Data management is a key ingredient for a good return on ad spend. But many companies can’t analyze the full slate of data using traditional software. Thankfully, you can find new ways to maximize your return on ad spend with artificial intelligence and machine learning.

Machine learning is a form of artificial intelligence (AI). It uses machines and applications to take in information, analyze it, and provide conclusions. Conclusions can improve a process or a task. From a marketing perspective, a machine is like a super-smart human. Additionally, a super-human who learns about you by analyzing your data and providing purchasing options accordingly.

With growing competition and cookie deprecation, it has become integral for businesses to optimize their strategy by focusing on first-party consumer data. The best marketing campaigns have clean first party customer data.

Machine learning has become the key for businesses to learn their customers’ habits and interests based on that data. So, whether a person is watching Connected TV (CTV) or scrolling on social media, they are likely to see advertisements for products and services of interest to them.

• How to Apply Machine Learning to Improve ROAS
• What are Some Benefits of Machine Learning in Advertising?
• How Does Machine Learning Help in Advertising?
• How do you Increase Return on Advertising Spend?
• How can we Use Your Machine Learning Skills to Generate Revenue?
• How can Machine Learning Increase Sales?
• How is Machine Learning Used in Digital Marketing?

How to Apply Machine Learning to Improve ROAS

Machine learning (ML) algorithms have many applications in AdTech. Here’s how companies use ML to increase ad revenue and lower expenses.

Anomaly detection

Anomaly detection software can monitor ad performance without burdening your human resources. The machine learning models learn from past campaigns and historical data to produce an expected performance model. This lets you detect and resolve any deviations (like changes in traffic and behavior metrics) that interfere with your campaigns. 

Read Also: How Machine Learning Can Improve Real-time Bidding in Digital Marketing

Trained algorithms can also detect ad fraud to reduce marketing expenses. Beat, a popular ride-hailing company, used anti-fraud software to monitor suspicious, reflected, and protected traffic sources. It allowed the company to decrease fraudulent activities by 92% and save over $54,000.

Enhanced programmatic bidding

Advanced machine learning platforms can understand which bids and ad impressions deliver the most revenue per dollar. For example, Google’s Smart Bidding platform optimizes auctions in real time based on strategies with higher conversion rates.

Manny’s, an independent musical equipment store, ran a campaign that used the Dynamic Creative platform and Google’s machine learning algorithms to optimize ads for users with strong buying interest. As a result, the company reduced the cost per acquisition by 14% and increased conversions by 18%, which led to a 66% improvement in return on ad spend.

Automated ad creatives

Dynamic creative software can modify and produce ad creatives. The system analyzes device type, product interests, installed apps, and other parameters that reveal the user’s preferences. Afterward, the machine learning algorithms create combinations of color schemes, ad formats, interactive elements, and text to engage audiences.

Here’s how Day 7 Interactive, a mobile game developer, improved its return on ad spend with an artificial intelligence platform from Meta. First, the company prepared the elements and text messages for the ads. The algorithm then produced new creatives and placed them on Facebook services. The results were astounding, they experienced 252% uplift in return on ad spend and 92% increase in conversions in two months after launching. 

Audience segmentation

Machine learning platforms analyze a wide range of parameters to tailor campaigns for different user categories. These parameters include geographical location, purchase timing, browsing history, in-app behavior, session depth, and click tendency. A system then groups visitors in cohorts to let you target them with the most appealing offers.

Bukalapak, one of Indonesia’s most prominent eCommerce companies, automated ads to Indonesian users with the help of the Smart Shopping platform. The system analyzed best-performing products and matched them to relevant audiences while also taking care of ad placement. It improved Bukalapak’s return on ad spend fivefold and drove four times more conversions.

Retargeting campaigns

The algorithm helps you target audiences that didn’t show explicit buying intent. To clear things up, it’s not about advertising products that people abandoned in a cart (or, even worse, items they already bought). Instead, it’s about identifying a similar product or better deals that visitors are likely to buy.

Solutions like Session Quality and Conversion Probability use machine learning to estimate a proximity to convert. For instance, it can identify users who have studied smartwatches or added them to carts. The well-placed ad with a list of better-priced alternatives can be a final nudge for a conversion.

User experience optimization

User experience isn’t the least important factor for achieving a good return on investment on marketing spend. Page speed, for example, is a critical search ranking and conversion factor for websites. 

