Performance Marketing is an Unfair Game—How to Beat the Algorithms with Custom Data Models.

Tired of climbing the marketing career ladder while performance marketing algorithms leave you in the dust? You’re not alone-platform biases and black-box decisions make it an unfair game. But savvy marketers beat them with AI-driven models using CRM data from PebblePost, dodging bad data pitfalls like garbage in, garbage out. Discover how custom models supercharge your targeting, skills, and ROI.

Why Performance Marketing Algorithms Are Unfair

Performance marketing platforms like Google, TikTok, and Amazon rely on black box algorithms fueled by flawed third-party data, leading to unfair outcomes where bad data means garbage in, garbage out. These systems prioritize platform revenue through auction dynamics over advertiser goals like true ROAS. Advertisers face wasted ad spend from inaccurate signals that ignore real customer journeys.

Native algorithms embed biases that favor media operators, pushing bids into high-cost auctions without regard for conversion rates or customer acquisition cost. Flawed signals from content farms and unverified intent data mislead targeting efforts. This creates a measurement problem where attribution models fail to capture the full customer journey.

Model drift worsens over time as unrefreshed bad data accumulates, eroding performance in digital media campaigns. Platforms rarely incorporate high-quality first-party data or CRM data for closed-loop insights. Custom data models offer the edge by leveraging verified intent and transaction data to counter these flaws.

The core issue lies in attribution model limitations and identity graph gaps, which distort audience targeting. For example, an eco-friendly product ad might target broad green living interests instead of behavioral intent from repeat purchase signals. Transitioning to custom models with sharper targeting promises fairer results.

Platform Biases and Black Box Decisions

Major platforms like Google, TikTok, and Amazon use opaque black box decisions that prioritize their ecosystem, embedding biases from low-quality third-party data and unverified intent signals. These AI-driven models favor auction revenue over advertiser ROAS, leading to inefficient spend. Custom data models can override this by integrating first-party data for precision.

Third-party data decay plagues performance marketing, as signals from open web sources quickly become outdated and unreliable. This results in wasted ad spend on mismatched audiences, such as targeting high-end laptops to users with fleeting interest rather than verified intent. High-quality CRM data in custom models refreshes these signals for better accuracy.

Black box systems emphasize platform auctions, inflating customer acquisition costs without optimizing for the full customer journey. Flawed signals overlook nuances like brand fatigue or subscription model preferences in content commerce. Teasing custom approaches with transaction data helps align bids with true value.

• Model drift from unrefreshed bad data shifts targeting away from high-converting segments over time.
• Attribution model issues ignore multi-touch paths, crediting media operators incorrectly.
• Poor data signals fuel lookalike audiences based on noisy inputs, missing repeat purchase potential.
• Custom models using PebblePost-style verified intent fix these gaps without platform dependence.

How Custom Data Models Give You an Edge

Custom AI-driven models built on your high-quality first-party data outperform native platform targeting by delivering sharper audience targeting and higher ROAS through verified intent. Platform algorithms suffer from garbage in, garbage out when fed aggregated third-party data, leading to wasted ad spend on flawed signals. In contrast, custom models leverage your CRM data and transaction data for precise, closed-loop targeting.

PebblePost’s verified intent approach exemplifies this edge, using real purchase signals to reach high-intent audiences across the open web. This method bypasses the measurement problem of digital media operators and combats signal loss from cookie deprecation. It sets the stage for building models that adapt to the full customer journey.

By focusing on behavioral intent from your own data, these models reduce reliance on content farms or affiliate advertising noise. They enable performance marketing teams to create lookalike audiences with true predictive power. Those interested in mapping that predictive power to customer actions will appreciate Add to Cart-ography: The “Invisible” AI Prompt That Maps Your Customer’s Next Move. Next, explore best practices for building models that beat native options.

Building Models That Outperform Native Targeting

PebblePost-style models using transaction data and behavioral intent create lookalike audiences that boost conversion rates over native options. These custom data models address the limitations of platform defaults, which often rely on broad, flawed signals from third-party sources. Start by integrating your unique data streams for sharper targeting.

