outcome we optimize for – namely, the value of those conversions.

The Era of Value-Based Bidding: Optimizing for Revenue and ROAS (2020s)

As digital marketing matured, advertisers increasingly recognized that not all conversions are equal. For an e-commerce advertiser, a purchase worth $500 is obviously more valuable than one worth $50 – yet a basic “maximize conversions” or Target CPA strategy treats them as one conversion each. Similarly, for a B2B advertiser, a lead from a target enterprise account might be worth far more than a generic lead, even though both count as one form fill. Enter Value-Based Bidding, the approach that focuses on maximizing the total value generated by your ad spend, rather than just the number of conversions. In Google Ads, the epitome of value-based bidding is Target ROAS and Maximize Conversion Value strategies, which have become increasingly prominent in recent years.

Target ROAS (Return On Ad Spend) is the strategy that explicitly optimizes for conversion value. The advertiser sets a target ROAS percentage (for example, 500% or 5:1, meaning you aim for $5 in revenue for every $1 spent). Google’s Smart Bidding system then tries to meet or exceed that ROAS goal by adjusting bids – bidding higher for auctions likely to produce high revenue and bidding lower (or not at all) for those likely to produce low revenue. To do this, the system must be able to predict the conversion value of each potential click. This is accomplished by leveraging historical data and myriad contextual signals. Every time a user searches, Google’s algorithms estimate not just the probability of a conversion, but also the likely value of that conversion if it happens . For example, if past data shows that a user with certain characteristics tends to make larger purchases, the system will bid more aggressively for that user’s click under Target ROAS. Conversely, it will bid conservatively for clicks that historically lead to low-value sales. The advertiser’s job here is crucial: you must feed the system accurate conversion values. This typically means implementing conversion tracking that records the actual revenue from each sale (via the Google Ads conversion tracking tag or an analytics integration) or assigning proxy values to conversion events based on their importance. By “training” Google with your conversion value data, you enable the algorithm to discern which clicks are truly profitable for you, not just which ones convert.

In practice, using Target ROAS requires a mindset shift to “buying value” instead of just buying conversions. Under a Target ROAS bid strategy, Google might decide to pay a higher CPC for a keyword if the expected order value is high – even if that click is expensive, it’s worth it because the sale might be big. On the flip side, for low-value potential conversions, Google will bid very little or not at all, because those clicks aren’t worth much to your bottom line. This is a stark contrast to manual bidding or even Target CPA, where two conversions would be pursued equally regardless of value. Now the focus is weighted towards the outcomes that drive revenue. It’s a conceptual leap from volume to value. Marketers who once optimized for say, 100 conversions at $10 each (CPA), might find under ROAS bidding that they get fewer conversions in number, but more revenue overall for the same spend – because the strategy prioritized the big-ticket sales.

For example, imagine an e-commerce scenario with three potential customers: Customer 1 is likely to buy $100 worth of products, Customer 2 might buy $300, and Customer 3 might buy $500. A conversion-focused strategy (ignoring value) might bid the same amount for each customer since each would count as “1 conversion.” In contrast, a value-based strategy would bid much lower for the $100 customer and much higher for the $500 customer (as illustrated above). This way, more budget is allocated to users who are likely to generate higher revenue, maximizing the total return . In the illustration, the conversion-based approach sets an equal bid (e.g. $10) for each potential customer, treating them uniformly, whereas the value-based approach bids only $5 for the low-value customer (Customer 1) and $15 for the high-value prospect (Customer 3). The outcome is that value-based bidding helps maximize total revenue, not just conversion count, by redistributing spend towards the most valuable opportunities.

