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Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• How do you manage reviews for a company that operates under multiple brand names?

  Assign named owners per brand, monitor each brand's scores separately, and make sure the entity signals on your owned properties cleanly attribute each brand to its own identity. The core risk is the brands blurring together in search and AI.

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• How do you build a review response strategy that protects reputation?

  A review-response strategy that protects reputation has five components: a named-owner process so every review is someone's responsibility; a defined response window (usually within 24 to 48 hours); templates by review type for consistent, non-canned replies; escalation rules for serious cases such as legal threats or coordinated attacks; and outcome tracking so the program is measured, not just performed.

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• My name returns a mugshot site as the #3 result. How is that even legal and what can be done?

  You can address it through three parallel routes: legitimate takedown (many mugshot sites have removal policies, often tied to dismissal or expungement of the case), state-law pressure (several US states regulate mugshot pay-for-removal and require removal for people never convicted), and content displacement (building authoritative, accurate results that push the page off the visible set). Removal is sometimes possible; displacement almost always is.

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• How do you create a review management dashboard?

  Aggregate rating, volume and velocity, sentiment by theme, response rate and time, and platform mix across every relevant platform, then layer on trend lines, spike alerting, and named-owner views per location so the data drives action rather than just reporting.

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• How do you handle a coordinated review attack?

  Handle it as an incident on four simultaneous tracks: mass-report the reviews through each platform's process with documentation of the coordinated pattern, escalate legally against the source where the originator can be identified and the conduct is defamatory, accelerate authentic reviews from real recent customers to dilute the attack, and monitor the affected platforms continuously because coordinated attacks come in waves. No single track is fast or reliable enough alone, so they run together toward rating recovery and a check of whether the AI engines have absorbed the attack.

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