In 2022, programmatic advertising is rising to new heights in its value for marketing strategies. Emarketer estimates an increase in ad spending to almost $97 billion, compared to about $82 billion in 2021. With 54% of marketing budgets devoted to programmatically purchased ads, both publishers and advertisers need super-efficient strategies to make every dollar count.

For publishers, choosing an advertiser, and consequently, the revenue the publishers earn, are influenced by several criteria, like the price of impressions, ad relevance, and response time. The good news is that machine learning algorithms can help publishers make the best choices for real-time bidding in digital marketing and help them maximize revenue. 

• How is Machine Learning Used in Digital Marketing?
• What is Real-Time Bidding in Digital Marketing?
• How can I Improve my Bidding System?
• What are the Benefits of Real-time Bidding?
• How Machine Learning Improves Marketing Strategies?
• Why is Machine Learning Important in Marketing?
• What is Real-Time Bidding Example?
• What Companies Use Real-time Bidding?
• Which is the Most Automated Way of Bidding Strategy?
• What are the Different Types of Bidding Strategies?
• How Does Machine Learning Affect Marketing?

How is Machine Learning Used in Digital Marketing?

We know that marketing teams don’t want for lack of data. Marketers struggle with making sense of all the data they have at their fingertips and then putting that data to use. This analysis is where machine learning comes in.

The primary reason to add machine learning to your marketing stack is that it can make sense of vast amounts of data much faster and much more effectively than humans.

Read Also: RTB in Marketing: Why you Should be Using it

This process can use data to identify patterns and make predictions almost instantly. Marketers can then use these insights to optimize a huge portion of their workflow, from running more tests and improving their website’s UX to personalizing the customer experience and automating consumer engagement.

The long and short of it is machine learning can be used to improve just about every part of your digital marketing efforts. Below we discuss some of the most important ways.

1. Recommendation systems

The essence of a recommendation system is to offer customers products they’re interested in at the moment.

What a recommendation system predicts: Goods that a customer is likely to buy.

How this data is used: To generate email and push notifications as well as “Recommended products” and “Similar products” blocks on a website.

Result: Users see personalized offers, increasing the likelihood of their making a purchase.

2. Forecast targeting

In general, the essence of all types of targeting is to spend the advertising budget only on target users.

Most used types of targeting:

• Segment targeting — Show ads to groups of users with the same set of attributes
• Trigger targeting — Show ads to users after they take a certain action (for example, viewing a product or adding an item to the shopping cart)

There’s also predictive targeting, in which you show ads to users based on the likelihood of their making a purchase.

The main difference between these types of targeting is that predictive targeting uses all possible combinations of tens or hundreds of user parameters with all possible values. All other types of targeting rely on a limited number of parameters with certain ranges of values.

What forecast targeting predicts: The probability that a user will make a purchase in n days.

How this data is used:

Example 1: To launch advertising campaigns. For this purpose, create segments based on the probability of a purchase and upload those segments to Google Ads, Facebook Ads, and other advertising systems.

Example 2: To analyze the effectiveness of advertising campaigns. For this purpose, create segments based on the probability of a purchase and upload those segments to Google Analytics and use them to analyze the effectiveness of advertising campaigns (which campaign leads to the most conversions).

Result: Advertising is shown to a more targeted audience, increasing the effectiveness of campaigns.

3. LTV forecasting

The best-known methods of calculating lifetime value, or LTV, are based on knowledge of the total profit from a customer and the time for which the customer has been interacting with the business. However, many modern business tasks require you to calculate LTV even before a customer leaves. In this case, the only solution is to predict LTV based on available data.

What LTV forecasting predicts: The LTV of each user by segment.

How this data is used:

1. Segments are loaded into push notification or email services and used for mailing to reduce customer outflows (the churn rate).
2. Segments are uploaded to Google Analytics and used to analyze the effectiveness of advertising campaigns based on predicted LTV.

Result: The advertising budget per user is determined based on LTV, which improves the effectiveness of campaigns.

