Core Mission and Approach

The primary objective of Sellforte is to solve one of the most challenging problems facing modern enterprise marketing teams: determining true incremental business impact and deciding where to allocate the next marketing dollar. Traditional marketing measurement tools often suffer from fragmentation. Ad platforms report inflated performance based on self-attribution, last-click models overvalue bottom-funnel demand capture while undervaluing upper-funnel awareness channels, and legacy MMM solutions traditionally deliver slow, high-level strategic insights restricted to broad marketing channels.

Sellforte addresses these gaps by unifying three critical measurement pillars into a single, always-on operating system: 1. Causal Marketing Mix Modeling (MMM): Utilizing advanced Bayesian probabilistic inference and time-series analysis as the architectural backbone to estimate baseline versus incremental sales. 2. Incrementality Testing: Incorporating real-world causal experimentation—such as geo lift tests, conversion lift studies, and A/B testing—directly into the models as informative priors to continuously calibrate accuracy. 3. Multi-Touch Attribution (MTA): Ingesting and reconciling existing platform and user-level attribution data rather than treating it as a competitor, correcting systemic biases to yield granular insights.

Products and Services Offered

Sellforte delivers an integrated software-as-a-service (SaaS) platform backed by specialized AI agents and dedicated data science support. The platform bridges the gap between high-level strategic planning and day-to-day tactical media buying.

1. The Sellforte Operating System & Unified Measurement Hub

The core platform acts as an automated analytics control center that integrates data from over 500 sources, connecting major advertising channels (such as Meta, Google, TikTok, programmatic display, video, and offline media like TV, radio, and print) with enterprise data warehouses and ecommerce platforms.

Key functional modules within the platform include: * Measurement Dashboard: Provides a unified view of total spend, incremental sales, and return on investment (ROI) broken down by channel, campaign, region, and customer type (new versus returning customers). It separates natural baseline sales from marketing-driven incremental volume and allows side-by-side comparison of MMM outputs against Google Analytics 4 (GA4) and native ad platform metrics. * Marginal Incremental ROAS (miROAS): Moving beyond historical average returns, Sellforte calculates the marginal return of the next marketing dollar at the granular campaign and ad-set level. This helps marketing teams identify point-of-diminishing-returns, spot saturated channels, and evaluate what happens to revenue if budgets are scaled up or reduced. * Scenario Planning & Simulation: Enables marketers to run predictive simulations before committing live budgets, forecasting the exact revenue impact of reallocating funds across different channels or geographic markets.

2. Sellforte AI Agents

Sellforte integrates specialized artificial intelligence agents designed to translate statistical models into operational workflows: * Media Planner Agent: Acts as an automated budget-planning analyst, providing ongoing campaign budget recommendations and media mix optimization at granular levels based on computed miROAS metrics. * Media Buyer Agent: Operates as a continuous optimization assistant that executes real-time bid and budget adjustments directly within advertising platforms like Meta, Google, and TikTok via secure APIs. It functions under defined guardrails in either assisted mode or automated workflows, removing the friction of manual platform-switching. * Experiments Agent: Automates the design, launch, and analysis of geo-lift tests and conversion lift studies, systematically feeding experimental results back into the Bayesian models to sharpen forecast precision over time.

3. Professional Services and Enterprise Support

To ensure successful implementation, Sellforte pairs its technology platform with dedicated human expertise: * Customer Success and Data Science: Every enterprise client is assigned a dedicated Customer Success Manager and Data Scientist to assist with onboarding, custom model tuning, and ongoing strategic reviews. * Enterprise Infrastructure: The platform complies with strict global data privacy standards, including GDPR, features role-based access controls, and supports direct data writing into enterprise data warehouses like Snowflake and Google BigQuery without utilizing customer proprietary data to train generalized models.