Unifying data for enhanced portfolio insights and reduced risk in private equity

Unifying data for enhanced portfolio insights and reduced risk in private equity

By creating a scalable, unified data infrastructure, OneSix enabled a private equity firm to gain a comprehensive view of portfolio performance, improve decision-making, and drive long-term profitability.

Data & AI Strategy

Data & App Engineering

Data Analytics

Portfolio AI Strategy

Snowflake

Matillion

Fivetran

Overview

Building a modern data infrastructure for strategic agility

A private equity firm managing a diverse portfolio of investments faced challenges in leveraging data for strategic decision-making. The firm recognized the need for a modern data infrastructure to consolidate disparate datasets, enhance analytical capabilities, and gain deeper insights into overall portfolio performance.

The existing data infrastructure was fragmented, leading to delays in reporting, inefficient data processing, and a lack of agility in adapting to evolving business requirements.

Our Solution

Data unification across portfolio companies

OneSix built a modern data infrastructure, which is essential for centralizing data and gaining deeper insights into overall portfolio performance. This involved creating a unified data repository to integrate and store data from various portfolio companies, providing a unified view for analysis and reporting.

As the firm increases the portfolio, the architecture was designed to allow seamless scalability, accommodating growing datasets without compromising performance. The solution leverages Snowflake’s data sharing features to securely share relevant data between portfolio companies and the PE firm, fostering collaboration and knowledge exchange.

Results

A holistic picture of portfolio performance

The implementation of a comprehensive data infrastructure provided the private equity firm with a holistic view of portfolio performance while ensuring data security and integrity.

This empowered the firm to make more informed decisions, enhance operational efficiency, and gain deeper insights into their investments, ultimately mitigating risks and fostering long-term profitability.

Ready to unlock the full potential of data and AI?

Book a free consultation to learn how OneSix can help drive meaningful business outcomes.

Building a Customer 360 data solution for Leader Bank

Building a Customer 360 data solution for Leader Bank

By implementing a unified Customer 360 data solution, OneSix enabled Leader Bank to enhance customer service, drive data-driven marketing, improve data quality, and boost operational efficiency.

Data & App Engineering

Data Analytics

Snowflake

Matillion

Fivetran

Power BI

Overview

Overcoming data silos to gain a unified view of customers

To maintain a competitive edge, community banks need to have a deep understanding of customer behavior and preferences. Leader Bank, like many financial institutions, faced a common challenge: fragmented systems and data silos. They had separate platforms for commercial lending, residential lending, and core banking operations, with little interaction between them. This siloed approach made it incredibly difficult to establish a comprehensive view of their customers.

Back-office processes were cumbersome, and customer support teams found it challenging to provide efficient service due to a lack of unified customer data. To compound matters, data quality issues, including inconsistent naming conventions and formats, added complexity to the situation.

Our Solution

Implementing a Customer 360 platform with a cloud-based infrastructure

OneSix partnered with Leader Bank to tackle these issues head-on, devising a comprehensive solution that aimed to provide a holistic view of each customer. The key components of this solution included:

Master Data Management (MDM) Strategy: OneSix developed a robust MDM strategy to standardize data across the organization, ensuring consistent naming conventions and formats. This laid the foundation for accurate data integration.
Cloud-Based Data Infrastructure: To create a unified platform for customer data, OneSix implemented a cloud-based data lake and data warehouse. This allowed for the storage and processing of vast amounts of data from different systems in a scalable and cost-effective manner.
Rules Engine: A rules engine was introduced to automate data validation, cleansing, and transformation. This ensured that data was accurate, up-to-date, and adhered to the defined standards.

The goal of these components was to establish a common data platform where information from various systems could seamlessly integrate. This would enable Leader Bank to tie together customer interactions, track their history, and gain valuable insights for personalized marketing and improved customer service.

Technologies Implemented

Snowflake served as the host to collect data into the data lake across various systems. A C360 data warehouse served the BI and Customer information needs of this bank.
Fivetran was used to capture data across a variety of data sources to move data into the data lake. In addition, Snowflake Data Share provided access to raw data for a Snowflake-based system.
Matillion was used as the rules engine to cleanse data and provide match/merge rules for the customer record and transform that into the data warehouse.
PowerBI was used to drive analytics and display Customer 360 information across the institution.

Results

Enhanced customer service, data-driven marketing, and operational efficiency

The implementation of the Customer 360 solution brought about significant positive changes:

Improved Customer Service: With a unified view of customer data, the bank’s support teams could quickly identify callers and access their interaction history. This streamlined the support process, leading to improved service and reduced call handling time.
Data-Driven Marketing: To create a unified platform for customer data, OneSix implemented a cloud-based data lake and data warehouse. This allowed for the storage and processing of vast amounts of data from different systems in a scalable and cost-effective manner.
Enhanced Data Quality: The MDM strategy and rules engine led to improved data quality. Data inconsistencies were eliminated, resulting in more reliable and accurate information for decision-making.
Increased Efficiency by 2X: Back-office processes were streamlined, reducing operational inefficiencies. This led to cost savings and increased productivity across the organization.
Increased Customer Satisfaction: With improved service and personalized experiences, customers were more satisfied, leading to stronger customer loyalty and potential for business growth.

Ready to unlock the full potential of data and AI?

Book a free consultation to learn how OneSix can help drive meaningful business outcomes.

Building a forecasting engine and media mix modeling pipeline for a FinTech firm

Building a forecasting engine and media mix modeling pipeline for a FinTech firm

OneSix implemented machine learning models for a financial services client to optimize marketing spend, resulting in a strategic reallocation that improved ROAS from 0.5-0.8x to 1.5x and achieved the client’s first quarter of positive marketing ROI.

