Data and technology can help clarify customer behavior at the earliest stages of the decision journey. Here’s how.
Agrowing share of the marketing budget is managed according to the principles of targeted-performance marketing: personalized messages, direct impact measurement at the level of individual users, near-time optimization, and partial automation. This is especially true for digital marketing activities that drive conversion and purchase.
But what about the media spend focused on the earlier stages of the consumer’s decision journey, such as brand awareness, including traditional media or “mid- and upper-funnel” portions of digital marketing? While these investments often account for more than 50 percent of the marketing budget, they are managed with far less rigor than online spending. This leads to an unclear understanding of performance and a tendency to rely on accepted wisdom. Or, worse, mid- and upper-funnel spend in digital are held to the same performance metrics as the lower-funnel performance, often leading to a false or misleading understanding of the impact of spending. As a result, marketers often rely on their media agency to tell them what to do.
Data-driven performance marketing, however, can now be applied much more effectively to branding and demand-generation activities. With a clearer understanding of consumer preferences and behavior at the early stages of their buying journey, companies report marketing efficiency gains of up to 30 percent and incremental top-line growth of up to 10 percent without increasing the marketing budget. On average, the impact is significantly higher than that of established marketing ROI (MROI) methods for branding activities.
This precision in branding is particularly crucial now as companies manage the effects of COVID-19 on the early stages of the consumer’s decision journey. While much is still unknown, we believe that data-driven performance marketing will give marketers an edge when it comes to reaching their target groups efficiently during and after the pandemic. In a consumer survey conducted in the US in April 2020, respondents said they intend to watch more live news (net intent +19 percent) and read more news online (+14 percent) to stay up to date. At the same time, almost 20 percent of consumers have switched their “go-to” brands due to COVID-19. As these habits evolve, granular data analysis and disciplined marketing-performance management will be essential for brands to stay in touch with their customers and drive MROI.
Existing MROI approaches have inherent limitations
Marketers spend $560 billion annually on global advertising.1 However, standard cross-channel MROI approaches, such as marketing-mix modeling (MMM) and multitouch attribution modeling (MTA), to track the effectiveness of that spend are often unsatisfactory due to the following:
- Existing models are overly focused on late-funnel stages.
- Because they depend on historical data, existing models are focused on the past.
- Existing data sets are not sufficiently granular (except for digital marketing).
- Existing approaches struggle to account for the impact of message and creation.
- There is a gap between MROI measurement and marketing execution.
- Most existing approaches do not support realtime optimization.