Adikteev Company Profile: App Retargeting & User Retention Platform
Introduction to the Adikteev Mobile Marketing Platform
Adikteev is a specialized Demand-Side Platform (DSP) and mobile marketing provider focused exclusively on app retargeting, user retention, and incremental revenue generation. Built for enterprise app publishers, this programmatic advertising engine re-engages dormant users through predictive churn modeling and customized bidding strategies. By shifting the traditional digital marketing focus away from initial user acquisition, the platform works to maximize the lifetime value (LTV) of existing application audiences. Modern performance marketers require tools that identify behavioral drop-offs, and this specific technology stack is engineered to address those exact lifecycle bottlenecks.
Operating on a high-frequency Real-Time Bidding (RTB) infrastructure, the DSP is designed to process millions of ad-exchange queries per second to identify optimal re-engagement opportunities. By analyzing Mobile Measurement Partner (MMP) postback data alongside contextual behavioral signals, precision-targeted campaigns are deployed across gaming, e-commerce, and non-gaming vertical applications. This strict data-driven approach allows Adikteev to deliver measurable incrementality, ensuring that advertising spend generates proven revenue lift rather than merely taking credit for organic user conversions. Furthermore, the specialized solutions offered by the company, such as churn-based bidding, empower technical directors to preemptively target audience cohorts identified as having the highest statistical risk of uninstallation. The architecture supports complex operational scalability for global application developers seeking sustainable ecosystem growth.
Adikteev Company Overview
When enterprise procurement teams evaluate mobile marketing partners, verifying corporate stability is a critical step in the due diligence process. The structural and executive foundation of this programmatic platform is outlined below, detailing its operational footprint and leadership.
Full Company Name: Adikteev. Operating as a dedicated mobile Demand-Side Platform (DSP), the entity focuses strictly on post-install app marketing and algorithmic ad delivery.
Founding Year: 2012. The organization was established to address the growing complexities of mobile advertising, evolving from early ad networks into a specialized machine-learning retention engine.
Founders: Xavier Mariani, Emilien Eychenne, and Frederic Leroy. This founding team built the technological infrastructure to bridge the gap between creative ad formats and real-time bidding efficiency.
CEO: Xavier Mariani. Serving as the Chief Executive Officer, Mariani directs strategic expansion, focusing on privacy-compliant mobile architecture and incrementality measurement for the company.
Global Office Locations: Paris (Headquarters), New York, and San Francisco. The headquarters in France serves as the primary engineering hub, while the US offices position Adikteev to service major North American app publishers and gaming studios.
Adikteev Company History & Milestones
For B2B procurement professionals evaluating mobile marketing platforms, tracking the corporate evolution of a technology partner provides necessary context regarding long-term viability and algorithmic maturity. The historical trajectory of the platform reflects a strategic transition from a generalized mobile ad network into a highly specialized app retargeting and churn prediction engine. The following timeline outlines the major operational milestones, product launches, and industry validations that have defined the market position of Adikteev.
Timeline of Key Events and Funding Milestones
2012: Adikteev was officially founded in Paris, France, establishing its initial operational framework within the mobile programmatic advertising sector.
2014–2015: The organization secured early seed funding from venture capital firms, including Ventech and ISAI, to build out its foundational advertising technology infrastructure.
February 2018: The company secured a $12 million Series B funding round. This pivotal capital injection was led by Ring Capital and BNP Paribas Developpement, enabling Adikteev to scale its Demand-Side Platform (DSP) architecture and aggressively expand its operational footprint within the North American market.
Acquisitions and Creative Capability Expansion
MotionLead Acquisition (May 2015): A critical turning point in the technological development of the DSP was the acquisition of MotionLead, a Y Combinator-backed startup specializing in interactive mobile advertising. This acquisition fundamentally enhanced the internal creative capabilities of the company. It allowed the platform to natively deliver animated, transparent, and playable ad units without disrupting user navigation—a technology that forms the backbone of the current Adikteev in-house creative studio.
Product Launches and DSP Evolution
2016 Retargeting Pivot: The company strategically shifted its product focus away from standard mobile media buying to specialize heavily in post-install app retargeting and retention.
Predictive Churn-Based Bidding: The commercial rollout of proprietary machine learning models allowed the platform to move beyond standard retargeting, utilizing predictive analytics to assign dynamic bid values to audience cohorts identified as having a high probability of application uninstallation.
