The Democratization of Predictive Analytics

How does it work?


Business cases

Success Stories


Leading medical company

Revenue forecasting by 2000+ SKUs and 80 regions with automatic selection of optimal model for each forecasting unit and options to include additional factors to improve accuracy

CHALLENGES

Large number of unique SKUs that need to be forecasted separately

Limited availability of data science specialists

Forecast accuracy issues

BENEFITS

Ability to calculate forecasts by each SKU and region

Support for managers in forecasting activities

Ability to add new factors and variables into forecasting process

Integration with Anaplan models

Leading internet company

Sales forecasting by 2 million of B2B customers taking into account internal and external attributes

CHALLENGES

No statistics indication for sales managers in forecasting

Judgmental estimations errors

No data science specialists are available for particular forecasting tasks

BENEFITS

Ability to calculate forecasts by millions of existing customers

Ability to provide basis for forecasting of new customers

Challenge for judgmental forecasting

Ability to add new factors and variables into forecasting process

Leading telecom company

Content recommendation service for each user based on consumption history and matching similar combinations of multiple content characteristics

CHALLENGES

Slow process of selecting recommendations using 50 parameters and 400 content characteristics

Hours of work to manage machine learning algorithms

Low data science specialists availability

BENEFITS

Confirmed the ability to tune machine learning algorithms without any programming by analysts

Increased prediction accuracy of ROC AUC from 72% to 86%

Accelerated machine learning models tuning by 2 times

Demonstration


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