Commentary

Will AI Become A Marketer's Best Friend?

  • by , Op-Ed Contributor, September 26, 2017

Much of the focus of artificial intelligence (AI) and machine learning (ML) in marketing to date has been on improving the consumer experience to drive more sales. And it’s not hard to see why.

Data-fueled algorithms that continuously learn and improve are making consumer recommendations more personalized and precise than ever before. Today, shoppers see helpful and timely recommendations as they finalize their Amazon carts; news junkies finish reading one online piece and find several related stories of interest presented below it; Netflix surfers browse “Because you watched XYZ” lists to find shows and movies closely aligned with their specific interests. 

Yet despite these advancements, marketers still largely hold myopic views of AI. To truly harness its power, they must start thinking beyond the consumer. It is time for marketers to focus on how AI can improve their own experiences as marketing professionals and help them do their jobs more efficiently and effectively. 

Progress on this front begins with transparency and empowerment. Though most organizations today are swimming in marketing performance data, many marketers rely on marketing analysts to dig for and dissect insights that could be of importance. It’s a nebulous, black-box approach that has most marketers operating blindly. And since data is typically generated by multiple, disparate parties and systems, lines are blurred and confusion ensues over who really “owns” what. If a marketing program is built on bad data, any efforts to leverage data-driven advancements like AI and ML are doomed to fail.

Why AI and ML in Marketing?

So what works and why should marketers tap AI to help? First, it’s important to consider what marketers really care about. So far, the focus on the marketer’s experience has been on low-level, programmatic issues versus high-impact ones that truly matter, such as strategic direction, execution focus and campaign and mix planning.

By combining a deep understanding of top priorities with accurate marketing data, AI tools can be applied to automatically discover insights worthy of attention and proactively deliver them in an automated, timely manner. Here are just a few examples of ways marketing applications should be reimagined and enhanced by AI and ML to improve the marketer’s experience:

Attribution advancements: With performance insights that automatically take into account contextual elements such as a business objectives and organizational structure, marketers will more precisely determine which media are driving purchases, and which creatives are (and are not) moving the needle.

Proactive notifications: AI will enable real-time alerts that automatically deliver information, observations and insights on potential issues, giving marketers the ability to act quickly, course-correct as needed and optimize performance, without having to halt campaigns.

Enhanced mix modeling: Simple, actionable recommendations based on past performance will help marketers more effectively plan their marketing mixes for future campaigns.

Anomaly detection: Machine learning software will also calculate the baseline performance for a metric based on historical data for marketers, and then automatically detect anomalies through dynamic baselining to identify unexpected behaviors.

Dynamic interaction: Instead of waiting for insights, marketers will be able to verbally and proactively ask their technology systems questions about their campaigns—similar to consumers asking Siri or Alexa for information.

Predictive modeling: A smart learning system can make (cautious!) predictions of future performance—letting marketers make more informed decisions around budget allocation, channel selection and even creative design. 

What’s Next?

When it comes to AI and ML, we’ve only reached the tip of the iceberg. As the above approaches take hold, marketers will also benefit from the emergence of experimental design. Data from A/B experiments will play a critical role in supporting all of the future AI applications for marketers. As a next step, the ML systems can help design those experiments to ensure the most accurate information is extracted from any active campaign.

Marketers must proactively seek out ways to leverage automation and AI innovation to help them uncover rich, new insights on everything from audience to creatives and channels. Rethinking the way they engage with data will ultimately change (and improve) how they do their jobs.

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