Connected TV (CTV) User Acquisition: Recognizing the shift in cross-device media consumption, the company launched specialized user acquisition products for the CTV ecosystem, allowing app publishers to target high-intent audiences on smart TVs using programmatic ad placements.
Adikteev Awards and Industry Recognitions
AppsFlyer Performance Index: The DSP consistently ranks at the top of the AppsFlyer Performance Index for global remarketing. Recent editions of the index have placed Adikteev as the #1 independent retargeting platform for gaming apps, securing the top position for Remarketing (Android) in Casual and Casino gaming worldwide, alongside top-tier rankings in the Animated Gaming creative category.
Adikteev Financials & Key Metrics
For enterprise organizations evaluating programmatic partners, verifying the financial stability and scalability of a Demand-Side Platform is an essential step in procurement. As a privately held entity, Adikteev does not maintain a public market capitalization; however, its corporate financial metrics demonstrate significant market penetration, profitability, and backing from established private equity and venture capital firms.
Below is a breakdown of the key financial and organizational metrics for the company:
Revenue Estimates and Run-Rate: The financial trajectory of Adikteev reflects substantial scaling within the global mobile ad-tech sector. In late 2025, concurrent with a corporate restructuring, the company projected an annual revenue run-rate of approximately €150 million. A significant indicator of its global scalability is that 98% of these sales are generated from international markets outside of its domestic French headquarters.
Funding Rounds and Investors: Historically, the platform accelerated its early technological infrastructure through strategic venture capital. Prior to its most recent restructuring, the company had raised over $13.4 million in venture funding. Key capital milestones include:
Seed Round (2014): Secured approximately $1.4 million in early-stage capital, backed by institutional investors Ventech and ISAI.
Series B (2018): Secured a pivotal $12 million funding round led by Ring Capital and BNP Paribas Développement, which funded its aggressive expansion into the North American mobile market.
Management Buyout (Late 2025): The founding team executed a €28 million management buyout (MBO) backed by AgilaCapital. This strategic financial maneuver allowed the founders to regain majority control, positioning the company to explore new acquisitions and further develop its AI-driven predictive architecture.
Employee Count: To support its high-frequency trading infrastructure and managed services, Adikteev maintains an organizational scale of approximately 120 global employees. This headcount is strategically distributed across data science, technical engineering, account strategy, and its in-house creative production studios in Paris, New York, and San Francisco.
Market Capitalization: Because Adikteev operates as a privately held corporation, it does not possess a publicly traded market cap. The company’s ongoing expansion and technological development are sustained entirely by its private equity backing and robust internally generated revenue from enterprise app publishers.
Adikteev Industry & Market Position
Industry Classification and Market Segment
Within the broader AdTech ecosystem, Adikteev operates specifically as a mobile Demand-Side Platform (DSP) focused on programmatic advertising. While many platforms operate as generalized user acquisition networks, Adikteev is classified strictly within the app retargeting and retention segment. By integrating directly with ad exchanges via Real-Time Bidding (RTB) protocols, the platform facilitates automated media buying for enterprise-level app developers. This highly specialized market positioning allows this specific architecture to capture high-intent search traffic from performance marketers seeking measurable, post-install ecosystem growth rather than top-of-funnel install metrics.
Core Competitive Advantages
For B2B procurement teams evaluating DSP options, distinguishing the platform’s proprietary technology from generalized ad networks is crucial. The core competitive advantages of Adikteev are rooted in machine learning and a deliberate focus on the post-install lifecycle:
Dedicated Focus on App Retargeting: Unlike generic networks that split computing resources between user acquisition and remarketing, the entire RTB infrastructure of the platform is dedicated to re-engaging existing or dormant users. This focus ensures higher processing efficiency for post-install data points.
Proprietary Churn-Prediction Machine Learning: The most distinct differentiator is the platform’s churn-based bidding methodology. By analyzing historical Mobile Measurement Partner (MMP) postbacks, Adikteev deploys predictive algorithms to identify user cohorts at high risk of uninstallation.
Preemptive Budget Allocation: Using these predictive churn scores, the DSP automatically adjusts programmatic bid values, allocating capital to re-engage users before they drop off, maximizing the return on retention marketing spend.
