How Machine Learning Can Improve Advertising
The biggest capability of machine learning and AI is that it can process huge amounts of data to make predictions. This can be used in advertising and marketing to detect signals and get the right ad in front of the right person, at the right time, even without the need of personally identifiable information. With the help and support of machine learning models and AI technology, ads are getting hyper-relevant and can deliver the ROI marketers expect out of digital campaigns.Predictive Targeting and Testing
Predictive targeting is a marketing technique that uses AI and machine learning to predict future customer decisions based on behavior patterns and historical data. The data is used to predict the probability or likelihood that a certain person will take an action, such as making a purchase, engaging with products, or converting in some other way. Predictive targeting tools can also help brands to create better customer personas, helping them to decide who to target with which campaigns, making sure that potential customers are getting the most relevant ads possible.AI Product Recommendations and Hyper-Relevance
One of the best and most efficient ways to move a customer through the buyer’s journey is with a product recommendation ad. But, it all comes down to how relevant your ads can be to a person at any given time. This is where an AI-powered recommendation model can help take the guesswork out of the process. Recommendation models are usually built on known customer attributes and habits. It allows the model to then recommend products to a new customer based on the information the model knows about them. You’ll see recommendation models in action on Netflix and Amazon for shows to watch and products to buy, as well as in popular search engines and social networks that rely on ads for their revenue streams.Advanced Recommendation Models
One of the major updates to recommendation models in the last few years is that they’re moving from using explicit feedback to implicit feedback. The earliest forms of recommendation models used explicit feedback from customers, information that customers supplied, such as their preferred product categories. Newer models are using more implicit feedback to make recommendations, looking for behavioral signals to understand intent. Advanced recommendation models are also getting more granular and specific. Instead of just using product categories to make recommendations, they’re now using SKU numbers and getting specific as individual products. By getting this specific with recommendations, the landscape of advertising is changing. It’s no longer about the products and product categories, it’s all about the customers and their path to purchase. Advertisers are now guessing what customers will want to buy even before a customer might know themselves. And, recommendation models aren’t stopping here. The future of AI and machine learning models for advertising will not only use historical user data but will also incorporate a user’s reaction to the ad. Recommendations will be updated, essentially, in real-time. As you can imagine, working with this volume and diversity of data requires a data partner that understands the challenge deeply, like Appen.Personalized Ad Targeting
People and organizations change their preferences all the time, and it’s more important than ever for advertisers to be able to detect and adapt to these changes in real time. What’s more, people are more likely to purchase if the ad is personalized to their journey, so.this means that personalized ads are no longer a luxury, they’re a requirement. Ads can be personalized through a number of different tacts and lenses, such as:- Seasonality
- Weather
- Region
- Individual traits
- Interests
- Culture
- Previous purchases
