At BGROW, we reduce the time spent on data collection, analysis, and reporting so that time can be redirected toward thinking about how a brand should grow. As part of our AX (AI Transformation) initiative, we built an in-house system called Data Wave and put it into practice starting in March 2026. The time required to aggregate ad data, analyze it, reach a decision, and act on it dropped from roughly one hour to around twenty minutes. That is approximately a 67% reduction compared to our previous workflow.
Faster data visibility accelerates how quickly teams can respond to performance shifts, and it reduces the recurring resource cost of routine reporting. The time recovered is used to examine what the data alone cannot show and to have more substantive conversations about where a brand should go next.
The data a marketer needs extends well beyond ad platforms
Customer signals from paid channels like Meta, Google, and Naver are only part of the picture. Marketers also need to understand which content is generating responses on social media and how visitors are arriving at and moving through a brand's own website. For fashion and beauty brands in particular, customer behavior is shaped by a combination of factors: product, content, influencers, and promotions all interact in ways that no single channel captures on its own.
BGROW recognized that a connected view of data spread across paid media, social, and web was not a convenience but a necessity. When channel data is examined in isolation, it becomes difficult to read the overall flow of a brand.
Data Wave: connecting what was scattered
As we advanced our AX work, we mapped which tasks consumed the most time on a recurring basis. Data collection, organization, analysis, and reporting were consistently among them. To address this, we built Data Wave, a proprietary system that automatically pulls data from multiple channels including paid media, social platforms, and web analytics, and connects it into a single flow that runs through AI-assisted analysis and reporting.
Every brand has different data priorities. A brand focused on revenue does not need the same dashboard as one centered on content engagement. Data Wave is designed to be customized: the data collected, the key metrics surfaced, and the reporting format can all be configured according to each client's business structure and goals. Marketers spend less time searching for and moving data, and can start analysis and decision-making earlier in the process.
Data automation reduced workflow time by approximately 67%
Since Data Wave entered active use in March 2026, the full cycle from ad data aggregation through analysis, judgment, and adjustment has been cut from roughly one hour to around twenty minutes. This represents approximately a 67% reduction compared to the previous process.
Shorter cycle times translate directly into faster response. When budget paces differently than expected, or when performance on a specific ad shifts sharply, teams can identify and act on the change before it compounds. This limits unnecessary ad spend and reduces the internal resources previously absorbed by manual aggregation and reporting.
Where does the time recovered through AX actually go?
BGROW puts that time toward broadening the scope of how we examine a brand. When ad performance declines, we look beyond media operations to consider customer responses to social content, changes in product and promotional activity, and the signals embedded in creative and brand messaging. Influencer activity and shifts in owned channels are reviewed as part of the same assessment.
The questions marketers ask naturally shift as well.