Optimization without measurement is guesswork. The Impact tracker connects each change you make to the AI answers that follow it, so you can see whether visibility for the affected prompts moved — and whether the evidence is strong enough to say so.
How a measurement works
When an action is marked as implemented, Narrashift stores the implementation date and a snapshot of the baseline: the monitoring results for the action's prompts over the previous thirty days. From then on, every new monitoring run adds observations to the after period. The comparison uses the same prompts, and you can filter it to a single provider.
For each period the tracker shows the number of analyzed answers, your brand mention rate, recommendation rate, citation rate, share of voice within the prompt set, the sentiment distribution and the most cited sources. Changes are shown in percentage points, and a daily trend chart marks the implementation date.
Honest about uncertainty
A before/after comparison needs enough data on both sides. If either period has fewer than the minimum number of answers, the result is “Not enough historical data to measure impact” instead of a misleading percentage. If the AI model that produced the answers changed between the periods, the tracker flags it, because a model update alone can change answers.
Results are phrased as associations: “Visibility for these prompts increased after this action was implemented.” Narrashift never claims that an action caused a change, never shows simulated improvements and never fills missing baselines with estimates. Every number comes from stored monitoring results.
Closing the loop
Measurement updates the opportunity behind the action. When the after period has enough data, the opportunity moves to improved — at least a ten-point increase in mention or recommendation rate — or to no measurable change. The thresholds are configurable, and an opportunity is never marked resolved because of a single favorable answer.
The tracker lists all implemented actions with their status, links to the opportunity and its evidence, and a provider breakdown. Over time it becomes a record of what has worked for your SaaS in AI search and what has not — valuable for prioritizing the next round of changes and for reporting to stakeholders.
Plan availability
Historical impact tracking is included in paid plans. Because it relies on repeated measurements of the same prompts, it works best with a regular monitoring schedule: daily or every three days gives you enough observations within a few weeks, while weekly monitoring needs more time to reach a reliable comparison.
Reading the results well
Look at the observation counts first, then at the provider breakdown, and only then at the headline change. An increase that appears in all providers and holds over several runs is far more convincing than a jump in one model on one day. If several actions were implemented close together, attribute changes cautiously: the tracker shows each action's prompts separately, but AI answers respond to everything that changed on the web in the meantime.
Example of a measurement
Imagine an action that adds a section about supported website events to your automation page. Before implementation, eight answers to the related prompts were collected and your product was recommended in one. After implementation, the tracker waits until enough new answers arrive, then shows both periods side by side with the exact counts. If the model changed in between, a badge says so, and the provider breakdown shows whether all assistants moved or only one.