Aman Sareen is the CEO of Aarki, an AI company that delivers promoting solutions that drive revenue growth for mobile app developers. Aarki allows brands to effectively engage audiences in a privacy-first world through the use of billions of contextual bidding signals coupled with proprietary machine learning and behavioral models. Working with tons of of advertisers globally and managing over 5M mobile ad requests per second from over 10B devices, Aarki is privately held and headquartered in San Francisco, CA with offices across the US, EMEA, and APAC.
Could you share a bit about your journey from co-founding ZypMedia to leading Aarki? What key experiences have shaped your approach to AI and AdTech?
My adtech leadership odyssey began with co-founding ZypMedia in 2013, where we engineered a cutting-edge demand-side platform tailored for local promoting. This wasn’t just one other DSP; we built it from the bottom as much as handle high-volume, low-dollar campaigns with unprecedented efficiency. Consider it because the precursor to the hyper-localized, AI-driven targeting we see today.
As CEO, I steered ZypMedia to $20 million in SaaS revenue and processed $200 million in media transactions annually. This experience was a crucible for understanding the sheer scale of knowledge that modern ad platforms must handle — a challenge tailor-made for AI solutions.
My stint at LG Ad Solutions, post-ZypMedia’s acquisition by Sinclair, was a deep dive into the world of device manufacturers and the way the control of viewership data can shape the longer term of Connected TV (CTV) promoting. We used quite a lot of AI/Machine learning in constructing the LG Ads business, where the information collected from devices was used to generate targeting segments, inventory blocks, and planning software.
As CEO of Aarki since 2023, I’m on the forefront of the mobile promoting revolution. I can say that my journey has instilled in me a profound appreciation for the transformative power of AI in adtech. The progression from basic programmatic to AI-driven predictive modeling and dynamic creative optimization has been nothing wanting remarkable.
I’ve come to see AI not only as a tool but because the backbone of next-generation adtech. It’s the important thing to solving the industry’s most pressing challenges; from privacy-compliant targeting in a post-device ID world to creating real and personalized ad experiences at scale. I firmly imagine that AI is not going to only solve the pain points the advertisers face but in addition revolutionize how operations are run at platforms like Aarki. The teachings from my journey — the importance of scalability, data-driven decision-making, and continuous innovation — are more relevant than ever on this AI-first era.
Are you able to elaborate on how Aarki’s multi-level machine-learning infrastructure works? What specific benefits does it offer over traditional adtech solutions?
My experiences have taught me that the longer term of adtech lies in harmonizing big data, machine learning, and human creativity. At Aarki, we explore how AI can enhance every aspect of the mobile promoting ecosystem; from bid optimization and fraud detection to creative performance prediction and user acquisition strategies.
At this stage, Aarki’s multi-level machine learning infrastructure is designed to deal with several critical facets of mobile promoting, from fraud prevention to user value prediction. Here’s how it really works and why it’s advantageous:
- Fraud Detection and Inventory Quality Control: It’s designed to guard our clients’ performance and budgets. Our multi-layered approach combines proprietary algorithms with third-party data to remain ahead of evolving fraud tactics. We ensure campaign budgets are invested in real, high-quality inventory by consistently evaluating user behaviors and maintaining an up-to-date fraud database.
- Deep Neural Network (DNN) Models: Our core infrastructure utilizes multi-stage DNN models to predict the worth of every impression or user. This granular approach allows each model to learn features most vital for specific conversion events, enabling more precise targeting and bidding strategies in comparison with one-size-fits-all models.
- Multi-objective Bid Optimizer™ (MOBO): Unlike easy bid shading utilized by most DSPs, our MOBO considers multiple aspects beyond price. It uses dynamic variables resembling campaign and inventory attributes, predicted user value, and CPM segmentation to optimize bids. This sophisticated method maximizes ROI while balancing multiple objectives, finding optimal bids that win, meet KPI goals, and pace appropriately to utilize campaign budgets fully.