Here, machine learning tools like PageSpeed Insights or Thor Render Engine help you optimize HTML structure, media, and style to optimize your page speed. Some models can pre-load the pages users are likely to visit next to improve their experience. This makes the browsing much more pleasant, making the user more likely to click on ads.

What are Some Benefits of Machine Learning in Advertising?

There are tremendous benefits of machine learning for companies and businesses in advertising and marketing. Below are five major reasons why your business should implement ML to attract more customers.

1. Improved advertising options

In marketing, using machine learning or AI helps businesses thrive. It does this as it collects the most accurate algorithms and data. Moreover, this it’s able to provide the best possible options for decision makers. However, when counting on decisions made solely by humans, different biases can arise. Biases come between analyzing data and providing the best customer service.

This can negatively impact your company’s desired results. You get decision without complications using machine learning. The machine optimizes data according to the targeted audience while improving your ROAS (return on ad spend).

It has become crucial for businesses to keep up with shifting advertising trends to put consumers’ needs at the forefront. While the machine makes the right choices for consumers based on the algorithm, your employees will have more time to solve complex problems, leading towards a more profitable business.

At the end of the day, the marketer still needs to understand the person behind the data-learning machine because machine learning by itself will not collect valuable data from only speaking to your customers directly. Therefore, a balance between the machine and the marketer is essential for successful advertising.

2. Personal conversations with one-on-one chats

Almost all new business and company websites have introduced chatbots for personal one-on-one conversations with their customers. Chatbots in machine learning take the sales process one step further by answering different queries regarding a purchase, store location, or other topics.

Customers can get the exact product they are looking for by sending a message to this bot, they will also be linked directly to the desired product, increasing the chances of them making an immediate purchase.

3. Personalized Experience

Personalization is key when it comes to shopping. When businesses give a more personalized shopping experience based on customers’ choices, they will likely return as they won’t have to worry about wasting time searching for the right product.

Machine learning gives more personalized advertisements to people by reviewing massive amounts of data in a short amount of time. This offers customers targeted advertisements based on personal choices, seasonal products, and region-based options.

4. Better Privacy

There are many speculations about businesses leaking customers’ data, but your company can protect consumers’ privacy with limited human interaction. Machine learning, with the right ML vendors, is privacy compliant as data is only provided with customers’ permission. Additionally, as data is analyzed entirely anonymously, it keeps the customer’s personal information safe while giving advertisements based on their interests.

5. Reaching the right audience

With machine learning, your business can target relevant advertisements to the right audiences. In the past, too much time and money were wasted on trial and error methods. Now, machines can easily match the appropriate products to a suitable audience. For this reason, your company can make a more personal connection with your targeted customers and increase engagement with machine learning.

How Does Machine Learning Help in Advertising?

There are numerous benefits for companies that are ready to embrace machine learning as part of their advertising efforts. Below, we have outlined five reasons organizations should implement ML as part of their strategy in attracting new customers and increasing ROI.

1. Better personalization

Personalization delivers a better customer experience by providing more relevant ads. Most consumers also prefer these types of advertisements. Some 8 out of 10* frequent shoppers note that they will only buy from companies that personalize the experience.

Additionally, about 6 out of 10* customers will not buy from organizations that are poorly leveraging personalization tactics. For companies looking to connect with consumers and drive sales, personalization is more important now than it has ever been.

Machine learning sifts through massive amounts of data to deliver more personalized advertisements. These can be delivered via conversational marketing tactics or seasonality, weather, and region. For example, in December, it may make more sense to advertise auto batteries or hot cereal. In January, you may want to advertise severe winter clothing or hiking boots according to consumer trends.

Regionality is also an important factor to consider when you’re considering personalization. For example, a 60-degree day will feel very different to someone who lives in Florida than someone who lives in New York. In New York, someone might put on a light sweater, while in Florida, someone might be wearing a coat and gloves. As a result, you’ll want to personalize your ads accordingly.

2. Better advertising decisions through machine learning and AI

When relying on human decision-making processes, organizations can fall prey to some hurdles that can lead to less-than-optimal choices. It’s hard to separate biases from the analysis and it can be even more difficult to discern what’s important from the vast amounts of data collected.

Companies recognize these challenges and are trying to address them with machine learning. Some 65% of organizations that are using, or planning to use AI, cite its importance for informed decision making and analytics as a key reason for implementing these kinds of technologies.

