5 Ways to Use Data to Boost Your Facebook Ad Campaigns

Most Facebook ad campaigns that fail do so not because of a single bad creative or a poorly written headline, but because of a fundamental lack of a data-driven strategy. For many advertisers, navigating the platform feels less like a science and more like a high-stakes gamble, characterized by rising ad costs, unpredictable results, and a constant struggle to prove return on investment. The feeling of pouring money into a black box, hoping for a positive outcome, is an all-too-common pain point.

The solution is to reframe the role of data. It is not merely a rear-view mirror showing what has already happened; it is a sophisticated GPS that provides a clear, actionable path forward. The key to unlocking scalable, profitable campaigns lies in building a systematic framework for collecting, analyzing, and acting upon performance data at every stage of the marketing funnel. This transforms advertising from an art of guesswork into a science of optimization.

This report outlines a comprehensive blueprint for transforming an ad account through a disciplined, data-first approach. It details five interconnected methods that build upon one another, creating a powerful flywheel of continuous improvement. These methods range from mastering performance analysis and building a robust data engine with the Meta Pixel to implementing a full-funnel retargeting system, scaling with value-based audiences, and driving down costs through a rigorous optimization framework. Following this blueprint will empower advertisers to move beyond simple metrics and build a truly intelligent advertising operation.

Way 1: Master Your Metrics with Advanced Performance Analysis

The foundation of any data-driven advertising strategy is the ability to correctly read and interpret the information presented in Facebook Ads Manager. This requires moving beyond surface-level or "vanity" metrics to diagnose campaign health, identify specific opportunities, and understand the causal relationships between different performance indicators.

The KPIs That Actually Drive Business Growth

While Facebook Ads Manager offers a vast array of metrics, only a select few directly measure business impact. Metrics like Reach and Impressions, which indicate how many people saw an ad, are useful for context but do not measure efficiency or profitability.¹ A successful data-driven approach prioritizes metrics that connect ad spend to tangible business outcomes.

• Cost Per Result (CPR) / Cost Per Acquisition (CPA): This is the North Star metric for the majority of campaigns. It is calculated by dividing the total amount spent on a campaign by the number of desired outcomes it generated, such as purchases, leads, or app installs.³ For example, if a campaign spends $500 and generates 10 purchases, the CPA is $50. This metric is the ultimate measure of efficiency. A "good" CPA is not a universal number; it is entirely relative to a business's profit margins and the customer's lifetime value.³ A $50 CPA might be highly profitable for a company selling a $500 product but unsustainable for one selling a $40 item.

• Return on Ad Spend (ROAS): For e-commerce businesses, ROAS is the primary measure of profitability. It is calculated by dividing the total revenue generated from ads by the total ad spend.⁶ A campaign that generates $2,000 in revenue from $500 in ad spend has a ROAS of 4.0, or 400%. A ROAS greater than 1.0 indicates that the campaign is profitable on a direct-response basis, though a ratio of 4:1 is often cited as a strong benchmark for a healthy, scalable campaign.⁷ This metric requires the Meta Pixel to be correctly tracking purchase values.⁸

• Click-Through Rate (CTR): CTR is a crucial diagnostic metric that measures the relevance of an ad to its target audience. It represents the percentage of people who clicked on an ad after seeing it.⁹ A low CTR is a clear signal of a disconnect between the ad creative, the copy, and the audience's interests.⁶ While a high CTR doesn't guarantee conversions, a low CTR almost certainly predicts poor performance, as it indicates the ad is failing at its first job: capturing attention.

• Cost Per Mille (CPM): CPM represents the cost to achieve 1,000 ad impressions.² This metric is a direct measure of the cost of reaching a specific audience in the Facebook auction. A rising CPM can indicate several factors, including increased competition for that audience, a decline in the ad's relevance score, or audience fatigue, where the same users have seen the ad too many times.¹⁰

A sophisticated understanding of these metrics involves recognizing their interplay. Advertisers often focus on CPA in isolation, but it is an outcome metric that is heavily influenced by the others. The CTR of an ad is a primary input for Facebook's algorithm; a high CTR signals high relevance.⁶ The auction system rewards this relevance, often by providing a lower CPM, as Facebook prefers to show users ads they find engaging.¹¹ Since the cost of a conversion (CPA) is directly linked to the cost of reaching users (CPM) and the rate at which they click and convert, a low CTR is a leading indicator of a high CPA. To diagnose and fix a high CPA, the first area to investigate is often not the budget or bidding strategy, but the ad creative itself. Improving creative to boost CTR can have a cascading positive effect, lowering CPM and, consequently, the final CPA.

