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Nataliya Portman | 360Insights.com

Nataliya received her Doctoral Degree in Applied Mathematics from the University of Waterloo in 2010, followed by postdoctoral training at the Neurological Institute in Montreal.

Following her postdoctoral assignment, she developed a novel approach to brain tissue classification in early childhood brain MRIs using modern Computer Vision pattern recognition and perceptual image quality models.

Nataliya has worked in many industries including neuroscience, biotech, the public sector, and various start-up software companies. Throughout her career, she has applied her expertise in Mathematics to develop numerous models including but not limited to machine learning algorithms, computationally efficient algorithms for model validation, and neural networks.

She is the co-inventor of “Bid-Assist”, a strategy for setting up an initial bidding amount to discourage low bidding behaviour, and “AutoVision”, a mobile app that allows automatic taking of pictures of vehicle views and damages recognized by an image classifier.

In January 2019, Nataliya took on a new role in Data Science at 360insights.com, committed to the development of predictive sales channel analytics that will help channel leaders maximize the return on investment of their channel incentive programs.

The Art and Limitations of Accurate Forecasting

Accurate sales forecasting is vital to marketing incentive program design, budget planning and strategic decision making. This seminar will focus on three business use cases:

1. Short and long-term forecasting of target KPI metrics

2. Estimation of sales lift due to promotional program choice and schedule

3. Trade customer/dealer purchasing behavior analysis and prediction of reward winners

Ms. Portman will discuss the challenges of building reliable forecasting time-series models, and how the forecast accuracy can be improved using a multi-model approach. The proposed approach involves preparation of various training datasets containing historic observations of KPIs on different time scales and factors that influence their outcome.

The task of prediction of reward winners based on their to-date and future purchasing activities involves learning of individual buying patterns and estimation of volatility/uncertainty in forecasted KPI values (e.g., volumes of products to be purchased). Knowledge of the uncertainty extent of predicted quantities will reveal individuals with a low degree of uncertainty (candidates to winners). She will show how to effectively evaluate uncertainty per individual that will lead to informed decision making on the final candidate list.