Transparent Incrementality Measurement: To prove tangible value, the system utilizes rigorous Ghost Bidding and PSA testing, ensuring advertisers can measure “true lift” and verify that ad spend is driving new revenue rather than cannibalizing organic app sessions.
App Retargeting & Re-engagement
Mobile App Retargeting Engine Capabilities
The programmatic backbone of the Adikteev platform is engineered specifically for scale and latency reduction. Unlike standard ad networks, the Adikteev mobile app retargeting engine relies on a high-throughput architecture utilizing advanced database platforms such as Aerospike and Apache Druid.
Query Processing Volume: The Real-Time Bidding (RTB) infrastructure operates at an enterprise scale, processing up to 1.5 million database queries per second (QPS) across more than 1.3 billion active user counters during peak traffic.
Sub-Millisecond Latency: To successfully win programmatic auctions, the Adikteev engine evaluates global ad exchange opportunities, cross-references user behavior histories, and executes bid decisions in milliseconds. The system maintains a p99 latency of less than 1 millisecond.
Platform Adaptability: To navigate modern privacy frameworks, the DSP provides robust iOS app retargeting capabilities optimized for SKAdNetwork protocols, ensuring stable attribution in post-IDFA environments alongside traditional Android ecosystems.
Target App Verticals
While the foundational technology processes massive programmatic data loads, the execution of these app retargeting campaigns is customized for distinct vertical requirements. Adikteev segments its strategic approach into specific application categories to maximize performance outcomes.
Gaming App Retargeting: The platform holds top-tier industry index rankings for re-engaging users within the mobile gaming sector. The Adikteev machine learning models are optimized to identify dormant players in casual, mid-core, and social casino categories, subsequently deploying interactive playable ads to drive repeat in-app purchases.
E-Commerce and Retail: For shopping applications, the predictive algorithms focus heavily on cart abandonment recovery. The system isolates audience segments demonstrating high commercial intent, serving dynamic creatives to facilitate secondary purchases.
Non-Gaming Applications: The platform actively extends its retention methodology to lifestyle, finance, fitness, and on-demand delivery apps. In these non-gaming verticals, the strategic focus centers on driving secondary feature adoption, promoting subscription renewals, and preventing user churn.
User Retention & Churn Management
Predictive Churn Machine Learning
For enterprise application publishers, reactive marketing is often less cost-effective than proactive audience management. To address this, the Adikteev platform utilizes proprietary predictive algorithms designed specifically for proactive app user retention. Rather than waiting for an uninstallation event to trigger a remarketing campaign, the machine learning models analyze historical event postbacks transmitted by Mobile Measurement Partners (MMPs).
The Adikteev churn prediction system evaluates complex user behaviors to identify dormant tendencies through several technical processes:
Behavioral Scoring: The algorithmic model assesses granular in-app actions—such as session frequency degradation, prolonged cart abandonment, or stalled progression in gaming environments—to assign a statistical probability of churn to individual user profiles.
Dynamic Cohort Segmentation: Based on real-time data, users are automatically categorized into distinct risk segments (e.g., low, medium, or high risk of uninstallation) before they completely disengage from the application ecosystem.
Preemptive Targeting Activation: By isolating high-probability dormant users early in the decay cycle, the DSP ensures that retention resources are deployed precisely when an intervention is most statistically likely to succeed.
Churn-Based Bidding Methodology
The predictive data generated by the machine learning models directly dictates the programmatic buying execution of the DSP. This precise integration forms the foundation of the proprietary Adikteev churn-based bidding methodology. While standard remarketing platforms often apply a uniform, static bid price across broad audience segments, that approach frequently results in wasted ad spend on users who would have remained active organically.
The Adikteev churn-based retargeting architecture optimizes capital allocation through dynamic risk profiling:
High-Risk Capital Allocation: The engine aggressively increases real-time programmatic bid values for users assigned a high churn score, ensuring that customized retention creatives successfully win ad-exchange auctions to re-engage valuable, at-risk audiences.
Low-Risk Bid Suppression: Conversely, the system suppresses or entirely eliminates ad bids for user cohorts identified as having a low probability of churn. This mechanism prevents organic cannibalization and ensures marketing capital is not expended on guaranteed active users.
Automated ROAS Optimization: By linking the programmatic bid price directly to the statistical risk of losing the user, this churn-based bidding architecture intrinsically improves overall Return on Ad Spend (ROAS), delivering measurable capital efficiency for performance marketing teams.
