Marketing executives are planning to spend more and more of their budgets on marketing technologies year over year. In 2018, CMOs budgeted 29% to mar-tech, up from 22% in 2017, making marketing technology the biggest spend item compared to any other category, including labor and paid media.
Within mar-tech category the largest spend items were data, ad technology, and marketing automation technologies. Why? Because marketers know that having powerful systems that can collect, clean, crunch and analyze data is table stakes today, required just to stay in the game. Many marketers have found, however, that to truly put the technology and analytics to work for them and their customers is easier said than done.
The few leading marketing organizations that have succeeded in using analytics technology to its full potential seem to have a few common elements in their strategy, says Google. These marketing leaders have:
- Made it a priority to integrate their disparate systems of record so they deliver full-cycle metrics and analytics that inform smarter marketing decisions.
- Merged their mobile and app teams to deliver a seamless brand experience regardless of where consumers choose to research, compare and shop.
- Invested in new technologies, such as machine learning, to help them react faster to their customers’ changing needs, wants, and tastes.
One of these companies, Sprint, succeeds because it integrates data and technology to understand their consumers while making timely connections. They tested machine learning to determine if paid search impact the volume of in-store traffic as well as online conversions. (Spoiler – it does.)
Two technologies often mentioned together or interchangeably, machine learning (ML) and artificial intelligence (AI), have the most potential for audience targeting and segmentation, simply because of their capacity to “tame” the avalanche of demographic, location, and search data users leave behind online. That’s perhaps why an overwhelming majority of organizations that have implemented AI for these tasks already report a positive return on their investment.
But the use of AI in marketing doesn’t just benefit companies. AI and ML will have (and have already) a profound effect on how brands talk to consumers using better and smarter conversations. Companies can use data they aggregate to serve more relevant ads to more qualified consumers, so in theory, everybody wins.
From a marketing management perspective, here are some questions to consider:
- While marketing technology may automate many manual daily tasks, it has yet to be able to replace the human touch. What are some necessary skills marketers need to succeed in the age of AI?
- How might one use AI to contribute to creative endeavors, not just analytics?
- What can marketing managers do to decrease risks of integrating AI in their marketing strategies, as well as to increase team adoption?