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Sıla Ermut

Sıla Ermut

Industry Analyst
69 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

AIJan 21

AI Presentation Maker: Gamma vs Google Slides

We evaluated the top 5 AI presentation makers across 9 dimensions with 4 different prompts to assess their context and prompt understanding, visual AI integration, and voice and brand style adaptation capabilities: AI presentation maker benchmark results See the methodology and evaluation criteria to understand how we determined these results.

AIJan 21

Generative AI Ethics: How to Manage Them

Generative AI raises important concerns about how knowledge is shared and trusted. Britannica, for instance, filed a lawsuit against Perplexity, alleging that the company illegally and knowingly copied Britannica’s human-verified content and misused its trademarks without permission. Explore what generative AI ethics concerns are and best practices for managing them. 1.

AIJan 20

Text-to-Speech Software: Hume & ElevenLabs

As AI capabilities evolve, text-to-speech (TTS) software is becoming more adept at producing natural, human-like speech. We evaluated and compared the performance of five different TTS and sentiment analysis tools (Resemble, ElevenLabs, Hume, Azure, and Cartesia) across seven core emotion categories to determine which could most accurately, consistently, and comprehensively recognize emotional tones.

AIJan 20

AI Image Detector Benchmark

As these synthetic visuals grow more realistic and accessible, the ability to detect them has become a critical concern for upholding generative AI ethics, combating misinformation, and ensuring image authenticity. We compared the top 7 AI image detectors across 5 dimensions and found that most perform no better than a coin toss.

AIJan 20

Top 6 Social Media Post Generator Benchmark

Generative AI is playing a significant role in the creation and management of social media content. As more tools offer features like caption writing, image selection, and post scheduling, it’s helpful to understand how they compare.

Enterprise SoftwareJan 12

IT Asset Management (ITAM) Pricing Comparison

Finding the right IT Asset Management (ITAM) solution is key to controlling costs, reducing risks, and gaining full visibility into your IT infrastructure. Designed for IT managers, procurement teams, and SMEs, this comparison highlights how different pricing models and feature sets align with varying business needs.

AINov 25

Audience Simulation: Can LLMs Predict Human Behavior?

In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.

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.

AISep 1

Large Quantitative Models: Applications & Challenges

Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.