Preparing marketing data for AI use

Preparing marketing data for AI use#

This guide explains how to prepare your marketing data in Adverity for analysis so it can reliably support AI use cases. Use it to reduce noisy, misleading, or unstable outputs caused by common data quality issues.

Overview#

What makes marketing data AI-ready?: Marketing data is AI-ready when it is prepared and maintained for analysis. AI-ready data is complete (right channels and metrics present), consistent (shared definitions and naming conventions), timely (updated frequently enough), reliable (tracking validated, duplicates controlled), and well-documented (clear field meanings). When data quality is poor, AI accelerates flawed insights rather than correcting them.
Where do you start?: Start from the analysis you want to enable (for example, budget allocation, forecasting, or creative performance). Work backwards to identify required sources, metrics, and dimensions (including the required granularity) and define a single source of truth for each outcome.
Who should use this guide?: This guide is for marketing and business analysts working with data owners or engineers to define requirements and implement data quality controls in Adverity.

Why this matters#

Marketing teams estimate that up to 45% of their data is incomplete, inaccurate, or outdated. Poor data quality costs organizations an average of $12.9 million annually and undermines trust in AI-generated recommendations. The most common issues are missing data (31% of teams), inconsistent definitions across platforms (26%), and duplicate records (16%).

Core principles for AI-ready data#

Consistency and standardization

Ensure the same field means the same thing everywhere.

Harmonize dimension values across data sources (for example, standardize channel names so “Paid Social” and “Social Paid” map to the same value).
Align metric definitions (for example, ensure that “Primary Conversion” uses the same rule across platforms).
Use unique, descriptive target field names and document units, currencies, and time zones in your Data Dictionary. For common marketing metrics, consider using Adverity default field names as a maintained best practice to ensure consistency across your organization.
Standardize naming patterns (for example, UTM structure and campaign naming) and parse them into fields where needed.

Completeness and coverage

Ensure you have the right data, at the right granularity, with enough history for your use case.

Connect all relevant channels and data sources for your analysis.
Ensure key identifiers and time fields are populated (for example, date, campaign IDs, and account IDs).
Ensure sufficient history and continuous time series for key segments (for example, avoid gaps for active campaigns).
Keep schedules aligned and define how you handle late-arriving data and backfills.

Documentation and governance

Make data processing repeatable and auditable.

Document what each key field means, how it is calculated, and how it should be used (including filters and attribution rules).
Document mappings and transformations that affect reporting and model inputs.
Establish basic change management for schema, naming, or tracking changes that can affect analysis results.

Data quality monitoring

Detect issues early and fix them before they impact downstream analysis.

Monitor volume drops, missing values, and unexpected zeroes for critical metrics.
Monitor duplicates and double counting, especially where multiple systems report the same event.
Monitor timeliness and schema changes and assign owners for alerts and investigations.
Create custom monitors for business-specific validation rules (for example, campaign spend thresholds, metric ratio checks, or expected value ranges).
Review monitor results regularly from the Data Quality page and document root causes and fixes.

Prioritizing based on your maturity#

Where you focus depends on your current automation maturity:

Building your data foundation: Prioritize completeness and timeliness. Focus on connecting all relevant data sources, establishing reliable schedules, and monitoring for missing data or volume drops.
Standardizing and scaling: Focus on consistency and accuracy. Harmonize dimension values, align metric definitions across platforms, and implement validation checks for data transformations.
Optimizing at scale: Address uniqueness and advanced consistency issues. Monitor duplicates, implement deduplication logic, and create custom validation rules for business-specific requirements.

Common issues and how to fix them#

Missing data or gaps#

Review schedules and dependencies and decide how to handle late-arriving data and backfills. Consider using Smart Schedule to automatically manage dependencies between related datastreams.
Add monitors for missing values and volume drops on critical metrics.

Inconsistent naming or dimension values#

Harmonize naming and categorical values with Data Mapping (for example, align channel taxonomy and status values).
Replace null-heavy categories with explicit defaults where appropriate (for example, “Unknown” or “Other”).

Duplicates or double counting#

Identify where the same event is reported by multiple systems and define a source-of-truth rule per outcome metric.
Add duplication monitoring and apply deduplication rules consistently before analysis.

Outliers and anomalies#

Investigate sudden spikes or drops and decide whether to exclude, cap, or label extreme values.
Track planned changes (for example, promotions or tracking updates) so they are not misinterpreted.

Schema changes#

Monitor schema drift and field type changes and update mappings, transformations, and documentation accordingly.
Record changes that can create structural breaks in time series (for example, new conversion definitions).

Best practices for specific AI use cases#

Budget allocation recommendations#

Ensure you have spend, impressions, clicks, conversions, and revenue by day, campaign, and channel.
Capture key campaign attributes (objective, country, product or brand) in separate fields.
Ensure at least 3–6 months of stable history and refresh daily after the previous day is complete.
Set up monitors for unexpected zero spend/impressions and define how to treat new campaigns with limited history.

Performance forecasting#

Build continuous time series by day and campaign or channel with minimal gaps.
Include seasonality indicators where available (for example, holidays, promotions, or launches).
Document structural breaks (for example, tracking changes or new conversion rules) and adjust comparisons accordingly.
Set freshness requirements based on planning cadence (daily or weekly).

Creative or audience recommendations#

Collect creative-level or audience-level performance metrics with descriptive attributes (format, placement, theme, segment).
Define a primary outcome metric (for example, cost per acquisition or return on ad spend) and keep its definition stable.
Flag very low-volume creatives or segments and treat outliers caused by testing budgets explicitly.

Quick checklist#

Before using data for AI use cases, verify:

Data is loaded for all relevant channels and time periods
Target field names are unique and descriptive, with documentation for custom fields
Data is harmonized across data sources using Data Mapping with consistent dimension values
Datastreams are scheduled reliably, with an approach for late data and backfills
Universal and custom monitors are configured for critical fields and metrics
Duplicates and double counting are addressed, especially for outcomes reported by multiple systems
Outlier handling is defined and documented (exclude, cap, or label)
Source-of-truth rules are defined for outcome metrics reported by multiple systems