Google introduced the Maximize Conversion Value strategy in 2019 as an easy way for advertisers to let the system pursue the highest total value within their budget . Maximize Conversion Value is similar to Target ROAS, but without a specific ROAS percentage constraint – its goal is simply to get the most conversion value for your budget. In effect, it will spend your daily budget across auctions to maximize revenue, regardless of the efficiency (it doesn’t mind if ROAS comes in at, say, 300% or 400%, as long as all budget is used to grab as much value as possible). This can be useful if you have a fixed budget and want to see how much revenue you can drive, or if you value some conversions (like high-margin sales) much more and trust the system to allocate spend optimally. Target ROAS, on the other hand, does care about efficiency – it will throttle spend if it can’t achieve the target return. For instance, if your ROAS goal is very high, the campaign might underspend because the system is waiting for only the very best opportunities. In contrast, Maximize Conversion Value will use the budget fully, which might result in a lower overall ROAS but higher total sales. Advertisers often choose between these based on their goals: if hitting a profitability goal (ROAS) is paramount, use Target ROAS; if fully spending the budget to drive as much value as possible is the aim, use Maximize Conversion Value.

In the late 2010s and into the 2020s, value-based bidding has become a key focus area. Google has actively promoted the idea of “bidding to business outcomes” and even allowing advertisers to import data like profit margins or lifetime value projections to inform bidding. According to Google’s own guidance, value-based bidding lets you “maximise the total value of conversions generated by your campaigns,” with Google’s AI optimizing bids in real time to find users who will bring more value to your business . This often means not just using revenue, but possibly assigning different values to different types of conversions (for example, giving higher value to a sale of a premium product, or even to a lead that is more qualified). The tools for this include setting conversion value rules (adjusting values based on audience or location), using offline conversion imports (feeding back which leads turned into actual sales and for how much), and of course robust conversion tracking for e-commerce. All of these enhancements feed the beast that is Google’s bidding algorithm, making it smarter about where to bid your money.

From an auction mechanics perspective, value-based bidding doesn’t change the fact that ads still compete in an auction of Ad Rank, but it changes what your bid represents. Your bid is no longer a fixed amount you’re willing to pay for a click – it’s a dynamic value that Google calculates based on your conversion value predictions and targets. In a sense, Google’s algorithm becomes your proxy bidder. It looks at an auction and says, for example: “This impression has a high expected value for Advertiser A, so based on their target ROAS, I’ll bid $5. For Advertiser B, that same impression has low expected value, so I’ll only bid $0.50.” Each advertiser’s bid is tailored to their own value goals. This means two advertisers in the same auction might be optimizing for different things (one for conversions, one for value), and their bidding algorithms behave accordingly. The auction is now a collision of multiple algorithms each trying to achieve their advertiser’s goal, under the hood of the familiar Ad Rank formula.

As we moved into the 2020s, Google further refined the simplicity of using these strategies. In 2021–2022, Google decided to streamline the Smart Bidding options by merging Target CPA into Maximize Conversions (with an optional target) and Target ROAS into Maximize Conversion Value (with an optional target) . This change, implemented around July 2022, meant that in the Google Ads interface you no longer explicitly select “Target CPA” or “Target ROAS” for standard campaigns; instead, you choose Maximize Conversions or Value, and if you have a specific efficiency goal you can set a target CPA/ROAS. The underlying capabilities didn’t disappear – this was largely a UI simplification – but it reflects Google’s push toward value optimization as a default. The platform is essentially nudging advertisers: “Tell us what you value (conversions or conversion value), and we’ll handle the bidding. If you have a specific ROI in mind, you can specify it, otherwise we’ll just try to spend your budget intelligently.”

Historical milestones in the value-based era:

• 2013: Target ROAS introduced, planting the seeds for value-driven optimization .
• 2019: Maximize Conversion Value strategy launched, signaling Google’s commitment to maximizing revenue, not just conversion counts .
• 2020–2021: Google introduces conversion value rules and other features to let advertisers adjust values based on factors like audience or device (e.g., valuing returning customers higher, or mobile app sales differently). These tools help businesses align Google’s optimization with their true business value.
• 2022: Simplification of bid strategies – Target CPA and Target ROAS options were deprecated as standalone strategies in favor of the more goal-centric Maximize approaches (with targets as a tweak) . This was essentially Google telling advertisers: “Focus on what outcome you want (conversions vs. value); we’ll handle the rest.”
• 2023: The trajectory continued with campaign types like Performance Max (introduced broadly in late 2021) which require value-based frameworks (Performance Max uses conversion goals and often works best with ROAS targets or value rules in place). Additionally, Google began phasing out some manual capabilities – notably, it even hinted at sunsetting manual CPC bidding entirely. By early 2023, industry experts noted that “Google Ads manual bidding is on its deathbed” and Google representatives suggested manual bidding might be fully retired in the near future . While manual bidding hasn’t been completely removed as of 2025, it’s clear the future Google envisions is one dominated by automated, value-driven strategies.