4. Churn rate forecasting

In marketing, the concept of churn or outflow refers to customers who have left the company and the associated loss of revenue and is usually expressed in percentage or monetary terms.

Churn rate forecasting allows you to respond to a customer’s intention to abandon your product or service before they actually do.

What churn rate forecasting predicts: The probability of users leaving by user segment

How this data is used: Segments can be uploaded to email or push notification services as well as to Google Ads, Facebook Ads, and other advertising systems. You can also pass this information to the retention department so they can personally reach out to customers with a high probability of leaving.

Result: Retain customers.

What is Real-Time Bidding in Digital Marketing?

Real-time bidding (RTB) is the process in which digital advertising inventory is bought and sold. This process occurs in less than a second.

On Authorized Buyers, you can use RTB to evaluate and bid on each available impression. This is available for any Authorized Buyer with an ad server or bid engine. No additional contract is required, but Terms of Use are referenced in your Authorized Buyers contract.

Terms to know

Publisher: An owner of a web page. 

Cookie: A small piece of data that might keep track of visits to a website, remember items in a digital shopping cart, or help personalize a web-page experience.

Personalized ads: Digital ads that let advertisers reach users based on their interests and demographics.

Advertising partners: Advertisers who work with Google and publishers to buy advertising space on a web page. In the European Economic Area (EEA), the publisher must obtain consent to process cookies, and provide information about which advertising partners data is shared with.

With real-time bidding, your network can receive and review pre-filtered inventory to establish and execute bids.

Once your account is set up, here’s what happens:

1. Specify inventory to receive with pretargeting

Filtered bid requests are sent based on the pretargeting configuration. These requests specify targeting criteria, such as day parting, language, geographical region, and so forth. You can specify the maximum number of requests (queries per second) that your system can receive.

2. You receive pre-filtered ad call parameters

The following parameters are passed to your system:

• HTTP header with referrer URL (for branded sites)
• Targeting information, such as geo and vertical
• Truncated user IP address (IPV4 and IPV6)
• Encrypted user cookie ID
• Ad unit restrictions, such as restricted advertisers, creative types or product verticals

3. Your system analyzes the ad call, layers additional targeting data, and submits your bid

If submitted, the following is included in the bid:

• CPM bid
• Creative redirect or Google-hosted creative ID

How can I Improve my Bidding System?

With Google Ads, businesses enter bids in an auction to have their results for specific keywords boosted and prioritized with the Google search engine. Those successful bids appear right at the top of the list as ads and will be the very first thing people see, even before the natural, organic search results.

The more successful bids you get, the more frequently you will appear at the top of these lists. So, it’s important to make sure you get the most out of your bids, and you can do that by following these tips.

1. Learn the System

As much as it might be tempting just to go in and start letting automated systems do your bidding for you, this is highly inadvisable as your starting point. Take the time to actually learn the Ads system, understand some of the basic principles behind Pay Per Click marketing, or PPC, and get a grip on the relationships between different factors like keywords, content quality, and how these affect your ranking.

Remember that if you go bidding with a website and content that rate high on Google’s quality rating system, you’ll pay lower amounts for your bids.

2. Use a Good Automation Strategy

Google Ads runs billions of auctions every month, and you don’t want to spend your time overseeing every single one. Fortunately, there are automated bidding processes built in that allow you to set the parameters for how your bidding is adjusted and submitted. However, the real question becomes, just how much automation should you be using?

This answer will vary from one business to the next, but you should find the right combination of automated and hands-on bidding to ensure that you’re getting the most for your money. You don’t want to waste time participating in thousands, or even billions of bids, but you don’t want to be out of the loop either on how the algorithms are handling your bids!

3. Choose Good Metrics

Metrics are the lifeblood of making good decisions. The information you get will help you to determine whether you’re doing something right or need to improve in some areas. The same with the metrics you get back from your bidding, as well as the clicks you’re getting on your ads.