Uncovering Hidden Insights with the Breakdown Feature

The "Breakdown" menu within Ads Manager is arguably the most powerful diagnostic tool available to advertisers.¹ It allows for the segmentation of aggregate campaign data into granular subsets, revealing hidden pockets of high and low performance that are masked by overall averages.¹²

• Delivery > Placement: A campaign's performance can vary dramatically across different placements like the Facebook Feed, Instagram Stories, Reels, and the Audience Network. Using this breakdown often reveals that one or two placements are consuming a large portion of the budget while delivering a very high CPA.¹ Identifying and disabling these underperforming placements is one of the quickest ways to improve overall campaign efficiency.¹²

• Delivery > Age & Gender: This breakdown segments performance by demographic groups. An advertiser might discover that the 45-54 age bracket converts at half the cost of the 25-34 bracket, or that women outperform men significantly.¹³ This data is invaluable for informing future creative decisions—for example, using different imagery or messaging that resonates more strongly with the top-performing demographic—or for creating entirely separate ad sets dedicated to that high-performing segment.¹²

• Delivery > Impression Device: This breakdown compares performance across devices such as iOS smartphones, Android smartphones, and desktops. Significant discrepancies can highlight technical issues, such as a landing page that is not optimized for mobile, or opportunities to create device-specific ad experiences.¹²

• Action > Carousel Card: For advertisers using the carousel ad format, this breakdown is essential. It shows the clicks, CTR, and cost for each individual card within the carousel.¹² This provides direct, empirical feedback on which product, feature, or image is most compelling to the audience, guiding future creative and product marketing strategies.

The choice of reporting layout also dictates the type of analysis that can be performed. Facebook offers three main layouts: Trend, Bar, and Pivot Table.¹² Many advertisers remain in the default view, but proactively selecting the right layout transforms analysis from passive observation to active, question-led investigation. A

Trend layout, which graphs metrics over time, is essential for diagnostics. A sudden spike in CPA can be pinpointed to a specific date, allowing the advertiser to investigate what changed that day—a new ad was launched, a competitor's sale began, or a technical issue arose on the website. A Bar chart is best suited for direct comparisons, answering questions like, "Which of my five active ad creatives has the highest ROAS?" or "Which country has the lowest CPA?".¹² Finally, the

Pivot Table layout offers the most granularity, enabling deep-dive analysis with multiple breakdowns simultaneously.¹² By consciously choosing the right layout for the task, advertisers can more effectively diagnose problems and identify opportunities.

Way 2: Build Your Data Engine with the Meta Pixel

If performance analysis is the skill of reading the map, the Meta Pixel is the tool that draws the map in the first place. It is not merely a tracking utility; it is the central nervous system of a sophisticated advertising operation, creating a vital bridge between user activity on an external website and their profile within the Meta ecosystem.

The Pixel: Your Bridge Between Worlds

The Meta Pixel is a small piece of JavaScript code that is installed in the header of a website.⁸ Its fundamental function is to observe the actions users take on the site—such as viewing a product, adding an item to the cart, or making a purchase—and connect that activity back to their anonymous Facebook and Instagram profiles.¹⁶ This connection is the technical foundation that makes virtually all advanced advertising strategies possible. Without a properly installed Pixel, an advertiser is effectively blind, unable to accurately track conversions, optimize campaigns for valuable actions, or build the audiences required for effective retargeting.¹⁸

Tracking the Customer Journey with Standard Events

The specific actions that the Pixel tracks are called "events".⁸ Facebook provides a set of "standard events" that correspond to the most common and important steps in a typical customer journey. Implementing these events correctly creates a digital map of the conversion funnel, allowing advertisers to see not only how many users convert but also where others drop off along the way.⁶ The most critical standard events include ⁸:

• ViewContent: This event should fire when a user visits a key page that signifies interest, such as a product details page, a service page, or an important blog post.

• AddToCart: This event fires when a user adds an item to their shopping cart.

• InitiateCheckout: This event fires when a user begins the checkout process, a strong signal of purchase intent.