These components offer significant benefits over traditional AdTech solutions:
- Superior fraud detection
- More accurate predictions and higher ROI through multi-stage DNNs
- Granular creative hyper-targeting with multi-objective bid pricing
- Scalability to handle vast amounts of knowledge
- Privacy-first targeting with contextual cohorts
Our AI-driven approach allows for unprecedented accuracy, efficiency, and flexibility in mobile promoting campaigns. By leveraging deep learning and advanced optimization techniques, Aarki delivers superior performance while maintaining a robust deal with privacy and fraud prevention.
How does the Dynamic Multi-object Bid Optimizer function, and what impact does it have on maximizing ROI on your clients?
The Dynamic Multi-object Bid Optimizer is a complicated system that goes beyond traditional bid shading algorithms. Unlike easy bid shading algorithms that focus solely on pricing just below the anticipated winning bid, our optimizer considers multiple objectives concurrently. This includes not only price but in addition campaign performance metrics, inventory quality, and budget utilization.
The optimizer takes into consideration a variety of dynamic variables, including campaign and inventory attributes, predicted user value, and CPM segmentation. These variables guide the optimization process around client-specific KPIs, primarily ROI. This enables us to tailor our bidding technique to each client’s unique goals.
One in all the important thing strengths of our optimizer is its ability to balance between acquiring high-value users efficiently and exploring latest, untapped user segments and inventory. This exploration helps us discover worthwhile opportunities that more rigid systems might miss.
In practice, this implies our clients can expect more efficient use of their ad spend, higher-quality user acquisition, and, ultimately, higher ROI on their campaigns. For instance, it would make sense to pay 50% more to bid for a user who’s 5 times more worthwhile (ROAS). The optimizer’s ability to balance multiple objectives and adapt in real-time allows us to navigate the complex mobile promoting landscape more effectively than traditional, single-objective bidding systems.
Aarki emphasizes a privacy-first approach in its operations. How does your platform ensure user privacy while still delivering effective ad targeting?
I’m proud to say that privacy-first engagement is considered one of the core pillars of our platform, together with our AI platform. We have embraced the challenges of the no-device-ID world and developed modern solutions to make sure user privacy while delivering effective ad targeting. Here’s how we accomplish this:
- ID-less Targeting: We have fully adapted to the post-IDFA landscape and are SKAN 4 compliant. Our platform operates without counting on individual device IDs, prioritizing user privacy from the bottom up.
- Contextual Signals: We leverage a big selection of contextual data points resembling device type, OS, app, genre, time of day, and region. These signals provide worthwhile targeting information without requiring personal data.
- Massive Contextual Data Processing: We process over 5 million ad requests per second from over 10 billion devices globally. Each request has a wealth of contextual signals, providing us with a wealthy, privacy-compliant dataset.
- Advanced Machine Learning: Our 800 billion row training model database correlates these contextual signals with historical consequence data. This enables us to derive insights and patterns without compromising individual user privacy.
- Dynamic Behavioral Cohorts: Using machine learning, we create highly detailed, dynamic behavioral cohorts based on aggregated contextual data. These cohorts enable efficient optimizations and scaling without counting on personal identifiers.
- ML-driven Creative Targeting™: For every cohort, we use machine learning in collaboration with our creative team to plot optimal creative strategies. This approach ensures relevance and effectiveness without infringing on individual privacy.
- Continuous Learning and Adaptation: Our AI models constantly learn and adapt based on campaign performance and evolving contextual data, ensuring our targeting stays effective as privacy regulations and user expectations evolve.
- Transparency and Control: We offer clear details about our data practices and offer users control over their ad experiences wherever possible, aligning with privacy best practices.
By leveraging these privacy-first strategies, Aarki delivers effective ad targeting while respecting user privacy. We have turned the challenges of the privacy-first era into opportunities for innovation, leading to a platform that is each privacy-compliant and highly effective for our clients’ user acquisition and re-engagement campaigns. Because the digital promoting landscape evolves, Aarki stays committed to leading the best way in privacy-first, AI-driven mobile promoting solutions.