That is why it’s critical that you ignore metrics that aren’t relevant to your business or goals and concentrate on those metrics that will meaningfully affect your operations. For example, if your targeted market is mostly using computers and laptops to conduct searches, then the metrics coming in from mobile platforms should play a much smaller role in your decision-making.

4. Make Use of Your Bid Modifiers

Once you know what metrics are important to you, make sure you take this into account and tweak your bid modifiers. Bid modifiers are a fantastic way to get even more detailed about micro-managing the way your bids play in the bidding system. There are a lot of different variables that will change given the time of day, the day of the week, or even the type of bidding plan that is being used.

By taking the time to really understand and exploit what bid modifiers can do for you, you can fine-tune and hone in on the results that you want. So if your metrics are telling you your New York City, New York results are better than your Austin, Texas results, you can change your modifiers to reflect where you want to direct your bids.

5. Pay Attention to Quality

Remember, one of the things that Google evaluates when deciding how to address your bidding and pricing is the ‘quality’ of your marketing. Ads quality is what determines just how much you’ll pay on your bid, and the higher your Ads quality, the lower the prices you’ll pay per click. But what determines your Ads quality?

A host of factors. Pay attention to things like having a good landing page, a proper balance of keywords, and your click-through rate. It essentially means having your website and content be in a good, search-friendly shape before you even start bidding.

6. Develop a Bidding Plan

Once you have an understanding of automation, which metrics to use, and how the interrelated systems work, you should come up with a solid bidding plan. Google Ads has a few different bidding plans and strategies that you can use—and automate—but you’ll choose different options based on the type of results that you want to see.

Someone who wants to maximize the conversions to actual sales, for example, will go with the Target Cost per Acquisition plan. On the other hand, someone who is more concerned with simply appearing at the top of Google searches, or within the first 1 to 4 pages, would benefit more from following a Target Search Page Location plan. What you want to get out of Google will determine your plan for your Ads efforts.

7. Don’t Make Constant Changes

The Google search engine is all about machine learning. That is, it is constantly analyzing incoming results and comparing them with the hoped-for goals, in order to better adjust its actions to help match the hoped-for results. Given enough time, the algorithms eventually ‘catch up’ to what you’re doing and can really help to push your goals along.

However, the opposite is true if you move too fast. If you’re impatient or restless, and you find yourself always changing your goals and your subsequent bidding plans and strategies, you are essentially forcing the Google algorithms to ‘start over’ and collect fresh data for analysis and comparison.

People who frequently change their strategies are, in other words, ‘starving’ Google of the data and time it needs to actually start helping you with your objectives. While there’s nothing wrong with changing plans when it’s clear they’re not working, doing so frequently will never let you know when a program actually is working because you haven’t given it time to thrive.

What are the Benefits of Real-time Bidding?

There’s no doubt there are still issues with programmatic advertising; fraud, poor creative, and a lack of transparency from media agencies cause much debate. Despite this, real-time bidding (the automated auction of ad impressions) has many obvious advantages for publishers.

1. Pricing precision

• Though successful bidders pay for ad impressions at the second-highest bid price (to provide advertiser value), publishers broadly extract greater value by using a supply-side platform (SSP) to enable RTB.
• This is due to pricing precision, with publishers able to extract value from each impression in real-time.

2. Increasing the value of remnant ad space 

• As real-time auctions are triggered by the arrival of a target visitor, ad space that was previously unwanted (remnant inventory) is increased in value.
• The Econsultancy Online Publishers Report (Dec 2013) revealed that 46% of publishers surveyed had seen an increase of 1-10% in remnant inventory value. One in 10 indicated that their remnant revenue rose by over 50% as a result of RTB.
• In the same survey, only 5% of publishers reported seeing no increase in revenue.

