PREDICTIVE MODELING

Predictive Analytics, Machine Learning, and AI are at the forefront of organizational initiatives across just about every industry today.  While they might be the hottest topics around right now, our team at Ketchbrook have been building predictive models since before it was "cool", and have continued to stay on top of the leading-edge advances in the field that are happening every day.  

Our team is armed with a decade of model-building experience, including statistical & probabalistic models, machine learning, recommendation engines, and risk forecasting.  With our technical data science acumen and business expertise across a wide variety of industries, you could see your sales strengthen, your marketing budgets leveraged more effectively, and your risk diminish.  

Forecasting & Linear Models

For example:

  • forecasting next month's sales based upon trend, seasonality, and algorithmically-derived time series elements

  • determining the relationship between your bank's loan volume and your marketing spend, the general economy, and the time of year; allowing you to optimize your capital and advertising efforts in order to maximize your bottom line

  • predicting the life expectancy of each piece of in-service equipment your business uses, enabling you to triage your fixed assets and focus maintenance costs; preventing machine failure before it occurs can save you from the avoidable expense of replacing it entirely

Machine Learning & Artificial Intelligence

For example:

  • identifying those most likely to respond positively to your marketing campaign, helping you target the individuals you want to advertise to before spending a single dollar

  • detecting anomalies or instances of fraud in your high-frequency transactional data, alerting you to concerns instantaneously and helping you prevent such cases in the future

  • predicting which customers will fail to repay their loan in full, allowing you to determine customer profiles that are most likely (and least likely) to repay debt

  • classifying x-ray images into those exhibiting a particular injury or health issue versus those that do not; aiding healthcare experts with additional insights to help them diagnose patients

Recommendation Engines

For example:

  • arm your sales team with the best cross-selling opportunity for each of their accounts, allowing them to narrow their pitch, save time, and help you boost revenue

  • in your online store, have the products most likely to be purchased in conjunction with the item(s) in the shopping cart appear automatically, maximizing additional sales or garnering the most likely impulse purchases

Model Validation

Do you have a vendor or internally developed model that you're evaluating, testing, or have already put into production?  Here are some signs that you're due for a model validation:

  • You have never had a model validation performed

  • Your board or regulator wants to ensure that the risks associated with your predictive model are accounted for from both a quantitative and qualitative perspective

  • You have received a vendor model that you feel may be statistically weak

  • You have received a vendor model that is statistically robust but is lacking detailed, interpretable documentation

  • Your model isn't making predictions with the accuracy you'd like it to, or you are concerned you have not been comparing model predictions to actual results proficiently

  • You have continued to collect data in your organization, but the model you are using has not changed or leveraged this new data to improve accuracy

If any of these scenarios sound like your organization, let us help you unlock the true return on your predictive modeling investment.

LET'S TALK PREDICTIVE MODELING

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