Advanced bidding strategies

Automated bidding's most significant contribution to advertisers is that it removes you from 90% of the decision-making process. Make sure you understand the 10% before you employ it.

The Ad Auction - how you get your ads to show

You can't escape Ads. Ads are everywhere. When you watch a video browse a website, open a mobile app, you see ads. Great advertisers put their ads in just the right place when you are most likely to engage with the ad. Mediocre advertisers just blast messages that are annoying or out-of-place.  Historically, we had to settle with a certain amount of disinterest in our ads in broadcast and outdoor media. You had to run a commercial to the entire audience just to reach the sub-segment you were after. Most of the 'Boomers and Gen-X audiences expect commercials and are pretty good at tuning them out.   Those days are going away fast.

Consumers are more empowered to avoid nuisance advertising. It's essential to select suitable ad placements for your advertisements and know what those placements are worth to you.

I'm going to teach you how the ad auction works and how bids and conversions feed machine learning to help you show your ads like a boss. In other words, I'm going to show you the power of effective bidding strategies.

First, understand the ad placement.

The space the ad takes up when you see it (both the time and location) is the ad placement. Most ad placements are auctioned off in real-time. Yes, the auction happens really quickly, and the bids include both the money you are willing to spend and other factors. Typically, the highest bidder gets the top spot, the second-highest bidder gets the second spot, etc.

Next, consider the components of your bid (hint: it's more than money)

Advertisers pay for clicks, so the platforms (like Google, Facebook and Microsoft) show ads that get clicked. To do that, they have created a formula to calculate your actual bid.

Actual bid = {$ Amt} * {Click History} * {Relevance}

Think about the purpose of these formulas.  They balance the likelihood that an ad will get clicked with the dollar amount of the bid.  In this way, it's in an advertiser's best interest to keep click history and relevance as high as possible.  The actual formulas are a touch more complex in their practical application, but the concept is the same.

Get the best ad placements for your unique product or service automatically

Don't worry. You don't need to measure each variable to determine what you will bid in each auction. Although many have tried to beat the system (with failing strategies like SKAG or software quant programs), understanding the system is enough. Google, Facebook, and Microsoft all provide dashboard reporting and details about your bids. The interfaces are intuitive, and they also want you to be successful. Why not use the best tool for the job?

Machine learning tools can monitor a much larger sample of attributes to build a user's context. These include device, location, time of day, remarketing lists, browser, language, and more.

I've beaten out third-party solutions repeatedly with a well-thought-out bidding strategy using the platform's built-in tools.

It's not magic; it's a tool

You still need to use it properly.

First, start with a little common sense. If you advertise used vehicles when people search for cucumbers, you miss the point (and should review the components of your bid.) Beyond common sense, you want to find and monitor all the metrics which indicate click history and relevance numbers for the auction. Let the machine do the bidding at auction time while keeping an eye on costs, relevance, and click-through rate. Assume that the automated bidding is "maximizing" within the constraints you set, and then continuously evaluate those constraints.

For discussion, your click history is your click-through rate (or CTR).  Google and Microsoft show you a Quality Score, and Facebook offers a Relevance Score. If you have a robot doing your automated bidding for you, what metrics do you want to monitor? Hint: It's a lot more than costs.

Automated bidding takes the heavy lifting and guesswork out of setting bids. It uses machine learning to maximize your desired outcome.

Select your bid strategy based on your outcome

Each type of automated bid strategy is designed to help you achieve a specific outcome. For machine learning to effectively bid on ad placements, we need to understand how results are tracked and fed back to the machine (or algorithm). Machine learning is just a sizeable equation running with the parameters we set. We constrain it to specific outcomes with our selected bidding strategy and by feeding it with conversion data.

Automated bidding requires active feedback from analytics with conversion tracking and event values. For example, when someone makes a purchase on your website after clicking through an ad, you want the platform to know that so it can get you more.  The Facebook Pixel, Google Analytics, Microsoft's Universal Event Tracker all serve this purpose.

Automated bidding's most significant contribution to advertisers is that it removes you from 90% of the decision-making process. Make sure you understand the 10% before you employ it.

If you like this article, I would love some feedback.  Reach out on Facebook or start a webchat, and we can set up a time to talk.