What agencies and brands need to know about collaboration in data analytics

What agencies and brands need to know about collaboration in data analytics

What agencies and brands need to know about collaboration in data analytics

Michelle Harness, division vice president, digital agency and platform sales, Wiland, Inc.The trend toward in-house agencies, or in-housing — most prominently represented by P&G bringing media planning and buying capabilities in-house — continues to be a hot topic in marketing.Depending on the perspective, it’s either a fundamental change or just another chapter in the ongoing story of in-house versus external agency teams. So, when it comes to agencies helping their clients analyze and activate their customer data, deciding who should lead the process increasingly matters.

Brand and agency approaches to data analytics illustrate differences in perspective

Research from Digiday and Wiland identifies the most common data-related challenges for marketers. The study began by asking brands and agencies who did the heavy analytical lifting. Among brand respondents, 77% said that their internal teams play a significant role in data analytics through in-house analytics (40%), hybrid teams (13%) or external infrastructure used in-house (24%), such as AWS, Azure and Snowflake.

Their counterparts on the agency side, however, reported being involved in data analysis for more than half of their client brands — either themselves (32%), through external infrastructure (19%) or by contracting with a third party (8%). That’s a much higher rate than the brands reported. This discrepancy seems to be partially rooted in one outcome of a remarkably and quickly changing world of data — that is, brands and agencies often struggle to fully align on what constitutes analytics. Brands typically have a micro-perspective focusing on details such as CRM data specifics, identifying ideal cohorts and achieving the best possible segmentation and personalization. Conversely, agencies have more of a macro-perspective that starts with learnings across multiple clients and campaign types and then works down to the client data itself — including what kinds of third-party data may be needed to fill gaps in the brand’s CRM data. In that sense, it’s often true that clients and agencies are analyzing customer data simultaneously — with overlapping and distinct objectives.

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