Multi touch attribution marketing8/25/2023 ![]() This information helps businesses improve customer experience and tailor marketing strategies.ģ. Customer Journey Analysis: MTA provides a granular view of the customer journey, revealing the effectiveness of specific touchpoints in driving conversions. Budget Optimization: By quantifying the contribution of different marketing channels, MTA allows businesses to allocate their marketing budget more efficiently, maximizing return on investment.Ģ. MTA models, especially those based on deep learning, provide valuable insights for businesses, including:ġ. Additionally, TCNs naturally handle sequences of varying lengths, making them well-suited for modeling customer journeys, which can vary in length across different customers. The gradients can be calculated using backpropagation, and the obtained attribution scores can be used to understand the effectiveness of various touchpoints and marketing channels.Ī key benefit of TCNs is that they can process the entire input sequence simultaneously, making them amenable to parallelization and leading to faster training and inference time. This allows the model to quantify the contribution of each touchpoint to the predicted outcome. Similar to LSTM and transformer-based models, credit attribution can be performed by calculating the gradient of the predicted conversion likelihood with respect to each touchpoint's input features. Given a sequence of touchpoints in a customer journey, a TCN-based attribution model takes the sequence as input and predicts the likelihood of a conversion. The dilation rate is increased at each layer of the network, allowing the model to capture patterns at different scales. The key mathematical property of a dilated causal convolution is that it applies convolutional filters to input data with spacing (dilation rate) between values, while ensuring that the convolution is causal, meaning that the output at time step \(t\) only depends on the input values up to time step \(t\). Additionally, TCNs incorporate residual connections that facilitate the training of deep networks by allowing gradients to flow more easily through the network. The architecture of a TCN typically consists of multiple layers of dilated causal convolutions, each followed by a non-linear activation function (e.g., ReLU). Due to their ability to handle sequential data effectively, TCNs have been employed for multi-touch attribution (MTA) The use of dilated causal convolutions ensures that the model respects the temporal ordering of the data and allows for the capturing of dependencies at different time scales. Unlike recurrent neural networks (RNNs) that process sequences in a recurrent manner, TCNs employ convolutional layers to capture both local and long-range dependencies in a sequence. Temporal Convolutional Networks (TCNs) are a type of neural network designed to handle sequence data. Temporal Convolutional Networks Temporal Convolutional Network Example Model Architecture Similar to the LSTM-based model, credit attribution can be performed by calculating the gradient of the predicted conversion likelihood with respect to each touchpoint's input features. \(d_k\) is the dimension of the key vectors. Where \(X\) is the input sequence of touchpoints, \(W_Q\), \(W_K\), and \(W_V\) are the weight matrices for query, key, and value projections, respectively \(Q\), \(K\), and \(V\) are the projected query, key, and value matrices \(A\) is the attention matrix, and \(Z\) is the output. The formula for this model can be represented as: Last touch attribution assigns 100% of the credit for a conversion to the last touchpoint encountered by the customer before the conversion event. We will also discuss the mathematical concepts behind these models and highlight their applications in optimizing marketing strategies and analyzing customer journeys. In this blog post, we will explore various approaches to multi-touch attribution, starting with traditional heuristic models (e.g., last touch, first touch, linear) and progressing to more sophisticated data-driven models based on deep learning architectures (e.g., long short-term memory (LSTM), transformers). Multi-touch attribution (MTA) models are an advanced class of marketing attribution models that attribute credit to multiple touchpoints in the customer journey, instead of assigning full credit to just one touchpoint. ![]() It involves determining the contributions of different marketing touchpoints (e.g., online ads, social media posts, email campaigns) to a desired customer outcome, such as a purchase or a sign-up. Marketing attribution is a critical challenge faced by businesses seeking to understand the effectiveness of their marketing efforts. Co-Founder, Principal Data Scientist Introduction
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