Marketing Analytics: Optimize Your Business with Data Science in R, Python, and SQL by Jacobs Dave

Author:Jacobs, Dave [Jacobs, Dave]
Language: eng
Format: azw3, epub
Publisher: Dave Jacobs
Published: 2016-07-12T04:00:00+00:00

print(c8_mydata_3)

Figure 8.19 Inputted values

Now, the model can be applied to this dataset to create the prediction. The results can be seen in Figure 8.20 and show that for the next two weeks, sales should be $7,297,382 and $6,857,488.

y_predict= model_1.predict(c8_mydata_3)

print(y_predict)

Figure 8.20 Predicted values

For directional purposes, this model is fine. We can go back to the Marketing Department and report that it appears like the most effective channels are Online Display and Direct Mail while TV, Radio, and Paid Search do not appear to be generating a positive ROI (Return on Investment).

Although this model is adequate for general application, there is one critical factor we are not accounting for and that is the effect that time and seasonality have on the dependent variable. In order to create a sound model with an accurate prediction, it is necessary to use a Time Series model. These types of models will be presented later in the book.

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