SERM: reputation in search results

Why search reputation affects sales

Someone searching for a company by name has usually almost decided and wants to confirm the choice. If the first page shows a poorly rated review site, an article about a dispute or a “company name scam” suggestion, they leave at the last step, after you have already paid to attract them. The rating in a snippet is visible before the click, and the AI answer above the results retells the most prominent sources. Since most of these ratings originate in reviews, SERM usually runs alongside online reputation management.

Branded search results

We begin with reputation queries: company, product, executive, or a disputed topic where it relates to the business. We record date, viewing region, and result composition so a random snapshot is not presented as a permanent picture. Then we identify the sources a searcher actually sees.

Sources and the right to respond

Different results need different actions: an owned page, platform profile, editorial article, or review. For every source, we determine whether the company can add accurate information, reply to a user, or file a substantiated complaint. Artificial publications and simulated positive demand do not replace this work.

Measuring changes

Reporting is tied to agreed queries, sources, and completed actions. A position change alone does not prove an outcome: we record accurate information that appeared, cases processed, and changes that can be demonstrated. This keeps SERM distinct from a promise to control results.

SERM: working with reviews on ranking platforms

Review sites and maps often take half of the first page for a branded query. We start with the platforms that rank highest: verify the profile, reply to accumulated reviews and separate genuine complaints from suspicious ones. Thanking customers for good reviews also supports reputation when the reply is specific. Reviews with a provable rule breach are disputed through the platform’s own procedure.

Search reputation in AI answers

For queries such as “company name reviews”, Google and Yandex increasingly show an AI answer above the results, while ChatGPT and Alice AI answer the same question in chat. Models build the answer from the same sources that occupy the results: review sites, maps, media, forums and the company website. So SERM work on sources directly shapes what an assistant says. The technical side, including AI crawler access, structured data and answer-ready pages, is covered by generative engine optimisation.

• We check what assistants say in response to reputation queries about the brand and save the answers with sources and dates.
• Cited sources are compared with the results map: most often they are the same pages.
• The website gains pages with verifiable company facts that a model can cite instead of outdated publications.
• Platform profiles are updated so models stop repeating old addresses, terms and service names.

When SERM matters most

Search reputation matters to everyone, but some companies lose more than others when their brand name brings up third-party or outdated sources.

• Online shops and marketplace sellers: branded results are taken by review sites and delivery discussions.
• Clinics and medical centres: patients search for the name together with doctors’ names and the word “reviews”.
• Banks, insurers and financial services: ratings, forums and dispute news sit side by side in results.
• Property developers: a development’s name brings up buyers’ forums and articles about completion dates.
• B2B companies: before a tender, clients study results for the supplier’s name and its executives.

What the work includes

SERM combines analysis of results for reputation queries, work with sources, and displacing negative content by white-hat methods. The scope depends on what occupies the results: reviews, media publications, or the company’s own pages. Displacing negative content runs through different sources in each case.

• Reputation query collection: brand, brand plus “reviews” and “scam”, products, executives.
• Result capture by region and search engine with date and result composition.
• Source classification: owned pages, review platforms, media, forums, aggregators.
• Work with owned assets: about pages, on-site reviews, social profiles.
• Completion and verification of profiles on platforms that rank for the brand.
• Official review responses and platform requests where breaches can be substantiated.
• Preparation of accurate material that can earn a place in results: reference pages, publications.
• Regular result monitoring and a report on changes in result composition.

What we need from you

SERM requires access to owned assets and facts that can answer complaints. Without facts a platform request has no chance, and a public reply reads as a brush-off.

• Access to the site and to company profiles on platforms and social networks.
• Facts on disputed situations: case history, documents, steps taken.
• Contacts for people responsible for responses and legal review.
• A list of priority queries, regions, and search engines.
• Constraints: topics that cannot be commented on publicly.
• Details of any previous attempts to influence results, if there were any: this affects the choice of actions.

What you get

You receive a map of results for reputation queries with a classification of every source and a decision for each: respond, supplement, dispute, or create an alternative. It comes with a work plan for owned assets and platforms, prepared official responses, and requests. A regular report records result composition for agreed queries, completed actions, and changes that can be shown.