Most marketers are stuck somewhere between first and last click attribution to value their marketing campaigns. However, due to increases in data availability and modern data science algorithms, marketers are able to predict attribution with more and more accuracy.

NStack's statistical approach to attribution allows marketers to uncover the value and ROI of their marketing initiatives and campaigns. This allows them to understand in-depth how interactions with their brand contribute in a path to purchase. Data-driven attribution removes the guesswork and replaces it with actionable output, which customers feed into bid strategies, dashboards, and other internal tools to increase marketing ROI.

What do I need to start?

To get started with data-driven attribution, you need to be collecting both session-level campaign data (such as UTM campaign tags) and be tracking conversions (e.g. transactional data).

How does NStack help me attribute marketing spend?

NStack's Data Driven Attribution Model can be easily connected to your data through our web interface, and you can start making predictions in under 20 minutes. This model takes aggregated customer sessions and transactions as input, and returns a list of channels, alongside their data-driven attribution value and number of conversions. As comparison and reference, this model also provides first-touch, last-touch, and linear calculations.

Read the documentation on this model

Additionally, NStack provides a companion module, the NStack Session Aggregator, which takes the most common formats of clickstream data, and converts these into the customer journeys which are required for modelling.

How do I use these models?

NStack provides a web interface where you can connect models to your data by building workflows. These workflows allow you to take data from various sources, such as CSV, or your data warehouse, run data through the model. Output can be returned simply as a CSV, or can be automatically written to data warehouses, visualisation tools, or bid-management platforms.

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