"As artificial intelligence transforms advertising analytics, many organizations are rushing to adopt AI tools, hoping for a "magic button" that instantly democratizes data access. It's an appealing vision: marketing teams asking questions in plain English and getting instant insights without SQL expertise or analyst bottlenecks. But generic AI tools often stumble when confronting the specialized world of advertising analytics. They lack a crucial understanding of attribution models, customer journeys and cross-channel measurement."
"Augmenting expertise, not replacing it Effective AI tools enhance analytical expertise by reducing time spent on tasks like SQL writing and dashboard creation. This shift enables analytics teams to focus on higher-value activities, such as extracting strategic insights, conducting sophisticated analyses and designing rigorous experiments. Consider the typical marketing analyst today. The majority of their time - say, 70% - may go to writing queries, debugging code and formatting dashboards, leaving only 30% for actual analysis."
Artificial intelligence can transform advertising analytics by enabling marketing teams to access insights without SQL expertise, but generic AI tools lack necessary advertising-domain understanding. Purpose-built solutions that combine deep advertising expertise with intelligent automation unlock the promise by preserving and multiplying analytical expertise rather than replacing it. Effective AI reduces time spent on SQL, debugging and dashboard creation, enabling analysts to focus on strategic insights, sophisticated analyses and experiment design. AI-powered analytics can invert typical time allocations so analysts act as strategic advisors who guide AI, validate results and link data patterns to business outcomes. Generic AI struggles with proprietary business data and domain-specific measurement.
Read at AdExchanger
Unable to calculate read time
Collection
[
|
...
]