3. The ability to tweak

• SSPs allow RTB campaigns to be carefully controlled with the application of business rules that specify the types of advertisers that should and shouldn’t be allowed to purchase. This can be more flexible than just setting up traditional blocklists.
• Publishers can set and adjust pricing, giving greater control over their entire inventory. This can include the ability to enforce a higher price on a particular advertiser.
• Price can be controlled no matter what parts of the site impressions are served.

4. Gaining audience insight

• Publishers can see what specific segments of their audiences are best performing and most sought after by advertisers.
• This insight can be used to influence strategy, maximizing the value of a publisher’s audience by building stronger relationships with these high-value customers and attracting new audiences that match this profile.

5. Private marketplaces maintain premium inventory

• Publishers can use private marketplaces (effectively limiting who can bid for the best ad space).
• This allows premium inventory to be sold with the transparency, controls and data protection available through RTB.
• Private marketplaces can be used as an incentive to encourage advertisers to hit a spend threshold required for exclusive access to a premium segment of inventory.

6. Improving direct sales strategy and pricing

• SSPs allow publishers a clearer picture of what they are selling, who they are selling to and what price works best for them.
• This intelligence gained from RTB campaigns can be used to inform direct sales pricing and strategy.

How Machine Learning Improves Marketing Strategies?

How exactly can marketers use AI and machine learning in marketing? Can any of these ways improve sales?
Remember that investing in machine learning applications and artificial intelligence tools isn’t a light decision to make, so measuring the ROI of AI initiatives is always a good idea.

Below are some of the most common ways that AI and machine learning models for marketing are being used:

Personalizing Customer Services

A big number of service-providing entities choose to focus on personalizing customer services, and these are usually the businesses on the right path. Take, for example, Netflix.

Netflix provides its customers with recommendations based on a wide variety of factors: previously watched movies, rated movies, ignored movies, search queries, and so much more. Netflix is also known to recommend personalized artworks catered to a viewer’s personal preferences, resulting in more views.

A Netflix series launched in 2019 called Love, Death + Robots even had four different episode orders that they say are based on the site’s understanding of your browsing behaviors and viewing habits.

Overhauling the Customer Service Experience

These days, the fastest way to get customer service is via chatbots. The rising popularity of chatbots can be attributed to zero waiting times, round-the-clock availability, and extensive knowledge databases.
With chatbots, you can get instant answers without having to talk to an actual human representative. You won’t have to call anyone on the phone or wait for an email reply.

You can pick or type your replies, and chatbots will provide you with answers or direct you to helpful articles. Providing this option to your customers lessens the chances of them wanting to get routed to customer service representatives.

Crafting Compelling Promotional Content

Machine learning and digital marketing are a match made in heaven. In most business models, saving time equates to saving money, because this means that more can be accomplished with more time. This is true with content creation, too.

Using machine learning marketing is a great way for content creators and copywriters to up their game.

• Automate keyword research
• Get better research materials to work with
• Make whole articles with just a topic input
• Craft shorts and product descriptions with natural language processing applications.

Machine learning tools like these can help speed up the content creation process. Though the final results will still mostly benefit from the scrutiny of a content editor, these tools can help produce interesting, engaging, and informative content.

Building Beautiful Websites

Website design is more than just good appearances. A good-looking website is nothing if the whole user experience is no good. Machine learning can help marketers make sense of visitor data to create websites that are stunning in both the UI and UX aspects.
WixADI (Wix Artificial Design Intelligence) is an example, and this is how it works.

• Step 1: You tell WixADI what kind of website you want to create.
• Step 2: You input some basic business information like business name, logo, and location.
• Step 3: WixADI searches the web for publicly available and business-related information, and crafts a website based on them.
• Step 4: You choose from a set of styles offered by the ADI.
• Step 5: You get the chance to make minor adjustments or start from scratch.
• Step 6: Push the site live or park your finished design.

Optimizing Marketing and Advertising Efforts

It’s difficult to undermine the many applications of machine learning in marketing and sales. They can be particularly helpful in marketing campaigns, predictive analytics, mix modeling, and even attribution. AI and machine learning consultants are even a thing now.