Incrementality & Performance Metrics
Adikteev Incrementality Testing Methodology
B2B performance marketers require definitive proof that remarketing spend generates new, measurable revenue. The Adikteev incrementality testing methodology provides a scientific framework to calculate the exact percentage of conversions driven exclusively by advertising exposure. As detailed within the Adikteev incrementality playbook, this rigorous testing isolates target app audiences into distinct treatment and control groups to determine actual revenue lift. This mathematical approach ensures that enterprise procurement teams can financially validate the direct impact of their retention strategies, moving beyond traditional, flawed attribution models.
Ghost Bidding vs. PSA Testing for True Lift Measurement
To conduct accurate Adikteev uplift measurement, the DSP architecture utilizes specialized control group mechanisms. The platform supports distinct methodologies to evaluate incremental performance and prove real financial yield:
PSA Testing (Public Service Announcement): This traditional methodology involves serving a generic, non-brand ad (the PSA) to the control group, while the treatment group receives the actual remarketing creative. By comparing the conversion rates between the two cohorts, the platform calculates the baseline organic conversion rate versus the ad-influenced rate.
Ghost Bidding: As a highly cost-efficient alternative, Ghost Bidding simulates the programmatic auction process for the control group without actively purchasing the ad impression. The Adikteev engine logs the exact millisecond it would have won the ad exchange auction, monitoring the subsequent organic behavior of that user.
Evaluating the Ghost Bidding vs. PSA testing frameworks allows marketing directors to deploy the most effective measurement architecture based on their specific budget constraints and data fidelity requirements.
Calculating Adjusted ROAS
Standard mobile attribution models frequently overestimate campaign performance by assigning ad credit for users who possessed a high probability of converting organically. To resolve this discrepancy, the Adikteev reporting dashboard utilizes Adjusted ROAS (Return on Ad Spend), a precise app monetization metric that integrates true incrementality data directly into the financial yield calculation.
Incrementality vs. Last-Click ROAS: Traditional last-click attribution assigns 100% of the conversion value to the final ad interaction, creating artificially inflated metrics. Adjusted ROAS corrects this by applying the proven incrementality multiplier (the percentage of true lift) to the gross revenue generated.
The Adjusted Metric Formula: Adjusted ROAS is calculated by taking the Gross Campaign Revenue, multiplying it by the Incrementality Percentage, and dividing the result by the Total Ad Spend.
By utilizing this advanced calculation, Adikteev ensures that performance reports reflect genuine, verifiable financial contributions, allowing application developers to scale budgets based on validated returns.
Connected TV (CTV) User Acquisition
CTV Advertising for Apps
Recognizing the evolving media consumption habits of digital audiences, Adikteev has expanded its programmatic infrastructure to encompass broader cross-device environments. A primary focus of this strategic expansion is Connected TV UA, a service line designed to capture high-intent users navigating between mobile applications and smart television screens.
By deploying high-fidelity video creatives across premium streaming inventory, the Adikteev Demand-Side Platform allows enterprise publishers to scale their user bases beyond saturated mobile-only channels. This CTV user acquisition methodology leverages advanced cross-device tracking protocols—including probabilistic and deterministic attribution models—to connect an ad exposure event on a smart TV to the final application installation on a secondary mobile device.
Through this integrated approach, Adikteev ensures that performance marketing teams can diversify their acquisition traffic sources while maintaining strict quantitative measurement standards. The utilization of CTV advertising for apps allows developers to reach untapped, high-value demographics on the largest screen in the household, subsequently feeding the core retention engine with a consistent volume of newly acquired, qualified users.
Core Capabilities and Advertising Technology
Dynamic Creative Optimization (DCO)
Beyond algorithmic bid adjustments, the Adikteev Demand-Side Platform utilizes Dynamic Creative Optimization (DCO) to personalize the visual and interactive elements of mobile advertising in real-time. This specific advertising technology ensures that the retargeting creatives served to an individual are algorithmically tailored to their unique behavioral history, operating system, and immediate context, maximizing the statistical probability of application re-engagement.
To execute this programmatic personalization at an enterprise scale, the Adikteev DCO engine leverages a complex matrix of real-time data inputs to dynamically assemble ad components—such as background images, call-to-action buttons, product carousels, and localized text—in the exact millisecond before the ad impression is served on the exchange.