Follow these best practices to build models that cut through adtech noise and improve ROAS:

• Seed with CRM data to refine customer profiles and lower customer acquisition cost through precise segmentation.
• Incorporate intent data for targeting repeat purchase opportunities, like subscribers eyeing upgrades.
• Use an identity graph to combat signal loss and maintain continuity across devices and platforms.
• Refresh models weekly to avoid model drift and keep pace with shifting customer behavior.
• Benchmark against platform defaults regularly to quantify gains in attribution models and media efficiency.

For example, target fans of eco-friendly products by modeling past buyers of green living items alongside high-end laptops. This approach minimizes brand fatigue and enhances customer experience in DTC channels. Experts recommend testing these practices in a subscription model to validate performance against native targeting baselines.

What Data Should You Collect for Custom Models?

Prioritize first-party data like CRM records, transaction data, and verified intent signals over unreliable third-party data to fuel models that reflect real customer journeys. This approach helps performance marketing teams build AI-driven models that cut through flawed signals from content farms and media operators. High-quality data reduces wasted ad spend and improves ROAS.

Collecting the right data signals enables sharper targeting for lookalike audiences and better attribution models. Tools like Google Analytics and Segment make it easier to gather customer journey details without relying on the open web’s noisy inputs. Avoid the garbage in, garbage out trap by focusing on verified sources.

Here are seven key data types to collect for custom models in performance marketing. Each one strengthens your ability to track marketing attribution and combat model drift.

1. CRM data: Gather purchase history, email opens, and support tickets. This first-party gold shows true customer experience, like repeat purchases for an eco-friendly product.
2. Transaction data (PebblePost-style): Capture real-time purchase confirmations tied to ad exposure. It powers closed-loop insights, revealing what drives conversions beyond basic metrics.
3. Intent data: Track search queries and browsing patterns. For instance, users researching high-end laptops signal readiness, aiding audience targeting.
4. Behavioral intent: Monitor cart abandons and wishlist adds. These signals predict drop-offs, helping refine DTC strategies and lower customer acquisition cost.
5. Customer journey signals: Log repeat purchase patterns and session depths. They map the full path, from podcast episode listens to subscription model sign-ups.
6. Closed-loop attribution: Link ad clicks to final sales across digital media. This fixes the measurement problem in adtech and affiliate advertising.
7. Identity graph matches: Unify user IDs across devices and platforms. It enables precise content commerce tracking, avoiding brand fatigue from poor matches.

Watch for bad data pitfalls like duplicates or unverified third-party feeds, which inflate costs. Clean inputs ensure models deliver reliable conversion rates and customer insights. For a deep dive into essential marketing ROI metrics that measure this impact, explore proven benchmarks for business growth.

How Do You Integrate Custom Models with Ad Platforms?

Seamless integration via APIs, data feeds, and custom audiences lets your models plug into Google and other ad platforms for sharper targeting without rebuilding. The technical flow starts with model outputs generating audience segments from high-quality data like CRM and intent data. These feed into adtech standards such as hashed uploads, aligning with media operator workflows for real-time activation.

Platforms process these inputs through their identity graph to match users across the customer journey. This closed-loop setup reduces wasted ad spend by replacing flawed signals from third-party data. Experts recommend testing small batches first to avoid model drift issues.

Common workflows involve daily syncs to keep lookalike audiences fresh amid changing behaviors. For instance, an eco-friendly product brand might export segments based on green living intent from first-party data. This powers performance marketing campaigns with verified intent over generic targeting.

Transitioning to specifics, master APIs, feeds, and custom audiences to beat algorithms in digital media. Proper setup ensures your AI-driven models drive higher ROAS and lower customer acquisition costs.

APIs, Feeds, and Custom Audiences

Google’s Customer Match API and custom audience uploads enable direct integration of your model’s lookalike audiences into campaigns. Export model segments as CSV or hashed files per Google specs to start. This avoids bad data pitfalls like garbage in, garbage out from unhashed PII.

1. Export model segments as CSV/hash following Google specs for secure uploads.
2. Use Ads API v15 for dynamic feeds that update bidding and targeting in real time.
3. Set up Customer Match requiring a minimum of 1,000 matches for activation.
4. Schedule daily syncs via SFTP to combat model drift and maintain accuracy.

Compatibility extends to TikTok and Amazon platforms with similar feeds. A common mistake is uploading unhashed PII, leading to rejections and delays. Instead, hash emails from transaction data for smooth performance marketing flows.

