Using journey mapping to optimize customer experience
Journey mapping helps brands understand the sequence of actions a customer is likely to take – and it has strategic implications.
Read moreOut of the lab series
A collection of 6 posts
Using spatial aggregation and consumer predictions to analyze markets
A fundamental part of location intelligence, spatial aggregation combines individuals into groups and generalizes information about them.
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K-means clustering: Finding the optimal number of personas
Our clients expect that their persona groups won't shift every time they convert a lead. This is why we are currently researching a new method for determining the optimal number of personas, which focuses on the stability of the persona group.
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K-means clustering: Developing customer personas
K-means clustering is an unsupervised clustering algorithm that dates back to 1957. At Faraday, we use this algorithm to target the optimal number of personas hidden within your data.
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Using data science to predict customer value
Knowing the expected lifetime spend of new customers fundamentally informs retention efforts.
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The weighted scale: Mitigating implicit bias in data science
An algorithm contains the biases of its builder. At Faraday, we have a handful of approaches we use to minimize these effects at each level of our machine learning pipeline.
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