Marketing Intelligence: Case Study

Enhancing Revenue Outcomes Using Time Series Modelling

The Challenge

The sales & revenue were seasonal, making it challenging to establish reliable sales targets.

Additionally, the client found it difficult to determine the effectiveness of their marketing efforts especially paid media.

  1. Proposed building a time-series model to predict sales for an important, upcoming period.
  2. Historical data was used to train the model, which would then forecast future sales. A combination of statistical techniques and machine learning algorithms were used to build and train the model.

The Approach


The implementation of the time-series model has been a game-changer for the insurer. The model has provided meaningful business insights for their sales team, which has helped to improve performance.

The new model has provided the insurer with a way of measuring the effectiveness of their marketing efforts. The insurer is no longer reliant on Google or Facebook attribution models, which has allowed them to gain more control over their marketing strategies. Overall, the implementation of the time-series model has improved the travel insurer’s forecasting abilities, and their ability to make data-driven decisions.

Time & Approximate Cost: 2 weeks, starting at $15,000