Keys to Successful Marketing Attribution

Even though the term “marketing attribution” has been popular among experts for quite a long time, many companies still don’t have the clear understanding of its role in their business development.

As a marketing method, attribution implies allocating the budget to various marketing channels, based on their role in the customer journey and aims to maximize audience engagement and companies’ revenue. As opposed to conventional trial-and-error approaches, marketing attribution allows businesses to understand their customers’ purchase behavior better and optimize their marketing spend faster.

Which attribution model to choose?

Over the past decade, the world’s marketing specialists have developed a wide range of potential beneficial attribution models, which is why businesses often find it hard to choose the best one.

The table below aims to provide the comprehensive and concise description of the most popular marketing attribution models, their specifics and potential drawbacks.

Model	Details	Potential Drawbacks
Last-click attribution	The model implies allocation of the entire value to the last customers’ click. About 20-21% of Advertisers currently apply it.	The model gives almost no credit to other stages that potentially affect customers’ purchases.
First-click attribution	The model implies allocation of the entire value to the first customers’ click. About 40% of ad agencies and 25% of brands still use it.	The model implies that subsequent stages have no effect on customers’ purchases.
Linear model (multi-channel attribution)	The essence of the model lies in the allocation of equal value to all stages of the customer journey.	The model ignores the different impact of various stages on customers’ purchases.
Time decay model (multi-channel attribution)	The closer to a conversion, the higher the value of the stage in a customer journey.	The value of first stages in a customer journey is often underestimated.
Position-based model (multi-channel attribution)	The model implies the allocation of the highest values to the first and the last clicks.	Allocation of values to different stages within a customer journey is mostly arbitrary.
Regression model (multi-channel attribution)	The allocation of values depends on their previously tracked channel performance.	If the amount of historical data is insufficient, the research results may be misleading.
Algorithmic model (multi-channel attribution)	The allocation of values depends on the tracked performance of both the converting and the non-converting stages.	If the amount of historical data is insufficient, the research results may be misleading.

As seen from the Table above, none of the attribution models is flawless, and marketers still have some serious challenges to overcome. Some of the major issues include:

• The use of marketing attribution separately, instead of integrating it with the general marketing strategy.


• Lack of marketers’ qualification: their inability to be both creative and capable of analyzing/ interpreting customer data.


• Lack of businesses’ willingness to apply multi-channel marketing attribution without guaranteed revenue increase.

Helpful Tips

Most experts in digital advertising agree that businesses should conduct the much deeper analysis of each stage in a customer journey to optimize their budget spend accurately. Moreover, in the recently published AdMonsters interview, Andrew Lebowski from Epom Ad Server also admitted the paramount task for digital businesses was to master the entire marketing funnel, not just the top or the bottom.

In this respect, it becomes vital for companies to invest more money and effort in their performance measurement activities, if they wish to increase the efficiency of their marketing channels and ROI. In fact, marketers have no other option than to research and apply innovative technologies to get deeper customer data insights and further optimize their budget spend on both the channel and the campaign levels, based on the tracked results.

Keys to Successful Marketing Attribution