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Practical Data Science.

The Machine Intelligence Revolution

The machine intelligence revolution is well underway yet most organizations struggle to successfully apply AI to their business. “The biggest impediments to winning with AI are an inability to understand and choose where to focus AI in your business, what types to use and how to manage AI-related risk,” (Gartner).


Beacon educates key stakeholders on AI / ML and helps them cut through the hype, jargon, and abstract nature of much AI discussion often divorced from concrete business value. Together with business and technology leaders, Beacon’s senior data science practitioners then rank identified AI / ML use cases based on lowest complexity and highest estimated business value. Beacon’s cross-functional team partners with clients on validating the right questions, targets, and datasets for testing viable models. Finally, in order for AI models to generate ongoing value, they must be integrated across diverse tech and marketing stacks to complete “last mile” deployment. Examples of proven Beacon AI / ML use cases with breakthrough impact include:

  • Churn Prediction: Predicting which profitable customers are at risk of churning in the next 90 days, generating Weekly Rescue Lists, and recommending top interventions to increase retention and repeat customers (i.e., fitness center members)

  • Inventory Optimization: Predict optimal inventory levels at the individual SKU level to optimize supply and demand thresholds, decrease idle rates, and minimize costly order back orders (supply chain services containers).

  • Product Recommendation Engine: Ingest customer data and use machine learning to identify high value micro segments and recommend highest relevancy products at the individual level. (online travel agency discount travel).

Avoiding Data Science Pitfalls

Beacon will also help client staff avoid common data science pitfalls such as sampling bias, irrelevant feature selection, failing to understand business context, data leakage, missing data, inaccurate scaling & normalization, neglecting outliers, miscalculated features, ignoring multi-collinear inputs, ineffective performance KPIs, starting with a poor data foundation, not giving yourself enough time to intervene on customers at risk of churning, failing to establish and calculate baseline up front, and not ensuring predictive model recommendations are actionable.

Schedule a free 30 minute assessment.

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