Emerging Trends in Customer Data Analysis for Brands and Agencies
Insights from new Wiland-sponsored Digiday research on roles and responsibilities in data analytics
Brands and agencies both know that first-party data, while vital to the success of their marketing programs, isn’t the whole story. Enriching that first-party data using data from reliable third parties is also tremendously important. Customer data analysis and enhancement have long been crucial to marketing success, but we’ve entered a new era of robust, machine learning-enabled data enrichment and analytics that is changing how data-driven marketers operate.
To assess where marketing data analytics stands today, we developed our newest research collaboration with Digiday. The State of Marketing Data Enhancement and Analytics: How Brands and Agencies are Using Data to Improve Outcomes identifies the most common challenges for marketers when it comes to the still-maturing field of data analytics and enhancement.
As part of this maturation, brands are continually assessing how much of the work to keep in-house and how much to allow their agency partners to run. “Internal team or external partner?” is an age-old question, of course, but we wanted to see how things are playing out today. So we asked our brand respondents who performs their data analysis—themselves, a partner, or some combination—and then asked the same set of questions to a number of agencies.
Who typically performs your customer data analysis?
Brands do the analysis:
Agencies do the analysis:
External infrastructure is used (AWS, Azure, Snowflake):
Non-agency third party is used:
Other (hybrid teams, varies by campaign, etc.):
Among our brand respondents, 77% said that their own internal teams play a significant role in data analytics through in-house analytics, hybrid teams, or external infrastructure. Their counterparts on the agency side, however, reported being involved in data analysis for more than half of their client brands—a much higher rate of agency involvement than reported by the brands themselves. Some variation should be expected, of course, since the brands and agencies surveyed don’t necessarily work together. Nevertheless, this difference in perception is interesting. What’s going on here?
It’s clear that many brands are choosing to keep their data and analysis inside their organizations for a variety of reasons. As a recent ANA study discovered, one of the key reasons for the trends towards “in-house agencies” is the perceived need for internal customer data to stay internal. But we’re also seeing many brands who are embracing a situational approach in which some analytic and data enrichment functions are outsourced while others are reserved for inside teams. This practice is represented in our research by the significant number of brands who responded that they’re using a hybrid strategy (13%), as well as agencies who responded that it varies by client preference (20%).
So what are the factors that work into the 2023 version of “in-house vs. external analysis”?
|Recruiting and hiring top analytical talent can be time-consuming and costly as an overhead expense for brands, while agencies are staffed and ready.
||Outsourcing data analysis can be expensive, whether it’s the agency or a specialized analytics firm that is used. An outside partner would need to demonstrate incremental value.
|Agencies have a holistic view of the marketplace that extends beyond individual brands or verticals.
||In-house teams have a deep, innate understanding of their brands, customers, and markets that an external partner may have difficulty acquiring within the scope of a single campaign or initiative.
|Clean rooms, secure transfer protocols, and collaborative data environments allow for privacy-safe sharing of data for analytical purposes.
||Brands need to ensure the security and confidentiality of their customer data, and many feel that keeping analysis functions in-house gives them stricter control over access to sensitive information.
|Agencies’ technical prowess and specialization means they are often able to provide faster turnaround times.
||In-house teams may in some instances be nimbler in responding to changes.
It is worth noting that different departments within an organization may have different policies about data collaboration. Also, it is possible for brands and their agencies to end up duplicating analytics work within their respective data environments rather than working collaboratively, even though they both have the same goals in view. Such overlap, if intentional, can be useful for validating findings. But to avoid unnecessary duplication of effort, good communication is essential.
Keeping data analytics in-house can be a difficult road to navigate—assembling a massive amount of customer data, enriching it with third-party data, resolving the data into a coherent ID graph, harnessing machine learning and predictive data science, and so forth. But it’s the path that many brands are choosing to make the most of their data assets.
That said, working with a best-in-class agency or partner can frequently yield superior results. It is important to build trusted data partnerships that bring incrementally new data elements into your analytics environment while maintaining the highest data protection standards. Any barriers that exist typically give way to a profitable relationship that benefits your data, your organization, and your marketing outcomes.
Tags: analytics data enhancement data enrichment marketing data research