In today’s data-driven marketing world, the importance of Multi-Channel Attribution cannot be overstated. Multi-channel attribution is the science of using advanced analytics to allocate proportional credit to each marketing touch point across all online and offline channels, leading to a desired customer action. In short, recognizing the channels which are contributing to a conversion. Sounds easy? Unfortunately, it’s not that straight forward.
What makes multi-channel attribution so complex?
The diverse digital media landscape, various media platforms, online & offline sales channels combined with a plethora of available customer metrics (like demographics, psychographics, purchase triggers and frequency) make the task of multi-channel measurement quite complex. In fact, according to a recent study, about 40% of marketers surveyed still rely on individual channel analysis and channel-specific metrics to measure the success of their marketing campaigns.
To top it all, there isn’t just one type of multi-channel attribution model. The attribution model chosen by an organization may depend on the channels being analyzed, but there is no one-size-fits-all solution.
Why is it so important?
When it comes to measurement and optimization of marketing campaigns across channels, one should address blind spots and focus on metrics that focus on consumer responses to a marketing stimulus.
Action metrics, which are revealed in the last click attribution model, are the simplest because the link between the stimulus and its business outcome is direct. For example, a user clicking on a search link leading to purchase is an action metric. However, other metrics such as engagement, perception and attention also play an important role in defining a consumer’s response to a given marketing opportunity.
The Measurement Solution
The attribution model needs to relate to the right data inputs across all touchpoints. There are three types of attribution measurement:
1. Cross-channel measurement:
This solution uses a machine-based algorithm to track and attribute every cookie-based consumer touchpoint. This is a user-level approach is often easiest to track and usually what many marketers use to attribute the performance of multiple channels. This insight about how individual channels are performing against one another, it is possible to identify the best performing channel and optimize ad spend accordingly.
2. Marketing Mix Modelling (MMM):
MMM is a way to measure the high-level impact of a range of marketing and media strategies on your business. It refers to the practice of analyzing historical data and use the same analysis to predict which channels and tactics will work better than others. It applies regression techniques for aggregating data to estimate the impact of marketing activities on a desired outcome, such as sales.
3. Online to Offline attribution:
The consumer journey travels both online and offline, desktop to mobile to outdoor and can seem impossible to track down. To address this challenge, a solution blending both cross-channel attribution and MMM is emerging that allows marketers to make strategic decisions almost in real-time.
Mature marketing leaders are beginning to understand the benefits of using a combined attribution method. This unified method takes user-level data from attribution and high-level sales predictions from MMM and enables marketers to properly measure multiple marketing channels.
Common Barriers to multi-channel attribution
One of the most common barriers to successfully implementing multi-touch attribution model are organizational. Although studies have found that more than 70% of CEOs expect marketing to drive revenue growth, most companies are unwilling to treat data-driven marketing measurement as a business strategy.
While there is no one right answer for all about which attribution measurement solution to pursue, it is incredibly important to ensure that marketers start analyzing the variety of information available. Marketers interviewed by Gartner have indicated that these measurement methods have yielded 20%-30% improvement in the efficiency of marketing budgets, primarily by optimizing marketing channels.