When advertisers use an AI or machine learning-based tool, the algorithm considers all information and data they have on a given topic and uses it to make the best decision possible. Over time, these decisions continue to improve as the algorithm collects more information. The tools can then make better recommendations tailored for the intended target audience.

Better decision-making has become increasingly important for advertisers who want to ensure ads are relevant to the target audience. The wrong advertisement can not only be an annoyance to users, but it can make brands less credible. According to one study, 90%* of consumers say that messages from companies that are not personally relevant to them are “annoying.”

Creative is not the only place where AI can help advertisers make better choices. The marketplace is continually shifting; as an advertiser, it’s essential to keep up with demand. For example, consumers are increasingly turning to digital shopping rather than entering stores, especially during the pandemic.

Machine learning can take these changes into account, decide what is relevant, and determine the next course of action so campaigns are more effective.

3. More personal interactions through 1:1 conversations

Machine learning and AI tools such as conversational marketing can also give customers a more personalized experience at scale. These kinds of tools will become increasingly important as consumers continue to move into a digital world, but still crave that in-store experience. By leveraging AI, advertisers can tailor experiences to provide them with that same personalized touch.

Conversational marketing tools can also help advertisers create unique, personalized engagements with customers throughout multiple touchpoints in the buyer’s journey. This could include customized, interactive banners at the top of their browser or an AI-powered chatbot that helps a customer answer questions and leads them one step further in the sales process.

Additionally, in today’s fast-paced world, consumers are looking for more immediate responses from companies. Some 82% of consumers want an instant response to questions related to sales and marketing. By leveraging the power of conversation marketing, you can address concerns or questions more quickly, improving customer satisfaction and retention.

4. Better creative based on data

AI can also go beyond traditional A/B testing to make predictions about how creative will perform before the campaign goes live. This is important because it helps marketers become more proactive in their approach to creative instead of reactive, which can lead to more conversions and higher rates of engagement.

One example of how machine learning can optimize the creative elements in your advertisements is by using historical data to determine what kind of colors and messaging will connect with consumers and drive sales. Machine learning can also determine the context of an ad to determine placement of creative.

A soon-to-be bride browsing wedding dresses online would be a primary target for ads related to the big event. Creative can also leverage personalization to target users based on location and weather insights. Before a snowstorm, promote mittens and hats at your store; during a hot summer day, encourage people to visit your store in-person. 

5. Improve performance without needing cookies

According to industry surveys, many marketers use their budgets inefficiently. However, most budget waste is a result of marketers focusing on reach rather than quality. Showcasing your message to the wrong audience can be a costly mistake.

However, without cookies, it can be difficult for advertisers to leverage data to drive results. Additionally, with new regulations and increased pressure from consumers looking for privacy, companies must also deliver personalized experiences without feeling invasive.

By leveraging machine learning and ad targeting, companies can identify which messages resonate with their audiences. AI can then use contextual signals and accurate weather data to determine which ad is most likely to drive conversions. Machine learning can deliver these campaigns and messages without using cookies, while respecting data privacy.

How do you Increase Return on Advertising Spend?

ROAS data is particularly helpful in measuring ad performance because it takes conversions into account instead of simply counting clicks. While it’s a good idea to measure click-through rate (CTR), it’s even more important to track your ROAS so you can develop advertising strategies that meet your budget.

How do you maximize your ROAS? Here are five ways you can significantly increase ROAS so you’re creating relevant, targeted, and effective ads—and making the most of your ad dollars.

1. Reduce your ad cost

There’s one straightforward way to improve ROAS: spend less on your ads. But there are different tactics to keep less money from going out the door. Here are a number of ways to lower your ad costs:

Experiment with your bidding strategy. Landing on a winning advertising strategy is often a trial-and-error process. Try not to get locked in to one way of setting up your ads. Even if one is successful, you’ll need to modify it at some point to keep up with marketplace changes. Consider these two approaches to mix up your bidding strategy when running Google ads:

• Manual bidding: You can manually adjust your maximum bid with manual or enhanced costs per click (CPC) to control ad costs without impacting advertising conversions.
• Automated bidding: There are a host of different ways to set up automated bids that are designed to help you achieve specific ad goals. Smart bidding uses machine learning to optimize conversions in every auction.