For example, a high-end laptop seller could feed behavioral intent from repeat purchase signals into these audiences. This sharpens targeting beyond content farms, boosting conversion rates. Media operators praise this for cleaner attribution models in DTC campaigns.

Why Start with First-Party Data in Marketing?

First-party CRM data from your owned channels trumps third-party data’s garbage in, garbage out issues, especially post-cookie era with rising privacy regs. This high-quality data comes directly from customer interactions on your site, app, or email lists. It powers performance marketing campaigns with reliable signals for AI-driven models.

Consider a DTC brand selling eco-friendly products. They used transaction data to target repeat buyers, avoiding brand fatigue from broad ads. This sharpened audience targeting and cut wasted ad spend on uninterested users.

Brands see clear gains from this shift. One benefit list breaks down the advantages:

• Verified accuracy: Data reflects real customer actions, not flawed signals from content farms or open web scrapes.
• Closed-loop ROAS tracking: Track from ad click to purchase without media operator black boxes, fixing the measurement problem.
• Model stability vs drift: First-party data resists model drift in AI-driven models, unlike volatile intent data. Curious about how to build AI models that leverage this stability for budget allocation?
• Compliance edge: Meets privacy regs head-on, building trust in the customer journey.

Teams report strong ROI, like 3-5x uplift in eco-friendly product campaigns. Start by auditing your CRM for transaction data and behavioral intent to build lookalike audiences that beat adtech defaults.

Step-by-Step Guide to Training Your First Model

Train your first AI-driven model in 4-6 weeks using first-party CRM data to sidestep platform biases and achieve reliable predictions. This approach helps performance marketers overcome the measurement problem in digital media. It focuses on high-quality data from your own systems, avoiding reliance on flawed signals from third-party data.

Gather at least six months of CRM data or transaction data with a minimum of 10,000 records to build a solid foundation. Include details like purchase history, customer journey stages, and behavioral intent signals. This first-party data ensures verified intent, unlike noisy inputs from lookalike audiences or the open web.

Cleaning the data is crucial to avoid the classic garbage in, garbage out trap. Use Pandas to remove bad data such as duplicates, incomplete records, or outliers. Expect some loss during this step, but it leads to sharper targeting and better ROAS down the line.

The full process takes about two weeks for preparation and one week for training. Follow the steps below to deploy a model that predicts conversion rates and cuts wasted ad spend. Watch for overfitting to historical biases and plan for model drift over time.

1. Gather 6+ months CRM/transaction data (min 10k records). Focus on sources like sales logs or customer databases. Export in CSV format for easy handling.
2. Clean via Pandas/remove bad data. Drop rows with missing values in key fields like email or purchase amount. Handle dates and normalize currencies.
3. Feature engineer (intent signals, journey stages). Create variables for repeat purchase likelihood or time since last visit. Add intent data from site interactions, such as views of eco-friendly products.
4. Train with XGBoost/scikit-learn. Split data 80/20 for train/test. Use cross-validation to tune hyperparameters like learning rate.
5. Validate on holdout set (aim for strong AUC). Test predictions against unseen data from recent campaigns. Adjust if performance lags on metrics like customer acquisition cost.
6. Deploy via AWS SageMaker. Set up an endpoint for real-time scoring. Integrate with your adtech stack for closed-loop attribution.

After deployment, monitor for model drift as customer behavior shifts. Retrain quarterly using fresh transaction data to maintain accuracy in audience targeting.

How to Test Custom Models Against Platform Defaults?

Rigorous A/B testing pits your custom models against platform defaults, benchmarking KPIs like ROAS and CAC for clear proof of edge. Amid attribution problems in performance marketing, a solid testing framework reveals how AI-driven models using CRM data and first-party data outperform flawed platform signals. Closed-loop measurement ties ad spend to real outcomes, avoiding garbage in, garbage out from third-party data.

Platforms often rely on last-click attribution, missing the full customer journey. Custom models incorporate transaction data and behavioral intent, cutting wasted ad spend. This setup exposes model drift early, ensuring sharper targeting for lookalike audiences.

Test in real campaigns to validate against media operator defaults. Track via clean UTM parameters to sidestep bad data. Success means higher conversion rates and lower customer acquisition cost.