• Purchase: This event fires on the confirmation or "thank you" page after a transaction is complete. Critically, for e-commerce, this event must be configured to pass back two parameters: value (the total value of the order) and currency. This is what enables ROAS calculation and value-based optimization.⁸

• Lead: For B2B or service-based businesses, this event fires upon the successful submission of a contact or lead generation form.

Many advertisers mistakenly believe the Pixel's primary job is to report conversion data back to them. While it does this, its most important function is to feed data back to Meta's powerful machine learning algorithm.¹⁰ When a campaign is optimized for "Purchases," the advertiser is effectively instructing the algorithm: "Here are thousands of data points describing the demographics, interests, and online behaviors of the people who have completed the

Purchase event on my website. Your job is to analyze these patterns and find me more people across Facebook and Instagram who look and act just like them".¹⁶

The more high-quality conversion data the Pixel provides, the more intelligent the algorithm becomes at predicting who is most likely to convert in the future. This explains the existence of the "learning phase" for new campaigns, during which performance can be volatile as the system gathers enough data to optimize effectively.²¹ It also underscores a critical strategic point: ad spend in the early stages of a campaign is not just an expense to generate immediate sales; it is an investment in

data acquisition. The advertiser is "paying" to educate the algorithm. This is why campaigns often become more efficient over time and why allocating a sufficient budget to exit the learning phase is vital for long-term success.²²

Furthermore, the events tracked by the Pixel are the foundational building blocks for all advanced audience segmentation. The power of creating Custom Audiences from website traffic comes not from targeting "all visitors" but from segmenting users based on the specific events they have triggered.²⁰ The ability to create a highly valuable audience of "everyone who triggered

AddToCart but not Purchase in the last 14 days" is entirely dependent on having those two events installed and firing correctly.²⁵ Likewise, building a powerful Lookalike Audience to find new customers requires a source audience built from the

Purchase event, ideally with value data attached.²⁶ A poorly implemented Pixel strategy directly cripples an advertiser's ability to create the audiences needed for sophisticated targeting and retargeting. The Pixel is not just a reporting tool; it is the essential infrastructure upon which all other data-driven strategies are built.

Way 3: Implement a Full-Funnel Retargeting Strategy

Once the Pixel is collecting high-quality data, the next step is to activate that data through a structured retargeting strategy. A common and costly mistake is to target all past website visitors with the same generic ad, ignoring their specific level of intent.²⁸ A sophisticated approach segments audiences based on their on-site behavior and matches the ad message to their specific stage in the marketing funnel, dramatically increasing relevance and conversion rates.²⁹

Building Your Core Retargeting Audiences

An effective retargeting system is built on a series of Custom Audiences created from website traffic data. Each audience represents a different level of user intent, from casual browser to high-intent shopper.

• Top of Funnel (Awareness/Interest): This segment includes users who have shown initial interest but are not yet deep into the consideration process.

• Audience: All website visitors from the last 30-90 days, or users who have engaged with the brand's Facebook or Instagram page.²⁵

• Message/Offer: The goal here is to build trust and stay top-of-mind, not to push for an immediate sale. Effective creative for this audience includes brand storytelling, social proof like customer testimonials or media mentions, and promoting high-value content such as informative blog posts.²⁹

• Middle of Funnel (Consideration): This segment includes users who have moved beyond general browsing and have shown interest in specific products or services.

• Audience: Users who have triggered the ViewContent event by visiting specific product pages or browsing particular product categories.²⁸

• Message/Offer: The messaging should become more product-focused. This is the ideal stage for Dynamic Product Ads, which automatically show users the exact products they previously viewed.²⁹ Other effective tactics include highlighting key product features, showing videos of the product in use, or presenting case studies and reviews related to that specific product category.

• Bottom of Funnel (Intent/Purchase): This is the most valuable segment, containing users who have signaled a clear intent to buy but have not yet completed the transaction.