For marketers, the role transformation is now complete: from bid operators to strategy pilots. In the value-based bidding era, a marketer’s time is best spent ensuring the right data and goals are in place. This means setting up comprehensive conversion tracking with correct values (for example, making sure your Google Ads tags or analytics are recording the actual revenue of purchases, or assigning sensible values to leads or other conversion types). It means choosing bidding strategies aligned with business goals – e.g. if lifetime value matters, perhaps optimizing for that via proxy metrics or uploaded data. And it means adjusting targets based on performance and business needs (for instance, if profit margins tighten, you might raise your ROAS target; if volume is more important, you might lower the ROAS target to capture more conversions at a lower efficiency). The marketer must also maintain oversight: automated doesn’t mean set-and-forget entirely. You’ll monitor ROI and make sure the algorithm’s decisions align with reality. But importantly, your lever is no longer the individual keyword bid – it’s the higher-level parameters of the strategy.

Another big change is in testing and learning. In the old days, a marketer would run manual A/B tests by splitting traffic between different bid amounts or using scripts to alter bids. Now, testing often involves running experiments like one campaign using Target ROAS vs another using manual or Target CPA to compare outcomes, or testing different ROAS target levels to find the sweet spot for revenue vs. volume. The skillset has shifted towards analytical design (setting up these tests correctly) and interpretation, rather than manually tweaking dozens of bids.

Crucially, the focus is on outcomes. Google’s value-based bidding essentially aligns the auction with the advertiser’s bottom line (as long as the inputs are right). As Google itself puts it, value-based bidding enables you to prioritize conversions based on their value to your business . This is why it’s so powerful: it closes the loop between advertising spend and business results. If you train it that a certain type of conversion is worth $500, it will aim to get more of those even if they are fewer in number, rather than chasing a bunch of $50 sales. It’s the closest thing to “tell the platform your business goals and let the AI handle it” that digital advertising has seen.

Navigating the Transition: From Manual to Automated Value Bidding (Actionable Tips)

Understanding this evolution is one thing – implementing it is another. Many experienced marketers who grew up in the manual CPC era find it challenging to relinquish control, and rightfully worry about pitfalls of automation. Here are some actionable steps and best practices for transitioning into automated and value-based bidding:

1. Audit Your Conversion Tracking and Values: Before turning on Target ROAS or Max Conversion Value, ensure that you are tracking conversions accurately and that each conversion action has an appropriate value. For retail/e-commerce, this means passing the exact purchase revenue for each transaction. For lead generation, assign surrogate dollar values to lead types based on quality or close rates (e.g. maybe a demo request is “worth” $100 while a newsletter signup is $5 – whatever makes sense for your funnel). The accuracy of your data is the bedrock of smart bidding. Garbage in, garbage out.
2. Start with Realistic Goals: When using Target CPA or Target ROAS, set targets that are achievable given your historical performance. For instance, if you’ve been manually achieving a 300% ROAS, don’t immediately set a 1000% ROAS target – this might throttle your traffic too much. Likewise, don’t set an unrealistically low CPA that the campaign has never hit before. Gradually tighten the goals once the algorithm shows it can meet the initial target. A common practice is to start with a target similar to your  average historical CPA/ROAS, then slowly push it once performance stabilizes.
3. Leverage Learning Periods and Seasonality: Be aware that when you switch to an automated strategy, there’s a “learning phase” during which the algorithm is adjusting. Performance may fluctuate in this phase. Avoid making hasty changes or turning the strategy on and off too quickly. Google’s interface will often indicate when the learning phase is over. Also, if you know your business has seasonal spikes or lulls, use the tools available (e.g. seasonality adjustments in Google Ads) or adjust targets preemptively to help the algorithm; otherwise, it might misinterpret sudden changes in conversion rate.
4. Use Experiments or Phased Rollouts: If you’re nervous about going all-in with automation, use Google’s campaign experiments or A/B testing tools. For example, run an experiment where 50% of traffic stays on manual bidding and 50% uses Target ROAS, then compare results after a few weeks. This controlled approach can give you confidence (and hard data) about whether the automated strategy can beat your manual approach. Many marketers are surprised to find that after the learning period, Smart Bidding can indeed match or exceed manual bidding’s efficiency – especially as it continues to get smarter.
5. Feed the Algorithm More Business Data: As you become comfortable with value-based bidding, consider feeding even more nuanced data to Google Ads. This could include importing offline conversions (e.g. when a sale is closed in your CRM, send that back to Google with the revenue or profit info), or using Conversion Value Rules (a feature that lets you tell Google, for instance, that conversions from a certain audience or region are worth +20% more to you than the base value). These inputs help fine-tune the algorithm’s understanding of value. For example, if you know repeat customers are more valuable, you might import a first-party data list and use value rules to amplify value for those users. This way, the bidding algorithm will further prioritize what truly drives long-term value.
6. Monitor and Optimize Strategically: Even with Google doing the heavy lifting on bids, your job is not over. Continuously monitor key metrics like ROAS, CPA, conversion rate, and total conversion value. Make sure they align with your goals and adjust your targets or budgets if needed. For instance, if your Target ROAS campaign is consistently exceeding the target (getting a higher ROAS than asked for), you might be able to lower the target to capture more volume. Conversely, if ROAS is falling short, you may need to either lower your target (if volume is more important) or troubleshoot (check if conversion tracking had issues, if competition increased, etc.). Also, keep an eye on search query reports and conversion quality – Smart Bidding with broad match keywords can sometimes venture into odd queries; you still want to negative out truly irrelevant traffic to guide the system.
7. Focus on Creative & CRO: With bidding optimization largely automated, invest your freed-up time into improving ads and landing pages. The quality and relevance of your ads (which affect click-through rate and Quality Score) still heavily influence results. Smart Bidding doesn’t replace the need for persuasive ad copy and robust account structure. Similarly, optimizing your landing pages and user experience will boost conversion rates and conversion values, which in turn gives the algorithm more conversions to work with and improves your ROI. In other words, let Google handle the bids while you handle the message and the conversion funnel – this partnership yields the best outcomes.
8. Stay Educated on Platform Changes: Google Ads is not static – as we’ve seen, strategies get renamed, new features roll out (e.g. Performance Max campaigns require a different way of thinking), and old options get deprecated. Stay up to date with Google’s announcements and industry blogs. For instance, knowing that Enhanced CPC was deprecated in 2024–2025 in favor of fully automated strategies can save you from relying on a tool that’s being phased out. Similarly, if Google raises the minimum data thresholds or changes how learning works, you’ll want to adapt your approach. The evolution didn’t stop in 2020; it’s ongoing.

In summary, the evolution of Google Ads bidding strategies has been a journey from manual control to automated intelligence, and from optimizing for clicks to optimizing for true business value. The Google Ads auction today is a very different beast than it was 20 years ago – it’s now a high-speed, AI-powered marketplace where each advertiser can have their own customized bidding algorithm fighting for their goals. As a marketer, embracing this shift can pay dividends. Those who cling to manual bidding in 2025 are finding it increasingly unsustainable (and indeed, Google may eventually remove the option entirely) . Meanwhile, those who harness value-based bidding are often seeing better returns on their ad spend, because they’re aligning their ad dollars with real business outcomes.