Experts recommend starting small to build confidence. Integrate verified intent from sources like PebblePost for an edge over content farms. This proves custom approaches beat native lookalike audiences in digital media.

A/B Testing and KPI Benchmarks

Run 50/50 budget A/B tests over 2 weeks, targeting ROAS >3x and CAC 20% below platform averages as success benchmarks. Split identical campaigns: one with your custom models trained on high-quality data, the other using native platform lookalikes. This isolates the impact of first-party data versus flawed signals.

1. Split identical campaigns into custom versus native lookalikes, ensuring same creative, audience size, and geo-targeting for an eco-friendly product launch.
2. Track via Google Analytics 4 with UTM parameters, feeding into closed-loop systems for accurate marketing attribution.
3. Run until minimum 1k conversions per variant to reach statistical significance, monitoring daily for anomalies like brand fatigue.
4. Measure ROAS, CAC, and repeat purchase rate; custom often hits higher marks by leveraging intent data from CRM.

A common mistake is short tests ignoring model drift, where performance fades as platforms adjust. For a high-end laptops campaign, custom models using transaction data sustained gains over native ones. Re-test quarterly to adapt to adtech shifts.

Benchmarks guide decisions: aim for ROAS that covers subscription model costs in DTC. Lower CAC signals better audience targeting. This framework turns measurement problems into proof of your edge in performance marketing.

What Career Skills Boost Custom Modeling in Marketing?

Marketers skilled in SQL, Python, and adtech APIs like those from PebblePost advance fastest in building custom models that slash wasted ad spend. These tools help process CRM data and first-party data to create AI-driven models. They turn bad data into high-quality signals for better ROAS.

Core skills start with SQL for CRM data queries, essential for pulling transaction data and customer journeys. Python with scikit-learn enables model training on intent data and behavioral intent. Attribution modeling using GA4 and BigQuery fixes the measurement problem in marketing attribution.

API integration with platforms like Google Ads connects closed-loop data from media operators. Statistics knowledge covers A/B testing and model drift detection to maintain accuracy. Alex Schleifer from the DTC podcast shared how these skills built verified intent models at PebblePost, improving audience targeting.

A 3-month bootcamp focused on these areas yields quick results. Start with SQL queries on real CRM datasets, then build simple Python models for lookalike audiences. Practice attribution on sample GA4 exports to see gains in conversion rates and lower customer acquisition cost.

Master SQL for CRM Data Queries

SQL powers most tasks in handling CRM data and transaction data. Query customer purchase histories to spot repeat purchase patterns. This uncovers high-quality data hidden in noisy datasets, avoiding garbage in, garbage out issues.

For example, write queries joining sales tables with user behavior logs. Filter for eco-friendly product buyers to build sharper targeting segments. Experts recommend daily practice on public datasets to build speed.

In performance marketing, SQL reveals flawed signals from third-party data. Combine it with intent data for custom models that boost conversion rates. DTC brands use this to cut brand fatigue in digital media campaigns.

Python and Scikit-Learn for Model Training

Python with scikit-learn trains AI-driven models on first-party data. Load CRM exports, clean bad data, and fit regression models for ROAS prediction. This beats generic algorithms reliant on open web signals.

Start with pandas for data prep, then scikit-learn for clustering lookalike audiences. Train on behavioral intent like high-end laptops searches tied to purchases. Monitor model drift with simple stats tests to keep predictions fresh.

PebblePost applies this for verified intent, as Alex Schleifer discussed in the DTC podcast. Custom models from Python reduce wasted ad spend in subscription models. They outperform content farms using generic affiliate advertising.

Attribution Modeling with GA4 and BigQuery

Attribution modeling in GA4 and BigQuery maps the customer journey accurately. Shift from last-click to data-driven models using BigQuery ML. This solves the measurement problem with closed-loop attribution.

Export GA4 events to BigQuery, then build multi-touch models. Weight channels by contribution to green living product sales. Integrate identity graph data for precise tracking across devices.

Practical advice: Run SQL in BigQuery to simulate A/B tests on attribution windows. This refines media mix for DTC brands, lifting customer experience. Schleifer highlighted similar tactics for content commerce efficiency.

API Integration and Statistics Essentials

API integration like Google Ads pulls real-time data into custom pipelines. Use Python requests to fetch performance metrics and feed them into models. This creates live updates for adtech strategies.

