By Germaine A.
For years now, machine learning has been an integral part of the advertising and marketing industry. With so much data pouring in from users, effective leveraging that data requires the help of machine learning. It is no wonder that advertising agencies are using machine learning to more effectively cater to potential customers through personalized advertising and returning customers by offering more effective customer service and support (Biewald 2016).
In the domain of web-based advertising, machine learning has made its mark known. Since ads are sold on a per-click basis it is useful to know how effective they are before committing time and resources to their deployment. In the past, advertising firms relied on simple human intuition. However, to gain a competitive advantage they need more: enter ad click-through rate prediction. Google’s AdSense is a juggernaut in this arena. Utilizing machine learning for predicting ad click-through rates, Google AdSense can maximize advertising effectiveness by honing in on a user’s particular wants and needs to provide a tailored experience using data pulled from search history, page dwell time, website content, and click behavior (McMahan 2013). A number of advertising applications such as sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have benefitted from the increased efficiency from ad click-through prediction.
Moving from cyberspace to human space, machine learning-augmented advertising continues to flourish. Data intelligence firms are seeking ways to tie in a customers’ digital footprint with their footprints in the real world. That is, matching a user’s browsing data on an online store to their buying habits at a physical store location. Firms like Qualia are using an opt-in location system to collect information by pulling from GPS and wi-fi signals and integrating them into machine learning algorithms so that advertisers can offer specialized services and recommend commodities (Qorbani 2017).
In the customer relations theatre of marketing, machine learning is also making large strides. Gone are the days of having the user fill out forms with varying degrees of accuracy, instead machine learning can produce help tickets by gleaning relevant information from the substance of the user’s complaint directly (Biewald 2016). The result is efficient ticket tagging and routing, saving time and money for any firm’s customer service department.
The benefits of machine learning aren’t limited to fully automating processes: it can also augment a marketers’ skills and bolster their toolset. Salespeople face the problem of personalizing messages to every potential customer while also reaching out to a large number of people. The email analytics platform offered by Nova provides a solution. Integrating directly into the salesperson’s gmail inbox, Nova’s software tracks the rates of potential customers’ clicks, reads, replies, and bounces to the salesperson’s correspondence and correlating it with public data (Matney 2016).
As with any new technology, machine learning is not without its limitations and concerns especially in the field of advertising and marketing. Even if information is publicly available it does not necessarily mean that accessing it and correlating it with other sources is not a breach of privacy. The main advantage of machine learning is in “filling the gaps” and it is precisely this feature that makes the potential breach of privacy so severe, especially if location services are involved. It allows, potentially, for a corporation to track a person’s movements and location at any time. Even assuming that corporation is entirely benign, a data breach can put many customers at risk. For customer service, machine learning cannot replace a true one-to-one client interaction no matter how much it can streamline.
Machine learning-augmented advertising and marketing is a powerful tool to reach and service customers. As this technology grows and matures, it will no doubt form a greater part of the advertising industry.