AI In Marketing: Where And When It Can Make A Difference
Today’s CMO is tasked with the challenge of understanding a far greater number of channels, platforms and technologies than ever before. Couple that with the never-ending flow of data coming from every device, method and channel and it’s a recipe for data-processing disaster. The right investment can determine whether a CMO lasts less or more than the average 18-month lifetime.
Artificial intelligence offers fascinating possibilities for marketing. While it’s still in its infancy, the power is in the hands of marketers to push for answers to the hard questions. Marketers looking to invest in new technologies must know how and why they’re going to apply them and evaluate how they will solve specific pain points. By working with teams made up of traditional marketers, who focus on the practical applications or technical investment, and more technically savvy computer scientists, who will be responsible for building out and deploying new solutions, CMOs can make far more informed decisions.
For AI or any new technology to make an impact, it should be able to solve a key pain point. I see a few particular areas where AI can improve performance and reach goals.
As marketers’ functions expand, AI can power better decision-making and help solve the data overload problem that most marketers face. Data’s depth and speed are the biggest issues. Adding a digital brain to the problem helps identify perfect storms that will free strategic teams from the basics of just corralling the insights.
Ad fraud is also an opportunity to apply machine learning to weed out fraudsters reducing the time spent manually building whitelists and blacklists. While AI-powered protection might be helpful for the most common patterns, it is still not a replacement for diligence.
Machine learning can help flag suspicious IP activity. Flagging things such as time spent, scrolling and mouse movements can help identify and train AI to scan for similar patterns. While certain types of bot traffic are predictable and can be identified easily, others are adopting more sophisticated fraud tactics, which is where AI-powered fraud tools can be useful for mitigation.
But marketers also need the right data and truth set so they can trust the accuracy of the AI tool they use. Just because a powerful tool is available doesn’t mean it’s always the best choice to solve a particular problem.
Many programmatic platforms apply AI to the decision-making process for which impressions should or should not be bid on. AI uncovers insights in the massive data streams in the digital ad-buying process that can be acted upon in real time. Even for assessing and determining audience data, AI can help go beyond what humans can do. With most big data sets, scale and speed can deliver new insights. In the end, AI serves as a tool to keep up with the dizzying array of resources that marketers can leverage.
For instance, marketers can reach the right customer who will convert the fastest and not waste media spend on the masses. Non-relational features and data points are perfect fodder for marketers to create custom marketing experiences on the fly. AI allows marketers to go beyond off-the-shelf data and leverage first-party data with third-party data sources. AI can pinpoint individual user features to leverage all forms of acquisition campaigns.
Alternatively, marketers can expand their pool of target customers by leveraging high-quality data for lookalike modeling. While lookalike modeling has been around for some time, a deeper level of AI coupled with a rich third-party data set refreshed in real time can help expand and select additional prospects. I believe it is far more accurate to call it “act-alike” models, if the power of AI is truly being applied.
