AI in weather forecasting, prediction and communication
Contextual advertising uses various factors to determine which content is most relevant to users when placing an ad. It targets potential customers by relying on context such as the content of a webpage, location or weather. Machine learning can apply data science to targeting and take these pieces of information to deliver the right ad to the right users. For example, if a user is reading an article about wedding planning, the user might see an ad for wedding dresses on the page.
While advertisers have traditionally practiced behavioral targeting, that is, using a potential customer’s data surrounding their browsing and shopping habits, rising concerns about privacy have led advertisers to find alternative options. Advertisers no longer must rely on cookies or behavioral signals to deliver relevant ads. By using insights surrounding the context of the ad, companies can still create messaging that resonates with audiences. Companies are beginning to make this shift in how they approach their advertising, with contextual advertising projected to reach over USD 376 billion1 by 2027.
Why is context important in advertising?
Context is important because it provides advertisers with useful information about the type of content a user is interested in. Advertisers can then target them with an ad that allures the user with related content and messaging.
While relevant behavioral data expires as consumers navigate an ever-changing environment, using context tells advertisers what users are interested in right now rather than relying on past behavior. Advertisers can use this to their advantage by delivering highly relevant and timely ads.
How does contextual targeting work and how to get started?
Contextual advertising uses AI and deep-learning algorithms to analyze content like text, speech, imagery, and geolocation in real time. Predictive advertising tools analyze all of this data and the content a user is browsing to determine if a user will take a particular action like clicking your ad. The deep analysis provided by AI is like what a human brain would do when deciding where to place an ad manually, which helps ensure your ad placement is relevant, timely and of interest to the user. This also creates a more personalized experience for consumers, which is becoming increasingly important to consumers when deciding what brands they want to align with.
To get started, each contextual ad needs a unique landing page to help ensure there is a central place for driving conversions. Contextual advertising requires a high level of creativity and relevancy.
The importance of contextual advertising in a cookieless world
Contextual advertising is having a resurgence due to the impact of privacy laws like the GDPR and announcements made by Google to scrap third-party cookies—meaning advertisers will no longer be able to track users across multiple sites to target them.
What is the difference between contextual advertising and behavioral advertising?
Behavioral and contextual advertising doesn’t need to be an either-or decision; there’s a place for both. The main difference between the two is that contextual advertising targets context, that is, the environment in which the user is browsing and the topic and content of the page they’re viewing. On the other hand, behavioral advertising is more focused on the actions a user made before reaching the web page, whether it’s clicking a particular link, product page or article. Both kinds of advertising have their own benefits and can work together to achieve the desired result.
Benefits of contextual targeting in advertising
There are many benefits to contextual advertising for advertisers. Companies are beginning to implement tools to deliver higher levels of personalization based on the context of their advertisements such as weather, content and other factors. Below are some of the benefits of implementing contextual advertising across your organization.
Contextual advertising doesn’t require using cookies and personal information
It’s no secret that consumers are becoming increasingly wary of giving out personal information. In fact, in a recent survey from Startpage, it was found that 72% of Americans are “very concerned” to “extremely concerned” about their online privacy. Contextual advertising doesn’t require cookies to deliver relevant ads, which can help you spend your budget more effectively while targeting the right people.
Contextual advertising is easier to implement
Behavioral targeting is notorious for requiring vast amounts of data, which requires not only the right tools and technology to collect it, but a team to analyze it. Contextual advertising, on the other hand, focuses on predictions that AI makes based on trends and other insights that can make it an easier tool to implement.
Context can be more telling than behavior
Past behavior is not always relevant to present wants and preferences. And with so much change happening regarding lockdowns and the ever-evolving state of COVID-19, consumers are rightfully shifting their habits to adjust to the new normal. While advertisers may feel anxious to keep up with these changing shopping habits, we have developed the COVID-19 Triggers solution, which can help advertisers adjust their strategy based on the course of the virus and what’s happening now.
