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Adwords management requires making decisions with limited data. A keyword might have 100 visitors and conversion rate of 20%. The campaign as a whole might have an overall conversion rate of 10%. This means that in the next 12 months, the conversion rate of the keyword will probably be between 10 and 20 percent.
Correctly predicting the conversion rate is important. We use machine learning to predict the future conversion rate for this keyword, and set the keyword bid to maximize revenue and minimize loss as defined by a Mean square error (MSE) loss calculation.
Machine Learning and Polynomial Regression
The fundamental challenge we are solving using ML is how much “trust” to give to datasets based on the type of data, and sample size. The best way to do this is to create a polynomial that maps levels of “trust” with the size of the sample set. Essentially, the larger the sample set, the more we trust it.
A system to minimize loss is not linear. As such, we use a regression model for to find a best fit line thru the data points. This best fit line minimizes the loss associated with either trusting data of an arbitrary sample size either too much, or too little. Once we have use machine learning to solve for the best fit line, we can use it to calculate how much to trust a specific dataset for any of our client based on the size of the dataset. More information about polynomial regression is available here.
The actual bid/bid adjustment computation turns into simple high school algebra after we have calculated how much to trust each dataset used in our keyword bid and bid adjustment calculations.