We spoke with Başak Zerman, Sales Director META at Adjust, about Türkiye’s position in the global mobile app ecosystem, the impact of artificial intelligence on user acquisition and marketing strategies, and the future of the industry.
Drawing on Adjust’s Mobile App Trends Report 2026 Türkiye, our conversation explores the competitive advantages of Turkish mobile companies, the differences between Generative AI and Agentic AI, data-driven optimization, and how the role of mobile marketers is expected to evolve in 2027 and beyond.
If you had to describe the current state of Türkiye’s mobile app ecosystem in a single sentence, what would you say? Adjust’s 2026 data on Türkiye highlights a rapidly growing market with a young population and high mobile penetration. In your opinion, what truly sets Türkiye apart in the global mobile ecosystem today?
If I had to sum up Türkiye in one sentence, I would say: “A global mobile app development hub that doesn’t just play the game, but rewrites the rules and has mastered the art of turning crises into growth opportunities.”
Everyone talks about Türkiye’s young population or high mobile penetration. But that’s not what radically differentiates us on the global stage. It’s our agility and speed of adaptation.
Mobile teams in Türkiye can pivot on the same day in response to changes that their international competitors spend weeks discussing in meetings and developing strategies around. Rollic’s aggressive transition from hyper-casual to hybrid-casual, or Loom Games’ ability to identify a new mechanic and achieve a breakthrough even in the most saturated market segments, are no coincidences.
Our people don’t just interpret data. They immediately turn the market opportunities they sense through that data into action.
The report highlights Türkiye as a market that not only consumes apps and technology but also develops them for global audiences. For Turkish-developed apps to succeed internationally, which capability is most critical today: product development, user acquisition, or turning the right data into action at the right time?
Let’s face it: We’re living in a graveyard of hundreds of apps that failed despite building great products and pouring millions of dollars into user acquisition.
Neither a great product nor UA alone can save you anymore. Today, the name of the game is turning the right data into action at the right time, with virtually zero latency.
Look at the regional and global expansion efforts of giants like Trendyol and Hepsiburada. Their success isn’t just about the range of products they offer. It’s also about their ability to identify the exact moment a user becomes at risk of churning and retain them with a personalized offer at precisely the right time.
Product and UA are the two pistons of the engine, but the attribution and measurement infrastructure that converts data into real-time action is the engine’s fuel delivery system. Unfortunately, if you deliver the fuel at the wrong time, the engine blows up.
One of the most striking findings in the 2026 report is the rise of AI-powered applications. How do you interpret the growth of AI apps in Türkiye? Does consumer interest in AI products represent a lasting shift in behavior, or are we currently experiencing an “AI discovery phase”?
We’re clearly in a phase where the AI bubble is being cleared out.
Users have moved beyond experimenting with superficial apps that simply slap an AI label onto everything. Retention rates for basic tools that are essentially just ChatGPT interfaces wrapped in a different design are hitting rock bottom.
However, this isn’t a passing fad. It’s the second phase of a permanent shift in user behavior.
Users no longer expect AI to be “cool.” They expect it to make their lives easier without even noticing it’s there.
The apps that survive are those that deliver hyper-personalization seamlessly, solving everyday frustrations without making users consciously aware of the AI working behind the scenes.
Just as Papara made the financial experience virtually frictionless, AI will achieve lasting adoption when it becomes an invisible yet indispensable part of the mobile product.
Generative AI is no longer just a feature offered to end users. App companies are increasingly incorporating it into their own growth processes. Which tasks should mobile marketing teams absolutely be using Generative AI for today, and where should they still rely on human expertise?
Teams that use Generative AI only to “generate images” or “write ad copy” are missing the boat.
GenAI has become essential for managing the explosion of creative variations. Marketers should no longer be producing five different ad creatives. They should be using GenAI to generate 500 variations.
But here’s where we draw the line: Strategy, empathy, and brand identity.
GenAI can write 500 different ad copies for you, but it doesn’t truly understand what will strike a nerve with the people in a particular market.
Understanding the cultural nuances, sense of humor, or current economic mindset of a user in Türkiye or LATAM, and then building a strategy around those insights, is still the responsibility of human marketers.
GenAI can be your factory worker, but it’s still a long way from becoming your orchestra conductor.
AI’s role in marketing is expanding beyond content creation. Systems that analyze user behavior, identify segments, evaluate campaigns, and answer the question “What should my next move be?” are becoming increasingly common. How does Adjust view this transformation?
At Adjust, we don’t see this as an option. We see it as the new standard.
In the old world, a marketer would download a campaign report on Friday evening, analyze it on Monday, and shift the budget on Tuesday. In 2026, it’s impossible to survive at that pace.
At Adjust, we’ve transformed data from a passive reporting dashboard into a live decision-making mechanism.
The system doesn’t just tell marketers, “Look, CPI is increasing on this channel.”
Instead, it recommends something like: “The audience segment on this channel has reached saturation. You should shift 20% of your budget to another channel where this segment is delivering higher D7 ROAS.”
The marketer’s role is evolving from manually processing data to making strategic decisions.
