Everyday Use Cases for Applying ML in Data Analysis

To further understand the appropriate level of machine learning that can be beneficial based on the analytical task, it helps to explore some everyday use cases within business functions:

Marketing

• Predictive lead scoring – High ML: Identify high-value leads most likely to convert based on dozens to hundreds of attribute combinations. Requires significant historical conversion data.
• Campaign propensity modeling – Medium ML: Estimate response rate to marketing campaigns through supervised learning techniques. It may need more historical responses.
• Message personalization – Low/Medium ML: Tailor messaging across channels using segmentation models with a hybrid rules-based approach.
• Market basket analysis – Low ML: Understand co-purchase trends with essential association rule mining. Useful for cross-sell.

Sales

• Territory assignments – Low ML: Set sales territory boundaries based on geographic concentrations of customers using clustering algorithms.
• Demand forecasting – High ML: Predict revenue by market based on historical performance, economic trends, and other signals. Requires advanced time series ML techniques.
• Churn analysis – Medium ML: Use classification techniques to determine customers most likely to cancel services. BeneficialIt is helpful, but many causes require operational fixes.

Finance

• Anomaly detection for fraud – High ML: Identify abnormal transactions not fitting expected patterns in complex high-volume data—advanced unsupervised ML capability required.
• Cash flow prediction – Medium ML: Forecast short and long-term cash positions. Time series ML is helpful, but causal understanding is also important.
• Credit risk assessment – Low/Medium ML: Supplement policy rules with ML to classify subprime applicants. Limited historical default data requires simple models.

HR

• Employee attrition modeling – Medium ML: Predict workers likely to leave based on tenure, performance, and engagement indicators. Focus on small data techniques.
• Job profile recommendation – Low ML: Suggest open jobs to the talent pool based on skills match and stated preferences—mainly rules-based matching.
• Learning personalization – Low ML: Recommend training content using collaborative filtering—beneficial but small and sparse dataset.

IT Ops

• Infrastructure optimization – Medium ML: Tune resource allocation across technology assets based on usage analytics. Reinforcement learning helps.
• Issue remediation – Low ML: Provide suggestions for solving system incidents based on matching error codes.
• Event correlation detection – High ML: Analyze a multitude of infrastructure events across apps and networks to identify failure correlations. Requires advanced ML.

The need for machine learning varies greatly depending on the function and use case complexity. Many impactful business decisions can improve with basic descriptive and diagnostic analytics – an important reminder not to over-index on ML. Cross-functional data analysis also plays a crucial role in contextualizing signals from different domains.

Data analysis has become a critical component of decision-making across industries. With the exponential growth of data, businesses are increasingly looking for valuable insights to stay competitive. Here’s where machine learning comes in. Machine learning provides advanced analytical capabilities to uncover patterns, make predictions, and optimize processes based on historical data. However, an essential question organizations face is: how much machine learning capability is needed for impactful data analysis?

In this article we will explore the considerations around finding the right balance of machine learning for optimizing data analysis.