Kazuki Ohta, CEO and cofounder, Treasure Data.
Gone are the days when business decisions were based solely on intuition and gut feelings. With the proliferation of data platforms, data-driven insights have become essential.
Regardless of industry, data science has grabbed a foothold and is only growing in importance. In the world of media and advertising, for example, a new powerhouse has emerged—a union between advertising operations (AdOps) and data science. The integration of these two disciplines has proven to be natural, but that doesn’t necessarily mean it is seamless. An effective collaboration requires a mutual understanding of respective roles and skill sets.
AdOps has an advantage because marketing departments have been working with data for decades. They know which KPIs best capture a successful marketing campaign and which don’t. Nevertheless, data is invariably limited if analytics is not complementing it. To improve online ad performance today, marketers need to start asking their data the right questions.
Deeper Testing
Most marketing departments have a good sense as to whether an online campaign was successful. Through A/B testing, marketers typically know which creative is performing better at the outset, allowing the campaign strategy to be adjusted in real time to account for the audience’s preferences.
This is an example of data’s long-time influence over advertising, but there are limitations. A/B testing typically focuses on comparing two variants or elements of a campaign. It fails, however, to capture the complexity and interdependencies of multiple variables that influence customer behavior.
In order to adapt to the increasing complexity of consumer preferences and the growing number of channels and touchpoints, marketers are exploring more advanced methods of testing, such as multivariate testing or bandit algorithms.
Multivariate testing allows marketers to simultaneously test multiple variants or elements within a campaign, providing a more comprehensive understanding of the combined impact of different variables. This approach allows for more nuanced insights and reveals interactions between different elements that may not be apparent through traditional A/B testing.
Without this deeper analysis, it’s difficult for brands to obtain a granular understanding as to why the campaign was able to succeed. By asking the right questions, marketers can begin to slice through the complexity.
Improving Segmenting
Most brands today have the ability to segment their audience. Customer segmentation is the process of organizing customers into specific groups based on shared characteristics. Marketers can segment based on demographics, geography and even psychology. Only savvy brands, however, are actually building different campaign configurations for each segment.
One way marketers can get a better understanding of their customers is through micro-segmentation, where customers are grouped into smaller, more specific segments based on the audience’s unique characteristics. This allows marketing teams to create hyper-personalized experiences, made easier with the proliferation of generative AI. By combining customer data with artificial intelligence, brands can tailor communications in real time to meet the needs of each customer.
To derive the most value out of each consumer, brands can segment their audience based on the customer’s lifetime value. This involves analyzing factors such as purchase frequency, average order value and customer loyalty score, which can be accomplished through a unified data platform to integrate information from various sources.
Once marketers and data scientists establish the data flows to synthesize this data, they can then begin to identify which customers should be targeted.
Questioning Assumptions
When AdOps and data science begin to work together effectively, there is often an “Aha!” moment early on that quickly leads to better marketing outcomes. As every data scientist can attest to, managing datasets that initially appear well-maintained (only to later be discovered flawed) is akin to a movie twist when a seemingly benevolent character is later revealed to be the bad guy. At first glance, the dataset appears immaculate, neatly organized and ready to yield valuable insights. However, after delving deeper into its intricacies, they stumble upon an anomaly or discrepancy that shatters the data’s initial perception.
In digital advertising, these discrepancies can make or break the overall success of an online campaign. Brands won’t realize they are working with faulty data unless they are constantly monitoring their datasets and asking the right questions to confirm integrity.
Like other industries, the digital advertising landscape is constantly evolving. Stakeholders must stay up to date with the latest trends, technologies and industry best practices to keep up. Business leaders can learn about their own data infrastructure by constantly questioning their methodologies and approaches. This constant introspection, combined with flexibility to adapt when necessary, can help ensure that customer data remains relevant and valuable.
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