From complex data to better business decisions.
Treece Strategy Group helps companies answer difficult questions about growth, customers, revenue, and performance — and build the analytical capabilities to answer those questions more effectively over time.
These examples highlight work spanning revenue intelligence, customer segmentation, marketing analytics, and AI implementation.
01 REVENUE INTELLIGENCE | Major Financial Institution
What actually drives deeper client relationships?
The Challenge
A large financial institution wanted to better understand what drove the size and value of its institutional client relationships.
With thousands of clients using different combinations of products and solutions, the challenge was determining which behaviors and product relationships were actually associated with deeper, more valuable client relationships — and where that could point to opportunities for growth.
The Approach
Analyzed client revenue, product adoption, and relationship data across more than 2,000 institutional clients.
Multiple statistical and machine-learning methodologies — including linear regression, log-target regression, elastic net, and random forest — were used to identify relationships between individual products and solutions and the overall size of the client relationship.
An automated modeling process was developed to test multiple model types and combinations of independent and dependent variables, making it possible to systematically identify the models that best explained different revenue relationships.
The Outcome
The analysis identified products and engagement patterns associated with larger client relationships, providing a more rigorous way to understand how clients interacted with the bank and where potential cross-sell and growth opportunities existed.
Rather than relying solely on historical performance or intuition, commercial teams gained a data-driven framework for identifying the characteristics of deeper client relationships and determining where to focus.
Capabilities
REVENUE INTELLIGENCE | CUSTOMER ANALYTICS | PREDICTIVE MODELING | CROSS-SELL STRATEGY
02 CUSTOMER & GROWTH STRATEGY | Major Financial Institution
What are the strongest opportunities within a complex client base?
The Challenge
A large institutional client base contained dramatically different types of customers with different product relationships, geographic footprints, revenue profiles, and growth trajectories.
Traditional segmentation could describe who those clients were, but the more important question was strategic: Which groups represented different types of growth opportunities, and how should the organization approach them differently?
The Approach
Applied cluster analysis to identify naturally occurring groups of clients based on characteristics including client type, products used, geography, revenue, and growth.
K-means clustering was used to evaluate patterns across the client population and develop distinct subsegments with meaningful differences in their relationships with the bank.
The resulting segments were then evaluated from a commercial perspective to identify whitespace, differences in product adoption, and potential growth strategies for each group.
The Outcome
The analysis transformed a large and complex client population into a more actionable set of subsegments, each with different characteristics and potential growth opportunities.
This provided a framework for moving beyond broad client categories toward more targeted decisions about where opportunities existed, which clients warranted greater attention, and how growth strategies could differ across the portfolio.
Capabilities
CUSTOMER SEGMENTATION | WHITESPACE ANALYSIS | REVENUE ANALYTICS | GROWTH STRATEGY
03 MARKETING ANALYTICS | Digital Publishing Company
Which digital activities are actually creating value?
The Challenge
A digital publisher operated across multiple content verticals and acquisition channels, with performance data distributed across Google Analytics, Google Search Console, Google Ads, and other platforms.
Each system provided part of the picture. What was missing was a unified view of performance that made it easy to understand which pages, channels, and activities were responsible for creating the most value.
The Approach
Built an integrated analytics and reporting system that combined data from multiple marketing and digital platforms into a consistent view of performance.
Automated dashboards organized results across business verticals while allowing performance to be evaluated at the individual page and channel level.
Rather than focusing only on top-line traffic metrics, the analysis was structured to identify the specific content and acquisition sources responsible for disproportionate value creation.
The Outcome
The resulting system replaced fragmented reporting with a more seamless and automated view of digital performance.
Leadership could more easily identify the pages and channels creating the greatest value, understand where performance was changing, and make more informed decisions about where to focus content, marketing, and acquisition resources.
Capabilities
MARKETING ANALYTICS | DATA INTEGRATION | PERFORMANCE MEASUREMENT | AUTOMATED REPORTING
04 AI & ANALYTICS | Major Financial Institution
How can AI make sophisticated analytics accessible across an organization?
The Challenge
A global financial institution had extensive institutional client and revenue data and access to increasingly sophisticated analytical methodologies.
But extracting meaningful insights from that information often required specialized analytical expertise. That created a broader challenge: How could the organization make advanced analytics available to business users without requiring every user to become a data scientist?
The Approach
Helped develop a bank-wide AI-enabled analytics initiative that integrated institutional client and revenue data with advanced statistical modeling capabilities.
The objective was not simply to add an AI interface to existing reporting. It was to give users a more intuitive way to interrogate complex data, explore business questions, and access analytical methodologies that would otherwise require specialized technical expertise.
The initiative brought together large-scale revenue data, statistical modeling, and generative AI to create a bridge between sophisticated analytical capabilities and the business users who needed them.
The Outcome
The initiative expanded access to sophisticated client and revenue analytics across the marketing organization.
Business users could explore questions and extract insights from complex data without relying on a specialized analyst for every request — helping democratize analytics and make advanced analytical capabilities more useful across the organization.
The result was not simply greater access to data. It was a new way for a broader organization to interact with data and use it to support decision-making.
Capabilities
AI STRATEGY | ANALYTICS ENABLEMENT | REVENUE INTELLIGENCE | DATA INTEGRATION | STATISTICAL MODELING
Different problems.
A common approach.
The specific tools and methodologies vary by engagement, but the objective is consistent: start with the business question, determine what the available data can tell us, and turn the analysis into a clearer decision.
Treece Strategy Group works at the intersection of strategy, analytics, and execution — particularly when the answer doesn’t fit neatly within a single dataset, dashboard, or department.
Have a difficult business question?
If you have plenty of data and information but aren’t sure what it’s telling you — or what to do next — let’s talk.