Once the "prepare" feature executes, the output table usually contains: : A unique identifier for the customer.
: In many MTA workflows, the "prepare" step separates paths that ended in a conversion from those that didn't, allowing the model to analyze "null" paths for more accurate probability calculations [4]. Typical Structure of the Prepared Data
: A concatenated string or array of channels (e.g., Social > Search > Email ). strongmta.sql
The "prepare" stage typically handles the heavy lifting of data cleaning and sessionization before any attribution logic (like Markov Chains or Shapley Value) is applied:
: It standardizes timestamps, user identifiers (UIDs), and channel names across different platforms (e.g., Google Ads, Facebook, Organic Search) to ensure a unified view of the customer journey [1, 3]. Once the "prepare" feature executes, the output table
: The script applies logic to filter out interactions that occurred outside a defined lookback window (e.g., 30 days) and identifies which touchpoints belong to a single conversion cycle [2, 5].
: A boolean or integer indicating if the path led to a sale (1 or 0). The "prepare" stage typically handles the heavy lifting
: The revenue or weight associated with that specific conversion [1, 2]. Why This Feature is Critical