Short answer: I do not attribute calls, ranking or AI mentions to a review authority score. Define relevant platforms, baseline, location identity and separate metrics: profile consistency, review recency, factual conflicts, source citations, referral and qualified leads. Google documents LocalBusiness and review-related structured data, but does not publish a formula by which the rating produces visibility.

Define the intervention

Write exactly what you do:

  • claim a legitimate profile;
  • correct the program or address;
  • align the category;
  • respond to reviews;
  • update the description;
  • correct service coverage;
  • implement a transparent request review process.

If you also change the site, pricing and campaigns, the attribution becomes weaker.

Baselines

For each location and platform save:

  • URL profiles;
  • rating and volume, just as context;
  • review recency;
  • name/address/program;
  • services;
  • material conflicts;
  • source citations in a query set;
  • detectable referral;
  • leads and eligibility, if measured.

Metric 1: critical-profile consistency

Numerator: priority profiles without material conflict. Denominator: previously selected eligible profiles.

Do not add favorable directories after intervention.

Metric 2: material conflict rate

False claims about address, program or service. Do not include negative opinions as "conflict".

Metric 3: review recency

The distribution of reviews by period. An aggregate rating can hide the fact that recent experiences have changed.

Metric 4: source citation observations

In local queries, note when review platforms are cited. The denominator is the eligible observations with sources.

Citation does not constitute endorsement.

Metric 5: referral

It measures clicks from the platform where they are detectable. Landing page and next step matter more than raw sessions.

Metric 6: qualified leads

For local services, sort leads by service area and request type. Multiple out-of-zone forms are not necessarily a good result.

It keeps only the necessary data and respects privacy.

Observation window

Seasonality can dominate local services. Compare similar periods and note holidays, special programs and campaigns.

For review recency, use a window sufficient for volume.

False-attribution risks

  • paid campaigns;
  • season;
  • changing the program;
  • new competitor;
  • local PR;
  • relocation;
  • volume spike review;
  • website redesign;
  • Search updates;
  • AI model changes.

Comparison group

If you have multiple locations, you can intervene on the profiles of one and keep another comparable, without leaving materially wrong information in control.

Match by type of service, maturity and volume of reviews.

How do you formulate the conclusion

Good: "Profile consistency increased from 6/10 to 10/10, and schedule conflict disappeared. Qualified leads increased, but a local campaign ran during the same period, so commercial attribution remains uncertain."

Poor: "Review authority +25% generated leads."

How do you treat the rating

The rating is a summary of experiences on a platform, not a universal authority score. Report it separately and avoid comparison between platforms with different populations.

How do you handle a review request

If the request process changes, mark the time in the change log. An increase in volume can come from process, not from a change in reputation.

Do not buy reviews and do not dictate formulations.

Null result

If the profiles become consistent, but the calls and citations do not change, the project can still be justified: you have reduced the wrong information and the time-to-resolution.

The denominators

Consistency uses profiles. Conflict rate uses claims. Citation rate uses observations. Qualified lead rate uses qualified leads. Do not combine them.

Stop criterion

Close the analysis when the profiles are stable, the observation window is complete and the new data does not change the conclusion. Then switch to monitoring.

How do you handle the difference between platforms

A rating of 4.6 on a platform with thousands of reviews is not directly comparable to 4.9 on a directory with a few dozen. Populations, moderation and user type differ. It keeps the rating in the context of the platform and avoids a global reputation average.

How do you measure the theme of the reviews

You can categorize themes such as punctuality, communication, price, quality or support, but the methodology must be versioned. Do not interpret the frequency of a theme as prevalence across the entire customer base without adequate sampling.

For factual claims, keep source-of-truth separate. A review can relate a legitimate experience without being the official documentation of the service.

How do you handle attribution without a click

A user can see the reviews or an AI response and then call directly. If there is no observable touchpoint, mark the influence as `not attributable', don't distribute it through an invented model. Surveys can add context, but they have their own biases.

Re-audit threshold

Resume analysis after relocation, major job change or review solicitation campaign. Between events, monitor profile consistency and P0/P1 conflicts.

How do you handle phone calls

If the business receives a lot of calls, use call tracking only in a way compatible with local identity and applicable legal requirements. A tracking number must not create contradictory external profiles or be automatically interpreted as a canonical number.

For attribution, keep the source detectable when it exists and unknown when it doesn't. Don't distribute unknown calls to review platforms via a convenient rule.

Claim ledger

  • FACT/EVIDENCE: Google documents LocalBusiness and review-related structured data in eligible contexts.
  • PRACTITIONER GUIDANCE: reputation, source observations and business outcomes must be measured separately.
  • INFERENCE: coherent profiles can reduce contradictory information.
  • NOT PROVEN: that the rating or volume of reviews directly produces ranking, AI citations or leads.

Conclusion

Mature attribution in local services starts with profiles and factual conflict, not a score. When you add seasonal context and qualified outcomes, you can tell what improved without turning correlation into causation.

Sources reviewed

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