High Frequency Data And Modeling In Asset Management

  • June 4, 2024
  • Case Studies
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  • In the face of stagnating customer growth, the largest retirement planning provider in the US recently acquired a successful App-based asset management category disrupter given its potential to attract a large cohort of new customers.
  • They faced a challenge of how to prioritize investments between developing the main brand and maximizing the customer acquisition rates of the newly acquired company. 
  • They also needed high frequency of model updates to be able to plan media at a cadence that meets the needs of the modern marketplace.


  • The company leveraged both the long-term modeling and the real-time attribution capabilities of the Marketscience Studio to measure the effectiveness of its historical investments. Models were built using daily data over the last 3 years augmented with hourly data for the last 3 months and we used a Bayesian technique to get the most recent performance outputs. 
  • Insights were provided at a granular level across tactics and sub-tactics for the various media channels employed; at the station, genre, creative, ad length and day part level for DRTV, at the partner level for Affiliates, at the platform and keyword level for Paid Search and at the audience level for Facebook.
  • Based on the granular modeling insights the client employed the Marketscience SimOpt tool to optimize their investments across channels and partners and monitor campaign progress. 
  • Monthly reporting was put in place comparing the cost per qualified lead of channel partners based on siloed in-channel models to the Marketscience holistic measurement across all channels to inform campaign effectiveness.


  • 120 media channels/partners measured and brought together in a common database with automatic feeds and processing.
  • +17% improvement in effectiveness across media channels and partners.

What We Did

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