Marketing Analytics by Mike Grigsby

Author:Mike Grigsby
Language: eng
Format: epub
Publisher: Kogan Page Limited
Published: 2018-03-20T16:22:28+00:00

PARAMETERS

EST

DM

1,960.6

EM

–297.4

SMS

5,679.4

The fit is much better using fixed effects and the insights from marcom make more sense. E-mail can be negative because of e-mail fatigue as shown in the fixed effects model. The random effects model had SMS as negative which is nonsensical and shows e-mail as positive.

Insights about time period (quarters)

Quarter 8 was removed (avoiding the dummy trap) and the resulting impacts of the quarters is insightful. Quarter 7 increases revenue on average by 19,000 and Quarter 5 decreases revenue on average by 50,000. All were significant.

Table 7.4 shows that the quarterly seasonality is important and predictable and has to be taken into account to confidently measure the marcom impact.

Table 7.4 Quarterly seasonality

PARAMETERS

EST

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