The Six Steps to Machine Learning PPC Bidding Optimization [eBook]

PPC bidding optimization has come a long way over the years. The bid calculation process for at-scale programs has many moving parts and can be intimidating for data scientists, let alone the day-to-day users, managers, and stakeholders in paid search programs.

Bidding calculation in the modern era has many flavors, but we’re interested in the best and most optimized version; the type of process that is designed to drive peak performance. Our new Guide, Machine Learning Powered PPC Optimization, walks through both the prerequisites of a thorough bidding process and the bid calculation stages that modern SEM optimization tools are utilizing to unlock the most from large PPC programs. Its focus is on machine learning PPC.

Machine Learning PPC Bidding Optimization

Foundations to Optimized Bidding

The infrastructural foundation to the best possible bid calculation starts with the data architecture to capture brand interactions as a series of events happening in real-time. The system must be flexible enough to ingest all of the data sources that track, measure, or influence the customer journey.

Once that infrastructure is in place, that data can be utilized for execution; for action; for the modeling and calculation that turns a data set into dollars.

Our new guide to modern bidding, using QuanticMind’s calculation stages as a model and exemplar, explains the concepts and process of fully optimizing PPC bid management. The optimal bidding platform leverages the latest advances in Data Science, including machine learning algorithms, Bayesian modeling, predictive performance methodology, and natural language processing, to optimize SEM performance toward specific business goals.

There are six high-level steps.

A Look Into The Bidding Process


One: Understand and Estimate Keyword Value

Stage one looks at the modeling that estimates the dollar value of each individual keyword. This process involves ingesting revenue data from any conceivable source and applying that back to the keywords in such a way as to estimate a dollar-value for each. This involves powerful machine learning models generating revenue-per-click graphs, and results in a dollar value that can be used in later steps to calculate optimal PPC bids.

When keywords lack sufficient data to make a meaningful model of the potential value, deep learning text recognition models are used to map semantically similar data-rich keywords to data-poor keywords. As a result, even low-click or low-conversion keywords still get the most accurate possible value assigned.

Two: Understand Click and Cost Elasticity

Stage two aims to understand click and cost responses to CPC changes, with the goal of generating a map of costs and the expected volume. This is where Bid Landscape Data from Google is highly useful, and applied at scale to an optimization practice.

Three: Calculate a CPC that Promotes Your Goals

Stage three combines the estimated dollar value determined from stage one’s artificial intelligence-powered PPC calculations and the cost ecosystem analyzed in stage two. The decision engine applies the advertisers targets, bidding strategies, and goals and then runs through and selects the best bids to maximize performance, given the data, calculations, and goals. Often, a Portfolio approach is used to bid against a target while maintaining an efficiency metric. This is the modern approach to PPC bid optimization that most bid management tools utilize – if they’re designed for medium to large SEM programs. QuanticMind differs in some ways from legacy tools, discussed further in the Guide.

Four: Calculate Bid Adjustments

Stage four repeats nearly the same process completed in the first three steps, but on a different set of data and with a different purpose: calculating and automatically applying bid adjustments. QuanticMind’s model shines at this point, using machine learning to optimize bid adjustments at scale. Device Bid Modifiers, Geo Location Bid Modifiers, and Audience Bid Modifiers can all be automatically calculated and applied, based on their relative successes in the SEM program. The data science algorithms used here are another advantage when attempting to calculate optimized bids at scale.

Five: Anomaly Detection

Stage five moves into the often understated – but highly important – anomaly detection. This is one of several areas where the infrastructure discussed at the top can “flex” its strength. When designed for effective capturing, cleaning, and piping of data from any source, the system provides better data for better execution. However, the opposite has negative effects: when data is missing or seems different than forecasts would suggest is reasonable, the performance can take a hit. Fully optimized bidding platforms prevent these problems by using multiple anomaly detection and issue-prevention steps, ensuring bids aren’t pushed based on bad data.

Six: Bid Push

Stage six is the execution! Push the bids and bid modifiers through the publisher and go live. Data collection is ongoing and fed back into the system. Other uses for more variable aspects of a program, like inventory management or a “maximum capacity of leads” per day or location, can be applied and fed to make decisions even quicker. Ultimately the process is repeated to create a virtuous cycle of optimized PPC bidding.

Learn How Fully Optimized Bid Calculation Works At-Scale

This guide will walk you through the modern solution to optimized bidding automation. It is a powerful tool to learn the leading process in paid search bid calculation and optimization. It helps paint the picture for how machine learning PPC is actually applied, how well-integrated data feeds the model, and how bid management decision engines step through the process of calculating and pushing bids. It helps answer the question: how can I optimize PPC bids?

Machine Learning PPC Bidding Optimization