• Audience: Users who have triggered the AddToCart or InitiateCheckout events but have not triggered the Purchase event.²⁹

• Message/Offer: At this stage, direct-response tactics are most effective. The ad should aim to overcome the final barrier to purchase. This can be achieved by creating a sense of urgency ("Your cart expires soon!"), reminding them of key value propositions like free shipping, or offering a small, time-sensitive incentive like a 10% discount to encourage them to complete their purchase.²⁹

The Critical Role of Exclusions and Frequency Caps

Two technical elements are crucial for a profitable and efficient retargeting system. First is the use of exclusions. It is wasteful and potentially annoying to continue showing purchase-oriented ads to customers who have already bought the product. This is prevented by creating a Custom Audience of all users who have triggered the Purchase event in the last 180 days and then excluding this audience at the ad set level of other retargeting campaigns.²⁸

Second is managing Ad Fatigue. When a user sees the same ad repeatedly, its effectiveness diminishes, leading to "banner blindness," declining CTR, and rising CPA.³³ Advertisers must monitor the "Frequency" metric in Ads Manager, which shows the average number of times each person in the target audience has seen the ad.²⁸ If frequency becomes too high (a common rule of thumb is to investigate when it exceeds 5-7 in a week), it's a clear signal to refresh the ad creative or broaden the audience.

The Data-Driven Retargeting Funnel

The following table synthesizes these concepts into a practical, actionable blueprint for building a multi-layered retargeting structure. It provides a clear guide for translating the abstract marketing funnel into concrete campaign settings within Facebook Ads Manager.

A successful retargeting strategy requires a balanced budget portfolio that reflects the inherent size differences between these funnel stages. The "All Visitors" audience will always be larger than the "Initiated Checkout" audience.³⁰ A common error is to over-allocate budget to the bottom-of-the-funnel ad sets simply because they exhibit the highest ROAS. While these audiences are the most efficient, their small size means they can be quickly saturated. Over-budgeting leads to excessive ad frequency, which exhausts the audience and rapidly diminishes performance.²⁸ Conversely, under-budgeting the larger top and middle-funnel audiences starves the entire system of new prospects to nurture. Therefore, the budget must be allocated strategically, with a significant portion dedicated to the mid-funnel to ensure a steady flow of qualified users, preventing the small, high-intent audiences from being over-exposed. This represents a crucial balance between maximizing efficiency (ROAS) and achieving sustainable scale (volume).

Way 4: Scale Profitably with Value-Based Lookalike Audiences

While retargeting is essential for maximizing conversions from existing traffic, true business growth comes from acquiring new customers. The most powerful tool in the Facebook advertising arsenal for this purpose is the Lookalike Audience. This technology allows advertisers to move beyond their known contacts and systematically find new, cold audiences that are statistically likely to be interested in their products or services.²⁰

Lookalike Audiences: The Engine of Scale

The concept behind Lookalike Audiences is straightforward yet powerful. An advertiser provides Facebook with a "source" or "seed" audience—typically a Custom Audience of past purchasers or high-value leads. Meta's algorithm then analyzes the thousands of data points associated with the users in that source audience, identifying common patterns in their demographics, interests, and online behaviors. Finally, it scans the platform to find a new group of users who "look like" the source audience based on those shared characteristics.²⁶ This process serves as an intelligent prospecting engine, enabling advertisers to scale their reach far beyond their existing customer base.

The Evolution: From Standard to Value-Based Lookalikes

The primary limitation of a standard Lookalike Audience is that it treats every individual in the source audience as equal. A customer who made a single $10 purchase is given the same weight in the algorithm's analysis as a loyal customer who has spent $1,000 over ten separate transactions.²⁷ This can dilute the quality of the resulting Lookalike.

Value-Based Lookalike Audiences are the advanced solution to this problem. This technique requires the advertiser to provide a source audience that includes a specific customer value metric, most commonly their Customer Lifetime Value (LTV) or total purchase value.²⁷ The impact of this additional data point is profound. The instruction to the algorithm changes from "Find me people who look like my customers" to "Find me people who look like my

most valuable, highest-spending customers".²⁷ This aligns the ad targeting directly with the business goal of acquiring high-quality customers, optimizing for long-term profitability rather than just short-term conversion volume.