Automating and optimizing your marketing and advertising efforts can lead to higher revenues and better leads. It can help you build more accurate customer segments so you can personalize your strategies and make better customer interactions. You can even use ML tools to help in deciding how much to spend on advertising and figuring out the best timing and duration for the advertisement.

Managing Social Media Marketing

Social networking sites are known to use AI and ML tools to provide better user experiences. Some of the most widely-used examples are Twitter’s curated feeds; Instagram’s customized content; and Facebook’s face recognition features.

Not all comments were made equal. Some require carefully-crafted responses because they can make or break your brand. Companies use ML to determine complaints and reviews that take top priority as part of a process more commonly known as reputation management.

Perfect timing is as important as the content itself, because what good is great content when the reach isn’t as wide? Sometimes, all it takes is the perfect timing for posts and brands to go viral, and AI and ML tools can help determine this perfect timing and analyze the consumers’ sentiment.

Handling Machine Learning Email Marketing

Email marketing is an old practice, but it doesn’t mean it’s not as useful as the other ways of marketing in the digital era. There have been so many changes to the way people use, read, and send emails.
You can automate content, optimize subject lines, retarget customers, and many more with the help of AI. Other AI tools can help with timing, segmentation, email delivery, and solutions for e-commerce, too.

Many e-retailers use customer retargeting. A few hours after browsing through several products and product recommendations, they’d send an email to customers to remind them about a product in their cart and offer the user vouchers and discounts to help seal the deal.

Why is Machine Learning Important in Marketing?

One of the major ways that machine learning will be integrated into human-mediated marketing is in the concept of the Smart Content framework. In this framework SEO and content marketing are converged, and machine learning is woven into discovery, optimization, and measurement phases of content development to help marketers’ campaigns keep parity with ever-changing search algorithims–which themselves are becoming increasingly managed through machine learning.

BrightEdge defines Smart Content as:

1. TARGETED to exactly what customers want and need when they need it
2. OPTIMIZED to make the content more visible and discoverable
3. ALWAYS ON and technically up-to-date
4. INTEGRATED, activated across devices and the entire marketing stack for maximum impact
5. PROFITABLE for marketers because the content delights and engages readers on topics they have intent for and primes them for conversion

Machine learning will be an important part of marketing in the future, as it will help brands better understand customer behavior and what people want to see online. Humans will always be in charge of the creative process, but this type of learning will make it easier to create a superior user experience.

What is Real-Time Bidding Example?

At any given moment, multiple advertisers can bid on a single impression of a publisher’s inventory, then the winning ad (with the highest bid) is shown to the user. Through RTB, advertisers can apply fine-tuned targeting and focus on the inventory most relevant to them. This, in turn, yields better ROI and higher eCPMs. RTB also allows advertisers to adjust their campaign budgets in real-time in order to optimize campaign performance.

Take, for example, the moment in a mobile game where the player watches an ad between game levels. At that moment, the mobile SSP runs an auction for all of the advertisers interested in showing an ad to that player. The advertisers make their bid and, in a split-second, the highest bidder is chosen. Their advertisement is then served to the player.

Publishers and advertisers can both set parameters for RTB, such as minimum prices and maximum bids, as well as prioritize specific deals and inventory.

RTB is effective for advertisers and publishers. Let’s take a look at how RTB is useful for each:

• For advertisers: RTB means more streamlined, efficient and targeted buying. It provides them with the ability to fine-tune targeting and focus on the most relevant inventory results in higher ROI. Ultimately, users see more relevant ads.
• For publishers: RTB increases revenue and fill rates by opening inventory to a wider variety of buyers in a competitive auction. Finally, publishers gain visibility of who is buying which inventory and can leverage this knowledge to charge more for their premium placements.

What Companies Use Real-time Bidding?

1. Ad Colony

Developed in 2011 by mobile developers specifically for mobile publishers, Ad Colony has since grown into one of the largest mobile advertising and monetization platforms in the world.