The Adikteev creative customization process relies on the following data-driven targeting parameters:
Behavioral History Integration: The Adikteev DSP synchronizes directly with Mobile Measurement Partner (MMP) postbacks to analyze past user actions. For e-commerce applications, the DCO engine will dynamically populate the ad unit with the precise retail product left in an abandoned cart. For gaming publishers, it will automatically display the specific level, item, or character the player last interacted with prior to churn.
Operating System (OS) Context: Advertising formats are automatically adjusted to match native UI/UX design guidelines based on whether the programmatic impression occurs on an iOS or Android device ecosystem, strictly improving overall ad relevancy and conversion rates.
Contextual and Environmental Signals: The Adikteev infrastructure evaluates contextual variables, such as the user’s granular geographic location, the time of day, and the network connection type (Wi-Fi versus Cellular data), to seamlessly serve the most appropriate video resolution or localized language creative without latency.
Managed Services and Strategic Solutions
While programmatic bidding forms the core technological infrastructure, the platform pairs its algorithmic capabilities with specialized managed services to ensure maximum operational efficiency for enterprise clients.
Adikteev In-House Creative Studio
A significant bottleneck in programmatic retention is the continuous production of fresh visual assets. To resolve this, the Adikteev in-house creative studio operates as a dedicated extension of the client’s marketing team. The studio is responsible for designing custom app retargeting creatives that prevent ad fatigue and drive conversions. Key production capabilities include:
Gaming Creatives: The development of Adikteev playable ads for gaming, allowing users to directly interact with a micro-level of the game before initiating a re-engagement session.
Dynamic Video: The production of high-performing video ads for mobile that utilize dynamic elements automatically customized to the user’s documented past in-app behavior.
Interactive Formats: The rapid deployment of various interactive mobile ad formats designed to maximize CTR (Click-Through Rate) and engagement metrics across diverse application verticals.
Pre-Launch Auditing & Strategy
Prior to executing any programmatic media spend, Adikteev enforces a rigorous consulting phase. This pre-launch auditing strategy ensures that technical configurations align precisely with enterprise business goals. The baseline setup involves:
Data Baseline Setup: Verifying Mobile Measurement Partner (MMP) postbacks, ensuring deep-link architectures are routing correctly, and mapping baseline user activity.
KPI Definition: Establishing exact financial benchmarks with the client, such as target Return on Ad Spend (ROAS), incrementality percentages, and Cost Per Action (CPA).
Audience Segmentation: Structuring distinct audience cohorts based on historical Adikteev performance data to guarantee optimal capital allocation and maximize early campaign yield.
Real-Time Analytics & Custom Dashboards
Transparent client reporting is critical for B2B performance marketers who must mathematically validate their retention budgets. To facilitate this level of observability, the Adikteev reporting infrastructure is built upon advanced enterprise database technologies, specifically leveraging direct Apache Druid integration.
Sub-Second Latency: The integrated infrastructure processes massive programmatic datasets instantly, allowing procurement and marketing teams to query complex campaign metrics without systemic delay.
Granular Visibility: Custom dashboards provide deep, unfiltered insights into ongoing incrementality testing results, real-time cohort degradation curves, and A/B creative-level performance metrics.
Cross-Segment Engagement Consulting
Beyond basic uninstallation prevention, Adikteev provides advanced consultative strategies designed to maximize the total lifetime value (LTV) of an application’s existing user base.
Secondary Feature Adoption: Consulting teams strategize campaigns guiding single-use customers (e.g., users who exclusively utilize an app for ride-hailing) to adopt newly launched or secondary in-app services (e.g., food or freight delivery).
Cross-Selling Opportunities: Structuring sequential programmatic formats that drive secondary purchases and premium subscription upgrades, leveraging Adikteev data insights to systematically increase the average revenue per user (ARPU) across the entire digital ecosystem.
Adikteev Technical Ecosystem & Integrations
To execute algorithmic bidding effectively, the Adikteev platform requires the seamless ingestion of real-time behavioral data. This technical ecosystem is engineered to synchronize directly with an enterprise app publisher’s existing marketing stack, minimizing latency and automating audience management across global programmatic channels.
