Team solutions

Antelope SAP

The current paper examines the factors that influence the increase of the
sales volume of a retailer. The aim of the study is to create an accurate model with high explanatory
power which accounts for the promotional and competitor effects on the quantity sold as well
as to identify the main volume uplift drivers. That information could be useful when designing
marketing strategies in order to gain a competitive advantage over the other market players.


2 thoughts on “Antelope SAP

  1. 0

    The most difficult part of this challenge is to understand the data, create new features and rerun the predictive models till you achieve a good accuracy.
    As you may mentioned if you run a predictive model with the initial dataset you will get an extremely low modelling accuracy.

    I will vote based on the below criteria:

    1. business understanding
    2. feature engineering
    3. modelling accuracy
    4. insights & final results

    Your approach was on the right path but you didn’t managed to create the new features that will help you identify the volume uplift drivers and increase the accuracy of the model.
    You understand the data and the business challenge but you didn’t manage to increase the accuracy of the 1st model by using feature engineering.
    You could increase the accuracy of the model by implementing a base price algorithm and then by taking the % of difference between the actual and the base price you could extract the weekly promotional price reduction and use it as input parameters for your regression model.

  2. 1

    Hi team,
    You have underestimated the data understanding, EDA and feature engineering. It is an important part of data science. Having a visual representations would be nice. Also tables and some numbers are welcome in the paper.
    – zenpanik

Leave a Reply