Ad Colony specializes in HD video advertising and playable technologies, making them a great choice for mobile game developers.

Ad Colony works across both Android and IOS devices.  

2. Ads Compass

Ads Compass is a global ad network that aims to work as a conduit for mutual cooperation between multiple sources including webmasters, advertisers, media buyers, and multiple ad networks. Ads Compass has its own ad exchange and self-serve platform, creating an extremely user-friendly UX.

3. Imonomy

Imonomy focuses on in-image advertising and work with over 13,000 publishers in the online space.

Perfect for publishers with a visually rich website, this platform also offers is in-line, in-screen, in-video, and header bidding technology. 

Integrating semantic programming theory with contextual analysis technology and Big Data analytics, Imonomy pride itself on serving all three major market stakeholders in the ad serving process, publishers, advertisers and users.

4. LiveIntent

Founded in 2009, LiveIntent specializes in email marketing campaigns. Perfect for publishers with a large email list and ongoing email campaigns, LiveIntent are also looking at solutions to help publishers to monetize without relying on third-party cookies.

5. Magnite

Previously operating as Rubicon Project, Magnite is a popular sell-side platform specializing in in-app and video advertising. They offer a header bidding wrapper and a private marketplace.

They offer ten different video ad formats, including linear, vertical, native, and out-stream.

Having worked with big-name clients such as  Spotify, Vox, and eBay, they are one of the most trusted names in programmatic advertising. 

6. OutBrain

Native advertising platform OutBrain is an industry-recognized leader when it comes to content recommendations for publishers. These content recommendations appear as boxes of interesting content with catchy titles and bright images used to attract readers. 

In 2019, Outbrain merged with Taboola and now serves an audience of over 2.5 billion people. 

OutBrain generally works with premium publishers who serve high-quality content to their readers. OutBrain’s ad format offerings include interstitial, native, in-stream, and in-article.  

7. Smaato

Smaato is another great choice for publishers looking to monetize through in-app publishing on the mobile web.

Smaato is a  publisher-centric, multi-offering platform that offers an RTB ad exchange, an ad server for publishers, and a private marketplace.

Working with around 90,000 mobile publishers and app developers, Smaato offers comprehensive support for a wide range of ad formats including native, in-stream, out-stream, and banner ads.

8. Splicky

Splicky is a demand-side platform that uses proprietary Real-Time Advertising (RTA) technology to serve relevant ads on mobile, desktop, DOOH screens, and CTV. Splicky uses programmatic technology to ensure advertisers only bid on impressions that match their targeting requirements.

Splicky offers a range of ad formats, including banners, interstitials, rich media and video, and a user-friendly interface with a self-serve platform and real-time analytics. 

RTB real-time platforms are a central part of the programmatic advertising ecosystem. By leveraging the right RTB platform digital publishers, app developers, and blog owners can join the RTB process and get the highest price possible from the advertising inventory. 

RTB platforms simplify the ad buying process and make the whole process fast and cost-effective. 

Which is the Most Automated Way of Bidding Strategy?

When you set up a Google Ads campaign, you have to tell Google how much you’re willing to pay for your ad to be shown. This is called your bid, and there are two different ways to set your bids: manual or automated.

With manual bidding, you tell Google the maximum amount you’re willing to pay per click on your ad (CPC), and you can make adjustments to that bid based on your ad’s performance as determined by the metrics available in your reports.

With automated bidding, Google uses automated rules to adjust your bids for you, based on the ad’s likelihood of getting a click or conversion. Automated bidding may use additional data points that aren’t available in reporting metrics.

There are eight types of automated bidding options in Google Ads:

1. Enhanced cost per click (ECPC)
2. Maximize Clicks
3. Maximize Conversions
4. Maximize Conversion Value
5. Target Cost Per Action ( tCPA)
6. Target Return on Ad Spend (tROAS)
7. Viewable CPM (vCPM)
8. Cost Per View (CPV)

Google Ads allows for automated bidding strategies to be set at either the ad group, campaign, or portfolio level, depending on the strategy you choose. This means you can direct different aspects of your account to rely on different bid strategies depending on their goals.

