Below is a high-level overview of how Rockerbox builds a multi-touch attribution model for you.

  1. Rockerbox categorizes all marketing touchpoints into a tier 1-5 taxonomy
  2. Rockerbox aggregates these touchpoints for all website visitors, building a "timeline" of sorts for all users
  3. Rockerbox groups users by whether or not they converted
  4. Rockerbox runs a logistic regression to find which marketing channels (by taxonomy) are most highly correlated with converters
  5. This produces weights for each marketing channel taxonomy, which are then used to determine how much credit each channel receives
  6. In the MTA reports, Rockerbox apply these weights to all the marketing touchpoint rows & then normalize (so weights add up to 100% for each converter)

If you'd like to review a more in-depth technical explanation of this process, check out our guide here.

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Model Refresh

When you make significant changes to your marketing mix, Rockerbox can refresh your Multi-touch attribution model so that the model weight are reflective of optimized performance or new channels.

Reasons for a model refresh might be:

  • New channel launches
  • Significant changes in tactics within an existing channel

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