The Problem with Marketing & Advertising Data (and how to fix it)

"Facebook Doesn’t Know What It Does With Your Data, Or Where It Goes: Leaked Document"

The above article from Vice was written in 2022, which was a tough year for Facebook, having lost $232 BILLION in stock value early in the first quarter, but that drop in value had NOTHING to do with the well-documented issue of fleeced numbers and data quality or control at Facebook.

In fact, it's such a non-issue to the market that 2024 is being touted as the year Mark Zuckerberg could get back into the trillion dollar club.

The Vice article highlights a significant (and persistent) gap in the data that is available to advertising platforms like Facebook and the types of data that are most useful for ad agencies.

Here's how the situation is detailed:

The Problem of Data Lineage at Facebook: Facebook’s internal systems are described as a chaotic "data free-for-all," meaning that once user data is collected, it spreads uncontrollably across the platform's open systems. The leaked document likens this to pouring ink into a lake: once it’s mixed in, it’s impossible to separate and control. This "ink" metaphor represents various types of data (third-party, first-party, sensitive categories) being scattered throughout the system. The key issue is that Facebook lacks precise control over where data goes, how it is used, and for what purposes.

Regulatory Expectations vs. Facebook's Data Reality: Regulators, particularly under privacy laws like the EU's GDPR, require platforms to ensure that data collected for a specific purpose is not repurposed without explicit consent. However, Facebook’s internal struggles to trace and control data usage make it difficult to comply with such laws. According to GDPR’s purpose limitation principle, every piece of data must be used only for the explicit reason it was collected, which Facebook seems unable to guarantee. This lack of data governance could lead to legal troubles, as Facebook cannot confidently promise that certain data won’t be used for unintended purposes.

Data Useful for Ad Agencies: Ad agencies rely on specific, actionable data for targeted advertising—data like user demographics, interests, and behaviors. However, for this data to be useful, it must be cleanly categorized and traceable. The article implies that Facebook's current lack of control over data flows hinders its ability to provide ad agencies with the precise data needed for effective, compliant advertising. The messiness of Facebook's data means that agencies might not receive the most relevant or legally compliant data to optimize campaigns.

Facebook’s Response to Data Control Issues: To address these issues, Facebook is working on new products like “Basic Ads,” which aim to allow users to opt-out of having their data used in ads, in compliance with regulations. However, the fact that this product is delayed past its intended launch suggests that Facebook is still grappling with how to meet these privacy standards while continuing to offer valuable ad services.

In essence, the article reveals that Facebook’s internal disorganization of user data complicates both its compliance with privacy regulations and its ability to deliver the useful, controlled data that ad agencies require for targeted campaigns. This creates a tension between regulatory demands and business needs in the advertising world.

So what can marketing and ad agencies do? First off, you have to really understand how your clients or brand ACTUALLY makes money, because you have to tether the data and performance metrics to these numbers.

Connecting marketing/advertising data to business impact

Connecting advertising and marketing data to business goals involves aligning the metrics and insights gathered from campaigns with the overall strategic objectives of the business.

Here’s a step-by-step guide to help agencies make this connection:

1. Define Clear Business Goals

Before diving into advertising data, it’s crucial to have a clear understanding of what the business is aiming to achieve. Business goals typically fall into broad categories such as:

• Revenue Growth: Increase overall sales or revenue.
• Customer Acquisition: Gain new customers or leads.
• Brand Awareness: Improve recognition and visibility.
• Customer Retention: Increase the lifetime value (LTV) of existing customers.
• Cost Efficiency: Reduce operational costs, including marketing spend.

Action: Identify the specific goals of your client or business, and make sure these goals are well-understood by both the agency and stakeholders.