Changes in weather or other external factors can also affect purchasing decisions. For example, a snowstorm approaching the Northeast will influence purchase behavior. As a result, advertisers can highlight relevant products such as shovels, gloves and hats to those in the geographic area the snowstorm is approaching.
Advancements in AI have improved context accuracy
Due to technological advancements, AI has become increasingly smart about analyzing page content and placing your ad in front of audiences who are more likely interested in seeing it, which can increase your number of leads and conversions. AI also removes the burden that your team is responsible for by virtually eliminating the manual work involved with segmenting audiences by identifying patterns and learning from past tasks.
Better weather targeting can drive sales
The weather is another important, but sometimes overlooked, component of contextual advertising. While the weather may seem trivial, you can gain a lot of insight into how a consumer will behave based on one’s location and weather. For example, a forecast of 50 degrees will drive a different behavior in Massachusetts than in Florida. Weather Targeting turns the relationship between weather location, consumer behavior, and complex data sets into actionable, proven solutions for advertisers that don’t rely on third-party cookies.
How do you use contextual advertising
There are a number of ways companies can use contextual advertising to better reach their target audience.
Weather triggered advertising
By combining location data and accurate weather information, businesses can target their audiences based on specific trends. On warm days, an ice cream manufacturer may choose to advertise its products to a specific location. On rainy days, a business may use contextual ads to increase foot traffic or showcase its umbrellas. Weather triggered advertising combined with location data can be a powerful tool in driving purchase decisions.
Dynamic Creative Optimization
Platforms leverage AI to understand consumer engagement and other relevant data signals to determine the best creative for each user. Dynamic creative optimization (DCO) can be used on a variety of platforms, including The Weather Channel digital properties, OTT, and video.
One of the most well-known contextual advertising examples is Google AdSense. Google robots serve ads automatically that are relevant to your users. For example, if you are running a movie review blog, AdSense may serve contextual ads that encourage users to buy movie tickets or sign up for movie streaming services.
In-game & in-video contextual advertising
Sony’s Wipeout HD was one of the first games containing in-game contextual advertising before the game loaded. Alternatively, an example of in-video contextual advertising would be YouTube ads for shampoo shown before video tutorials on cutting your own hair.
Native advertising is a form of contextual advertising. It uses sponsored ads that are designed to look like the native content on a website. This kind of advertising can use location and other data to personalize these ads.
Conversational marketing can be leveraged to deliver more personalized recommendations to customers. By leveraging a sophisticated solution, you can help customers find what they need while delivering more relevant messaging.
Machine learning can take various inputs to determine how likely it is that a user will take a specified action. This can be based on numerous factors, including weather data, location data, conversational insights, and the content of a page. AI can then determine how to engage the user to generate the best possible outcome.
Example of contextual advertising
CVS uses contextual ads to reach 42 million consumers in moments
CVS came to The Weather Company looking to engage with consumers in high-risk flu areas through contextual ads. The goal of the campaign was to drive flu-shot vaccinations to help prevent people from getting sick.
With millions of Americans at risk of contracting the flu, CVS’s sponsorship of the AI-enabled Flu Insights tool on The Weather Channel app was an optimal method for CVS to drive contextual relevance. Furthermore, the solution effectively connected with consumers when and where it mattered.
Through the Flu Insights sponsorship on The Weather Channel app, CVS reached millions of consumers in critical planning moments. Results included 42 million unique visitors, 644 million total ad impressions, and over 77% of module clicks via “Find Your Local CVS” messaging.
The Weather Company offers a suite of contextual advertising solutions that are designed to automate complex processes, predict future behavior and optimize employees’ time—without the use of third-party cookies. Contact us to learn more about AI in advertising.
The performance data discussed herein is presented as derived under specific operating conditions. Actual results may vary.
All client examples cited or described are presented as illustrations of the manner in which some clients have used The Weather Company products and the results they may have achieved. Actual environmental costs and performance characteristics will vary depending on individual client configurations and conditions. Contact The Weather Company to see what we can do for you.
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