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Sıla Ermut

Sıla Ermut

Industry Analyst
110 Articles
Stay up-to-date on B2B Tech

Sıla is an industry analyst at AIMultiple focused on email marketing and sales videos.

Research interests

Sıla's research areas include email marketing, eCommerce marketing campaigns and marketing automation.

She is also part of AIMultiple's email deliverability benchmark. She is designing and running email deliverability benchmarks while collaborating with the AIMultiple technology team.

Professional experience

Sıla previously worked as a recruiter and worked in project management and consulting firms.

Education

She holds:

  • Bachelor of Arts degree in International Relations from Bilkent University.
  • Master of Science degree in Social Psychology from Başkent University.

Her Master's thesis was focused on ethical and psychological concerns about AI. Her thesis examined the relationship between AI exposure, attitudes towards AI, and existential anxieties across different levels of AI usage.

Latest Articles from Sıla

DataJul 24

AI Data Collection: Risks, Challenges & Tools

AI builders need fresh, high quality data:  However, data collection comes with its risks. For example, enterprises need to avoid unethical data collection practices and ensure that data is collected ethically to minimize reputational risk.

AIOct 28

10 Steps to Developing AI Systems

IBM identifies the top AI adoption challenges as concerns over data bias (45%), lack of proprietary data (42%), insufficient generative AI expertise (42%), unclear business value (42%), and data privacy risks (40%).These obstacles can hinder AI implementation, slow innovation, and reduce the return on investment for organizations adopting AI technologies.

Enterprise SoftwareOct 27

Control-M for Enterprise Workload Automation

Control-M by BMC Software helps teams coordinate and automate data and application workflows across environments, including mainframes, the cloud, and hybrid systems. It gives users a single place to schedule jobs, track progress, and handle dependencies.

AIMay 20

Customer Engagement Automation: 5 Tools & Examples

Businesses face rising customer expectations and limited resources, especially as 80% of customers now value their experience as much as the product itself.Meeting these expectations requires clever use of customer engagement automation tools, which we divided into two categories: individual and comprehensive tools.

AIJul 21

Generative AI in Manufacturing: Use Cases & Benefits

Generative AI is becoming a strategic tool for manufacturers facing challenges such as supply chain disruptions, labor shortages, and rising cost pressures. It helps automate design, predict maintenance needs, and optimize supply chains, while driving efficiency, reducing costs, and speeding up innovation.

DataMay 28

Applying RLHF: Techniques, use cases, and challenges

Training AI systems to align with human values can be a challenge in machine learning. To mitigate this, developers are advancing AI through reinforcement learning (RL), allowing systems to learn from their actions. A notable trend in RL is Reinforcement Learning from Human Feedback (RLHF), which combines human insights with algorithms for efficient AI training.

Enterprise SoftwareJun 19

eCommerce Data Collection: Best Practices & Examples

As online shopping grows and customer expectations shift, eCommerce businesses face increasing pressure to stay competitive. Real-world data is key to making faster, smarter decisions. Failing to collect and utilize data properly can result in missed sales, inefficient operations, and poor customer retention.

Enterprise SoftwareOct 13

Top 5 Price Monitoring Tools

A key challenge for businesses is maintaining competitive pricing while adapting to market fluctuations. Price monitoring tools help solve this challenge by tracking competitors’ prices and providing insights for more dynamic pricing decisions.

AIOct 3

Conversational AI for Sales: Applications & Real-Life Examples

By combining natural language processing, machine learning, and integration with customer data systems, conversational AI tools enable sales teams to handle routine tasks, qualify leads, and engage in personalized conversations with prospects and customers.

AISep 3

Generative AI for Sales Use Cases

Unlike traditional AI in sales applications, which typically focuses on data analysis and pattern recognition, generative AI actively contributes to the sales process by creating content, drafting communications, and improving customer engagement. Explore the future of generative AI for sales, including use cases with examples that align with the steps of a typical selling process.

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