Why online reputation management searches declined: where demand went and how the market is changing

Declining searches for reputation management: statistics and facts

Over the past year, Yandex has seen a substantial decline in interest in traditional online reputation management (SERM). Monthly impressions for 'reputation management' fell roughly threefold, from 6,407 to 2,086. The query 'company reputation online' showed a similar trend, declining from 210 to 34.

Although direct demand for reputation management services has declined, checking companies and looking for reviews remain common activities. These searches continue at a high level, exceeding 100,000 a month. Users still investigate potential business partners carefully and gather information about companies. The most popular searches now concern reviews and checks, rather than purchasing reputation management services.

Declining interest in SERM can be explained by changing customer behaviour. Traditional reputation management methods are losing relevance as trust grows in personal recommendations and other ways to assess partners' reliability. Demand is shifting from formal SERM services towards more transparent, direct ways of verifying a business's trustworthiness.

Changing reputation checks: the role of personal recommendations and chat conversations

When choosing suppliers, businesses increasingly turn to personal recommendations and conversations in professional chats. Many clients now prefer information directly from industry communities or acquaintances to a conventional web search. Recommendations based on real experience can provide a more objective and timely assessment.

If a personal recommendation is unavailable, clients turn to assessing the provider through available data, reviews and materials, but this is more of a fallback option. Finding neither public reviews nor recommendations is seen as a warning sign. Without concrete evidence of reliability, employees and business owners feel uncertain and tend to seek reassurance through direct personal contact.

For clients choosing a provider, being able to speak with its previous customers directly or through chats is particularly valuable. This feedback offers a clearer picture of the quality of delivery, how responsibly the provider works and what to expect from the relationship. It is especially relevant to integrated digital marketing and ongoing marketing support, where confidence in a reliable partner matters alongside results.

Personal recommendations and chat conversations are becoming central to a comprehensive reputation assessment, reducing reliance on traditional search results and automated reviews. This helps entrepreneurs minimise risk and build trust in a supplier before working together.

New challenges and limitations in traditional online reputation management

Traditional online reputation management faces growing challenges that reduce its effectiveness and raise doubts among users. One key issue is how positive reviews are perceived: they often seem forced or fabricated. This can create the impression that negative information is being dishonestly removed, further increasing distrust.

Attempts to manipulate reviews can have negative consequences, including suspension of business listings on maps and other services. Platforms and search engines take these measures to combat fake activity, but the situation also reduces the value and credibility of review systems overall.

Service clients also often decline to provide previous customers' contact details, citing non-disclosure agreements (NDAs). Being unable to verify information personally is perceived as a warning sign and increases scepticism about the services and their reputation.

Reviews themselves, positive or negative, are not always trustworthy. Interested parties often post them, reducing their objectivity and value to potential customers.

Traditional search results for reputation queries are losing their role as a trusted information source. Users turn to alternatives, such as recommendations in professional chats and personal networks, where direct conversation can provide a more honest, transparent assessment.

AI models and generative AI in reputation management: myths and reality

The potential of neural networks and generative AI has been widely discussed in marketing and reputation management in recent years. Reality often falls short of inflated expectations. In practice, using AI to check company reputations has not yet become a mainstream way to choose suppliers or employers.

Most customers do not yet use neural networks to research companies, as AI answers often rely on incomplete or incorrect data. This can produce inaccurate or distorted conclusions and reduce trust in these information sources. Company websites rarely have a substantial influence on AI assessments, which more often draw on well-known encyclopaedias and assorted incidental sources.

As a result, the market sees AI primarily as a supplementary source of reference information rather than an established channel for reputation management or effective promotion. Its main role today is to support decisions and help process large volumes of data. The technology will clearly continue to develop, and its capabilities in reputation management may expand over time.

Promising areas include:

• improving the quality and reliability of data available to AI;
• incorporating AI into integrated digital marketing systems with analytics;
• automating monitoring of mentions and review sentiment;
• Combining specialist expertise and artificial intelligence to support decisions.

Given continuing scepticism and limited practical adoption, AI should be treated as one tool within integrated digital marketing rather than a magical reputation-management solution.