Native MMP Integrations
The DSP relies on direct API connections with all major Mobile Measurement Partners (MMPs) to process event attribution and user data. These native connections form the foundation of the platform’s predictive modeling and are highly structured for snippet extraction.
Adikteev AppsFlyer integration: Facilitates the automated syncing of in-app events and uninstallation data, enabling the engine to update audience cohorts instantly without manual CSV uploads.
Adikteev Adjust postback setup: Provides the secure, real-time transmission of conversion data and custom event triggers strictly necessary for accurate attribution and incrementality measurement.
Kochava integration for app retargeting: Ensures granular data ingestion, allowing developers to track highly specific lifecycle events and deploy customized retention campaigns across isolated audience segments.
CRM & Audience Sync
Connecting paid retention media with internal lifecycle marketing requires robust Customer Relationship Management (CRM) connectivity. This architecture allows publishers to bridge the gap between owned channels (like email or push notifications) and programmatic display advertising.
Braze Adikteev audience sync: This specific integration enables digital marketing teams to automatically push dynamically updating user segments from their Braze CRM directly into the DSP. This automated data flow ensures that programmatic ad campaigns operate in perfect alignment with internal retention strategies, explicitly preventing message duplication and wasted media spend.
API Availability and Custom Builds
For enterprise publishers requiring specialized data routing beyond standard MMP or CRM connections, the platform provides robust, secure API availability.
Open Architecture: The infrastructure utilizes RESTful APIs to facilitate custom programmatic data syncing for proprietary software environments.
Custom Data Pipelines: Engineering teams can pipe proprietary first-party behavioral metrics directly from their internal enterprise data warehouses into the machine learning engine for bespoke model training and highly tailored bid calculations.
Deployment Options
The technical infrastructure is designed for maximum availability and low latency, utilizing a deployment framework optimized specifically for global mobile environments.
Cloud/SaaS Architecture: Adikteev operates entirely as a managed, cloud-based Software-as-a-Service (SaaS) platform. It requires no on-premise server installation, allowing procurement teams to integrate the technology strictly through secure server-to-server (S2S) API endpoints.
Mobile Ecosystem Compatibility: The platform is natively equipped to process global programmatic inventory across both major operating systems. It executes seamless media buying for the Android ecosystem while strictly maintaining privacy-compliant frameworks designed specifically for modern iOS architecture.
Privacy Compliance & Post-IDFA iOS Strategies
With the deprecation of the Identifier for Advertisers (IDFA) and the tightening of global data regulations, enterprise publishers require programmatic partners capable of navigating complex legal frameworks. The Adikteev platform prioritizes secure data processing, ensuring that user retention campaigns execute flawlessly without violating consumer trust or international privacy laws.
Adikteev SKAdNetwork (SKAN) 4.0 Strategy
Apple’s App Tracking Transparency (ATT) framework fundamentally altered mobile marketing. To provide viable iOS 14+ app retargeting solutions, the DSP relies on advanced contextual signals rather than deterministic device IDs. The platform utilizes a robust framework to maintain performance in a restrictive ecosystem:
Privacy-First Attribution: The Adikteev architecture integrates directly with Apple’s SKAdNetwork (SKAN) 4.0, utilizing aggregated postbacks and conversion values to measure campaign efficacy without tracking individual user identities.
Contextual Bidding Algorithms: In the absence of an IDFA, the machine learning engine evaluates non-personal contextual data—such as time of day, device model, and publisher app category—to predict user behavior and assign precise programmatic bid values.
Probabilistic Modeling: By leveraging SKAN data alongside anonymized historical engagement trends, the system reconstructs accurate incrementality metrics, ensuring that iOS retention campaigns maintain high profitability.
GDPR-Compliant Mobile DSP
For international app publishers and enterprise procurement teams, strict adherence to global data handling protocols is a mandatory vendor requirement. Adikteev operates natively as a highly secure, GDPR compliant mobile DSP, engineering its infrastructure to mitigate legal risks associated with global remarketing data flows.
Data Minimization Protocols: The Adikteev DSP processes strictly the exact event postback data necessary for algorithmic bidding, automatically stripping out personally identifiable information (PII) at the point of ingestion.
CCPA & COPPA Adherence: Beyond European mandates, the corporate data architecture aligns fully with the California Consumer Privacy Act (CCPA) and the Children’s Online Privacy Protection Act (COPPA), restricting the unauthorized retention or sale of consumer profiling data.
