Data-driven marketing attribution
Marketing teams use multiple channels to drive website traffic. Visitors often return to the website numerous times and from different channels before they finally make a conversion. The question is which channel should be attributed the conversion to optimize ad spend. And the answer could be generated with the help of data-driven marketing attribution implemented by DataSentics PX.
Business rules or heuristic models are still the most common way how to solve the attribution problem. However, whereas heuristic models are crude and straightforward and might work reasonably well if there is just a small number of channels and short customer journeys, they do not provide actionable results to optimize your marketing spend.
Data-driven algorithms can significantly improve the attribution accuracy; they are invariant to the length of the customer journey and reveal which marketing channels have a tangible impact on conversions. Furthermore, data-driven models can protect the overspending of not-working approaches and optimally redistribute the budget to the proper channels at the right time.
Combining cloud computing services and modern data-driven models can authorize digital attribution to fully understand the customer experience and the key marketing channels that allow us to realize which marketing channels empower digital business.
Implementing the attribution data-driven model allows us to obtain marketing insights making the process more efficient. Additionally, there is a wide variety of options in the attribution model, ranging from Markov Chain models, supervised models to models based on Shapley values or artificial recurrent neural network (RNN).
- Understanding the digital/offline behavior of customers and their reaction to the marketing decisions
- Identifying the problems in the budget allocation and hidden potential for growth
- Predicting and simulating the outcomes of business decisions
- The attribution model is scalable for more channels and products
- Automated, reliable, reproducible