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Advanc but try not to get carri away. Here’s some example use cases of this tab for you to consider. Enter whether brand PPC was active (0 or. Enter whether you’re running TV ads or not Enter COVID lockdowns Enter algorithm updates that were significant to your business (one column per update) Why are my estimates different to your old tool? Is one of them wrong? There’s two major differences in method between this template and my old tool. The old tool us Google’s Causal Impact library. The new template uses an Ordinary Least Squares regression.

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The old tool captur non-linear trends by Israel Phone Number List using time period squar as a prictive variable and trying to fit the traffic curve to that curve. This is call a quadratic regression. The new tool captures non-linear trends by fitting each time period as a multiple of the previous time period month 2 where can be any value). This is call an AR(1) model. If you’re seeing a significant difference in the forecast values between the two, it almost certainly comes down to the second reason, and although it adds a little complexity, in the vast majority of cases the new technique is more realistic and flexible.

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It’s also far less likely to prict zero or negative Phone Number QA traffic in the case of a severe downwards trend, which is nice. How does it work? There’s a hidden tab in the template where you can take a peek, but the short version is the “LINEST” spreadsheet formula. The inputs I’m using are: Dependent variables Whatever you put as column B in the inputs tab (like traffic) Independent variables Linear passing of time Previous period’s traffic Dummy variables for 11 months (12th month is represent by the other 11 variables all being 0) Up to three “advanc” variables The formula then gives a series of coefficients” as outputs.

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