We’re also making significant investments in AI. Many brands already using Adjust are benefiting from these capabilities and growing rapidly as a result.
This brings us to the critical distinction between Generative AI and Agentic AI. While Generative AI helps marketers work faster, Agentic AI can make decisions and execute actions based on specific objectives. How will Adjust’s “Agentic Growth” vision change the way mobile growth teams operate?
The distinction is exactly this: Generative AI gives you recommendations. Agentic AI puts those recommendations into action.
Growth teams spend 70% of their time on manual budget adjustments, channel-specific spending limits, and monitoring routine alerts.
Adjust’s Agentic Growth vision eliminates this cumbersome process entirely.
The marketer simply sets a goal for the system: “Bring me users who generate a D30 ROAS of 15%, and keep CAC below this threshold.”
When performance drops on an advertising channel at 3 a.m., Agentic AI reallocates the budget without waiting for human intervention.
Marketers are no longer managing marketing operations themselves. They’re becoming strategists who manage AI agents.
The next stage involves AI not only explaining “what happened,” but also deciding “what should be done.” For example, when a campaign’s performance declines, can an agentic system adjust budgets, targeting, or channel allocation without human intervention? What does Adjust make possible in this area today?
Absolutely. It can, and it’s already happening!
Let me give you some concrete examples from the brands we work with globally.
Praktika, an English-learning app, automated its data workflows using Adjust’s automation and deep integration solutions. By moving away from manual campaign optimization and entrusting the process to data-driven AI systems, the company achieved a 50% increase in LTV and improved ROAS by 20%.
Another example is Axlebolt, the developer of the globally popular mobile FPS game Standoff 2. By using Adjust’s advanced Smart Banners and data automation tools, Axlebolt increased organic conversion rates in its web-to-app journeys by 23% and significantly reduced user acquisition costs.
So yes, the infrastructure that can automatically shift budgets to the right channels within milliseconds, without human intervention, and achieve performance targets is already live and operational with Adjust today.
As AI takes over more decision-making responsibilities, measurement and data quality become even more critical. An AI system working with inaccurate or incomplete attribution data could make the wrong decisions much faster. How does Adjust approach the relationship between reliable measurement, privacy, and accurate attribution in the age of AI-driven optimization?
Your question highlights what is arguably the most dangerous blind spot in the market.
The GIGO principle (Garbage In, Garbage Out) becomes far more dangerous in the AI era.
A human marketer working with bad data makes mistakes slowly. But an Agentic AI system operating on inaccurate data can burn through a company’s entire marketing budget in just a few hours.
At Adjust, our position is clear: Zero tolerance when it comes to data hygiene.
With our Fraud Prevention solutions, we block fraudulent traffic at the source before attribution even takes place.
We’re also incorporating AI-powered predictive analytics into SKAdNetwork/Conversion Hub and privacy-centric measurement models.
If AI is going to optimize campaigns, the raw data we provide must be 100% filtered, accurate, and fully compliant with privacy standards such as SKAN and Privacy Sandbox.
Without high-quality data, AI simply accelerates disaster.
This transformation also has an economic dimension for mobile companies in Türkiye. With rising user acquisition costs and intensifying competition, can AI, particularly agentic optimization, deliver a measurable advantage by generating greater growth with smaller budgets? If so, which KPIs would reflect this improvement?
Absolutely, because AI doesn’t increase your budget. It eliminates budget waste.
Today, at least 20–30% of mobile marketing budgets are wasted due to delayed decisions, poorly timed offers, and saturation that goes undetected.
The moment agentic optimization eliminates that waste, the impact becomes directly visible across three key KPIs:
- Lower eCPI and CAC: By immediately pulling budgets away from underperforming channels, average acquisition costs decrease.
- Higher D30 ROAS and LTV: Rather than simply acquiring users, AI identifies those with higher spending potential and optimizes budgets accordingly. This is what drives significant LTV improvements, as we saw with Praktika.
- Greater Budget Management Capacity per Marketer: Previously, a marketer might have struggled to manage three channels. With AI support, that same marketer can manage 30 different channels with the same level of efficiency.
Finally, let’s look toward the future. How will a successful mobile growth team operate in 2027 and beyond? If humans increasingly focus on strategy, creativity, and critical decisions while AI takes on more responsibility for analysis, optimization, and daily operations, how do you think this transformation will redefine the role of mobile marketers? And which capabilities should teams invest in today to prepare for that future?
By 2027, roles like “media buyer” or “dashboard monitor” will no longer exist.
The era of marketers sitting in front of screens and clicking buttons is over.
The mobile marketer of the future will be a senior strategist who designs and builds growth architecture.
Teams that want to prepare for this future must immediately invest in three essential capabilities:
- Becoming an AI Prompter / Agent Manager: The ability to define the right constraints, objectives, and business rules for AI systems.
- Deep Product and Business Model Literacy: Understanding not just advertising metrics, but also an app’s in-app economy, monetization structure, and user psychology.
- Data Hygiene and Architectural Understanding: Developing the technical expertise to question the accuracy of the underlying data and its compliance with privacy standards, rather than blindly accepting AI-generated decisions.
