Neural Networks
Neural Networks build accurate predictive models for marketing attribution and causal analysis. They provide deeper insights into customer behavior and campaign effectiveness.
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By Joris van Huët, Founder & CEOUpdated 5 min read
What are Neural Networks?
Neural Networks, inspired by the human brain's interconnected neuron structure, are a class of machine learning models designed to recognize complex patterns in data. Originating in the 1940s with the perceptron model, their evolution was propelled by advances in computational power and algorithms, leading to deep learning architectures capable of handling vast datasets. Technically, neural networks consist of layers of interconnected nodes (neurons) where each connection has a weight adjusted during training using iterative improvement techniques like backpropagation. In marketing attribution, especially for ecommerce brands, neural networks analyze multifaceted customer journeys across channels, sessions, and touchpoints to model nonlinear relationships that traditional attribution models overlook. For example, a beauty brand on Shopify can use neural networks to understand how a combination of influencer campaigns, email marketing, and paid social ads collectively drive conversions rather than attributing success to a single channel.
Why Neural Networks matter for ecommerce
For ecommerce marketers, neural networks provide a competitive edge by delivering nuanced insights into customer behavior and campaign effectiveness that linear models cannot capture. Additionally, neural networks facilitate real-time adaptation to changing consumer trends and campaign dynamics, important in fast-paced sectors like beauty and apparel.
How to use Neural Networks
- Define Business Objective: Start by clearly defining the marketing problem you want to solve. This could be improving customer segmentation, personalizing product recommendations, improving ad spend for better ROAS, or forecasting customer lifetime value. A precise goal is important for designing an effective neural network model.
- Gather and Prepare Data: Collect relevant data from your ecommerce platform (like Shopify), analytics tools, and CRM. This includes customer demographics, purchase history, website browsing behavior, and ad engagement data. Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables to prepare it for the model.
- Select and Design the Model Architecture: Choose a neural network architecture that fits your objective. For instance, a Feedforward Neural Network (FNN) can be used for predicting customer churn, while a Recurrent Neural Network (RNN) is better suited for analyzing sequential data like customer journeys.
- Train and Validate the Model: Split your data into training, validation, and testing sets. Train the neural network on the training data, and use the validation set to tune hyperparameters like the learning rate and number of hidden layers. This iterative process helps prevent overfitting and ensures the model generalizes well to new, unseen data.
- Deploy and Integrate for Attribution Analysis: Once the model is trained and validated, deploy it into your marketing workflow. Integrate it with your attribution platform to analyze the causal impact of different marketing touchpoints. Use the model’s outputs to understand the true ROI of your campaigns and reallocate your budget towards the most effective channels.
- Monitor and Refine Continuously: A neural network is not a “set it and forget it” solution. Continuously monitor its performance in a live environment. As market dynamics and customer behaviors change, retrain and refine your model with new data to maintain its accuracy and effectiveness in driving data-driven decisions.
Common mistakes
- Neglecting Data Quality and Preprocessing: Garbage in, garbage out. Using incomplete, inconsistent, or noisy data will lead to a poorly performing model, regardless of its complexity. Failing to properly clean, normalize, and preprocess data is a frequent error that undermines the entire process and can lead to flawed marketing insights.
- Overfitting the Model to Training Data: Overfitting occurs when a model learns the training data too well, including its noise, and fails to generalize to new data. This is a common issue in complex models like neural networks and results in overly optimistic performance during training but poor results in practice. Use techniques like cross-validation and regularization to avoid this.
- For example, using a simple feedforward network for complex sequential data like a customer’s journey through your sales funnel will yield suboptimal results compared to an RNN or LSTM designed for that purpose.
- Lack of Interpretability and Actionability: A “black box” model that provides predictions without explanation is of limited use to marketers. It's a mistake to focus solely on predictive accuracy without understanding why the model is making certain decisions.
- Treating the Model as a One-Off Project: Customer behavior and market trends are constantly evolving. A common mistake is to build a model and then fail to update it. Without continuous monitoring and retraining with fresh data, the model’s performance will degrade over time, leading to outdated and ineffective marketing strategies.
Frequently asked questions
How do neural networks improve marketing attribution for e-commerce brands?
Neural networks capture complex, nonlinear interactions between multiple marketing channels and customer touchpoints, enabling more accurate attribution of conversions. This helps ecommerce brands understand the incremental impact of each campaign element, optimize budget allocation, and improve ROI.What role does causal inference play alongside neural networks in attribution?
Causal inference helps distinguish true cause-effect relationships from mere correlations that neural networks might find. Integrating causal inference ensures that marketing decisions based on neural network predictions actually lead to incremental improvements rather than coincidental associations.Are neural networks easy to interpret for marketing teams?
Neural networks are often considered black boxes, but techniques like SHAP values and LIME can provide interpretable insights. Combining these with causal frameworks enhances transparency and trust among marketing stakeholders.How often should neural network models be retrained for e-commerce marketing?
Models should be retrained regularly, typically monthly or quarterly, to adapt to changing consumer behavior, seasonal trends, and new campaign data. Automated retraining pipelines help maintain predictive accuracy over time.