Statistics skills handle A/B testing and drift detection. Compare control groups for conversion lifts, or detect when models degrade from changing user intent. Focus on p-values and confidence intervals in daily workflows.

Together, these build resilient systems against cost structure shifts. PebblePost’s approach, per the DTC podcast, uses APIs for transaction data loops. This drives higher ROAS in competitive performance marketing landscapes.

How Does This Strategy Scale Across Campaigns?

Custom models scale from DTC podcasts to subscription models by automating audience refreshes, maintaining ROAS across high-volume digital media buys. This approach beats platform algorithms in performance marketing by using high-quality data like CRM data and first-party data. It avoids bad data pitfalls, where garbage in means garbage out for AI-driven models.

Scaling relies on sharper targeting with verified intent and transaction data, reducing wasted ad spend on flawed signals from the open web. For instance, a DTC podcast can expand from niche listeners to broader affiliate advertising without diluting conversion rates. Experts recommend quarterly checks to combat model drift and brand fatigue.

Key benefits include stable customer acquisition cost and improved customer experience through closed-loop attribution. Media operators note that hybrid setups with lookalike audiences preserve ROAS at scale. This tactic turns the measurement problem into an advantage across content commerce and adtech channels.

• Automate feeds for higher volume using media operator tips on data signals.
• Segment by customer journey, like from eco-friendly products to high-end laptops.
• Combine with platform lookalikes for behavioral intent matching.
• Monitor drift quarterly to sustain repeat purchase patterns.

Automate Feeds for 10x Volume

Media operators advise automating feeds to handle 10x volume in digital media buys. Pull from CRM data and intent data to refresh audiences daily, ensuring fresh signals for AI-driven models. This prevents stagnation in high-volume campaigns like subscription models.

For a DTC podcast, automate transaction data feeds to target verified intent listeners. It scales without spiking customer acquisition cost, focusing on first-party data over third-party data noise. Regular automation cuts through content farms and AI content distractions.

Segment by Customer Journey

Divide audiences by customer journey stages, such as eco-friendly product buyers moving to high-end laptops. This uses behavioral intent from marketing attribution to refine targeting. It boosts conversion rates by aligning with green living enthusiasts’ repeat purchase habits.

Start with broad DTC podcast reach, then segment to nurture paths. High-quality data segmentation avoids single-model overextension and brand fatigue. It creates tailored experiences across the identity graph.

Hybrid with Platform Lookalikes

Blend custom models with lookalike audiences for balanced scaling in performance marketing. Platforms provide volume, while your models add precision via PebblePost-style verified intent. This hybrid lowers wasted ad spend in competitive adtech spaces.

Example: Pair platform lookalikes with CRM data for subscription model growth. It maintains ROAS by fixing attribution model gaps with closed-loop insights. Avoid over-reliance to prevent model drift.

Monitor Drift Quarterly

Check for model drift every quarter to keep custom models sharp across campaigns. Shifts in customer behavior, like changing green living trends, demand data refreshes. This practice sustains ROAS in evolving digital media landscapes.

Use quarterly audits on data signals to spot fatigue early. For high-end laptops or DTC podcasts, realign with fresh first-party data. It ensures long-term wins over platform algorithms.

Real-World Case Studies from Performance Marketers

PebblePost powered Epic Gardening and Food52 to 3-5x ROAS using transaction data and verified intent, proving custom models’ real impact. These examples show how first-party data beats flawed third-party signals in performance marketing. Marketers cut wasted ad spend by focusing on high-quality data.

In each case, teams built AI-driven models from CRM data to predict behavioral intent. This closed-loop approach fixed the measurement problem in digital media. Results included sharper audience targeting and lower customer acquisition costs.

Lessons highlight the garbage in, garbage out risk with bad data from content farms or the open web. Custom models reduce model drift and improve conversion rates. Performance marketers now prioritize verified intent over broad lookalike audiences.

These stories offer practical steps for your strategy, from DTC podcasts to subscription models. Experts recommend starting with CRM/transaction data for reliable data signals. The payoff is clear in repeat purchases and customer journeys.

PebblePost and Epic Gardening: Behavioral Intent Wins

PebblePost teamed with Epic Gardening, an eco-friendly product brand, using CRM and transaction data. They modeled behavioral intent for gardeners ready to buy seeds or tools. This first-party focus delivered a 400% ROAS lift.

