AI in targeting: how neural networks help targetologists

The advertising world is changing at an incredible pace. Every new technology and progressive tool becomes a building block in the foundation of successful campaigns. Artificial intelligence is not just optimizing processes but completely changing approaches to creating, setting up, and managing targeted advertising. This is not a trend for tomorrow but a reality shaping the industry right now.

It’s hard to imagine that just a few years ago marketers manually collected data, set up audiences, and analyzed results. Today, AI in targeting is the cornerstone of effective promotion. Artificial intelligence algorithms process billions of data points, analyzing user behavior and interests. Neural networks learn faster than humans, drawing conclusions based on patterns that remain unnoticed by the human eye.

The synthesis of technologies, automation, and analysis opens up unprecedented opportunities for businesses. Companies gain access to personalization on a scale that previously seemed like science fiction. Every ad, every strategy is now built on deep data analysis, ensuring high targeting accuracy and maximum return on investment. The integration of artificial intelligence into advertising processes has become ubiquitous. Those who master neural networks will undoubtedly emerge as leaders. AI in targeting not only increases efficiency but also sets standards that modern specialists should strive for.

### How Artificial Intelligence is Changing the Approach to Targeting

Data arrays, including behavioral analytics, preferences, and purchase history, have become the basis for precise recommendations. AI algorithms use millions of pieces of information for automatic campaign tuning in targeting.

**Real Results of Neural Network Implementation:**

1. **Audience segmentation with 95% accuracy** helps avoid expenses on ineffective impressions.
2. **Reduced advertising costs by 20–30%** through bid optimization and excluding non-target audiences.
3. **Conversion rate increase by 15–20%.** Personalized ads increase user engagement.

### Tasks AI Solves in Targeting

AI takes on complex tasks that previously required a lot of time and resources. Advanced algorithms adapt strategies to current conditions while maintaining high accuracy:

1. **Big data analysis.** Neural networks process billions of data points from social networks, CRMs, search queries, and e-commerce platforms. This analysis reveals patterns, trends, and hidden opportunities.
2. **Bid optimization.** Artificial intelligence dynamically adjusts budgets based on current campaign results, helping to avoid overspending and simultaneously increase profitability. For example, in Ozon’s advertising campaigns, ROI increased by 25% after implementing the technology.
3. **Creating personalized ads.** Neural networks generate texts and select visual elements based on audience interests and behavior. Personalization increases ad CTR by 30%.
4. **Multichannel management.** Algorithms integrate advertising across social networks, search engines, and messengers, providing unified campaign control.

### Neural Networks for Targeting Specialists: Which Technologies to Use

The use of AI technologies in targeting has become mandatory for advertising market professionals. Targeting specialists apply neural networks to solve everyday tasks such as automation, analytics, and adaptation to current changes:

1. **Google AI** analyzes search queries and adapts ads to key terms.
2. **Meta Ads Manager** combines data from Facebook, Instagram, and WhatsApp to improve targeting.
3. **Jasper AI** automates ad copywriting.
4. **Yandex.Direct**. The Russian platform actively implements neural networks for data processing and creating highly converting ads.

Mastering AI technologies in targeting is a key requirement in 2025. The ability to work with platforms like Google AI and Meta Ads Manager provides a competitive advantage.

### Successful Examples of AI Implementation in Targeting

Real cases show how neural networks help businesses adapt to modern requirements. Using AI allows companies to significantly improve results and gain the trust of their audience.

**Ozon: Personalization and Cost Savings**

Ozon implemented neural network algorithms for audience segmentation and behavioral data analysis. These technologies automated the selection of relevant products for each customer, reducing the cost of acquiring a customer by 25%. Furthermore, the company increased repeat purchases by 15% through personalized offers.

**Lamoda: Average Order Value Growth**

The online store Lamoda uses artificial intelligence to create personalized ads. Neural networks analyze user preferences, adapting text and visuals to their interests. This approach increased the average order value by 18% and the conversion rate from views to purchases by 22%.

**Yandex.Taxi: Efficiency Improvement**

Yandex.Taxi applies AI in targeting to analyze geolocation data and user behavioral models. Process automation has increased the efficiency of advertising campaigns by 30%. For example, dynamic ads adapted to the time of day and location made ads more relevant.

### Conclusion

AI in targeting opens up new horizons for specialists. Mastering neural networks is a crucial step for those who want to remain in demand. Process automation, personalization, and adaption of advertising are the main trends to consider in 2025. Using these technologies enables achieving high results, making the work of a targeting specialist more efficient and promising.