When choosing a bid strategy, evaluate at the campaign level to determine if the strategy will help accomplish your goal and if you have enough data to make it work (e.g., enough conversion volume to make Target CPA effective). If not, you might benefit from another bid strategy, a portfolio level strategy, or even an adjustment in account structure to better leverage it later on.

Automated bid strategies in Google Ads are a fantastic way to save time while leveraging algorithms to optimize your account, but only when evaluated and chosen wisely.

And even though automated bidding strategies require less maintenance than manual bidding, there’s no such thing as “set it and forget it.” Once these strategies are being leveraged in your account, set reminders to check in on them to ensure they’re still accomplishing the goals they set out to hit.

What are the Different Types of Bidding Strategies?

There are seven different automated bid strategies:

1. Maximize clicks

• Goal: Increase site visits
• Available In: Single campaigns or across multiple campaigns, ad groups, and keywords
• Description: The Maximize Clicks strategy aims to increase the number of visitors to your site. The strategy automatically sets bids to help you maximize clicks within your set budget. The strategy is available as a standard strategy in a single campaign or a portfolio bid strategy across multiple campaigns, ad groups, and keywords.
• Best Used When: You have a solid conversion funnel and you want to send as many visitors to your website as possible.

2. Target search page location

• Goal: Increase visibility on the first page of a Google search results page or show in one of the top positions
• Available In: Campaigns
• Description: Target Search Page Location automatically sets bids to help increase the chance that your ads appear on the first page of a Google search or in one of the top ad positions. It is only available as a portfolio bid strategy on the Search Network.
• Best Used When: You want to rank as one of the top positions in a Google search.

3. Target outranking share

• Goal: Increase visibility over other websites
• Available In: Campaigns
• Description: With Target Outranking Share, you can choose another advertiser’s domain that you want to outrank in ad position and how often you’d like to outrank it. Google will then automatically set your search bids to help meet that objective. Target Outranking Share is only available as a portfolio bid strategy.
• Best Used When: You want to outrank a specific competitor in your industry.

4. Target cost-per-acquisition (CPA)

• Goal: Get more conversions with your target CPA
• Available In: Campaigns and ad groups
• Description: Target CPA allows for more control over your automated bidding. With the strategy, Google automatically sets Search or Display bids to help you receive as many conversions as possible at your set target cost-per-acquisition (CPA). Some conversions may cost more or less than your target.
• Best Used When: You’ve established a CPA that you know you can spend to acquire a customer while still maintaining a profit.

5. Enhanced cost-per-click (ECPC)

• Goal: Increase conversions while staying in control of your keyword bids
• Available In: Campaigns, ad groups, keywords
• Description: With ECPC, Google automatically adjusts your manual bids to help you generate more conversions while trying to achieve the same cost-per-conversion. ECPC is available as an optional feature when using Manual CPC bidding or as a portfolio bid strategy.
• Best Used When: You are using a manual bidding strategy but still want to take advantage of automated bidding.

6. Target return on ad spend (ROAS)

• Goal: Meet a target return on ad spend (ROAS) when you value each conversion differently
• Available In: Campaigns, ad groups, keywords
• Description: Target ROAS automatically sets your bids to help you receive as much conversion value as possible at your set ROAS (the average value you receive in turn for every dollar you spend on ads). Some conversions may have a higher or lower return than your target. Target ROAS is available as a portfolio bid strategy and a standard strategy for individual campaigns.
• Best Used When: You want to focus efforts on driving the highest value of conversions versus trying to receive the highest number of conversions.

7. Maximize conversions

• Goal: Receive more conversions while spending your budget
• Available In: Campaigns
• Description: This strategy automatically sets bids to help you receive the most conversions for your campaigns while spending your entire budget. With this strategy, Google optimizes for a higher volume of conversions. Maximize Conversions is available as a standard strategy for individual campaigns, but not as a portfolio bid strategy.
• Best Used When: You have a large budget and want to automate your ads to drive more conversions.

