CHALLENGES
Franchise hierarchies, multi-brand portfolios, regional rollups, and partner channels rarely map cleanly into generic marketing platforms. The result: unreliable reporting and tools the team routinely bypasses.
Attribution and dashboards explain what has already happened. They do not direct who to engage next, when to engage, what to offer, or where to shift spend. Marketing investment ends up justified, not optimized.
When every question routes through the data team, decisions slow and opportunities pass. Marketing requires direct, governed access to its own data and the capacity to act on it without delay.
CRM, marketing automation, web and product analytics, ad platforms, ecommerce, loyalty, and offline systems each hold a fragment of the customer. Unifying them for a complete view requires manual reconciliation that slows campaigns, undermines personalization, and leaves segmentation built on incomplete data.
CAPABILITIES
Most marketing platforms collect and measure data. We build the systems that turn it into action: who to engage, when, with what, and where to spend next. Our expertise across artificial intelligence, data science, and machine learning is applied in environments where data is fragmented, business models are complex, and the cost of slow execution is material.
Identify the highest-return marketing use cases across acquisition, retention, and growth, and architect a prioritized roadmap
Design the customer data ownership, consent, and access framework required to make marketing data trustworthy and compliant
Unify CRM, marketing automation, web, product, ad platform, and offline data into a centralized customer platform engineered for production reliability
Real-time visibility into campaign performance, customer behavior, and channel return, configured to the structure of your business
Deploy propensity models, churn prediction, next best action, marketing mix modeling, and bespoke customer segmentation
Automate data quality monitoring, consent enforcement, and policy compliance across your customer data environment
A dedicated OneSix team embedded for continuous delivery across marketing workstreams
Jacob Zweig
Managing Director, AI
Use Cases
Use Case
What it does
Use Case
What it does
A unified, real-time view of each customer across systems and channels, providing the foundation for segmentation, lifecycle analysis, and lifetime value modeling.
Use Case
What it does
Models that recommend the right offer, message, or engagement for each customer at the right moment, refined by every interaction.
Use Case
What it does
Predictive scoring that identifies the prospects and customers most likely to convert or expand, so investment goes where return is highest.
Use Case
What it does
Early identification of at-risk customers, surfacing the drivers and triggers behind the risk so teams can intervene before they leave.
Use Case
What it does
Media mix modeling, multi-touch attribution, and campaign intelligence that tie marketing activity to revenue and optimize spend across channels.
Use Case
What it does
Agentic systems that support proposal generation, RFP analysis, content production workflows, and other repeatable marketing tasks.
FAQs
Most marketing platforms collect and measure. We engineer on top of that: integrating existing tools, modeling the structure of the business, and adding the AI layer that converts data into decisions. Custom integration and custom visualization are where generic platforms fail, particularly for franchise hierarchies, multi-brand portfolios, and other structurally complex organizations.
No. We architect around the environment in place and make recommendations based on what is working, what is absent, and where the highest-return gaps exist. The objective is to extract more from existing investment, not to add another platform to operate.
It's the norm, not the exception. Fragmented systems, inconsistent identifiers, and incomplete data are why most marketing AI initiatives stall. OneSix treats integration and data quality as part of the engagement, not a prerequisite for it. Many marketing engagements combine Data Foundations and Artificial Intelligence into a single coordinated workstream.
Foundational work and a first production use case typically deliver inside ninety days. Full transformation across multiple use cases is a multi-quarter engagement. The cadence is set during scoping and validated against your roadmap.
