Why B2B Marketers Should Care About Algorithmic Attribution

20 Feb

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Successful companies in the B2B market space must prioritize attribution in order to fully understand how marketing affects sales. Attribution creates a convenient and precise way to match marketing efforts to the successful movements of the sales funnel. The data that attribution cultivates revenue, lead generation, etc., that each channel generates – and helps to map out the customer journey.

One of the most successful methods of quantifying attribution is the algorithmic model. Algorithmic attribution was created as a response to simpler, less effective models such as the first touch and the last touch model. It is a hybrid model that avoids much of the criticism of the simpler models; algorithmic attribution employs less of the predetermined rules that reduce the complexity of the customer journey too far.

Creating a Viable Model for Marketing

As analytical technology improves, it is no longer necessary to reduce model complexity in order to fit into limitations on the delivery or timeliness of information. As models become more detailed, accuracy naturally increases. The improved number of touchpoints within the algorithmic attribution model as compared to, say, the first touch model, greatly improves the viability of the conclusions lifted from its data inputs.

An AdRoll report seems to show that marketers believe the algorithmic attribution model to be more effective than any other methodology. Models that utilized machine learning as the data intake method were the most impressive.

The Method Behind the Algorithmic Madness

The major difference between algorithmic attribution and its simpler cousins is the custom weight that the algorithm places on data as it moves through the sales funnel. Instead of relying on assumptions or old data, algorithmic models rely on historical data that is already proven to be valid and useful to your company.

Why the B2B Market Should Prioritize Algorithmic Attribution

Many B2B marketers make the mistake of treating customers like businesses instead of individuals. Although the scale of B2B sales is usually larger than B2C, individuals within the company are making those purchases. These individuals exhibit the behavior of a commercial consumer in most cases, and treating them as such has many positive effects.

The successful B2B company of the future learns its customer most accurately. The algorithmic attribution model allows a company to access the data that is most appropriate for the current analysis. B2B marketers are able to see the stages in the customer journey that should be of the highest priority. This is especially important for a company with a multichannel or omnichannel approach to sales – the channels that are most successful can be identified accurately as well.

The Main Differences Between the Algorithmic Model and Simpler Models

  • A company may be able to add stages to its customer funnel that would otherwise go overlooked
  • Data is more actionable, because it falls into more precise and easily identifiable channels
  • Sales credit is given to the appropriate channels, allowing unsuccessful channels to be completely removed or reduced
  • The model itself translates more easily between departments, because the modeled customer journey closely resembles the actual customer journey

Knowing where to invest the human and financial assets of a company is essential to its ongoing marketing efforts. The algorithmic attribution model provides a more accurate depiction of the customer journey for marketers, leading to a more successful journey for marketers as well.

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