2. Select the Right Metrics

Once business goals are clearly defined, align your advertising metrics with those objectives. Here’s how different goals map to key metrics:

• Revenue Growth: Metrics like return on ad spend (ROAS), customer lifetime value (CLV), or conversion rate help track the direct impact of ads on revenue.
• Customer Acquisition: Focus on customer acquisition cost (CAC), lead generation, or click-through rates (CTR) to measure how efficiently your ads are attracting new customers.
• Brand Awareness: Impressions, reach, and engagement metrics (likes, shares, comments) are useful for measuring how visible and interactive your brand is.
• Customer Retention: Track metrics like churn rate, repeat purchase rate, and customer satisfaction surveys (often done through ads that promote customer engagement).
• Cost Efficiency: Cost per click (CPC), cost per lead (CPL), and overall campaign ROI give insights into how efficiently the ad budget is being utilized.

Action: Ensure that every advertising campaign is tied to at least one specific, measurable key performance indicator (KPI) that directly relates to the business goal.

3. Implement Advanced Attribution Models

Advertising often involves multiple touchpoints before a conversion happens. To effectively link ads to business outcomes, agencies need to go beyond basic attribution models, such as last-click attribution, and instead use advanced models:

• Multi-Touch Attribution: This model credits all touchpoints (ads, organic search, social media, etc.) leading to a conversion, allowing agencies to better understand which ads contribute to the business goal.
• Data-Driven Attribution: Using AI and machine learning, this model analyzes vast amounts of data to determine which touchpoints have the most influence on conversions. It is highly customizable to specific business needs.

Action: Choose an attribution model that best fits the client’s business structure and helps map customer journeys more accurately to conversions.

4. Link Data to Customer Journeys

Advertising data must be contextualized within the broader customer journey. This involves understanding the different stages of the funnel and how advertising impacts them:

• Awareness Stage: Ads aimed at creating initial awareness should be measured by reach, impressions, and engagement metrics. The connection to business goals could be building market share or expanding brand presence.
• Consideration Stage: Ads that encourage product consideration should be analyzed for metrics like click-through rate (CTR) and landing page engagement. These should be linked to goals like lead generation or increasing qualified traffic.
• Decision Stage: Ads targeting conversion (such as retargeting campaigns) should be linked to business outcomes like sales, sign-ups, or form submissions. ROAS and conversion rates are key here.

Action: Map your advertising campaigns to the different stages of the customer journey and ensure that each stage’s metrics connect back to relevant business outcomes.

5. Set Clear, Quantifiable Success Metrics

For each business goal, agencies need to establish clear metrics that will define success. These metrics should be both measurable and time-bound to enable continuous optimization:

• SMART Metrics: Use Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART) goals to ensure advertising data is actionable. For example, “Increase customer sign-ups by 20% in Q4 through a targeted Facebook ad campaign.”
• Benchmarking and Iteration: Compare current data against historical performance and industry benchmarks to assess whether you are on track to meet business goals.

Action: Set specific targets (e.g., 5% increase in conversion rate, 10% increase in customer lifetime value) for each campaign and measure performance regularly.

6. Analyze Data to Provide Insights and Recommendations

The process doesn’t stop at data collection. Agencies must analyze the data to provide actionable insights that demonstrate how advertising is driving business goals:

• Data Segmentation: Break down data by different segments (e.g., by audience type, device, location) to understand which variables impact success.
• Data Visualization: Present data in clear, understandable formats (dashboards, graphs, charts) to stakeholders. This helps communicate how specific campaigns are influencing business goals.
• Ongoing Optimization: Use AI tools to continuously analyze performance and suggest adjustments to campaigns for better alignment with business outcomes.

Action: Use data analytics platforms to regularly review the performance of ad campaigns and refine strategies based on real-time insights.

7. Regularly Communicate Results to Stakeholders

Connecting advertising data to business goals requires regular communication with stakeholders (clients, internal teams, etc.). This includes:

• Monthly or Quarterly Reporting: Provide detailed reports that tie advertising metrics directly to business outcomes, showing the progress made towards set goals.
• Tying Insights to Business Strategy: Make it clear how advertising insights are feeding back into broader business strategies. For example, if a specific audience segment is responding well, recommend expanding investment in that demographic.

Action: Regularly communicate with stakeholders through well-structured reports that tie ad performance to overall business health.

By aligning advertising data with clearly defined business goals, selecting the right metrics, and continuously analyzing performance, agencies can demonstrate the true impact of their efforts on the client’s bottom line. This not only makes advertising more effective but also ensures that it directly contributes to achieving business objectives.

