Marketing attribution in modern marketing analytics involves the process of identifying and crediting which marketing tactics contribute to sales or conversions.
Marketers gain insights into buying behavior by tracking how and where customers interact with their brand online and in-app — data that can be collected and analyzed to develop and refine strategies. Marketing attribution tells you which channels and messages are the most effective at influencing consumers to buy or move closer to a decision. This information, in turn, helps you to make better decisions about how and where to invest your marketing budget.
Because consumers no longer follow a straight path from awareness to consideration to purchase, the practice of attribution modeling is increasingly important. For example, marketers need to know how much return-on-investment (ROI) to credit to Facebook, Google AdWords, or other channels such as events or email to determine whether they should increase or decrease spending with each vendor or channel.
Benefits of Marketing Attribution
The primary benefit of marketing attribution is a clear understanding of which marketing tactics are most effective in driving sales and conversions. Broken down into its components, this means:
- Knowing which channels are your top converters based on ROI
- Understanding customer journeys, including the touchpoints that lead to conversions
- Optimizing your marketing spend, as you allocate budget towards top-performing channels
- Increased conversions and ROI
- Improved personalization of marketing, messaging, and product based on customer preferences>
Marketing Attribution Models
A marketing attribution model is a framework for assigning credit for sales and conversions to touchpoints in a customer’s conversion path. Each model assigns and distributes the value of a conversion across touchpoints differently, relying on different analytical techniques.
The most common marketing attribution models are:
Last click attribution
Last click attribution credits 100 percent of a conversion to the last clicked ad and associated keyword, prioritizing branded search and other “bottom-of-funnel” campaigns in which the customer converts based on an aggressive pitch, most likely. Last click is the most commonly used attribution model and the default for most marketing platforms.
First click attribution
First click attribution credits 100 percent of a conversion to the tactic that initially brought a customer to your digital property. First click prioritizes campaigns that build traffic, find new audiences for your messaging, and increase brand awareness.
A simple form of multi-touch attribution, linear attribution distributes credit for a conversion equally across all the clicks a customer makes on their conversion path. While this model credits all interactions, it doesn’t necessarily help identify which channels are most impactful.
Time decay attribution
Like the last click attribution model, time decay attribution gives the most priority to interactions occurring immediately prior to a conversion but also credits earlier interactions that led to the conversion
U-shaped attribution, AKA position-based attribution, gives equal priority to the first and last clicks. These interactions are credited with 40 percent of the conversion each; the remaining 20 percent is spread out among the intermediate touchpoints
Algorithmic attribution, AKA custom attribution or data-driven attribution, is a model that relies on historical data and applies machine learning to determine which touchpoints deserve the most credit for a conversion.
Marketing Attribution and Big Data
Leveraging big data is an essential aspect of effective marketing attribution. But leveraging large data sets across online and offline channels is a challenge, and having access to these data sets doesn’t necessarily make them useful. Understanding what marketing data and data types are required for attributions, gaining access to that data, and distilling that data into actionable insights are crucial for success.
Snowflake’s platform virtually eliminates data silos to create a single repository for a single copy of all types of marketing data. As a result, marketing teams can extract deep insights and deliver timely and relevant customer offers and messaging.The platform's ability to seamlessly integrate various data types ensures a holistic view of customer interactions, significantly enhancing marketing campaign effectiveness.
Snowflake's Marketplace provides marketers the ability to enrich their internal data with diverse third-party datasets, thus offering a more nuanced understanding of customer behaviors and user preferences.
The growing emphasis on Measurement & Attribution, Customer Data Activation, and the integration of AI & Machine Learning tools reflects a broader industry move towards intricate, analytics-driven marketing approaches. The increased focus on maintaining customer privacy, as seen in the rise of data clean room relationships, underscores Snowflake's commitment to responsible innovation.
Snowflake's platform not only provides the tools and data necessary for insightful marketing decision-making but also aligns with the latest trends and technologies, ensuring marketers can enhance their relevance and maximize ROI in an ever-evolving digital landscape.