Aim for a lower position in search results. You can save money by not having your ad appear in the top spot on search engine results pages (SERPs). If you go for the third position, say, your ad will cost less and it will still be in front of your visitors’ eyes on the first page—and still above the fold.

Target the right audience. By identifying your target audience, you spend less on advertising while reaching more prospective users. For example, you can identify users by geolocation, job title, or device. Consider segmenting your audience based on multiple markers and then creating ads and post-click landing page experiences that resonate with those segmented groups. Segmentation improves just about every aspect of your campaigns, such as Google Quality Score, click-through rate (CTR), and your users’ landing page experiences.

Pay attention to keywords. Researching and implementing the right keywords can dramatically improve your Google Quality Score and advertising impact. Don’t forget about using negative keywords to exclude certain search terms so your campaigns focus on very specific keywords that matter to your audience. And while it might seem counterintuitive, bid on competitors’ brand terms to ensure that you appear in those search results as well.

2. Improve advertising conversions with relevant landing pages

Increasing conversion rates is the other half of your advertising strategy, and it can have a big impact on ROAS. The key is to connect ads to relevant post-click experiences so that your users’ intent (based on how they search) is reflected through the campaign from first click to landing page.

Here are three ways to make landing pages more relevant and improve conversion rates:

• Create personalized post-click landing pages. Optimize your landing pages using landing page design best practices to ensure a positive user experience. Personalize your landing pages by not only message-matching your users’ pre-and post-click experiences but also by appealing to the needs and goals of your target audience segments.
• Optimize page speed. Speed is a critical factor in user experience. Users are increasingly impatient, and pages with longer load times can drive up your bounce rates and drive down average time on page. Make sure your page load speed is optimized by fixing common speed issues, such as unoptimized images, large media files, and JavaScript issues. Instapage’s Thor Render Engine™ performs a full rewrite of landing pages to speed up response and load times.
• Deploy conversion-driven storytelling. When writing your ads, include compelling microstories to keep visitors engaged throughout the advertising journey. Leverage audience data—such as psychographics and demographics—that taps into your users’ beliefs and values.

3. Increase your customer lifetime value

A long held adage in advertising is: “Your best customers are the ones you already have.” In fact, holding on to current customers costs significantly less than acquiring new ones and the probability of selling to current customers is much higher than trying to convert users unfamiliar with your brand. So, part of increasing your ROAS is to increase your customer lifetime value (LTV), the estimated average revenue that a customer will generate throughout their lifespan.

Consider these methods to increase LTV:

• Retarget campaigns. Use cookies and pixels to track visitors who have left your page without converting. Deploy retargeting ad campaigns to win them back.
• Email campaigns. Use email nurture campaigns to reiterate your offer, keep subscribers interested, and convince them to convert.
• Rewards and loyalty programs. Launch loyalty programs to encourage and reward customers in exchange for an action you want them to take.
• Upsells. Offering customers temporary upgrades, bundled products, and free shipping are some ways to upsell and increase revenue and ROAS.

4. Optimize Google Shopping Ads

Google’s product listing ads (PLAs) continue to be one of the most popular advertising channels. PLA ad spend continues to increase, especially on mobile devices. So, it’s a good idea to optimize your PLA shopping ads by making them relevant, targeting your segmented audience, and collecting and analyzing performance data.

5. Step away from the data

Some ROAS issues may be unrelated to your advertising strategies. Take time to think about obstacles that might be connected to your products themselves or the purchasing process. Could you bundle products in a way that would make them more appealing? Should you rethink your price point?

Also, consider the call-to-action (CTA) route you’re asking your customers to navigate. If it’s confusing or requires them to click too many times, that could result in a drop in conversions and negatively impact ROAS.

How can we Use Your Machine Learning Skills to Generate Revenue?

Machine learning is a well-known term in the current industry. With its help, we can use our projects in a more powerful and smarter way. There are some obvious uses of machine learning in the real world.

Given the huge proportion of open data and multiple possibilities, you need to consider and locate the mandatory request. If you answer these questions correctly, people and organizations will start paying more attention to them.

Any industry that requires creativity can benefit from machine learning. As a result, there are many options that you can use to earn passive income with machine learning, and you can carve a bright career in machine learning.

1. Publish Book Online

You can write and publish online books related to any field in machine learning. It may be related to neural networks, cortex, intensive learning, sensors, or any aspect of technology. Kindle Direct Publishing has many platforms where you can publish.

