How does smart bidding work with Google Ads?

Smart bids are automated bidding strategies in Google Ads that integrate ad­vert­ising metrics such as keywords, target groups or user profiles into campaign planning for greater success. Machine learning is used to set the best bids for your strategies.

What is smart bidding?

Smart bidding is con­sidered a subset of automated bidding strategies. Based on machine learning, the algorithm draws on various signals and values to place campaign bids. Instead of costly and time-consuming manual bidding, smart bidding strategies make automated decisions. This results in more suc­cess­ful, cost-efficient campaigns and optimal return on in­vest­ment (ROI) or return on ad­vert­ising spend (ROAS).

How does smart bidding in Google Ads work?

Without Google smart bidding, you would have to develop your own strategies and analyse target groups, keywords and millions of signals for search engine ad­vert­ising (SEA) and search engine marketing (SEM). Based on these market and ad­vert­ising analyses, you would also have to set bids for lucrative ad­vert­ising positions in auctions within mil­li­seconds which is hardly con­ceiv­able without ar­ti­fi­cial in­tel­li­gence and auto­ma­tion.

Smart bidding does this by cal­cu­lat­ing available signals for ad positions and campaign targets. It then bids for the best ad slots for the keyword or signal on digital ad exchanges such as Ad Exchange, all in a few seconds. Strategies are adapted to your needs by in­cor­por­at­ing ad­vert­ising context and business goals. This means your campaign budget is more ef­fect­ively im­ple­men­ted, along with automated advert buying and selling through pro­gram­mat­ic ad­vert­ising, pro­gram­mat­ic buying and real-time ad­vert­ising.

Business goals and bidding strategies for Google Ads

Google Ads smart bidding is easily set up from your Google campaign settings. But first you must activate or implement con­ver­sion tracking with Google Analytics or the Google Tag Manager.

Google dis­tin­guishes between two smart bidding goals:

• Increase sales/leads: con­ver­sions with defined budget or fixed return on in­vest­ment (ROI)
• Increase profit: con­ver­sions with a defined budget or fixed return on ad­vert­ising spend (ROAS)

The dif­fer­ence between ROI and ROAS is that ROI refers to actual profit after expenses and de­duc­tions. ROAS describes total revenue. While ROI is a measure of financial prof­it­ab­il­ity and the revenue-cost ratio of a campaign, ROAS refers to the success and the expense-revenue ratio of in­di­vidu­al campaign elements such as display ad­vert­ising.

Google smart bidding strategies in detail

Depending on your business goals, the following smart bidding strategies may be useful:

Maximise con­ver­sions

Con­ver­sions refer to specific marketing ob­ject­ives to increase sales or leads. This includes con­ver­sions from visit to lead/purchase interest and con­ver­sions from lead/purchase interest to purchase. You need as many prospects, buyers, or clicks as you can get for your set budget to maximise con­ver­sions and increase return on in­vest­ment.

Target CPA/tCPA for optimised con­ver­sions

tCPA (target Cost per Action) defines the greatest possible con­ver­sions at a fixed budget for ad­vert­ising costs or the con­ver­sion value. As the ad­vert­iser, you determine the price of a con­ver­sion. The algorithm ef­fi­ciently allocates your ad­vert­ising budget to selected con­ver­sions. One tip is to start with a few con­ver­sion types and allow the algorithm ap­prox­im­ately 30 days to learn.

Maximise con­ver­sion value

A value is assigned to each con­ver­sion. The strategy sets bids in the budget framework to maximise the overall value, i.e. the return on ad­vert­ising spend (ROAS) to increase targeted con­ver­sions and the overall con­ver­sion value. It does this ir­re­spect­ive of the different meas­ure­ment and con­ver­sion values.

Target ROAS/tROAS for optimised con­ver­sion value

You define the Return on Ad­vert­ising Spend you want to get your tROAS. Note the ROAS value doesn’t have to be identical to the tROAS value. tROAS is only worth­while if suf­fi­cient data is available, around 50 con­ver­sions over the past 30 days should suffice.

Maximise clicks/automate CPC

The automated cost per click (CPC) is used when the algorithm sets bids as soon as clicks have a high prob­ab­il­ity of leading to con­ver­sions. If the prob­ab­il­ity is low, the bid is lowered.

Per­cent­age of potential im­pres­sions

You define whether ads should be placed above or below the com­pet­i­tion. The upper limit for Cost per Click limits high bids.

It is important to try to use bidding strategies when the algorithm can draw on plenty of data. Con­ver­sion values or strategies that are changed too often and too quickly during the learning phase can have a negative effect on ef­fi­ciency.

What are the pros and cons of Google smart bidding?

