Case Study: a large US retailer achieves 20% higher customer conversion with Tredence's multi-touch attribution and spend optimization

Optimized the Marketing Acquisition Spend Across Multiple Channels for a Large Retailer in the US

The client, a large US retailer, was struggling to accurately measure the effectiveness of its multi-channel marketing campaigns. Their existing model failed to properly attribute how each channel contributed to converting a user, which was negatively impacting ROI and their ability to optimize acquisition spend. To solve this challenge, they partnered with data science firm Tredence.

Tredence implemented a multi-touch attribution model using the Markov chain concept to properly value each channel's role in the customer journey. Their solution calculated a new channel index and used linear programming to optimize marketing spend for maximum ROI. As a result of Tredence's work, the retailer achieved a 20% higher customer conversion rate with the newly reallocated budget.