Make sure you have a deep understanding of the subject you are writing. In addition, to make this book more reliable, some recognized resources are needed. After you start publishing the book, you can earn a few dollars by selling the book.

2. Earn by Creating Massive AI Data

In order to fuel AI and training algorithms which are part of AI solutions, gigantic volumes of data are necessary. By observation, human intelligence develops. Human beings have tons of experience in both visual and sound. An AI system needs comparable learning and dynamic data that are used to promote them at a significant level.

You develop and move enormous AI data at a significant expense. Enormous money for huge knowledge is ready for gigantic instructional research foundations. Privacy, security, inclusiveness, fairness, confidence, transparency, and accountability are the core requirements of AI arrangements. Cash is only influenced by the correctness of the data.

3. Develop a Simple AI App

Developing applications can be a great way to earn money from machine learning. You can develop a subscription app to pay for some premium features to be unlocked.

According to a recent study, it is found that subscription apps are estimated to earn at least 50% more money than other apps with a variety of in-app purchases.

First, you can try developing simple new smartphone AI apps and make money. The new AI apps for smartphones illustrate AI’s ability to convert our society and become an important part of our daily lives.

We all know how AI helped Facebook delete thousands of fake accounts and “suspicious behavior.”

4. Collect Data and Sell it to Companies

As machine learning enthusiasts, we all know that “data is the driving force of machine learning, and computing power is the engine of machine learning.”

You can make money by collecting data. For example, for a project to predict the population of India in the next 10 years, we need data on the number of family members in the house. You can collect this data in your area and then sell it to companies that need it.

The only challenge is to find companies that need this data, which can be found online.

5. Freelancing Machine Learning Jobs

After a full-time opportunity, we started working independently. Freelancing seems good and easy to do, but it is far more difficult than full-time 9 to 7 jobs.

Do you know why?

Because it requires crazy discipline.

Think there are:

No one cares about you.

No one pokes you.

No one asks you for updates.

If you have a team working on the project, you will also have to manage human resources.

Will you still work? 90% of people will not.

However, if you master self-discipline and master the relevant skills to solve customer problems, you can easily generate passive income from machine learning by engaging in freelance work.

6. Use AI Social Media Functionalities to Increase Business Sale

Social media platforms are necessary to promote companies in many industries. Social media sites use machine learning to benefit themselves and their users.

AI lets you optimize and better target your news feed.

Machine learning recognizes your experience on Facebook, taking into consideration your hobbies, your work, the type of market you can connect with, and recommend activities.

The Pinterest platform employs computer views to identify items or pins shown in photographs and can thus propose comparable pins.

This method enables machine learning to extract useful information from movies and pictures, along with an important computer vision component.

7. Make a Product that You Can Sell

The new AI chatbot is a gold mine for making money.

Produce and sell Alexa, Google Home, Siri, Bitsy, and other products. We also know how Apple’s Siri platform maintains the Google Assistant quality standard, which uses the same algorithms as Google Translate and Google Image Search to change the game.

Therefore, you can build a smartphone chatbot framework in the background, and on the front end, you can build a machine learning engine and make money with machine learning.

8. Provide Service in the Domain of ML

You can easily develop a new product and offer it as a service to as many potential customers, and make more money by doing these simple tasks.

You don’t need to spend much money to work continuously; you will have free time to spend on yourself and your family. For example, you can create a trained chatbot yourself or hire an AI chatbot development company that can build a bot to respond to multiple messages/queries without human intervention.

This will save website owners’ time and allow them to devote time to other things to expand their business. Nowadays, it is a very necessary tool, and they will provide you with a lot of money for this important service.

9. Analyze Stuff

Most learning machines are about stuff forecasting. A popular IT consulting company NYC makes a list of everything you see when you look at it and trains a machine learning model to attempt and anticipate what you’ll be watching next. You utilize this prediction to ensure that the material is available on your nearest server.

This implies for you that the film plays fast and in the finest quality. That does not mean that all they own on every server in the globe is saved for the development firm of machines.

How can Machine Learning Increase Sales?

1. Sentiment analysis and reputation scoring

Thanks to advances in the Natural Language Processing (NLP) subfield of machine learning, it is now possible to rank text, such as online reviews and social media posts, by favorability. We can even predict the feelings – such as anger, fear, or excitement – that are likely expressed by the author.