Secure Infrastructure Audits: Regular third-party security audits ensure that all data pipelines, API connections, and real-time database queries meet rigorous encryption standards, providing enterprise-grade liability protection.
Adikteev vs. Competitors in Mobile Retargeting
When evaluating programmatic mobile marketing partners, enterprise procurement teams must compare the platform against other leading Demand-Side Platforms (DSPs). Bottom-of-funnel evaluation requires analyzing specific technical capabilities, bidding models, and platform architectures. The following comparative data highlights how this specific infrastructure aligns against primary industry alternatives.
Market Comparison Matrix: Mobile Retargeting Platforms
| Feature / Capability | Adikteev | Remerge | Criteo | Jampp |
| Primary Platform Focus | Post-Install App Retargeting & Churn | App Retargeting & UA | Commerce Media & Open Web | Programmatic UA & App Retargeting |
| Bidding Methodology | Proprietary Churn-Based Bidding | Standard RTB & Audience Bidding | Predictive Commerce Bidding | Predictive Bidding (CPA/ROAS) |
| Incrementality Testing | Native Ghost Bidding & PSA | Ghost Bidding & Control Groups | A/B Lift Measurement | Native A/B Testing |
| Creative Production | In-House Studio (Playables, Video) | Custom Creative Studio | Dynamic DCO Templates | DCO & Creative Studio |
| Privacy Compliance | SKAN 4.0 / GDPR / CCPA | SKAN / GDPR / CCPA | First-Party Data / Privacy Sandbox | SKAN / GDPR / CCPA |
Adikteev vs. Remerge
Remerge is the most direct structural competitor to the DSP, as both entities focus heavily on high-frequency mobile app remarketing.
Algorithmic Focus: While Remerge excels in processing sheer transaction volume and rapid execution, the Adikteev architecture differentiates itself by heavily prioritizing predictive churn machine learning.
Budget Allocation: Remerge utilizes advanced real-time bidding algorithms across segmented lists. Conversely, this specialized DSP adjusts programmatic bid values specifically based on the assigned churn probability of the individual user.
Adikteev vs. Criteo (Commerce Growth)
Criteo operates as a massive, omni-channel commerce media platform, whereas the platform remains a highly specialized mobile-first tool.
Ecosystem Scope: Criteo services global retailers across desktop, mobile web, and connected television, leveraging an extensive graph of shopper data. The retention engine strictly limits its programmatic buying to in-app mobile inventory and targeted CTV app acquisition.
Target Verticals: Criteo dominates broad e-commerce retargeting. While the competing DSP supports retail, its core technical framework and interactive playable ad formats are heavily optimized for mobile gaming publishers.
Adikteev vs. Jampp
Jampp provides a full-stack programmatic platform addressing both top-of-funnel acquisition and retention.
Operational Breadth: Jampp divides its computational resources between scaling user acquisition campaigns and driving re-engagement. Adikteev directs its processing power almost entirely toward post-install retention and uninstallation prevention.
Incrementality Measurement: Both platforms offer robust performance analytics; however, the retention engine provides highly granular Ghost Bidding methodologies specifically designed to prove pure revenue lift for enterprise measurement.
Frequently Asked Questions (FAQ) About Adikteev
This FAQ section addresses the most common technical and operational inquiries regarding the platform, optimized for quick reference by enterprise procurement and performance marketing teams.
What is Adikteev?
Adikteev is a specialized mobile Demand-Side Platform (DSP) focused on app retargeting, predictive churn modeling, and user retention. The software engine evaluates real-time ad exchange inventory to re-engage dormant application users, maximizing lifetime value (LTV) and generating measurable incremental revenue for mobile publishers.
How does the churn-based bidding model work?
The proprietary bidding model utilizes machine learning algorithms to analyze historical event data and assign a churn probability score to individual users. The platform then dynamically adjusts programmatic bid values based on this score, aggressively targeting users at high risk of uninstallation while suppressing bids for users likely to remain active organically.
How is adjusted ROAS calculated?
To provide accurate financial metrics, Adikteev calculates adjusted Return on Ad Spend (ROAS) by factoring in proven incrementality. The calculation multiplies the gross campaign revenue by the exact percentage of true lift (conversions proven to be driven exclusively by the ad), divided by the total ad spend, eliminating credit for organic conversions.
