PPC automation can be a great driving force behind any marketing campaign, provided you know how to use it properly. Several factors go into how well your automated bidding algorithms work and mismanagement can mean a completely wasted ad budget and a useless PPC ad campaign.
The first thing to think about is the fact that automated bidding algorithms are essentially learning machines. This means that you’re trusting that machine to do work for you that you will otherwise have to do yourself. By now you’re probably going “gee I already know that, why are you telling me this?”
We say this because oftentimes the automated machine is not allowed to function as it normally should to make the PPC ad campaign as effective as it could be. That’s why we’re going to go over some things that every marketer should know about Automated Bid Strategies and how to make them work properly for your PPC ad campaigns.
Yes, that’s right, budget constraints can negatively affect your automated bidding strategies Or algorithm. The way the program grows is by learning behavior and adjusting to match that behavior to get the best results possible based on ad data.
However, if your budget is such that you can only spend ad dollars on auctions that will result in clicks and your return needs to be exceptionally high, this will result in your automated bidding strategy algorithm only Automated bidding strategies/bid strategies on auctions very sparingly and may result in diminished traffic and diminished returns.
Instead, your automated bidding targets should be set based on realistic expectations of return on ad spend based on actual cost per ad data that you’ve used recently.Relevant data helps the algorithm learn what it needs to do in a particular situation as opposed to setting target guidelines with no predefined data.
The algorithm must learn the patterns of data over time. Setting unrealistic goals on little to no data will only result in wasted spending or ad spend.
It’s important to remember that you are teaching the algorithm how to target the best auctions for display ad space on your behalf. To do this you should follow a few certain steps to get the best results.
Target strategies focus on hitting a certain ROAS or CPA and not on the bulk-buying of ad space. Because of this, how you manage these values in telling the algorithm what to do is crucial to having any success what so ever in your ad campaign
If this is your first time using an automated bidding strategy algorithm and you’re trying to target auctions accurately rather than forcefully, you should start by feeding the algorithm your CPA and ROAS data from about the last month or so.
It takes the algorithm a couple of weeks to get up to speed so you’ll want to avoid making any changes until the program has ended its initial learning phase.
When attempting to make adjustments to your ad targets, you should work in small increments. For instance, if your ROAS is 10x, don’t wait two weeks and immediately try to jump to a 15 or 20x number, move slowly from 10 to 11, wait a few weeks and then move again. This gives the algorithm time to learn and adjust.
Otherwise, you end up in the same situation as we stated before, the algorithm will likely seldom Automated bidding and result in diminished returns.
Lastly, allow your algorithm to observe other sets of data by setting it to observe other audiences. As we said, you’re teaching the algorithm, so the more data it has access to, the better it will perform.
For ad campaigns that run on a stringent budget, you can set your automated smart bidding algorithm to maximize the number of maximize conversions/maximize conversion value on a particular ad. While these strategies are indeed effective if your ad campaign is strapped for cash, they have some limitations that make them ineffective for certain types of ad campaigns.
For one, setting your automated smart bidding to maximize conversion is like telling it to charge into battle without considering anything but beating the enemy. What this means is that the algorithm will forego any other data other than maximize conversions, such as demographic data, customer preferences, or even automated bidding strategies adjustments.
It will also continue to bid and buy ad space so long as the budget allows until it hits the pre-defined limit.
This type of strategy also doesn’t care about cost per click targets. If you have an ad campaign with these targets set, this strategy will be wholly ineffective.
Just like with target-based strategies, maximize strategies require time to learn the strategies you want them to perform. Automated smart bidding algorithms aren’t designed to make quick and sudden changes and if you try to do so, you’ll end up with a non-functional algorithm that is constantly readjusting.
This rule applies doubly to marketers who are constantly seeking concrete proof of success and every time they don’t achieve it they want to switch and try something new. You’re more likely to achieve success with your algorithm if you allow it the time and space to grow properly. You may even find that a given strategy that you thought was underperforming, may do better than as time goes on.
Success is not guaranteed, but allowing the algorithm to work as it is meant to be better than constantly changing strategies and hoping something sticks.
Automated smart bidding algorithms are great for taking a lot of the procedure out of Smart bidding Strategies for ad space and can save you a lot of time and effort in your marketing strategy that would likely be better spent elsewhere.
The problem is, many marketers want instant gratification and maximum results. If you can manage to follow through with the advice we’ve outlined here though, you’ll find that an automated smart bidding algorithm is much more efficient and successful than you ever though.
In addition, many of today’s automated algorithms are strategically built so the house always wins. Instead of immediately accepted all bid adjustments suggestions, it may be worthwhile to take the time and do it manually. Remember, Google Ads are strategically designed to make Google money, not necessarily to always perform the best in every situation for every business.
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