A national QSR brand had data across their loyalty program, online orders, mobile, messaging, and Google Analytics 4. They had roughly $500 million in annual orders from known customers. What they did not have was a single view of that customer base that tied it all together and made it usable for marketing.
This is how Actable deployed the Intelligence Factory end to end for the first time with a client, what that process looked like, and what it produced.
The Problem: Data Everywhere, No Unified Customer View
The brand had data assets. Loyalty program data. Online orders from their website and mobile app. Messaging data from their messaging platform across email, SMS, and in-app interactions. Google Analytics 4 data. And transaction data flowing in from third-party aggregators like DoorDash.
What they did not have was a unified view of their customer. Without that, segmentation is guesswork. Targeting is blunt. And the opportunity to increase repeat purchase rates across a customer base generating roughly $500 million in annual orders from known customers stays largely untapped.
The first step was not modeling. It was not activation. It was discovery. Actable deployed a team to conduct a thorough audit across every data source, identify gaps in current marketing use cases, and map where the real opportunities were for improving marketing effectiveness.
Building a Unified Customer View in Four Months
From the start of the engagement to the completion of a full unified customer view, the timeline was four months. That view incorporated all owned interaction data, online orders from both direct and aggregator channels, loyalty transactions, and engagement history across every messaging touchpoint.
It required assembling and reconciling data from multiple internal and external systems, resolving identity across channels, and building a structure that could support ongoing analytics and activation. That order volume represented a significant opportunity to increase repeat purchase rates. The unified customer view made it targetable.
The result was a client-owned data foundation the brand could activate against immediately. This is the core of what the Customer Intelligence Hub delivers.
The Analytics Roadmap: Structure Before Activation
Actable developed a structured analytics roadmap with multiple tracks running in parallel.
The insights track focused on overlaying the brand’s first-party data with third-party data providers to enrich customer profiles. Cohort analyses mapped how customers were acquired, how they engaged over time, and where drop-off occurred. Audience deep dives gave the marketing team a clear picture of their highest-value segments and where growth potential existed.
On the modeling side, Actable launched predictive models covering buying propensity, repeat purchase likelihood, lifetime value, and product recommendations. These models became the core inputs for the Insights Engine. They translated raw customer data into actionable targeting signals that the marketing team could actually use.
This phased, structured approach mattered. Skipping it would have meant running campaigns against assumptions rather than evidence.
The First Activation: Meta Advertising Test
The first real-world test of the Intelligence Factory outputs was a Meta advertising campaign targeting a high lifetime value audience identified through the Customer Intelligence Hub and Insights Engine.
The results were clear. The high-LTV audience outperformed the control group by 12% on return on ad spend. Conversion rate lifted 16% compared to control.
These are not projections. They are production results that have held as the program has scaled. The test validated that the data foundation and the predictive models were working.
Expanding the Intelligence Factory Across Channels
The Meta test was the first activation. It is not the last. The brand is now expanding these targeting strategies into email and SMS, with ongoing work to push into as many use cases as possible.
The Customer Intelligence Hub serves as the backbone for all channel activations. The Insights Engine provides the decisioning criteria that determines who gets what message and when. The collaboration between Actable and the brand’s marketing team is ongoing, with a shared focus on increasing repeat purchase rates and maximizing customer lifetime value across the full customer base.
The brand now has a repeatable, scalable intelligence foundation they own. The Meta results validated the approach. The expansion into email, SMS, and additional channels is already underway.
The data was always there. The outcomes required a structure to make it usable.
Frequently Asked Questions
What is the Intelligence Factory? The Intelligence Factory is Actable’s end-to-end customer intelligence framework. It combines three components: the Customer Intelligence Hub, which centralizes customer data and creates a unified customer view; the Insights Engine, which applies predictive modeling and third-party enrichment to generate actionable customer intelligence; and the Action Engine, which connects those insights to marketing activation across channels.
How long does it take to build a unified customer view? In this engagement, Actable built a full unified customer view in four months. That covered data from the loyalty program, online orders, third-party aggregators, messaging platforms, and Google Analytics 4. Timeline varies based on the number of data sources and the complexity of the integration, but four months is a realistic benchmark for an enterprise brand with multiple systems in play.
What data sources does the Intelligence Factory work with? The Intelligence Factory is designed to work with the data a brand already has. In this case, that included loyalty program data, website and mobile app orders, messaging data from MoEngage (email, SMS, in-app), Google Analytics 4, and third-party aggregator transaction data. First-party data is then enriched with third-party overlays as part of the Insights Engine build.
What kind of results can predictive modeling drive in paid media? In this engagement, deploying a high lifetime value audience built from predictive models to Meta advertising produced a 16% lift in conversion rate and a 12% improvement in return on ad spend versus the control group. These results held in production and are being extended to additional channels.
What channels can the Intelligence Factory support? The Customer Intelligence Hub is built to support activation across any marketing channel. In this engagement, the first activation was Meta advertising, with expansion underway into email and SMS. The framework is designed to scale across channels as use cases are validated and priorities are set.
How is Actable’s approach different from a standard analytics engagement? Actable operates as a strategic enabler, not a vendor dropping in a tool. The work starts with discovery, not deployment. The team audits existing data, identifies gaps, builds a unified customer view, and develops a structured roadmap before any activation begins. The goal is not to add complexity. It is to make the data a brand already has more valuable.