Instead of third-party data, they used verified intent from recent purchases. Custom models created precise lookalike audiences, cutting adtech waste. The strategy emphasized high-quality data over volume.

Key lesson: First-party focus outperforms identity graphs plagued by flawed signals. Epic Gardening saw better attribution models and customer experience. Apply this by auditing your CRM for intent signals.

Food52: Boosting Affiliate Advertising with Content Commerce

Food52 leveraged PebblePost for content commerce in affiliate advertising. Transaction data fueled models predicting kitchenware buyers from recipe engagement. This drove significant affiliate boosts.

They shifted from open web signals to closed-loop transaction insights, refining media operator buys. Custom AI models handled the cost structure of high-end cookware promotions. Results showed stronger ROAS in a competitive space.

Lesson: Transaction data sharpens content-driven campaigns against AI content noise. Food52 reduced brand fatigue with targeted ads. Test similar models on your affiliate channels for conversion lifts.

Milk Street: Driving Repeat Purchases

Milk Street, a cooking brand, used PebblePost’s repeat purchase models from CRM data. They targeted loyal subscribers with verified intent for spices and books. This lowered customer acquisition costs.

Custom data beat third-party decay, focusing on subscription model signals. Models tracked customer journeys to prevent churn from generic targeting. ROAS improved through precise digital media spend.

Takeaway: Combat model drift with ongoing first-party refreshes. Milk Street enhanced marketing attribution for sustained growth. Integrate repeat signals into your performance marketing stack.

DTC Podcast: Sharper Targeting Pays Off

A DTC podcast host partnered with PebblePost for sharper targeting using listener transaction data. Models predicted downloads tied to product interests, like green living gear. This DTC approach minimized wasted ad spend.

They replaced flawed signals with podcast episode intent from CRM. Custom AI handled attribution challenges in audio adtech. Outcomes included higher conversion rates and ROAS.

Core lesson: High-quality data from niche sources like podcasts trumps broad audiences. The host refined customer journeys for better engagement. Build your models around unique data signals for similar gains.

Frequently Asked Questions

What does it mean that ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’?

In performance marketing, platforms like Google and Meta dominate with their proprietary algorithms, giving an unfair edge to big spenders while smaller players struggle with generic targeting. This FAQ explores how building custom data models levels the playing field by leveraging your unique first-party data for precise, algorithm-beating strategies-essential career advice for marketers aiming to outperform in a biased ecosystem.

Why is performance marketing considered an ‘unfair game’ in the context of ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’?

The ‘unfair game’ stems from ad platforms’ black-box algorithms that prioritize their own data signals, often sidelining niche or new advertisers. Custom data models counter this by integrating your proprietary customer insights, enabling hyper-personalized campaigns that bypass platform biases and drive superior ROI-key marketing career advice for thriving against algorithmic giants.

How can custom data models help beat algorithms in ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’?

Custom data models use your CRM, purchase history, and behavioral data to predict user intent better than platform algorithms. By feeding these into machine learning pipelines, you create tailored audiences and creatives that outperform generic targeting, turning the ‘unfair game’ of performance marketing into your advantage-a must-know tactic for advancing your marketing career.

What tools are needed to implement custom data models for ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’?

Start with accessible tools like Google BigQuery for data warehousing, Python libraries (e.g., scikit-learn) for modeling, and platforms like Segment or RudderStack for data unification. This approach democratizes advanced analytics, helping marketers beat platform algorithms without massive budgets-valuable career advice for performance marketing professionals.

Is building custom data models beginner-friendly in ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’?

Yes, even mid-level marketers can start with no-code tools like Google Analytics 4’s BigQuery integration or Zapier for data flows, then scale to custom ML models. This empowers you to challenge the ‘unfair game’ of performance marketing algorithms, accelerating your career growth through hands-on, high-impact data skills.

What results can I expect from applying ‘Performance Marketing is an Unfair Game-How to Beat the Algorithms with Custom Data Models’ strategies?

Marketers report 20-50% lifts in ROAS and conversion rates by using custom models for lookalike audiences and bid optimization, as they exploit data edges platforms can’t match. This transforms performance marketing from an unfair game into a winnable one, positioning you as a data-driven leader in your marketing career.