1. Introduction and Purpose
This study aims to develop an integration model of digital marketing and data-driven sales management specifically tailored to Iranian startups. Existing models in the literature predominantly focus on mature or large-scale enterprises and fail to address the unique characteristics of Iran’s startup ecosystem—including severe resource constraints, pressure for rapid growth, organizational agility, and environmental uncertainty. Studies indicate that only approximately 15% of leads generated through digital campaigns convert to purchases, revealing a deep gap between marketing and sales functions. The complexity is amplified in the Iranian context by the multi-channel and fragmented customer journey. Customers navigate through domestic messaging platforms (Rubika, Eitaa, Bale), international platforms (Instagram, Telegram), search engines, marketplaces, and independent websites. This diversity, combined with limited access to advanced analytics tools, international sanctions restricting access to cloud services and marketing platforms, and varying levels of data maturity among startups, transforms integration into a formidable challenge. The innovation of this research operates on three levels. First, it develops an integrated conceptual framework combining digital marketing knowledge, data-driven sales management, and customer relationship management within the startup context. Second, it presents a localized integration model for Iran’s startup ecosystem that accounts for structural characteristics (small, multi-functional teams; central role of founders), resource constraints (limited budget, restricted tool access), and domestic digital infrastructure (local messaging apps, local payment gateways). Third, it employs grounded theory to discover patterns, mechanisms, and solutions that successful Iranian startup managers and founders have implemented in practice, converting their tacit knowledge into explicit knowledge.

2. Methodology
This applied-developmental research adopts a qualitative approach based on grounded theory methodology, specifically employing the systematic framework developed by Strauss and Corbin. The study seeks to generate substantive theory grounded in empirical data from Iranian startup practitioners rather than testing pre-existing Western frameworks. The research population comprised digital marketing managers, growth managers, data-driven sales managers and specialists, digital marketing consultants, and CRM/data analytics experts working in successful Iranian startups. Using purposive sampling with maximum variation strategy, 35 participants were selected from prominent companies including Beeptunes, Smartech, Ovaanuto, Khanomi, Chilivery, Pichak, Jetbaar, Fidibo, Dr. Sayna, and Namava. Sampling continued until theoretical saturation was achieved. Data collection utilized in-depth semi-structured interviews, allowing participants to articulate their experiences regarding marketing-sales integration challenges and solutions. Analysis followed the constant comparative method through three systematic coding stages: open coding (identifying and labeling concepts), axial coding (establishing relationships between categories), and selective coding (integrating categories into a cohesive theoretical framework). Research quality was ensured through four criteria: confirmability (audit trail documentation), credibility/believability (member checking and peer debriefing), transferability (thick description provision), and dependability (consistency verification). The final model underwent validation by four independent experts: two senior industry practitioners and two faculty members from University of Tehran’s marketing department.

3. Findings
Analysis identified 29 categories organized within the paradigmatic model: Causal conditions: data disconnection between marketing and sales units; limited financial and human resources; dependency on domestic platforms due to sanctions; absence of shared lead qualification process; investor pressure for rapid growth. Core phenomenon: data-driven integration of digital marketing and sales; alignment of performance metrics; integrated customer journey management; seamless data flow between units. Contextual conditions: digital maturity level; flexible startup structure; business model type; organizational culture; growth stage. Strategies: establishing integrated data infrastructure and system connectivity; defining shared processes and SLAs for lead delivery; forming cross-functional teams; implementing integrated funnel models and shared KPIs; developing data-driven organizational culture. Intervening conditions: sanctions and tool access limitations; domestic platform dependency; budget constraints; investor pressure; economic volatility. Consequences: increased conversion rates; reduced CAC and improved ROI; better alignment and reduced conflict; enhanced forecasting accuracy; improved customer experience and loyalty.

4. Discussion and Conclusion
The primary weakness in Iranian digital businesses is not lack of tools but the disconnect between attraction, conversion, fulfillment, and retention caused by failure to utilize data systematically. Integration requires multidimensional strategic capability beyond tools and budgets. This research presents a localized model accounting for Iran’s unique characteristics: infrastructure limitations, multi-channel behavior, sanctions, and domestic platforms. Unlike Western models tested in developed markets, this study utilizes lived experiences of Iranian practitioners to develop context-specific integration mechanisms.
Practical implications include: establishing centralized data infrastructure using domestic tools; implementing SLAs with clear MQL/SQL definitions; forming regular cross-functional teams; deploying multi-touch attribution models; creating shared dashboards with integrated KPIs; and building continuous feedback loops. Limitations stem from focus on Iranian startups, limiting generalizability beyond this context. The qualitative nature provides depth but not statistical generalizability. Future research should employ quantitative validation, investigate AI’s role in attribution models, analyze company size impact, conduct comparative international studies, and explore industry-specific adaptations.