1. Lookalike audience targetingPredictive advertising accesses customer data and third-party behavioral data to identify potential customers, allowing businesses to expand their user base. Tools like Google’s Lookalike Audience and Facebook Similar Audiences use predictive advertising to study audience behavior, anticipate customer needs, increase click-through rates, and drive business profits. These networks compare information pertaining to your website visitors with people who have similar traits and buying behavior and use the insights derived to build a new audience. They also use information, such as the location, age range, interests, and recent online activity to find new users who look and act like your existing visitors. Most savvy marketers are using predictive advertising techniques to build lookalike models based on historical user data. Lookalike audiences help businesses expand their promotional strategies to new audiences who have the potential to become loyal customers. Since lookalike audiences have characteristics similar to your target audience, you can be assured that your ad budget is being used prudently.
2. Automatic and relevant content deliveryArtificial intelligence (AI) has the ability to observe buying behavior and use this data to predict future buying patterns. Marketers can use cluster models to segment their audience and deliver relevant content that converts. Thus, content automation with AI can help ad-makers produce high-quality content tailored to a specific audience or persona type. Amazon is already using predictive advertising to deliver relevant ads and cross-sell and up-sell its range. The ecommerce giant is coming up with new ways for brands to target their own customers and shoppers. Amazon’s advertising services can remove a lot of the guesswork by showing ads to the most relevant audience. For instance, using cookies and other technical tools, Amazon can tell that a person who recently bought a protein bar on the website is now reading a post on the wellness blog, “Nerd Fitness”. Thus, the customer can be targeted on this site with a relevant fitness product. Predictive advertising is allowing marketers to learn about customers based on their browsing history. They can determine where customers consume content and what kind of content they prefer. So, businesses can automate ad personalization based on user demography and situational factors, namely the device ID, domain, location, buying history, and interests to create the most relevant copy for that audience. Target recently used its data-crunching ability to formulate a predictive model that helps them identify which of their female customers are pregnant and will buy diapers in the near future. The American retail store discovered that women who are in their early trimester typically purchase a combination of 25 different products. Later, female buyers who exhibited this buying behavior were sent coupon booklets through emails or the post.
3. Optimizes ads for micro-momentsPredictive advertising is making it possible for marketers to gain insights valuable for a limited period of time. In other words, businesses can use real-time data and optimize their ad placement strategy to deliver appropriate advertising and encash on the micro-moments. According to Google, micro-moments are intent-rich moments when a user turns to a device (especially a smartphone) to learn, do, watch, discover, or purchase something. These are the moments when preferences are shaped or the user acts on a need. The key micro-moments that marketers often target are:
- I want to know moments
- I want to go moments
- I want to do moments
- I want to buy moments