Logo-black

Boosting energy demand forecast accuracy by 24% for thousands of NYC buildings

  • Energy

Boosting energy demand forecast accuracy by 24% for thousands of NYC buildings

OneSix developed a deep learning-powered forecasting engine that improved demand forecast accuracy by 24% and automated long-range, hourly projections across 1,000+ buildings to support energy program participation.
Data Science
AI & Machine Learning
Forecasting & Prediction

Overview

Manual energy forecasting process limited accuracy and scalability

An energy management firm needed to forecast electricity demand across approximately 1,000 buildings in New York to support demand-response and energy-efficiency programs such as NYISO’s Special Case Response.

The existing process was slow, manual, and error-prone—relying on spreadsheets and rough estimations to determine seasonal commitments. The forecasts had to be both granular (hourly) and long-range (up to six months), while accounting for complex, interacting seasonal patterns across a wide variety of building types.

Our Solution

AI-powered forecasting engine enables smarter, scalable planning

OneSix developed a forecasting and simulation engine powered by deep learning. The solution included:

  • A hybrid model that combines classical state-space forecasting with a neural network architecture, allowing the system to capture nuanced seasonal patterns and their interactions.
  • Fourier-transformed inputs to effectively encode cyclical time-based features such as hour-of-day, day-of-week, and season-of-year.
  • A shared model architecture fine-tuned to each building, enabling scalability across diverse asset types—from subway stations and K–12 schools to wastewater treatment facilities.
  • Simulated future weather and load conditions to estimate global, correlated risk and support smarter demand-response planning.

The system was built using PyTorch and integrated with Torchcast to manage temporal data modeling and training workflows.

Results

Improved accuracy, automation, and program impact

  • 24% improvement in forecast accuracy during high-demand scenarios.
  • Automated, reliable seasonal projections across 1,000+ buildings, reducing manual effort and minimizing risk of human error.
  • Now serves as the foundation for setting program commitments, replacing the legacy manual process with a scalable, data-driven forecasting approach.

Ready to unlock the full potential of data and AI?

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

Contact Us

Boosting sales by 15% through marketing optimization for a spa franchise

  • Travel & Hospitality

Boosting sales by 15% through marketing optimization for a spa franchise

OneSix helped a luxury spa franchise optimize its marketing investments using data-driven analytics, leading to a 15% increase in prospect sales. By implementing a marketing data warehouse, marketing mix modeling (MMM), and automated budget optimization, the client gained real-time visibility into performance.
Data Science
AI & Machine Learning
AI-Driven Marketing
Power BI

Overview

Lack of visibility and ineffective marketing spend hindered growth

For a luxury spa franchise with hundreds of locations, ensuring marketing investments drive real value across national, regional, and franchise levels was a growing challenge. Without clear visibility into spend and performance, it was difficult to optimize marketing strategies and allocate budgets effectively. The client faced two key issues:

  • Limited Visibility: Minimal and delayed reporting of channel spend at national, regional, and local (franchise) levels made it difficult to assess marketing performance.
  • Inability to Improve Marketing ROI: Due to the lack of an empirical impact measurement strategy, the client struggled to make strategic investment decisions across marketing channels.

Our Solution

Data-driven marketing optimization to enhance budget efficiency

To solve these challenges, OneSix implemented a data-driven marketing optimization framework, leveraging advanced analytics and automation. Our approach was specifically designed to support a franchise model by providing both high-level and location-specific insights. Key aspects of our solution included:

  • Implementing a structured approach that accounts for national-, regional-, and franchise-specific effects on media efficiency and non-media influences.
  • Customizing adstocking and saturation functions, with parameters adaptable to specific groups.
  • Handling missing data and uneven time series across different franchise locations.
  • Incorporating multiple methods for time-based effects analysis.
  • Developing an optimization model tailored to the client’s specific needs, including group-specific constraints and budget allocations.

To bring this strategy to life, OneSix deployed a comprehensive suite of solutions:

  • Marketing Data Warehouse: Centralized all marketing and adjacent data into a unified data warehouse. Power BI was used to enable real-time, customizable reporting across all franchise levels.
  • Marketing Mix Modeling (MMM) Pipeline: Implemented an advanced MMM framework to evaluate the impact of various marketing channels on key performance indicators (KPIs), such as sales and customer acquisition. The MMM pipeline provided:
  • A structured method to quantify the contribution of each marketing channel.
  • Predictive insights to optimize future budget allocations.
  • Actionable recommendations to shift spending to high-performing channels.
  • Automated Marketing Optimization: Our proprietary MMM tool facilitated continuous, real-time budget optimization by integrating historical performance data, market conditions, and client-specific constraints.

Results

Improved marketing efficiency and measurable sales impact

By implementing these solutions, the client gained the ability to make data-driven marketing decisions with confidence. Key outcomes included:

  • Full automation of marketing budget allocation and spend/impact reporting, eliminating manual data reconciliation efforts.
  • 15% increase in prospect sales as measured in an initial experiment, directly attributed to optimized marketing budget allocations across channels.
  • Enhanced strategic decision-making through real-time visibility into national-, regional-, and franchise-level marketing performance.