Are you able to explain the concept of ML-driven Creative Targeting™ and the way it integrates together with your creative strategy?
ML-driven Creative Targeting™ is our methodology for optimizing ad creatives based on the behavioral cohorts we discover through our machine learning models. This process involves several steps:
- Cohort Evaluation: Our ML models analyze vast amounts of contextual data to create detailed behavioral cohorts.
- Creative Insights: For every cohort, we use machine learning to discover the creative elements which can be more likely to resonate most effectively. This might include color schemes, ad formats, messaging styles, or visual themes.
- Collaboration: Our data science team collaborates with our creative team, sharing these ML-derived insights.
- Creative Development: Based on these insights, our creative team develops tailored ad creatives for every cohort. This might involve adjusting imagery, copy, calls-to-action, or overall ad structure.
- Dynamic Assembly: We use dynamic creative optimization to assemble ad creatives in real-time, matching essentially the most effective elements to every cohort.
- Continuous Optimization: As we gather performance data, our ML models continually refine their understanding of what works for every cohort, making a feedback loop for ongoing creative improvement.
- Scale and Efficiency: This approach allows us to create highly targeted creatives at scale without the necessity for manual segmentation or guesswork.
The result’s a synergy between data science and creativity. Also considered one of our core pillars, Unified Creative Framework, ensures that our ML models provide data-driven insights into what works for various audience segments. At the identical time, our creative team brings these insights to life in compelling ad designs. This approach enables us to deliver more relevant, engaging ads to every cohort, concurrently improving campaign performance and user experience.
What role does your creative team play in developing ad campaigns, and the way do they collaborate with the AI models to optimize ad performance?
Our creative team plays an integrated role in developing effective ad campaigns at Aarki. They work in close collaboration with our AI models to optimize ad performance. The creative team interprets insights from our ML models about what resonates with different behavioral cohorts. They then craft tailored ad creatives, adjusting elements like visuals, messaging, and formats to match these insights.
As campaigns run, the team analyzes performance data alongside the AI, constantly refining their approach. This iterative process allows for rapid optimization of creative elements.
The synergy between human creativity and AI-driven insights enables us to provide highly targeted, engaging ads at scale, driving superior performance for our clients’ campaigns.
How does Aarki’s AI infrastructure detect and forestall ad fraud? Are you able to provide some examples of the forms of fraud your system identifies?
As I discussed earlier, Aarki employs a multi-layered approach to combat ad fraud. We’re approaching fraud deterrence as a pre-bid filter with post-bid analytics of the information that comes through our systems. While I’ve already outlined our general strategy, I can provide some specific examples of the forms of fraud our system identifies:
- Click flooding: Detecting abnormally high click rates from specific sources.
- Install farms: Identifying patterns of multiple installs from the identical IP address or device.
- Abnormal click-to-install time (CTIT): Spotting abnormal click-to-install times as a signal for bot activity.
- Low Retention Rates: Identifying users from publishers that repeatedly exhibit low retention rates after install.
Our AI constantly evolves to acknowledge latest fraud tactics, protecting our clients’ budgets.
How does Aarki’s approach to user acquisition and re-engagement differ from other platforms within the industry?
Aarki’s approach to user acquisition and re-engagement sets us apart in several key ways:
- Privacy-First Strategy: We have fully embraced ID-less targeting, making us SKAN 4 compliant and future-ready in a privacy-focused landscape.
- Advanced AI and Machine Learning: Our multi-level machine learning infrastructure processes vast amounts of contextual data, creating sophisticated behavioral cohorts without counting on personal identifiers.
- ML-driven Creative Targeting™: We uniquely mix AI insights with human creativity to develop highly targeted ad creatives for every cohort.
- Dynamic Multi-object Bid Optimizer: Our bidding system considers multiple objectives concurrently, balancing efficiency with exploration to maximise ROI.
