In performance marketing, AI-driven models are king. They enable brands to target the right customer, at the right time, with the right message.
But here’s the catch: they’re only as smart as the signals that fuel them.
Train AI-driven models on noise and you’ll get wasted spend, low conversion rates, and misleading insights. We see this all the time.
Put simply, garbage in, garbage out.
3 Examples of Marketing Misfires Caused by Bad Data
Brands often rely on various data sources for audience targeting and performance marketing. However, not all data sources are equally trustworthy or predictive. Bad data can mislead AI-driven models and derail marketing performance. Here are some examples.
Example 1: CRM Data Gone Wrong
A brand’s tech partner trains an AI-driven model on historical CRM data (names, emails, prior purchases, addresses, etc.) to predict which customers will likely make a repeat purchase. But within the CRM, there are issues:
- Outdated or invalid household addresses
- Typos from manual entry
- Shared emails across household members
- Duplicate records
The model misattributes purchases and even wrongly segments customers vs. prospects, leading to flawed customer profiles. As a result, it builds lookalike audiences based on bad assumptions, causing campaigns to underperform.
Example 2: Flawed Third-Party Data
A brand looking to promote its new eco-friendly home product line taps a third-party data provider to identify segments like “Green Living” or “Environmentally Conscious.”
The data provider builds an audience based on shallow signals and false positives, like:
- Browsing behavior (i.e., visited a blog post about recycling once)
- Aggregated, biased survey responses
- Demographics and psychographics
By mistaking low intent for high intent—like a user doing research for a school project, not shopping—the brand ultimately pays for impressions that never convert.
Example 3: Misinterpreting Intent Data
An electronics retailer uses behavioral intent data (page views, site visits, search activity, etc.) to find users “in-market” for high-end laptops. But the AI-driven model can’t tell whether a user is shopping for themselves, researching for a friend, or simply comparing specs out of curiosity.
This misinterpretation of intent slows brands down and can lead them in the wrong direction, wasting valuable time and marketing dollars.
5 Consequences of Weak Marketing Signals
Here are five potential consequences of AI-driven models drawing inaccurate conclusions from flawed or ambiguous signals:
1. Wasted Ad Spend
If the AI-driven model wrongly assumes users are ready to buy, the retailer might allocate budget toward high-cost performance channels (like retargeting or paid social) for users who were never actually in-market. This drives up CAC (Customer Acquisition Cost) without delivering conversions.
2. Lower Conversion Rates
Campaigns underperform when brands target people who are not ready to purchase, and underprioritize high-intent shoppers, based on a flawed model’s assumptions. Even if impressions or clicks look decent, the downstream conversion rates will lag, making the campaign appear ineffective and less scalable.
3. Misleading Attribution and Reporting
The model may attribute success to flawed segments, reinforcing incorrect assumptions. Marketing teams might double down on the same bad data and repeat the mistake in future campaigns, thinking they’re optimizing when they’re not.
4. Poor Customer Experience
Consumers who aren’t in-market may find ads intrusive or irrelevant, leading to brand fatigue or even negative perception—especially if they’re bombarded with high-ticket item promotions that feel pushy or mistimed.
5. Model Drift and Compounding Errors
AI-driven models are designed to learn from outcomes. When trained on faulty intent signals, they reinforce inaccurate patterns, further deteriorating over time unless corrected with high-quality data.
What High-Quality Data Looks Like
To avoid these pitfalls and get the most out of your AI-driven marketing, focus on building your models on high-quality first-party data that checks all the right boxes:
- Relevance: Captures signals that reflect true buying intent, not just surface-level engagement
- Recency: Updates frequently to stay aligned with consumers’ evolving needs
- Accuracy: Verified against actual outcomes, like purchases, not inferred from weak signals
- Connectedness: Can be tied to identity across channels for consistent targeting and measurement
- Completeness: Offers a holistic view of the customer journey, including interactions with other brands, not just your own
The PebblePost Advantage: Transaction Data as a Modeling Superpower
Most performance marketing solutions and platforms are forced to model on proxy signals, like clicks or time on site, because that’s all they have. PebblePost takes a different approach. We model on verified outcomes: real transactions tied to real people.
We use data from hundreds of top brands—$100B+ in online and offline transactions and billions of intent signals—to power our AI-driven models.
Why does this matter?
- Verified Intent: Purchase data reflects real buying decisions, not hypothetical interest
- Sharper Modeling: Lookalike audiences built from transaction-backed data are more precise and predictive
- Faster Learning: Closed-loop measurement allows for rapid re-optimization based on real outcomes
When brands use PebblePost, they tap into a Performance Marketing Engine fueled by verified shopping behavior, not assumptions. That means sharper targeting, smarter decision-making, and stronger performance.
In fact, brands that leverage our AI-driven models see an average 20% lift in performance over those using third-party data.
Read more about why transaction data drives better marketing outcomes.
Better Data In, Better Outcomes Out
Machine learning is a force multiplier: it amplifies what it’s given. If your inputs are flawed, your outcomes will be too.
But if your models are trained on rich, accurate, and timely first-party data—like the kind PebblePost helps you activate—the result is sharper targeting, higher ROAS, and a better customer experience.
The best part? Our closed-loop approach (media → transaction → re-optimization) reinforces the model and ensures your campaigns drive even better outcomes over time.
In short: Better data isn’t a nice-to-have—it’s a competitive advantage.
Ready to supercharge your performance marketing? Reach out to learn how PebblePost’s Performance Marketing Engine helps leading brands drive measurable outcomes at scale.