How to Create Your High-Value Source Audience

There are two primary methods for creating the necessary value-based source audience:

1. The Customer List with LTV: This is the most direct method. It involves exporting a customer list from a CRM or e-commerce platform (like Shopify) into a CSV or TXT file. This file must contain standard customer identifiers (such as email addresses or phone numbers) and, crucially, a dedicated column that lists a numerical value for each customer, such as their total historical spend or predicted LTV.²⁶ This file is then uploaded into Ads Manager to create a value-based Custom Audience, which can then be used as the seed for the Lookalike.³⁵

2. The Value-Optimized Pixel: This method is often easier to maintain over time. It relies on the Meta Pixel's Purchase event being correctly configured to pass back the value parameter with every transaction.²⁶ When this is in place, Facebook automatically collects the purchase value data associated with each converting user. This data can then be used directly within the Lookalike Audience creation tool to build a value-based source without the need for manual CSV uploads.²⁷

For either method to be effective, the source audience must be of sufficient quality and size. The minimum requirement is 100 people from a single country, but performance generally improves significantly with a source audience of 1,000 to 5,000 individuals, as this provides the algorithm with a much richer dataset to analyze.²⁶

Strategic Deployment of Lookalike Audiences

When creating a Lookalike Audience, advertisers must select an audience size, which ranges from 1% to 10% of the total Facebook user population in the selected country.²⁷ A

1% Lookalike is the most precise, containing users who are most similar to the source audience, but it offers the smallest reach. A 10% Lookalike provides massive scale but is much broader and less similar to the source.³⁶

A highly effective strategy is to create nested Lookalike Audiences. This involves creating several distinct audiences—for example, a 0-1%, a 1-2%, and a 2-5% Lookalike—and placing each one in its own ad set.⁴⁰ This structure allows the advertiser to test which level of similarity provides the optimal balance of performance and scale. It may be that the 1-2% audience delivers the best ROAS at a reasonable volume, allowing the advertiser to confidently allocate more budget to that specific segment. Finally, it is imperative to always

exclude the source Custom Audience (e.g., all past purchasers) from any Lookalike prospecting campaigns to ensure that ad spend is focused exclusively on acquiring new customers.³⁸

The ability to scale profitably is fundamentally tied to a business's understanding of its own unit economics. Many advertisers calculate their target CPA based on the profit from a single, initial transaction, which severely limits how much they can afford to spend to acquire a new customer.⁵ A deeper understanding of Customer Lifetime Value (LTV) completely changes this equation.³⁹ If the average customer is known to make three purchases over their lifetime, the business can afford to spend significantly more on the initial acquisition and still remain profitable in the long run. This higher allowable CPA provides greater bidding power in the competitive Facebook auction, enabling the advertiser to reach higher-quality users and scale campaigns more aggressively.

Value-Based Lookalike Audiences are the technical execution of this financial strategy. They create an operational bridge between a company's internal financial analysis (LTV calculation) and its external marketing execution. Businesses that do not know their LTV cannot use this powerful tool effectively and will consistently be outbid and out-scaled by more data-savvy competitors who do. The ROI of cold traffic prospecting campaigns is therefore directly and causally linked to the quality of a company's first-party data. Investing in accurate Pixel tracking, clean CRM data, and robust LTV analysis generates a compounding return by dramatically improving the performance of the single most important scaling tool in the platform: Lookalike Audiences.

Way 5: Drive Down Costs with a Rigorous Optimization Framework

The final component of a data-driven advertising system is a commitment to continuous, iterative optimization. The goal is not to find a single "perfect" ad and run it forever, but to build a framework for constant testing and refinement that systematically improves performance and drives down costs over time.

The A/B Testing Mindset: Always Be Testing

A/B testing, also known as split testing, is the application of the scientific method to advertising.⁴⁴ It involves creating two or more variations of an ad where only a single element is changed, showing them to similar audiences, and analyzing the results to determine which version performs better against a specific objective. The cardinal rule of A/B testing is to

test only one variable at a time.⁴⁶ If an advertiser tests a new image and new headline simultaneously, it is impossible to determine which change was responsible for the resulting performance uplift or decline.²¹

The core testing process follows a clear structure:

1. Form a Hypothesis: Start with an educated guess, such as "I believe using a video testimonial instead of a static image will increase our conversion rate because it builds more trust".⁴⁶

2. Create Variations: Build the different versions of the ad within Ads Manager.

3. Run the Test: Allocate a sufficient budget and run the test for an adequate duration, typically at least 4-7 days, to gather enough data and account for daily fluctuations in performance.⁴⁷

4. Analyze Results: Compare the performance of the variations against the primary KPI (e.g., CPA or ROAS) and determine if there is a statistically significant winner.²¹

A Prioritized Framework for What to Test

Not all tests have an equal impact. A disciplined approach involves prioritizing tests based on their potential to move the needle. The hierarchy of testing, from highest to lowest potential impact, is generally as follows:

