Our team at Becasu is building an easy-integration machine learning tool to optimize your online marketing campaign spend. Our team uses interpretable LSTM neural nets to model customer journeys and calculate an ROI for each campaign that takes into account the complex dependencies that are a customer journey. Using our model, we automatically run simulations against your marketing and conversion data to optimize budget allocation. We’d love for you to try out our product.
To provide visualizations and analysis about predicted conversions when one changes marketing spend, we train a model to predict whether a customer will convert. Using this model, we run a simulation of budgets against your dataset to test how different amounts of marketing spend affect how many customers you’d likely see convert. We search over the space of potential marketing spend allocations to help you find the best allocation based on your previous customers’ behaviors.
Marketers usually use multiple channels–such as sponsored search, display ads, and emails–to reach their customers, and each channel usually includes multiple activities or has multiple parameters that are associated with various costs.
Every two years, The CMO Survey asks hundreds of senior marketing executives hard-hitting questions about their marketing performance, hiring, and budget allocation to track industry changes and predict new trends.
MIDS Students at UC Berkeley School of Information
