Underfitting is a modeling failure mode where an AI/ML model is too simple (or too constrained) to learn the underlying relationships in the data. As a result, it performs poorly on both the training data as well as on new, unseen data.
In practice, an underfit model fails to capture meaningful signal, producing predictions that are systematically inaccurate or overly generic.
How it relates to marketing
Underfitting is common in marketing AI when the modeling approach cannot represent the real drivers of customer behavior, such as:
- Nonlinear effects (frequency saturation, diminishing returns, threshold effects)
- Interactions (offer × channel, audience × creative, time × device)
- Time dynamics (seasonality, dayparting, lifecycle stage shifts)
- Heterogeneous segments (different motivations and constraints by cohort)
Marketing consequences of underfitting often include:
- Propensity models that barely outperform a baseline (e.g., “everyone is equally likely”)
- MMM or budget optimization that suggests flat allocations because it cannot detect incremental impact
- Personalization that repeats generic content because it can’t distinguish context
- Churn models that miss early warning signals and trigger interventions too late
- Lead scoring that fails to separate high-intent accounts from low-intent noise
How to calculate (the term)
Underfitting is not a single metric, but it is typically identified by consistently low performance across training and validation/test datasets.
Common indicators:
- Mtrain is low and Mval is also low (for “higher is better” metrics like AUC/accuracy)
- Training loss remains high and does not meaningfully decrease (for “lower is better” metrics like log loss/RMSE)
A practical diagnostic is comparing model performance to a baseline:
- Classification baseline: predict the majority class or use a simple logistic regression with few features
- Regression baseline: predict the mean (or seasonal mean) of the target
- Ranking baseline: random or heuristic ranking (e.g., recency-only)
If your model does not materially beat these baselines on training data, it is usually underfitting.
How to utilize (the term)
Underfitting is primarily used as a diagnostic concept to guide improvements in model design and data representation.
Common marketing use cases include:
- Choosing model families: moving from linear models to tree-based models or neural nets when relationships are nonlinear.
- Feature engineering: adding interaction terms, lag features, frequency features, or lifecycle stage indicators.
- Segmentation strategy: introducing hierarchical models or segment-specific models when customer heterogeneity is high.
- Measurement design: improving labels (what counts as “conversion,” “churn,” “engagement”) so the model can learn consistent signal.
- Experiment planning: identifying when ML is not the bottleneck—sometimes the real issue is instrumentation, taxonomy, or inconsistent campaign setup.
Compare to similar approaches, tactics, etc.
| Concept | What it is | Typical symptom | Common mitigation |
|---|---|---|---|
| Underfitting | Model is too simple to capture signal | Poor training and poor validation/test performance | Add features, increase capacity, reduce constraints |
| Overfitting | Model learns noise and dataset quirks | Training performance ≫ validation/test performance | Regularization, simpler models, better splits |
| High bias | Systematic error due to overly simple assumptions | Predictions consistently “miss” in the same direction | More expressive models, better features |
| Poor data quality | Signal is weak, noisy, or mislabeled | Models plateau regardless of tuning | Fix labels, instrumentation, identity, missingness |
| Concept drift | Relationships change over time | Degradation after deployment (even if trained well) | Monitoring, retraining, robust features |
Best practices
- Start with baselines, then earn complexity
- Use simple models first to validate signal; then graduate to more flexible models when needed.
- Increase model capacity deliberately
- Add nonlinear learners (GBDTs, random forests, neural nets) or allow more interactions, depth, or parameters.
- Improve feature richness
- Include recency/frequency/monetary features, channel exposure history, creative attributes, lifecycle stage, and context signals (device, geo, time).
- Model interactions explicitly
- Many marketing outcomes are driven by combinations, not single factors (e.g., offer × audience × channel).
- Use appropriate temporal structure
- Add lag features and seasonality signals; avoid treating time as an afterthought.
- Check whether the label makes sense
- If “conversion” combines radically different intents (brand, transactional, support), the model may look underfit because the target is incoherent.
- Evaluate by segment
- A model may underfit high-value cohorts even if overall metrics look acceptable; use cohort-level diagnostics.
Future trends
- More automated feature discovery
- Automated interaction search and representation learning will reduce manual feature engineering, especially for channel/creative context.
- Better multimodal modeling
- Models that incorporate creative text/image/audio signals will improve the ability to learn drivers of performance (reducing underfitting caused by missing creative variables).
- Hybrid causal + predictive approaches
- More marketing stacks will combine predictive models with experimentation and causal inference to avoid “blunt” models that can’t capture incremental impact.
- Greater focus on label governance
- As organizations standardize event taxonomies and definitions, underfitting caused by inconsistent outcomes should decrease.
Related Terms
- AI Development Lifecycle
- Machine Learning (ML)
- Machine Learning Operations (MLOps)
- Predictive Analytics
- Generative AI
- Overfitting
- Bias-variance tradeoff
- Model capacity
- Feature engineering
- Population Stability Index (PSI)
- Baseline model
- Regularization
- Cross-validation
- Data quality
- Concept drift
- Generalization
- Underfitting