Every data science team eventually discovers a frustrating truth: features that perform brilliantly in one project often fail spectacularly in another. A customer churn model's key predictors become useless when applied to fraud detection. Healthcare risk scores don't translate to manufacturing quality control.

Yet buried within this chaos are universal patterns—feature engineering techniques that consistently improve predictions regardless of industry, data type, or business problem. These aren't clever domain-specific tricks. They're fundamental transformations that capture how the world actually works.

Understanding these transferable patterns changes how you approach new projects. Instead of starting from scratch each time, you build on proven foundations. The best feature engineers aren't necessarily the most creative—they're the ones who recognize which techniques travel well and why.

Time-based features work everywhere because change matters more than state. A customer's current balance tells you something. Their balance trajectory over the past six months tells you far more. This principle holds whether you're predicting loan defaults, equipment failures, or employee attrition.

The classic RFM framework—Recency, Frequency, Monetary value—emerged from retail but transfers remarkably well. Recency captures decay and engagement. Frequency reveals behavioral consistency. Monetary value indicates commitment or capacity. Reframe these for any domain: when did a patient last visit, how often do they comply with treatment, how severe are their conditions?

Velocity features push this further. Calculate the rate of change, not just the change itself. A credit card with spending that doubled matters less than one where spending doubled in the past week versus the past year. Acceleration—the change in velocity—often predicts inflection points that static or even velocity measures miss entirely.

The pattern extends to temporal aggregations at multiple windows. Seven-day, thirty-day, and ninety-day averages capture short-term noise versus medium-term trends versus baseline behavior. The ratio between these windows often outperforms any single aggregation. Someone spending twice their ninety-day average this week signals something, regardless of whether you're modeling fraud, churn, or health deterioration.

Takeaway

Time-based features work universally because they encode how things change, not just what they are. Recency, velocity, and multi-window comparisons capture behavioral dynamics that static snapshots miss.

Raw values lie. A factory producing 10,000 defects sounds terrible until you learn they manufactured 50 million units. A patient's blood pressure reading means nothing without knowing their baseline. Normalization through ratios reveals the signal hidden in raw numbers.

This pattern transfers because different entities operate at fundamentally different scales. Comparing a multinational corporation's transaction volume to a small business's using absolute numbers produces meaningless models. Express each transaction as a percentage of typical activity, and suddenly the same features work for both.

Self-referential ratios prove particularly powerful. Compare an entity's current behavior to its own historical baseline rather than population averages. A 20% spending increase means different things for someone who varies by 50% monthly versus someone rock-steady for years. The coefficient of variation—standard deviation divided by mean—captures this behavioral consistency in a single number.

Cross-feature ratios unlock relationships invisible in individual variables. Debt-to-income ratios predict loan default better than either debt or income alone. Inventory-to-sales ratios catch supply chain problems. The technique generalizes: whenever you have two related measurements, their ratio often carries more predictive information than either measure independently. Domain knowledge helps identify which ratios matter, but even mechanical pairwise ratio generation frequently surfaces useful features.

Takeaway

Relative measures outperform absolute values because they control for scale differences and reveal proportional relationships. When features don't transfer between entities or contexts, normalization through ratios often fixes the problem.

Point-in-time features assume the present moment contains all relevant information. It rarely does. The path someone takes often matters more than where they currently stand. A customer who browsed, added to cart, abandoned, then returned behaves fundamentally differently from one who purchased immediately—even if both end at the same conversion point.

Encoding sequences starts with state transitions. Define meaningful states in your domain, then count transitions between them. How often does a patient go from stable to critical? How frequently does a machine cycle between normal and warning states? These transition matrices compress complex behavioral histories into manageable feature sets.

N-gram approaches borrowed from natural language processing work surprisingly well. Treat each action or state as a token, then encode common two-step and three-step sequences. A customer with pattern browse-browse-buy differs from buy-buy-buy differs from browse-buy-return. Sequence frequency and sequence recency both carry information.

More sophisticated approaches capture sequence similarity. How closely does a current behavioral trajectory match known patterns? Distance measures like Dynamic Time Warping let you compare sequences of different lengths. You don't need deep learning for this—clustering historical sequences and measuring distance to cluster centroids creates powerful features. The key insight is that sequences are their own data type, requiring encoding strategies beyond simple aggregation.

Takeaway

Sequential patterns capture behavioral dynamics that snapshots miss. Encoding state transitions, common action sequences, and trajectory similarities transforms temporal data into features that predict how situations evolve, not just where they are.

These three pattern families—temporal dynamics, ratio normalization, and sequence encoding—form a transferable foundation for feature engineering. They work across domains because they capture fundamental aspects of how systems behave: things change over time, context determines meaning, and history shapes outcomes.

The practical implication is significant. When facing a new prediction problem, start with these universal patterns before investing in domain-specific feature development. You'll often find that 70% of your predictive power comes from applying these techniques thoughtfully.

The remaining 30%—that's where domain expertise and creative feature engineering earn their keep. But building on proven foundations beats reinventing wheels every time.