As part of a series, we’ve asked industry leaders to share what they expect to see in the year ahead.
Next up is Isabel Han – Country Head of Data and Digital Media, Media.Monks Korea.
She shares with us three trends that she foresees in the coming year.
Looking Ahead – Three Trends I See in 2023:
Personalization in commerce
Personalization in commerce will still be a huge growth area and will evolve to be tailored to a more detailed “Nano-personalization” level.
Personalization is a topic that has been discussed for many years. We expect this trend will be continued in Y2023 and accelerate the need, especially for the commerce area. Customers are not pleased with non-tailored content anymore.
Beyond hyper-personalization, nano-personalization would be the trend. With the support of AI technologies, thousands of combinations of customized experiences could be realized in the digital space.
Owned-data-based marketing approaches will be more important to get brands ready in the cookie-less era.
Google had originally said it would stop third-party cookie tracking by 2022 to protect consumer privacy. Now, Google has changed the time frame to late 2024.
This means 2023 is the last year before the change, and also the last window to prepare for this change. Due to these changes, collecting and managing owned data, with one wall, is becoming a more obvious trend.
In that context, shaping owned-data platforms, whether that would be CDP or CRM, is the essential topic to consider for the data function of brands and companies.
Cloud, DaaS (Data-as-a-Service) will become more mainstream for companies.
Cloud is not just data storage anymore. Google Analytics or Adobe Analytics was the main analytics platform marketers wanted to explore in the past.
Now, many companies are considering analytics tools as-a-service. Data accessed through DaaS is typically used to augment a company’s proprietary data that it collects and processes itself in order to create richer and more valuable insights.
It allows businesses to work with data, without needing to set up and maintain expensive and specialized data science operations.