Conclusion: From Bid Tactician to Strategy Navigator

The role of the performance marketer has changed from being a bid tactician to a strategy navigator. In the manual CPC era, your expertise lay in tweaking bids and keywords; in the Smart Bidding era, it shifted to setting the right targets and letting the machine find the best path; and now in the value-based era, it’s about steering the entire ship towards the right definition of success (value) and ensuring the machine has the data it needs to get there. This has freed us to concentrate on higher-level strategy and creative optimization, which are human strengths, while leveraging machine learning for the number-crunching and pattern-recognition at scale.

Embracing automated and value-based bidding is no longer just an option – it’s increasingly a necessity to remain competitive and efficient in Google Ads. Google’s algorithms learn from the millions of auctions happening every hour, far beyond what any individual advertiser can see. By plugging into that intelligence with strategies like Target ROAS and Maximize Conversion Value, you essentially hire a tireless, super-intelligent assistant to manage your bids 24/7 in alignment with your goals. The key is to give that assistant the right instruction (your targets) and context (your conversion data). When you do, the results can be game-changing: higher return on ad spend, more efficient scaling of budgets, and insights that might be hard to glean manually (for example, discovering new high-value query patterns that convert well).

In closing, the evolution from manual CPC bidding to value-based bidding in Google Ads represents a paradigm shift from buying clicks to buying value. Each stage – manual, automated, and value-driven – built on the previous one, and today we have an auction ecosystem where what matters most is the outcome, not the input. As you apply these insights to your own campaigns, focus on the fundamentals: track and value what matters, choose the right bidding strategy for your objectives, and continuously refine your approach with data. The advertisers who master this new way of thinking are not only rewarded with better performance but also gain a competitive edge in the ever-advancing world of paid search marketing. By aligning your bidding strategy with business value, you ensure that every dollar of your budget works harder and smarter – a win-win for you and your marketing ROI.

Sources:

• Google launched AdWords as a self-serve advertising platform in 2000 . Quality Score was first introduced in 2005 to improve ad relevance in the auction .
• Google’s Conversion Optimizer (a precursor to Target CPA bidding) was launched in 2007, allowing automated bid adjustments to improve ROI . Google introduced Target CPA bidding in 2010 as an enhancement, letting advertisers specify a desired average CPA .
• Enhanced CPC (ECPC), an automated bid adjustment feature, was introduced in 2010 to help busy advertisers by dynamically adjusting bids for likelihood of conversion . It initially allowed bid changes within a 30% range , a limit later removed as smart bidding improved .
• Target ROAS automated bidding was introduced around 2013 to let advertisers optimize for conversion value (revenue) rather than just conversion volume . This strategy predicts the conversion value of each click and raises or lowers bids accordingly .
• Google’s term “Smart Bidding” was introduced in 2016, marking a major advancement with machine learning-driven, auction-time bid optimization using many signals and conversion data .
• Maximize Conversion Value, a bidding strategy to automatically maximize total conversion value within budget, was rolled out in late 2019 . This strategy uses historical data and ML to allocate budget based on the potential value of each conversion.
• In 2022, Google simplified search campaign bid strategies by merging Target CPA into Maximize Conversions and Target ROAS into Maximize Conversion Value (as target options), streamlining choices and reducing the learning curve for switching strategies .
• By 2023, Google signaled a move away from manual bidding entirely. Industry commentary noted that “Google Ads manual bidding is on its deathbed,” as Google’s push toward automation (e.g. Performance Max campaigns and auto-applied recommendations) accelerates . Google representatives even hinted that manual CPC bidding could sunset in 2023 , highlighting the company’s full commitment to automated bidding.
• Google’s own documentation emphasizes that value-based bidding maximizes the total conversion value by optimizing bids in real time toward users likely to bring more value (revenue, profit, etc.) – advertisers should specify the value they care about (like sales revenue or margins) when setting up tracking . This approach allows prioritizing high-value conversions over low-value ones, effectively buying value instead of just buying clicks or conversions .