Five benefits of machine learning in advertising

By IBM Watson Advertising

The advertising industry is in a constant state of evolution. Innovations in technology, such as artificial intelligence (AI) and influencer marketing, are changing how consumers interact with brands. Companies are also benefiting from better analytics and a more in-depth understanding of how their advertising works. Machine learning can take the guesswork out of capitalizing on vast amounts of data and providing insights that can be applied to AI-based advertising campaigns.

Machine learning in advertising is a process in which technology takes information, analyzes it, and ultimately formulates a conclusion that can improve a task or a process. The insights derived from this technology can be applied to audience targeting, personalization, media buying, and more.

How machine learning works

Machine learning is a form of AI. Its goal is to teach technology to think and operate similarly to humans, allowing machines to continuously learn from past experiences to make predictions about the future.

Machine learning leverages historical data and identifies patterns without the help of human interaction. Essentially any task that utilizes information or requires a set of rules can be streamlined through machine learning technology. Companies can save time and money by automating processes that previously would need human intervention. Employees then have time to solve more complex business problems, leading to more profits and less time on tasks that are time-consuming and mundane.

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Five reasons why machine learning can be right for your business

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 AI 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. Download our 2020/2021 IBM Weather Targeting Seasonal Activation Calendar to learn how IBM Watson Advertising can help you plan your 2021 weather strategies and activations.

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. 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. Reach the right audience by finding the right influencers

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. That’s why influencer marketing has skyrocketed during the past few years. It’s a great way for brands to create more personal connections and generate leads. With AI, companies can better determine who to partner with to drive the most impact.

With a tool like IBM Watson Advertising Social Targeting with Influential, a brand can fully understand the demographics that make up an influencer’s audience and set up timed messages to this group with images and postings that are reflective of what your audience wants to see. By leveraging influencers backed with AI, your team can find the right thought leaders to generate content, which can lead to an increase in engagement across platforms.

Applications of machine learning advertising

AI can be a successful tool for companies looking to better connect with customers and prospects. Here are two examples of how brands can combine machine learning and advertising to achieve their business goals.

The Weather Channel uses an AI-powered chatbot for COVID-19 Q&A

The Weather Channel came to IBM Watson Advertising with the realization that it was difficult for its users to know which COVID-19 related information they could trust and where they can find this information. They utilized IBM Watson Conversations technology to develop a COVID-19 Q&A, which is an AI-powered chatbot, reachable on mobile and app. This led to more than 400,000 conversions a month, over 2.6 million total user inputs, and an average of 1 minute engaged with the chatbot. 

Subaru advertising uses dynamic creative to boost engagement

Subaru approached IBM Watson Advertising looking to reinforce its commitment to safety on the road, especially during harsh weather conditions. To provide drivers with information to keep them safe on the road in all weather conditions, Subaru created a Driving Difficulty Index Commuter Forecast Tool: a new map layer displayed on The Weather Channel’s interactive maps across tablet, mobile, and desktop. This implementation led to increased engagement across the board with a 4-minute average visit to The Weather Channel web and mobile web and 66% of page views were repeat visitors.

Where Watson Advertising can help

Ready to learn more about how machine learning can help you make better advertising decisions? Book a demo with Watson Advertising today!

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