- Contextual Intelligence: We leverage trillions of contextual signals to tell our targeting, going beyond basic demographic or geographic segmentation.
- Continuous Optimization: Our AI models constantly learn and adapt, ensuring our strategies evolve with changing user behaviors and market conditions.
- Unified Approach: We provide seamless integration of user acquisition and re-engagement strategies, providing a holistic view of the user journey.
- Scalability: Our infrastructure can handle immense data volumes (5M+ ad requests per second from 10B+ devices), enabling highly granular targeting at scale.
- Advanced Fraud Deterrence Mechanisms: Our in-house pre-bid fraud filters, post-bid analytics of massive data volumes, combined with Third-party data, put us on the forefront of saving our clients’ money from fraudulent traffic.
This mix of privacy-centric methods, advanced AI, creative optimization, fraud deterrence, and scalable infrastructure allows us to deliver simpler, efficient, and adaptable campaigns.
My experiences have taught me that the longer term of ad tech lies in harmonizing big data, machine learning, and human creativity. I take pride within the incontrovertible fact that, along with our technology, we even have an excellent team of analysts, data scientists, and inventive professionals who add human creativity to our tech.
Could you share some success stories where Aarki’s platform significantly improved client ROI and campaign effectiveness?
The AppsFlyer Performance Index recognizes Aarki as a pacesetter in retargeting, rating us #1 for gaming in North America and #3 globally. We’re also rated as a top performer across all Singular promoting ROI indexes. This case study can be a testament to our global leadership. Not only for gaming, but we’ve recent case studies showcasing our ability to drive results across various app categories.
I’m proud to spotlight our partnership with DHgate, a number one e-commerce platform. Our retargeting campaigns for each Android and iOS delivered exceptional results, showcasing Aarki’s ability to drive performance at scale.
Leveraging our deep neural network technology, we precisely assembled user segments to maximise retargeting effectiveness. This resulted in a 33% growth in higher-intent user clicks and a 33% increase in conversions.
Most impressively, while DHgate’s spend with Aarki increased by 52%, we consistently exceeded their 450% D30 ROAS goals by 1.7x, achieving an excellent 784% ROAS. This case exemplifies our commitment to delivering superior results for our clients. Read more about it here.
For a food and delivery app, we implemented a retargeting campaign to reactivate users and acquire latest customers efficiently.
This resulted in a 75% decrease in Cost Per Acquisition (CPA) and 12.3 million user reactivations. The important thing to success was utilizing our Deep Neural Network models to focus on the fitting audiences with tailored messaging, keeping the campaign fresh and fascinating. Read it here.
These case studies reveal our ability to drive significant improvements in key metrics across different app categories and campaign types. Our privacy-first approach, advanced AI capabilities, and strategic use of contextual data allow us to deliver outstanding results for our clients, whether in user acquisition or re-engagement efforts.
What future advancements in AI and machine learning do you foresee as pivotal for the mobile promoting industry?
Looking ahead, I anticipate several pivotal advancements in AI and machine learning for mobile promoting:
- Enhanced privacy-preserving techniques: The large scale of knowledge we process will result in unprecedented learning capabilities. Deep neural networks (DNNs) will leverage this to create superior privacy-first engagement strategies. Actually, the concept of “targeting” will evolve so dramatically that we’ll need latest terminology to explain these AI-driven, predictive approaches.
- Generative AI for real-time creative optimization: We’ll see AI that cannot only optimize but in addition create and dynamically modify ad creatives in real-time. It will revolutionize how we approach ad design and personalization.
- Holistic Predictive Models: By combining our deep neural networks for product insights with our Multi-Objective Bid OptimizerTM (MOBO) for pricing, we’ll develop highly effective and efficient models for each user acquisition and retargeting. These will provide incredibly accurate predictions of long-term user value, allowing for smarter, more strategic campaign management.
These advancements will likely result in simpler, efficient, and user-friendly mobile promoting experiences.
