Artificial intelligence (AI) is influencing marketing and advertising in so many ways. At the core, it enables the personalization of online experiences by displaying the content in an appropriate time, place, and format that interests the customers when they see it!
Additional developments in machine learning, coupled with advanced big data analytics and data science, increasingly allow advertisers and marketers to run highly targeted, personalized, and dynamic ads and campaigns for the consumers at an unprecedented scale.
This hyper-personalized advertising offers significant benefits to companies and consumers alike. For companies, it increases sales and the return on investment (ROI). For consumers, these online services funded by advertising revenue can significantly improve their online productivity, buying experience, and satisfaction.
This post presents a non-exhaustive list that outlines some developments in AI that could have a massive impact on marketing and advertising around the world.
1. Natural language processing
Natural language processing (NLP) is a subfield of AI, used to increase the personalization of ads and marketing messages online. It enables the tailoring of marketing or ad campaigns based on linguistic contexts, such as the customers’ emails, social media posts, online interactions, and product reviews. NLP algorithms learn and identify patterns in words in everyday human language and infer a customer’s preferences and buying intent.
Also, NLP can improve the quality of the search results and create a better match between the customer expectations and ads presented, leading to a higher advertising efficiency. For instance, if a customer searched online for a specific shoe brand, an AI-based advertising algorithm could trigger a targeted advertisement for a particular brand, while the customers are browsing through websites. It can even send phone notifications if the customer walks by a shoe store that offers discounts.
2. Data analysis and customer insights
AI can analyze all forms of data, both structured and unstructured, and derive insights in real-time. Because of this, online recommendation algorithms can vastly outperform historical ratings from users and provide customized recommendations. For example, Netflix creates personalized suggested lists by considering which films a person has watched or the ratings given to those films. It also analyzes, though, which movies are repeatedly watched, rewound, and fast-forwarded. YouTube, with its “Recommended Videos,” is another vivid example of AI recommendation engine usage.
3. Predicting customer propensity
In online advertising, click-through rate (CTR) is an essential metric for assessing ad performance. It is the number of people who click on an ad divided by the number who have seen the advertisement. Predicting CTR is a massive-scale learning problem that is central to the multi-billion-dollar online advertising industry. It is critical to many web applications, including search advertising (sponsored search), recommender systems, and display advertising.
Thanks to ML-based click prediction systems, reinforced learning algorithms can now predict the outcome and help advertisers optimize ads that incorporate the characteristics that would maximize CTR in a targeted population. This maximizes the impact of sponsored ads and online marketing campaigns, significantly boosting the revenue and additional sales.
4. Dynamic pricing
Today’s ecommerce is highly dependent on dynamic pricing to provide the best offers to the consumers for a specific item or service. Using AI, the dealers or service providers consequently fluctuate the products’ costs based on supply, demand, competition, and external influences, to rapidly adjust to changes and improve profit. This enables companies to offer prices that are continually adjusting to consumer behavior and preferences. Also, ML algorithms can predict the top price a customer will pay for a product. At the point of engagement, these prices are tailored uniquely to the individual consumer.
5. Augmented reality (AR)
Unlike images or banners, augmented reality creates digital representations of products superimposed on the customer’s view of the real world. The AR ads are lifelike and interactive. Consumers can see them and even interact with them, forming an emotional connection. AR, combined with AI, can give customers an idea of how the product would look once it is placed in a physical context.
AI-powered AR systems can also learn from customer preferences and adapt the computer-generated product images accordingly, enhancing the customer experience and the likelihood of buying. AR could expand the online shopping market and thus boost the revenue from online advertising.