Automated bidding strategies mean the algorithm sets bids for your campaign in mil­li­seconds and bids for cost-effective, suc­cess­ful ad­vert­ising positions. Machine learning in­cor­por­ates a vast number of context signals and creates bids according to pre­defined goals. At­tri­bu­tion models and business goals are used to train the algorithm on the con­ver­sion values that are important to you. Analytics tools and per­form­ance reports allow you to see how suc­cess­ful your campaign is.

Overview of the most important pros:

• Cost-efficient, con­tex­tu­al placement of bids and bidding for suitable ad­vert­ising positions
• Automated bidding strategies based on machine learning
• Pre­defined per­form­ance targets take into account business ob­ject­ives and signals
• Analytics tools provide trans­par­ent eval­u­ations and reports
• Time and cost savings for more suc­cess­ful and efficient campaigns

One con is that the algorithm depends on data. It has a learning phase and needs data to learn from. This can include in­form­a­tion from purchases or user behaviour, sales, profits and page traffic collected using con­ver­sion tracking and data mining tools.

Which signals are important in smart bidding?

Millions of signals and data on usage and pur­chas­ing behaviour play a role in placing bids. The most important signals and char­ac­ter­ist­ics include:

• Age/gender
• Residence/location
• Device/browser
• Operating system
• Estimated income
• Day/time
• Interests/search behaviour
• Purchase behaviour
• Click depth/click behaviour
• Accessing important pages
• Reaction to CTAs
• Website behaviour
• Re­market­ing lists

How to implement a smart bidding strategy correctly

Firstly, ensure pre­requis­ites such as con­ver­sion tracking, defined target groups, realistic business goals and ad­vert­ising budgets are met for an efficient Google Ads bidding strategy. Important questions you should be able to answer include:

• Do you wish to recover maximum ad­vert­ising costs? In this case, the Google Ads bidding strategy ‘Maximise con­ver­sion value’ is re­com­men­ded.
• Do you want to recover a defined multiple of your ad­vert­ising costs? In that case, we recommend ‘Target ROAS’ set at ‘600%’ for six times the con­ver­sion value.
• Do you want to define the cost of an ad­vert­ising measure? Use ‘Target CPA’ and set the maximum budget for as many con­ver­sions as possible.
• Are you looking to achieve the maximum number of con­ver­sions through certain ad­vert­ising measures and actions? Select ‘Maximise con­ver­sions’ to get the maximum number of con­ver­sions for the cor­res­pond­ing action/ad­vert­ising position.

Examples of best practices

To use smart bidding more ef­fi­ciently you can follow the best practices below:

Activate con­ver­sion tracking

Con­ver­sion tracking is at the heart of smart bidding. You need data for a cost-effective campaign with lots of con­ver­sions. Enable con­ver­sion tracking and collect data that Google smart bidding can work with. The in­form­a­tion allows you to more ef­fect­ively evaluate user signals and place bids based on reliable cal­cu­la­tions. It’s important you don’t just track macro con­ver­sions like ‘buy’ or ‘don’t buy’. Micro con­ver­sions such as click behaviour and dwell time on pages or ads also play an important role.

Consider learning phase and create data master

The algorithm undergoes a learning phase to collect data for automated bid placement. One to two weeks are re­com­men­ded for the AI to collect and evaluate the data. The learning phase should be taken into account when ac­tiv­at­ing smart bidding for the first time and when adjusting your campaign strategy. Data can be increased in a targeted manner by or­gan­ising ads into smaller campaigns. This will help you to increase the amount of data.

Set realistic business goals

With strategies such as tCPA or tROAS, avoid setting targets that are too high or too low, but rather base them on pre­vi­ously achieved per­form­ance targets. It’s best to approach the desired goals in small 10-percent steps.

Success stories take time

Try not to get impatient when it comes to eval­u­at­ing your campaign. Organic con­ver­sions take time before they show up in per­form­ance analytics.

‘Maximise con­ver­sions’ at the start

Begin with max­im­ising con­ver­sion. This strategy allows you to collect important data later used for tCPAs or tROAS.

Address target groups

Use re­market­ing lists and identify your most important target and customer groups. Re­market­ing lists can be set in Google Ads, for example.

Consider Google Ads re­com­mend­a­tions

Take a look at the ‘Google Ads Re­com­mend­a­tions’ to find important tips for your campaign.

Con­clu­sion: smart bidding for cost-efficient ad campaign

It’s hard to imagine online marketing without machine learning, ar­ti­fi­cial in­tel­li­gence, data mining and audience targeting. The same applies to smart bidding which is cost-efficient and saves time for ad­vert­ising campaigns. The algorithm evaluates millions of user signals and data to help boost sales and profit through your Google Ads bidding strategies. Although it requires fa­mil­i­ar­isa­tion and patience, smart bidding should be a feature of your Google Ads campaigns.