PPC conversion value development assigns an estimated value to each type of conversion, which are assembled into a model for the site. See related articles on this site. Conversion value data is fed back into automated bidding machine learning algorithms to boost performance dynamically.

For every PPC account and search ad campaign, we create a conversion value model for each type of conversion event and a plan for click attribution modeling. This allows us to configure campaigns so that each conversion is assigned a financial value, and distribute credit for clicks throughout the sales funnel in an optimal fashion.

In our conversion value model, what we are after is the gross margin each conversion action is producing. In the case of a Shopping (e-commerce) campaign, the conversion value would likely be (sales price-sales tax)*(average gross margin). For a services business running a lead generation campaign we use a planning window, e.g. 1 year then compute the average value of a converted customer over the planning window, so the formula would be (average value of customer)_*(avg. close rate of leads).

Lead value modeling is important because it allows for calculating return on ad spend, and net advertising margin. But more importantly, it provides valuable information back to the automated bidding algorithms, which uses machine learning to maximize the performance of the campaign.

For click attribution modeling, we start with some light research about the nature of the sales conversion process for your vertical market but view that information as assumptions that could prove incorrect.  If there is campaign history in Google Analytics or the PPC platform, we will mine that data and map it into a click attribution model that fits the campaign. There are articles pending on this site about the somewhat complex topic of click attribution, a crucial method for driving strong campaign performance, especially for campaigns running with bid automation.

Pending: conversion value modeling blog article