Data Science

AI & Machine Learning

Media Mix Modeling

Overview

Improving marketing efficiency in a fast-growing financial services firm

A rapidly expanding firm in the consumer financial services industry, offering both traditional and cryptocurrency brokerage solutions, faced challenges with low and declining Return on Ad Spend (ROAS), estimated at 0.5-0.8x.

Despite impressive growth driven by a surge in interest in stock and alternative assets since 2020, the company’s marketing spend had consistently outpaced revenue. OneSix was tasked with implementing machine learning and data science solutions to enhance marketing efficiency by accurately measuring spend effectiveness and building an automated pipeline for optimized media allocation.

Our Solution

Building a marketing efficiency and optimization platform

To tackle the client’s challenge, OneSix developed two custom models designed to provide insights into current marketing spend efficiency and inform future optimization strategies:

LifeTime Value (LTV) Model

OneSix created a predictive LTV model capable of forecasting each newly acquired user’s value within 12 hours of signup. This model offered near real-time insights into customer acquisition health by forecasting future revenue over multiple time horizons for users and cohorts. Integrating this model with direct attribution data from the client’s Mobile Measurement Provider (MMP) and custom attribution logic enabled precise calculations of Customer Acquisition Costs (CAC) at both user and cohort levels. The model decomposed LTV predictions into key metrics like time-to-convert, time-to-churn, subscription revenue, and non-subscription revenue. This breakdown highlighted specific channel performance issues, revealing, for instance, that some channels suffered from retention issues while others had low conversion rates.

Media Mix Model (MMM)

OneSix also developed a Media Mix Model (MMM) that used historical LTV estimates and spend data to calculate the LTV/Spend (ROAS) ratio for each marketing channel. The MMM accounted for marketing and non-marketing factors, including market sentiment, seasonality, holidays, and product/pricing changes. By optimizing for aggregate forecasted LTV rather than short-term metrics like new users or first-month revenue, the model avoided common pitfalls and focused on maximizing long-term marketing ROI. Both models were deployed and automated using Flyte on Kubernetes, enabling weekly retraining with fresh data and pushing results to a data lake for real-time reporting.

Our marketing/media mix model predicted total aggregate LTV acquired on a daily basis through a combination of marketing and non-marketing drivers.

Results

Improvement in ROAS and a strategic pivot in marketing allocation

The new insights provided by these models led to a major shift in marketing spend allocation. The analysis revealed that certain channels previously deemed effective were attracting low-value, high-churn customers, while others seen as saturated actually delivered higher customer value.

Following MMM’s spend recommendations, the client projected an increase in ROAS from 0.5-0.8x to 1.4x. Within two months of adopting the optimized spending recommendations, the client achieved a 1.5x ROAS, doubling historical returns and achieving their first quarter of positive marketing ROI. This marked the beginning of a new era of accelerated growth and customer value for the company.

Ready to unlock the full potential of data and AI?

Book a free consultation to learn how OneSix can help drive meaningful business outcomes.

Eliminating data silos to drive efficiency and growth in title insurance

Eliminating data silos to drive efficiency and growth in title insurance

By implementing a unified cloud data solution, OneSix helped a leading title insurance company eliminate data silos, improve data quality, and enable real-time insights for greater operational efficiency and growth.

Data & App Engineering

Data Analytics

Snowflake

Power BI

Overview

Breaking down data silos for streamlined operations

Our client is a title insurance company that provides title, escrow, settlement, and related products and services. They have been in business for over a century and have grown to become one of the largest title insurance companies in the United States.

Due to the scale of their business, growth due to M&A, and fast pace of the real estate industry, our client had multiple systems for title and escrow processing based across separate geographical locations. This resulted in a series of data silos and data inconsistencies and frequent delays in delivering enterprise-wide reporting and analytics.

Our Solution

Implementing a scalable cloud data architecture

OneSix implemented a solution that uses a modern cloud data architecture that would reduce maintenance overhead, improve data quality, integrate data, and allow our client to scale as their business and data needs change over time.

The solution consisted of replicating all the data from various source systems into a data lake. The transformation layer was used to apply business rules, and clean, and consolidate the data into a data warehouse model solution. The new platform provided a new standard across the organization for data storage and information access.

Technologies Implemented

Snowflake was leveraged to create a highly secure, scalable data lake and multi-tenant data warehouse environment.
Rivery was utilized for acquiring data from various source types.
Informatica was the core data transformation tech used to integrate and cleanse an individual tenant's data, loading it into the multi-tenant data warehouse in preparation for visual analytics.
Power BI provided reports for actionable insights and trends.

Results

Unified data insights for efficiency, cost savings, and revenue growth

With the modern data solution, the client eliminated data silos, improved the data quality, streamlined workflows, improved efficiency, and increased their ability to make data-driven decisions. Using a single source of truth from the cloud data warehouse, the client generated consistent and accurate reporting across the organization.

The operations team created dashboards to identify trends and patterns in order volume which helped the client proactively plan for staffing and resource allocation. The revenue dashboards provided a near-real-time view of KPIs across various business units and geographical locations. The workflow dashboards and reports are being used to identify bottlenecks in the product workflow and improve turnaround time for client services. Together, these analytics tools have helped the organization improve efficiency, reduce costs, and increase revenue.

Ready to unlock the full potential of data and AI?

Book a free consultation to learn how OneSix can help drive meaningful business outcomes.