How Does Machine Learning Affect Marketing?

Although Machine Learning seems like a modern Marketing buzzword, it’s been around since the 1950s. It’s a technological powerhouse of which Marketing is only one application.  

There’s good reason Machine Learning has earned its tag as one of the most powerful trends in Digital Marketing. Machines can analyze huge swathes of data, quickly and efficiently without human error. A machine’s ability to identify trends and patterns from data can easily outstrip a human’s ability to do the same. 

Read Also: Top 10 Email Personalization Strategy to Increase Your Sales in 2023-2025

By removing the need for your Marketing team to analyze data, you free up team members from one of their most time-consuming tasks. It’s led to many industry experts building their own, custom-built ML systems to help deliver better results for customers and clients. 

Machine Learning is already being utilized in many areas of Digital Marketing. Here are just four ways businesses are using the tech to enhance their Marketing efforts.

1. Personalization

Customers want to feel like a brand is speaking directly to them. Salesforce says that 52% of consumers are likely to switch brands if a company makes no effort to personalize its communication with them. Thanks to advances in Machine Learning, you can make your customer experience more personal than ever.

Netflix is a pioneering company of personalized user experience. Every time you log in, the TV decisions you make are feeding their machine more and more data. In return, it feeds you with content that it thinks you’ll want to watch. 

For your business, personalization means hitting the customer with the right message at the right time. This might be with an email filled with content they’d be interested in, or products they might like to buy in the “recommended products” section on your website. Amazon is a master of this kind of personalization; 35% of their revenue is generated from their recommended product machine. 

2. Optimized Content 

There are ways you can harness Machine Learning to craft content that resonates better with your audience. Tools are available that learn which messages, tone, and individual words impact your audience the most effectively. You can even use a machine to write the perfect email subject line or Facebook post. 

To come to these conclusions, Machine Learning uses a form of A/B testing to learn more about those engaging with your product or service. On every email, article headline, or paid social media ad, you can experiment with different content to discover which delivers the best return. 

Google is a huge exponent of this kind of Machine Learning. The search engine’s ability to learn your search intent has improved significantly in recent years – all thanks to its Machine Learning capabilities. Today, it’s able to present you with more relevant search results than ever.

3. Smart Bidding 

Pay per click (PPC) is marketing’s most data-heavy channel. Where PPC executives and managers previously needed to spend hours analyzing huge datasets to gain workable insights, Machine Learning can now aid in many areas of the process.

Google’s Smart Bidding uses Machine Learning to optimize each campaign for conversations to increase ROI. Automation is also useful for budget pacing and allocation, optimizing creative, reporting and targeting the right audience. 

The role of the PPC manager is shifting accordingly. Today and in the future, they are required to take on a more strategic advisor role; put the strategy and direction in place, then let the machines do the work. 

Social media platforms like Facebook, LinkedIn and Instagram are developing ever-more sophisticated Advertising platforms too, allowing you to gain the same benefits across your social channels. 

4. Chatbots 

Chatbots are revolutionizing the face of customer service and support. It’s a common sight to see a chatbot pop up in the bottom corner of a website today. The technology is reliant on machine learning technology to deliver better service to customers. 

Chatbots are used by businesses to answer simple questions and queries instantly. They constantly learn from customer responses, expanding their knowledge base to better answer future queries. Available 24/7 and with no customer waiting time, they’re a great way to use Machine Learning to improve your service. 

It’s one of the best ways to meet the demands of the modern customer too. Insight from 99firms tells us that live chat support has the highest satisfaction level of any customer service channel. Chatbots are a great way to provide this for your customers at any time of day without a huge outlay on support staff. 

Machine Learning is no longer a futuristic, far-away technology. It’s being used every day by brands to save time, optimize your offering and improve the quality of your marketing.