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Lagging Signal Tracking ​

The prediction metric, support volume, retention. The verdicts that arrive in weeks — and the data the prediction check draws on. Slow to move, hard to argue with, expensive to hear wrong.

Owners: PO, Data Phase it lives in: After We Build (Volume V) The corpus principle this enacts: A prediction not checked is indistinguishable from a guess.

Where it lives in the chain ​

The lagging signals ​

  • The prediction metric itself — "Gal completes grading in under 15 minutes." Measured on the date the brief named, with the method the brief named. Not by survey. Not by impression.
  • Support volume — total tickets, tickets by category. A spike in "how do I" tickets two weeks after release is content design feedback; a spike in "this doesn't work" is a scenario gap.
  • Retention — for features users opt into, what fraction are still using them at 30/60/90 days? Drop-off at 7 days is novelty wearing off; drop-off at 30 days is the feature failing to fit the workflow.
  • VRI (Value Realization Index) — fraction of the predicted value that actually reached the person. Predicted 30 min saved per session × actually 24 min × actually used 70% of sessions = VRI 0.56.
  • Satisfaction (CSAT, NPS) — the relationship at the affective level. Slow to move, but the most expensive to hear wrong.

What good practice looks like ​

The lagging signals are wired at release time, watched monthly, and fed into the model update the cycle after they stabilise. Lagging signals do not produce immediate action — they produce learning. A lagging signal that contradicts the prediction is not an outage; it is a model correction.

The discipline: lagging signals are read alongside leading. A leading signal saying "adoption is high" combined with a lagging signal saying "VRI is 0.3" is a clear story — people are using it but it isn't producing the change we promised. That is the cycle's most important finding. The brief was wrong in a specific way; the next brief inherits that lesson.

200apps · How We Work · NWIRE