Data$-$driven attribution modeling differs from other models because it: ignores direct traffic and attributes $100\%$ of the conversion value to the last channel that the customer clicked through attributes $100\%$ of the conversion value to the last Google Ads channel that the customer clicked through before converting attributes $40\%$ credit to the first and last interaction, and the remaining $20\%$ credit is distributed evenly to the middle interactions. adopts machine learning algorithms to evaluate both converting and non$-$converting paths.