Now all we need to do is tackle the internal ways we handle data management.

Data chaos is an excuse. Here's what marketing and ad agencies can do to take back control

Data management can be mishandled by social media platforms in several ways, which directly impacts how ad agencies can utilize that data for their clients. Here’s a breakdown of these issues and how agencies can regain control to drive actual business impact:

Data Fragmentation and Silos

Social media platforms often maintain control over the data they collect, providing limited access or fragmented datasets to advertisers and marketers. This leads to disconnected insights, where agencies only see pieces of the puzzle but not the full picture. For example, platform-specific metrics may not integrate seamlessly with other channels, leading to inconsistent data that is hard to compare or analyze holistically.

How Agencies Can Take Control:

• Centralize Data Collection: Agencies should focus on integrating data from multiple platforms into a unified marketing data management system, ensuring a 360-degree view of campaign performance.
• Utilize Third-Party Data Integrations: Many AI-driven tools can aggregate data from different social platforms and other channels, making it easier for agencies to perform cross-channel analysis and track performance metrics that align with their business goals.

Limited Data Ownership

Social platforms are notorious for keeping ownership of user data, which limits how agencies can leverage the information for long-term insights or campaign optimization. Data access is often restricted to high-level analytics dashboards or anonymized datasets, which limits deeper strategic analysis.

How Agencies Can Take Control:

• Implement AI-Powered Analytics Tools: By leveraging AI-driven tools, agencies can bridge the gap between platform-specific data and actionable insights. AI can also help by identifying trends and patterns in the data that may not be immediately obvious.
• Advocate for First-Party Data Collection: Encourage clients to gather their own first-party data through direct customer interactions or surveys. This data can be more valuable and provides more control over customer insights without relying on social platforms as intermediaries.

Inconsistent or Inaccurate Attribution

Social media platforms may use "last-click" attribution models or offer their own attribution systems, which can skew the perception of how campaigns perform across multiple touchpoints. This inconsistency can lead to misaligned strategies and inaccurate ROI reporting, making it difficult for agencies to prove campaign effectiveness.

How Agencies Can Take Control:

• Adopt Multi-Touch Attribution Models: Agencies should use AI-driven, multi-touch attribution models to gain a more accurate picture of how different channels and touchpoints contribute to conversions. This holistic approach ensures that every customer interaction is accounted for, offering better insights into which parts of a campaign are truly driving results.
• Develop Custom Dashboards: Instead of relying on built-in platform analytics, agencies can create custom dashboards that aggregate data from multiple sources. This allows for more flexibility in how success is measured, ensuring that it aligns with the agency’s and client’s goals.

Poor Data Quality and Accessibility

Data provided by social platforms may lack the granularity needed for in-depth analysis, or it may contain inaccuracies, such as bot activity or duplicate entries. This low-quality data can lead to flawed decision-making, reducing the effectiveness of AI tools and other analysis methods.

How Agencies Can Take Control:

• Focus on Data Quality: Implement rigorous data cleaning and validation processes to ensure that the data ingested by AI systems is accurate and reliable. This involves removing duplicates, correcting errors, and ensuring that the data is complete.
• Enhance Accessibility Through Integration: Agencies should ensure their data infrastructure supports seamless integration of social media data with other business systems (e.g., CRM, marketing automation platforms). This helps AI tools access clean, comprehensive datasets, enabling better insights.

Scalability Issues

As social media campaigns scale, the amount of data generated grows exponentially. Many agencies struggle with managing this data effectively, especially when working with legacy systems that cannot handle large data volumes or the complexity of multi-channel campaigns.

How Agencies Can Take Control:

• Invest in Scalable Infrastructure: Agencies should ensure their data management systems can scale with the growth of their campaigns. This might involve investing in cloud-based solutions that can handle increasing data loads without compromising performance.
• Automate Data Reporting: Automation tools that consolidate and generate reports at scale can help agencies stay on top of campaign performance and make real-time adjustments.

By addressing these issues, ad agencies can take full control of their data and use it to generate meaningful insights that drive measurable business outcomes for their clients.