Chris is a business analyst who likes to practice data modeling in her free time.
She particularly enjoys building analytical models to achieve marketing objectives. For example: clustering models for auto segmentation, propensity models for customer lifetime value predictions, and attribution models for channel evaluations.
This is a blog for Chris to practice her analytical skills and connect with like-minded people.
All in Forecasting
Site selection problem interests me as it usually involve data sets with more explanatory variable than there are stores. Each potential location, geocoded by longitude and latitude, represents a point on a map and may be associated with multiple variables including but not limited to population, housing, traffic, and economic conditions.
To accurately forecast sales when the data has seasonality and trends, we’ll need to identify and separate them from the data series. Sometimes it is as simple as using moving averages to smooth data and eliminate seasonality (e.g. excel scatter plot with trend line. When monthly seasonal influence is suspected, use a 12-month moving average, when analyzing trend in quarterly data, use a four-quarter moving average). Yet, sometimes it is more complicated than that.