We can subsequently extract all the text in which a person, brand, or product was mentioned to compile a reputation index, and suggest ways to improve it. This is made possible by deep learning advances such as recurrent neural networks with attention mechanisms.

2. Customer personalization

The more you learn about a customer, whether it is behavioral data such as click pattern, or descriptive data such as age, gender, and hobbies, the more machine learning models can devise a personalized experience to them. Customers have higher than ever before and this trend will not slow down any time soon, so delivering a high quality customized experience and support should be on the priority list of every marketer.

Another area of customization is custom pricing: accurate risk models allow the company to quote the best price to a customer based on their situation and history, increasing the likelihood of converting a lead.

3. Lead scoring

Several classical lead scoring tools exist and can be purchased or developed in house. They often rely on human intuition and experience of what constitutes a good lead and whether they are marketing qualified leads, sales qualified leads, or dead ends. The main drawback of these tools is their generic nature. As seasoned professionals know, no two markets or industries are the same, and customer psychology changes greatly with context.

Neural network-based models cut the guesswork and find hidden patterns in the data that are often impossible for a human to identify, and provide a more accurate scoring. Most importantly, these predictive learn patterns from YOUR data, not somebody else’s, and therefore give you the best and most accurate predictions for your leads.

4. Customer churn and lifetime value modeling

Customer lifetime value and churn modeling go hand in hand. The churn risk of a customer measures how likely they are to stop interacting with the business while customer lifetime value is a prediction of how much revenue they are likely to generate for the business if they remain a customer.

Machine learning can model both of these quantities to allow the marketing and sales teams to intervene with high churn risk, high lifetime value customers and bring them back with incentives. The retention strategy can even sometimes be automated.

5. Customer segmentation and discovery

Unsupervised algorithms such as K-means clustering or DBSCAN can identify patterns in your data and reveal how your customers are clusters by factors such as age, income, address, interests etc… The more information you obtain about your customers, the more accurate the clusters will be, which is a general rule in machine learning. You can then label those clusters and customize your approach to their needs and expectations.

6. Recommender systems

Propensity models serve to upsell and cross-sell customers during their online purchases. 35% of what consumers buy on Amazon and 75% of what they watch on Netflix come from dynamic product recommendations.

Recommender algorithms can also be used to optimize message accuracy while targeting customers, thus reducing marketing waste. The latter would recommend ways to approach customers and topics that might be of interest to them.

7. Chatbots and virtual assistants

If you’ve recently interacted with virtual assistants such as Siri or Alexa, you might notice how far they’ve come. In fact, the future of marketing might just be full of highly sophisticated chatbots and virtual assistants.

The field of natural language processing and voice recognition have evolved by leaps and bound in the past few years and their progress has shown no sign of slowing down. What’s more, AI giants such as Google and OpenAI provide pre-trained models for a reasonable price or even sometimes free.

8. Minimizing marketing regret

If you’re a marketer, you’re certainly familiar with A/B testing. The issue with A/B testing is that a lot of opportunities are lost while testing. With algorithms such as multi-armed contextual bandits and reinforcement learning, the opportunity loss – or marketing regret as it is often called – is minimized, as the algorithms naturally sample the better options more often.

9. Text extraction and summarization

Another exciting application of natural language processing is the ability to automatically extract and summarize text. Applications of this can be the quick processing of news articles after a major product launch to gauge the market reception and correct the course of the launch if necessary.

10. Marketing mix optimization

Often, marketing portfolio mixes are based on experience and intuition. While those can work remarkably well, machine learning can once again take out the guesswork and provide optimized solutions. Algorithms will typically look at previous marketing spend on various channels (online CPC, CPM, radio, TV, etc…) as well as sales, and output an optimized allocation of funds to each channel to maximize return on investment.

11. Computer vision

Image recognition is used by some companies to detect when their branded collateral is posted on social media or in blogs. A famous study by Gumgum and Miller Lite utilized both text analysis and computer vision to analyze millions of user-generated content (on social media, blogs and such) to uncover ways to connect with their consumers and promoters.

In the end, they revealed that they found 1.1 million posts, 3.2% of them were images with no relevant text, meaning they were found by the power of computer vision alone.

How is Machine Learning Used in Digital Marketing?

