How To Shrink Your Marketing Analytics Stack
For top mobile brands, marketing analytics has become dramatically more in important in recent years as the emergence of a multitude of tools to track advertising spend, attribution, creative performance and in-app analytics has advanced mobile app marketers’ ability to measure their UA efforts across channels and optimize their campaigns.
Digital Marketing Analytics Are on the Rise — But “Utility” Is Stagnant
Despite the challenges digital marketing teams face in corralling and crunching performance data, they recognize the growing importance of marketing analytics in driving decision-making. A recent survey of CMOs commissioned by Deloitte and The American Marketing Association found that companies plan to increase their spending on marketing analytics by 376 percent in the next three years.
Yet while most companies have increased their focus (and budget) on their marketing analytics stack, and the sophistication of analysis tools has grown, for many companies, the investments are yet to pay off. According to data derived from the same survey of CMOs, the use of marketing analytics in decision-making has actually remained stagnant for the last five years.
Marketers surveyed in the study cited a lack qualified people who can utilize marketing analytics tools as well as a lack of data tools measuring “success” through analytics as the primary factors preventing their companies from using more marketing analytics.
A separate survey commissioned by Google found that only 13 percent of digital marketers were confident in their ability to measure marketing performance data. The number one reason marketers gave for why they have such a hard time exposing marketing performance data is a lack of integration between their marketing analytics tools.
Fragmentation in the modern-day marketing stack creates workflow inefficiencies as well as holes in performance data. Simple tasks like aggregating performance data across channels often require marketers to toggle between multiple dashboards and manually update metrics in unwieldy Excel files. The process is particularly painful for marketers who wish to analyze their performance data not merely by click-through rates or raw install metrics but rather by the actual quality of those users, as measured by ROI.
Hence, Singular’s name serves as a constant reminder of our mission: To build a single marketing analytics platform that unites all your disparate data feeds, enabling marketers to do their jobs more efficiently and more effectively.
In essence, when your marketing analytics are centralized under one single source of truth, your stack’s output becomes smaller and more manageable, making reporting, analyzing and optimizing performance across channels less error-prone and time-consuming.
Take cross-device attribution, for instance. In the past, we’ve seen marketers make investments in the wrong marketing campaigns because cross-device data was not properly integrated into their marketing analytics. This highlights the fact that just because a campaign drives high performance metrics on one device or platform, doesn’t mean it drives high performance metrics on other devices or platforms. When user engagement data across devices and platforms is taken into account, marketers are able to expose the true ROI metrics of their campaigns to drive better spending decisions and optimizations.
Analytics-Driven Experimentation, Without the Data Deluge
At the core of effective UA strategies is experimentation. As new channels, media formats and targeting strategies emerge, they can lead to outsized returns for marketers, especially in their early days. For example, when they first launched in 2016, Apple’s auction-based Search Ads drove low CPIs and high conversion rates as demand for Search Ads remained relatively low and curious users noticed the new ad slots.
Thus, the advantage of testing new channels and media formats is clear – but marketers must do so in a way that is deeply integrated with existing analytics systems. Yet due to the lack of standardization among ad networks, extracting detailed data from partners for analysis often requires custom integrations and constant maintenance. This can add layers of complexity to your analytics stack — which is why outsourcing partner integrations to a third-party analytics and attribution provider will allow for smarter testing and analysis without the data deluge.
Using this approach, one Singular customer says their team dedicates roughly 20 percent of their time to experimentation with new partners that they think could be promising, including BD, relationship management, media buying and testing. The remaining 80 percent of their time is spent investing in larger platforms they know well. In both cases, Singular serves as the analytics backbone, providing the most detailed and flexible performance data across nearly 2000 networks, thereby allowing the team’s stack to stay small, nimble and powerful.