This is a continuation from part one, if you haven’t read that yet you can start here.
Retargeting is a powerful tactic that gives you the opportunity to create hyper-targeted messages for people who have already visited your site. When you combine AI with retargeting, you go beyond the same set of messages that go out to everyone who visited your website.
For example, a typical retargeted ad, such as the ones used by Amazon, may contain items that the customer already looked at. AI can reference this information and make predictions based on similar consumer behavior. Your ads could recommend items that people buy after purchasing the products that the prospect looked at on your site already.
AI processes this information in real-time, so you get a continually optimized retargeting campaign. The solution can see the tests and experiments that had gone on before, and find opportunities to improve.
Complementing Other Campaigns
Your advertising data doesn’t need to sit siloed off from your other marketing campaigns. AI solutions can use this information to complement other campaigns and coordinate activities between them. For example, you can use real-time advertising data to track the popularity of certain products. Your marketing campaigns can focus their efforts on building more awareness of top performing products, use the most effective messaging from the ads and continually optimize based on this data stream.
Predicting Effective Advertising Channels
Many factors go into determining which advertising channels work the best for your company. AI can look at historical trends in your data, bring in third-party data sources to broaden its perspective and make data-driven evaluations of the most effective channels.
You can limit the time you spend on channels that don’t generate the ROI that you need for a successful advertising campaign. When you’re testing new channels out, you also limit how much money gets spent on options that ultimately end up failing.
Don’t overlook opportunities to maximize your return from your advertising data. AI solutions give you the powerful tools that you need to analyze large sets, produce actionable information, and offer many options for improving your campaigns.
What’s the Difference Between Machine Learning and Artificial Intelligence?
When you start learning about AI solutions for your organization, you end up running into a lot of terms. Machine learning, natural language processing, and deep learning are a few terms that you’ll read about. If you’re wondering exactly what these terms mean, and how they relate to AI, keep on reading.
Machine learning is one of the first terms that you’ll run into when you begin learning about AI and what it can do for your organization. Machine learning refers to technology that allows AI solutions to learn through experimentation and data analysis, rather than being programmed to act in a particular way.
These applications mimic the learning process and make it possible to put intelligent automation in place in your business. For example, machine learning chatbots can discover the most frequently accessed resources requested by people, as well as the formats that get the most positive responses.
Machine learning relies on various methodologies to analyze and process the data, enabling the AI to begin making its own decisions. Over time, the AI becomes more adept at optimizing the processes it uses and giving you better results.
Deep learning is a type of machine learning that focuses on building neural networks, which seek to mimic the function of the human brain. They aren’t trying to accomplish a specific task, but rather simulating learning through open-ended algorithms.
For example, one implementation of deep learning managed to beat one of the top human players at the game of Go, known for its complex ruleset and strategy. This technology also aids in image classification, creating image captions and tagging specific people in photos.
Natural Language Processing
Natural language processing refers to an AI solution that can process conversations and understand the context and meaning behind it. This technology is often used in AI chatbots that act as customer support through social networks and websites.
The person talking with the bot doesn’t need to use keywords or phrases to access the resources they need. Instead, the AI responds to the person like a customer service agent and retrieves helpful resources for them. In some cases, customers might not even know they’re talking to an AI bot.