1. Audience: Testing different target audiences is often the highest-leverage activity. A campaign's success is fundamentally dependent on reaching the right people. An example would be testing a 1% Value-Based Lookalike Audience against a broad, interest-based audience to see which delivers a lower CPA.²¹

2. Offer / Creative Concept: Before tweaking minor details, it's important to test fundamentally different value propositions or creative angles. This could involve testing a discount-based offer against a free-shipping offer, or a testimonial-driven video against a product-demonstration video.⁴⁹

3. Creative Format: Different ad formats engage users in unique ways. Testing a single image ad versus a video ad or a multi-card carousel ad can reveal which format is most effective for communicating the message and driving action.⁴⁷

4. Copy and Headlines: Once a winning concept and format are identified, testing can move to more granular elements like the ad copy. This involves testing different hooks, highlighting different pain points, or experimenting with various calls to action (e.g., "Shop Now" vs. "Learn More").²¹

5. Landing Page: The user's experience does not end with the ad click. The landing page they arrive on plays a massive role in conversion. Testing ads that direct traffic to different landing pages can uncover significant opportunities for improvement.²¹

Graduating to Data-Driven Bidding Strategies

As campaigns mature and accumulate a history of performance data, advertisers can move beyond the default "Lowest Cost" bid strategy. This allows for greater control over campaign costs and profitability by providing Meta's algorithm with a specific financial target to optimize towards.⁵²

Choosing Your Advanced Bidding Strategy

The various goal-based bidding strategies are a source of confusion for many advertisers, yet choosing the right one is critical for financial performance. The following table serves as a decision-making framework to match a business objective to the appropriate bidding strategy.

The choice of bidding strategy is inextricably linked to the budgeting approach. When using Campaign Budget Optimization (CBO), where Facebook automatically allocates a single campaign budget across multiple ad sets, the bidding strategy acts as the core instruction for that allocation.⁵⁴ A CBO campaign with a "Lowest Cost" bid strategy will aggressively shift budget to the ad set that can generate

any conversion for the cheapest price. In contrast, a CBO campaign with a "Minimum ROAS" bid strategy will intelligently allocate budget to the ad set that can meet the specified profitability threshold, even if its CPA is higher than other ad sets. Therefore, budget optimization cannot be considered in isolation from the performance goal defined by the bidding strategy.

Ultimately, data-driven advertising is a cyclical process, not a linear one. The five methods detailed in this report form a powerful flywheel. Insights from A/B testing (Way 5) might reveal that a new message resonates with a specific demographic. This performance data (Way 1) can then be used to build a more refined Custom Audience for retargeting (Way 3). That new, higher-performing audience can, in turn, be used as a superior source for a more powerful Lookalike Audience to find new customers (Way 4). This entire virtuous cycle is only possible because the Meta Pixel (Way 2) was installed correctly and tracking every interaction. The goal is not to "finish" optimizing, but to build and accelerate this cycle of continuous, data-fueled improvement.

Conclusion

Mastering Facebook advertising in the modern digital landscape is not about discovering a single secret hack or a magic formula. It is about building a robust, intelligent system grounded in the disciplined collection, analysis, and activation of data. The five methods outlined in this report—mastering performance analysis, building a data engine with the Meta Pixel, implementing a full-funnel retargeting strategy, scaling with value-based audiences, and driving down costs with a rigorous optimization framework—are not independent tactics but interconnected components of a single, powerful flywheel.

The process is cyclical: performance analysis uncovers opportunities that inform audience creation. These new audiences are then used in targeted retargeting and prospecting campaigns. The results of these campaigns are meticulously tested and optimized, generating a fresh stream of data that flows back to the start of the cycle, fueling the next round of analysis and improvement. Each rotation of this flywheel makes the entire advertising operation smarter, more efficient, and more profitable.

While the complexity of this system may seem daunting, the path to implementation is incremental. It begins with taking control of the most immediate bottleneck, whether that means finally installing the Meta Pixel correctly, dedicating time to explore the "Breakdown" reports in Ads Manager, or launching a first, simple A/B test. By committing to a data-first approach and systematically building out this framework, advertisers can move away from the uncertainty of gambling with ad spend and toward the confidence that comes from making decisions based on evidence. This is how sustainable, scalable growth is achieved on Facebook and beyond.