Machine Learning in Advertising

Innovations in Machine Learning are having a radical effect on how consumers and brands interact. Machine Learning also greatly expands the toolkit companies have, both to gather analytics, and to use that data to craft a deeper understanding of their consumers. Better customer profiles lead to better sales and happier customers, but there are pitfalls to be wary of too.

Machine Learning, a subfield of artificial intelligence, consists of algorithms that can iterate through large sets of data and then make smart conclusions and helpful correlations that would be difficult for a real person to detect. They can also learn over time to become better at their given task and improve their results with more exposure to data. These algorithms can digitally replicate the mind of a consumer, or the mind of an advertising researcher, or they can be utilized to communicate with consumers on behalf of brands. 

Gone are the days when armies of support staff wearing phone headsets are the only option companies have to communicate with inquiring customers. Now chatbots are the norm for many companies; Machine Learning allows chatbots to converse with human beings in a natural, human-like way that doesn’t frustrate users. The algorithms used by chatbots are able to pull bits and pieces from previous interactions and use them to infer answers to future questions, so they only get more effective at communicating over time. Chatbots are attractive to brands because they greatly reduce the overall costs they spend on customer service. Chatbots provide fast answers to the day-to-day queries of customers, resolving their queries immediately and simplifying the user decision process. Suppose a customer comes to your site and has trouble locating a certain product. In such a scenario, a chatbot on your site will solve the customer’s dilemma quickly. And unlike human agents, chatbots provide round the clock services.

Machine Learning can be used for a lot more than just chatbots though. Predictive targeting is a marketing technique that uses Machine Learning to predict customer decisions based on behavior patterns. Algorithms iterate through large data sets to predict the probability that a customer will take a certain action. They can help answer questions like: will a customer likely purchase this item or that? Will they be interested in engaging with a certain campaign, or would they react negatively to an advertisement? Machine Learning tools can help advertisers analyze the performance of ads, or help optimize marketing content, or they can analyze images posted on social media.

Social Media platforms can be an incredible source of data for advertisers. People tend to use Social Media to talk about their interests, comment on the places they have visited, and share their personal experiences with products and services. For example, a system that uses Machine Learning might conclude, after analyzing some social data, that young women who like a particular tv show and who are also interested in a certain celebrity are statistically more likely to purchase tickets for a certain vacation package. That could be very handy information for a brand. Suppose instead that you wanted to search through photos posted on Instagram to find images that contain your branded products: Machine Learning based tools could do this, and then analyze the content of those images to form a complex customer profile by observing characteristics about the people and environment in them. Using these customer profiles, a company could then launch new advertising campaigns that are more persuasive to this finely targeted audience.

There are however some potential downsides of using Machine Learning in advertising. For instance, Machine Learning only works with very large data sets, which will have to be harvested before any analyzing can take place. Machine Learning also struggles to reliably analyze sentiment because, despite advancements, algorithms simply aren’t capable of human intuition. Some tasks that are simple for humans are very difficult for a program to replicate. Sometimes AI is not a human enough replacement for an actual person. There are also companies who use machine learning in less-than-ethical ways. For instance, by sending out bots that can post deceptive content on social media platforms to falsely promote their brand, or falsely besmirch a competitor.

Despite these challenges though, there are many tasks that better suited for a Machine Learning based approach, and there is great potential in this emerging field. As the technology advances, there will be lots of new creative ways for advertisers to capitalize on the unique benefits of Machine Learning. Companies will need to adapt to these new methods if they are to stay competitive in the marketplace.  

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