Accurately identifying which sources and strategies contributed to sales is the Holy Grail of advertising. Without accurate attribution, advertisers can’t make good decisions about which channels to prioritize and which approaches to double down on. But today’s buyers don’t follow a neat, linear path from awareness to consideration to purchase. Instead, the modern customer journey is a winding path that includes a diversity of touchpoints, making it exceptionally challenging to identify high-yield conversion catalysts. A modern, data-intensive approach is required. Algorithmic attribution analysis is essential for understanding which channels, which activities, and which messages are generating the highest return. This article explores why algorithmic attribution is the best model for attribution in marketing.
6 Common Attribution Models
Let’s start with an overview of the most common attribution models and why they fall short of being an accurate metric for assigning credit.
As the name implies, first-click attribution gives 100% of the credit for a conversion to the first point of contact with your brand. First-click attribution is heavily weighted toward activities that build brand awareness and identify new audiences. But it doesn’t provide visibility into the rest of the buyer’s journey.
With last-click attribution, 100% of the credit is awarded to the last ad clicked or the keyword used to present the visitor with the ad. This is the most widely used attribution model and heavily favors bottom-of-the-funnel campaigns, but it doesn’t reveal the influence of brand affinity.
Linear attribution spreads credit out equally among each customer touchpoint in the conversion journey. This simplistic approach fails to identify which sources were most influential in a customer’s decision to convert.
The time-decay attribution model assigns more credit to the clicks closer to the point of conversion. This framework works on the assumption that the most recent touchpoints are the most influential, potentially discounting the effectiveness of older, top-of-the-funnel marketing campaigns.
U-shaped attribution attempts to recognize the importance of the first and last click by providing 40% of the credit to both. The remaining credit is spread evenly between the remaining intermedia touchpoints. But the percentages awarded in this model don’t necessarily reflect the reality of which were most influential.
Algorithmic attribution analysis
Algorithmic attribution is an advanced form of analysis powered by machine learning (ML). Rather than automatically assigning a predetermined amount of credit based on a simplistic formula, this marketing attribution model analyzes large data sets from a variety of sources to determine which sources deserve what amount of credit for a conversion.
Why Algorithmic Attribution Analysis Is Ideal
Algorithmic attribution is the most robust, data-driven method of attribution analysis. By gaining a more accurate view of which channels are top converters, marketers can optimize their marketing spend, resulting in increased conversions and higher rates of returns on their advertising investment. Accurate attribution in marketing makes it possible to identify which line items in the marketing budget should be eliminated, reduced, or increased, and by how much. Algorithmic attribution analysis also offers opportunities for improved personalization of marketing, messaging, and product offerings based on customer preferences. Here are three reasons algorithmic attribution analysis has become the gold standard for marketers.
Allows a fuller picture of the customer journey
The number of potential pathways buyers travel to arrive at a purchase decision is truly mind-boggling. This complex journey involves dozens of contact points, including interactions on social media platforms, streaming media platforms, websites, apps, online search, and more. Without the help of powerful ML algorithms, accurately assigning a value to each touchpoint would be a near-impossible task. Algorithmic attribution analysis is capable of analyzing thousands of these unique digital pathways, including those used by visitors who ultimately converted and those who didn’t.
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Highly accurate attribution
The results of these advanced analyses are used to determine precise probabilities of conversion based on each previous path taken. These probabilities are then used to allocate value to each marketing channel and point of contact that visitors encountered based on the likelihood of a future conversion. Analytic attribution doesn’t rely on simplistic, rule-based algorithms such as single- and multi-touch attribution methods. Instead, algorithmic attribution analysis analyzes probabilities to help identify the marketing actions that resulted in the highest levels of conversions.
Ability to better allocate ad spend
Identifying the touchpoints that drive the highest levels of conversions allows marketers to make better decisions on how they spend their advertising budget and how they allocate staff resources. But algorithmic attribution analysis is capable of even more. It can identify individual content pieces that increase the likelihood of eventual conversion. For example, this method of analysis can recognize that visitors who view a YouTube explainer video or read a particular blog post or white paper are much more likely to convert than those who didn’t.
Challenges Involved in Algorithmic Attribution Analysis
With so many benefits over less-precise methods of attribution in marketing, the use of algorithmic attribution analysis should be a no-brainer. But many organizations still aren’t taking advantage of it. There are several reasons companies struggle to implement this type of attribution analysis.
Algorithmic attribution analysis relies on data from many sources to accurately chart out the unique pathways buyers take as they interact with ads, video and text-based content, websites, and more. Organizations that lack a unified, single source of truth face huge obstacles in implementing this type of advanced analysis. A modern cloud data warehouse solves this issue. Modern CDWs are capable of storing data in numerous formats, including semi-structured and unstructured.
Accurately tracking an individual’s interactions with your brand across multiple channels requires access to large data sets from a diversity of sources. Successful implementation of algorithmic attribution analysis requires organizations to carefully evaluate whether they are currently collecting data from all available sources. And if so, would the data they currently collect need to be supplemented with additional public or third-party data sets such as those found in Snowflake Data Marketplace?
Data infrastructure doesn’t support ML
Most legacy data systems aren’t capable of storing, processing, and analyzing the sheer volume of data that algorithmic attribution analysis requires. Constraints on available compute power, limited data storage, and the inability to process and store data in certain formats make running the ML-based algorithms required for this type of analysis impossible. However, cloud-native data platforms with robust data ingestion and processing features are more than capable of powering ML.
Snowflake for Algorithmic Attribution Analysis
Effective marketing attribution requires using sufficient data and the ability to mine these massive amounts of data. The Snowflake Data Cloud solves the problems that commonly prevent marketers from benefiting from the valuable insights that algorithmic attribution analysis reveals. Snowflake serves as a single source of truth for all types of marketing data, eliminating data silos that can skew results.
Additionally, Snowflake integrates structured, semi-structured, and unstructured data across all sources, creating a unified view of your customer and their marketing touchpoints. This capability allows marketing teams to not only better allocate resources, but also to extract deep insights and improve the relevancy of customer offers and messaging. With the Snowflake Data Cloud, marketers gain access to faster insights, improved relevance, and increased ROI.
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