Gennady Nagornov: “Now is not the time for spending, but for optimizing efficiency”

Gennady Nagornov: “Now is not the time for spending, but for optimizing efficiency”

On the podcast "All About Traffic", Genius Group founder Gennady Nagornov and host Alexey Romanenkov discussed the state and prospects of programmatic advertising in Russia. The digital advertising market is undergoing a major transformation: growth is slowing, budgets are shrinking, and competition is intensifying. Now is the time not for spending, but for optimizing efficiency.

You can watch the full conversation at this link.

Genius Group is a Russian AdTech holding company providing solutions in the field of digital advertising and marketing. The company manages several divisions: Genius Desk (an omnichannel advertising buying platform), Genius Production (creative and production), and Genius X (performance and strategy). Its technology stack includes an audience marketplace, analytics, and advertising tools with AI elements. The holding also operates Pharmatic — a data platform for pharmaceutical companies.

Main trends:

  • Key priorities for advertisers and agencies are efficiency, cost, service, speed, and expertise.
  • Programmatic has matured: requirements have increased significantly for analytics, fraud protection, brand safety, and quality engagement with the target audience.
  • The importance of working with data, targeting, self-service platforms, reliability, and depth of analysis continues to grow.

Full-service means the platform’s team handles everything for the advertiser: setting up campaigns, choosing targeting, creating creatives, and monitoring performance. It's convenient if the client has no in-house marketer or time to deal with campaign management.

Self-service means the advertiser works independently: accessing the platform interface, setting up the campaign, choosing the audience, uploading banners, and tracking stats. It gives more control but requires understanding of advertising tools.

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What the market is experiencing:

  • Decline in consumer activity.
  • Cautious approach to budget planning.
  • Priority on data protection and fraud prevention.
Programmatic doomsday

Key signals for publishers and agencies:

  • Self-service is becoming a must — advertisers and agencies demand transparency and convenient interfaces.
  • Solutions focused on PostView, measuring sales impact, and integration with key business metrics are in demand.
  • High value is placed on functional depth and strong R&D teams.

Post-view is a conversion attribution method (e.g., purchase or signup) when a user saw the ad but didn’t click it, yet later performed the desired action. For example, someone saw a banner, didn’t click, but visited the site a day later and made a purchase — this could be credited as a post-view conversion.

This approach helps track the influence of display ads on user behavior, even when they don’t cause immediate interaction. Post-view tracking is important to understand whether brand ads are working and influencing recognition and purchase decisions.

What happens if global platforms return:

  • Russian companies still lag behind in anti-fraud, retargeting, and data management.
  • However, over the past 3 years, trust in local players has grown, and their teams have become stronger.
  • Advertisers have become more cautious — no one wants to “put all their eggs in one basket.”

The market is overheated: over 50 DSPs, but only about 15 are real:

  • The rest are white labels, resellers, and sales houses without their own stack or expertise.
  • Agencies must distinguish who has a real product and who only has marketing.
  • A sign of maturity is having an in-house team, interfaces, and real features.

Operators’ deals with DSPs:

  • All major mobile operators have acquired one or two platforms.
  • However, gaining legal and technical access to data is difficult — it requires resources and team maturity.
  • Successful are those who not only own a DSP but also develop the product and build synergy with infrastructure.

How agencies work: full-service and self-service models:

  • Some require the full cycle: planning, launch, analytics.
  • Others only want access to the dashboard for independent campaign management.
  • The platform must adapt to both scenarios and build relationships at all levels: from buyer to advertiser.
The battle for attention: attention as currency

Pharma as an example of media deflation and efficiency:

  • In 2024, the pharma sector is showing a decrease in traffic cost.
  • Reasons include in-banner video, accurate ML targeting, quality traffic sources, and optimization based on brand metrics.
  • This is an example of how a smart approach to programmatic can lead to growth while reducing expenses.

ML targeting — technologies that use machine learning to select the right audience. They analyze user behavior (such as sites visited or ad clicks) and predict who is likely to engage with an ad. These algorithms can find patterns people may miss and show ads to those most likely to convert — like making a purchase. This helps advertisers use their budgets more effectively.

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Attention economy:

  • Focus is not just on impressions, but on engagement, contact duration, viewability, and creativity.
  • Success comes to those who can measure and optimize these parameters — directly impacting brand lift and retention.
  • Banner blindness and unviewable impressions are major challenges requiring new solutions.
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Self-service as a maturity indicator for platforms:

  • Today, no one takes words at face value — the interface must demonstrate all claimed capabilities.
  • If a feature isn’t available in the dashboard, it likely doesn’t exist.
  • Transparency and the ability to independently manage campaigns signal trust and maturity.

Outlook:

  • Data operations are reaching a new level: segmentation, transparency, custom CDP solutions.
  • Artificial intelligence: GPT targeting, automated assistants for campaign optimization.
  • The concept of attention economy: the fight for quality contact and user engagement.

ML targeting uses traditional machine learning algorithms to process large sets of numerical data (clicks, views, purchases) to determine who should see an ad. GPT targeting works differently — it uses large language models to analyze text (such as search queries, articles, or messages) and understand a person's interests in context. ML targeting is about behavior, while GPT targeting is about meaning. GPT enables deeper understanding of user motivation, but requires different data and training approaches. Together, they can complement each other.

Conclusion:

2025 will be a test of technological strength, resilience, and quality customer engagement. Success will come to those who overcome challenges, create user-friendly interfaces, ensure transparency, and capture audience attention.

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