Intelligent machine learning applications can be used to increase the outcomes of digital marketing implementations. This advancement can help companies with personalization, big data management, and delivering a better customer experience.

Apparently, if you want to improve your digital marketing, you will need to focus on Analytics, Personalization, and Optimization. And machine learning can help you with all these.

Moreover, ML can be used across all aspects of digital marketing, be it search engine optimization & marketing, social media, email marketing, paid advertisements, or even content marketing.

Here are some of the ways ML-based applications and techniques can be used in digital marketing –

1. Improved customer experience 

Delivering a high-end customer experience is one of the objects in whatever an organization does. Integrating ML-enabled chatbots in your digital marketing process – to be specific, on your website can add to the customer experience you deliver.

About 80% of customers want their chat queries answered quickly. That’s when an AI and ML-based chat software can be your savior.

Apart from zeroing the wait time for customers, chatbot integration ensures 24 x 7 x 365 availability and allows you to broaden your database without any manual interference, once trained, and implemented successfully.

Chatbots also allow you to transfer calls or chats to human agents at any point of time during the conversation.

There’s another way chatbots can be used in marketing. You can train and make the chatbot learn (because this machine is designed to learn) to send emails, chat messages, and follow up messages on their own.

This way, you can send your customers important information related to your new offers and product launch.

And in turn, you will get a sea of information related to consumer behavior and product performance. These metrics can be used for developing strategies in the future.

2. Content creation and curation

Even in 2021, content is still the king. Content rules all forms of marketing. Without content, you wouldn’t be able to market your brand.

Content creation and content curation both require hours of brainstorming and digging. Here, machine learning tools can save you plenty of time which you can use in other crucial areas demanding your attention. It can help you improve what you write and publish.

Curata, Flipboard, Pocket, and Vestorly are some of the most popular content curation tools you can use to create highly engaging stories to post and share.

These ML-based content curation solutions can organize information and content, suggest bytes and contents, and create compelling curated content with their brilliantly designed templates.

The use of machine learning applications has also eased the way people write or create content. The auto-correct on your cell phone or Gmail compose box are a perfect example of machine learning technology.

We are yet to develop a technology that can write on its own without human intervention. But there is a tool, Quill that can create descriptions from a large set of data.

There are many AI and ML-based content creation tools such as frase.io that can help you research the topic and summarize long texts quickly. Built on conversational AI, the tool can continuously optimize the content on your website for better engagement and experience.

Emails are still a powerful marketing tool. The tool called Phrasee can help you create result-bound email copies and subject lines.

3. Website UI/UX

The user interface (UI) and user experience (UX) of a website are among the most important aspects of digital marketing. They are directly related to your website’s search ranking and visibility.

Machine learning tech can also help improve website design. We have already mentioned that you can analyze and find valuable insights related to the behavior of users and the performance of your website using advanced ML tools.

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

The technology will allow you to create websites that your users find practical and useful. Leading website building tools – such as Wix, Weebly, and WordPress – use technology and analytics to build simple yet effective and useful websites.

4. Marketing automation

If you use marketing automation tools, you can expect more than 10% revenue growth within a year. That’s the reason why over 79% of renowned brands have started using marketing automation in the last three years.

Automating your marketing will take your growth strategy to the next level. Tools built on machine learning can decode and learn from trends, suggest actions based on history and past experiences, and provide accurate analytics to help you develop strategies and take actions that yield.

Customer segmentation, pitching to the exact audience, and sending follow-ups become non-human tasks with advanced ML-based marketing tools. After every implementation and campaign, the tool learns automatically and shows scopes for improvement for future campaigns.

5. More optimized advertising

The traditional way of advertising includes choosing the right ad content and selecting the right channel or platform to display your ad. You will also need to work on finalizing the right time to show your advertisement. This is more a manual job with high chances of ads not delivering to expectations.

With AI-based advertising tools, such as Facebook Ads or Google Ads, you can pitch well-optimized ads to your audience. They allow you to find the right audience for your ad and cut your advertising cost significantly.

Apart from reaching your potential customers, you can display ads in a variety of formats and multiply the outcomes from different angles using these advanced ad channels. Just for a glimpse, you can find potential customers based on the traits of your existing customers or the customers of your competitors.

Not just that, you can send your ads when your average audience is most active. This new-age technology saves your time and spends while improving your returns on investment.