Is the platform compliant with iOS 14.5 and SKAdNetwork?
Yes, the DSP is fully compliant with modern privacy frameworks, including Apple’s App Tracking Transparency (ATT) policies. The Adikteev infrastructure utilizes SKAdNetwork (SKAN) 4.0 data, probabilistic modeling, and contextual bidding signals to execute high-performing iOS app retargeting campaigns without relying on the deprecated Identifier for Advertisers (IDFA).
What are the main alternatives?
Primary structural competitors to the Adikteev platform in the programmatic mobile retargeting space include Remerge, Jampp, and Criteo Commerce Growth. While these alternatives offer robust DSP capabilities, they often split computing resources between top-of-funnel user acquisition and remarketing, whereas this specific DSP strictly prioritizes post-install retention.
Does the ecosystem support Connected TV (CTV) advertising?
Yes, the Adikteev ecosystem supports Connected TV (CTV) user acquisition. This service allows app developers to target high-intent audiences on smart TVs via programmatic video inventory, utilizing cross-device tracking protocols to measure subsequent mobile application installations.
Which Mobile Measurement Partners (MMPs) integrate with the software?
The DSP requires direct data ingestion to function and maintains native API integrations with all major MMPs. Supported tracking platforms include AppsFlyer, Adjust, Kochava, Branch, and Singular, ensuring automated syncing of behavioral postbacks and conversion data directly into the Adikteev engine.
What is Ghost Bidding?
Ghost Bidding is a specific incrementality testing methodology utilized by the Adikteev architecture to measure true revenue lift. Instead of purchasing a control ad (PSA), the system logs the exact millisecond it would have won a programmatic auction and monitors the user’s organic behavior, providing a highly cost-efficient control group metric.
How does dynamic creative optimization (DCO) function?
The dynamic creative optimization technology automatically personalizes mobile ad formats in real-time. By processing contextual signals and behavioral history—such as an abandoned shopping cart item or a specific game level—the system dynamically assembles the ad creative instantly to maximize the statistical probability of user re-engagement.
How is the pricing structured?
While specific enterprise contracts vary based on media volume, Adikteev pricing is typically structured around standard programmatic media buying models, charging based on Dynamic CPM (Cost Per Mille) or optimized CPA (Cost Per Action) goals. The financial model scales dynamically with the volume of real-time bidding queries processed and the level of managed creative services required.
Adikteev Leadership Team:
Adikteev Profile Structure:
Name: Adikteev
Industry: AdTech / Mobile Programmatic Advertising (Demand-Side Platform)
Founded: 2012
Founders: Xavier Mariani, Emilien Eychenne, and Frederic Leroy
CEO: Xavier Mariani
Headquarters: 51 Rue Saint-Georges, 75009 Paris, France
Global Footprint: Paris (Headquarters), New York, and San Francisco
Ownership Structure: Privately held (Operating under majority founder control following a management buyout backed by AgilaCapital in late 2025)
Total Funding & Stage: Raised over $13.4 million in early venture capital (including a $1.4M Seed and $12M Series B), followed by a recent €28 million Management Buyout (MBO) stage.
Annual Revenue: Approximately €150 million projected annual run-rate (as of late 2025)
Number of Employees: Approximately 120 global employees
Target Audience: Enterprise app publishers, mobile gaming studios, e-commerce platforms, and non-gaming mobile application developers
Core Product Lines: Mobile App Retargeting Engine, Predictive Churn Machine Learning, Connected TV (CTV) User Acquisition, Dynamic Creative Optimization (DCO), and Managed Creative Studio Services
Key OEM Partnerships & Integrations: Mobile Measurement Partners (AppsFlyer, Adjust, Kochava, Branch, Singular) and CRM platforms (Braze)
Regulatory Clearances & Certifications: GDPR, CCPA, and COPPA compliant; Apple SKAdNetwork (SKAN) 4.0 compatible for privacy-first iOS attribution
NAICS and SIC Codes: Note: These specific codes were not detailed in the web post draft. (However, DSPs typically operate under NAICS 541810 for Advertising Agencies / 518210 for Data Processing, and SIC 7319 for Advertising, Not Elsewhere Classified)
Website: adikteev.com
Location:
51 Rue Saint-Georges, 75009 Paris, France