Through OneSix’s expertise in data-driven marketing optimization, the luxury spa franchise gained unprecedented control over its marketing investments. With real-time insights and predictive modeling, the client can now continuously refine their marketing strategies, ensuring maximum return on investment across all franchise levels.

Ready to unlock the full potential of data and AI?

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

Contact Us

Automating food inspection using deep learning for Sunsweet Growers

  • Manufacturing

Automating food inspection using deep learning for Sunsweet Growers

OneSix developed an AI-powered computer vision system for Sunsweet Growers, automating prune inspection with real-time defect detection while preserving high product quality standards.
Data Science
AI & Machine Learning
Computer Vision

Overview

Automating the prune inspection process while maintaining quality

Sunsweet Growers, the world’s largest prune distributor, aimed to automate its century-old manual inspection process to ensure product quality at scale. The new solution needed to match the reliability of the traditional process and operate as an edge device in areas with limited connectivity.

Our client’s goal was to build a single integrated provider marketing strategy powered by artificial intelligence. To do so, we had to predict not only how likely individual providers are likely to engage, but also how to intervene to change their probability of engagement. Additionally, as one of the largest pharmaceutical companies in the world, the data volume meant our solution had to be robust and architected to operate at scale.

Our Solution

Building an computer vision system for real-time defect detection

OneSix developed a computer vision solution using deep learning, deployed on portable edge devices to inspect prunes on-site. The solution comprised three key components: a hardware device capturing images from multiple angles, a cloud-based computer vision pipeline analyzing images for defects using custom-trained models, and a data warehouse for storing and visualizing inspection data. The system included a ‘human-in-the-loop’ component, allowing annotators to provide feedback on defect analysis to continuously improve model accuracy.

Results

Reliable, automated inspection ensuring quality and scalability

Since deployment, the system processes millions of prunes each season, delivering high-quality products with real-time defect detection. Sunsweet Growers’ team relies on the integrated monitoring and reporting dashboard throughout each production season, benefiting from an efficient, scalable inspection process that preserves product quality.

“OneSix designed, built and deployed a solution that integrated computer vision models with a monitoring/reporting dashboard that our team relies on throughout each production season. Since its initial deployment, as new challenges and opportunities have arisen, OneSix remains a valued collaborative partner to Sunsweet.”

Harold Upton, CTO

Ready to unlock the full potential of data and AI?

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

Contact Us

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

  • Financial Services

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
AI-Driven Marketing

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.

Contact Us

Optimizing personalized marketing campaigns at scale in real estate

  • Technology

Optimizing personalized marketing campaigns at scale in real estate

OneSix developed a personalized, data-driven marketing platform for a real estate client, driving increased revenue, customer engagement, and establishing a scalable foundation for long-term, automated personalization.
Data Science
AI & Machine Learning
AI-Driven Marketing

Overview

Adapting marketing strategies to achieve personalized engagement

Our client, a leading consumer-facing real estate company, had achieved substantial market penetration with near-total product awareness. However, this market saturation led to diminishing returns from traditional, broad-based marketing campaigns. A one-size-fits-all approach no longer captured customer attention effectively, nor did it foster meaningful engagement.

To drive continued growth, the company needed to transition to a highly personalized, data-driven marketing strategy. However, several obstacles stood in the way: the company lacked the infrastructure to analyze customer data effectively, tailor outreach strategies based on customer behaviors, and automate campaigns at scale. Additionally, they needed a reliable way to measure campaign impact to ensure continuous optimization based on real-world performance.

Our Solution

Building a scalable marketing platform for automated personalization

To address these challenges, OneSix partnered with the client to design and implement a production-grade marketing platform that would support scalable, automated, personalized campaigns. This platform was engineered to ingest real-time customer data across various business lines, using insights from behavioral data to drive micro-targeted marketing efforts. Key elements of the solution included:

  • Automated Micro-targeted Campaigns: The custom platform empowered the client to run personalized, data-driven campaigns by automating the ingestion and processing of user-level behavioral data. This allowed marketing teams to create campaigns tailored to individual preferences, targeting customers with high relevance across the best-fit channels.
  • Incrementality Measurement: The platform incorporated robust A/B testing to rigorously measure the incrementality of campaigns across audience segments, content types, and distribution channels. By comparing control groups and treatment groups, the client could accurately gauge the impact of campaigns, optimizing messaging and channel strategies based on real-world data.
  • Strategic Campaign Verticalization: We developed distinct campaign “verticals,” each focused on different customer segments within specific business lines. This approach allowed us to deploy highly specific campaigns with unique creative content and messaging, driving relevance and engagement across diverse segments.
  • Audience Segmentation and Experimentation: The platform automatically segmented the client’s customer base according to affinity with specific campaign verticals, using autonomous experimentation to test various content types and channels. The platform maintained matched control groups for each action, fueling ongoing analysis and optimization.
  • Next-Best-Action Optimization: Leveraging uplift modeling and next-best-action recommendations, we analyzed experimental results to understand the incremental impact of campaigns across different audience segments. This enabled the client to optimize future campaigns with content and channels proven to resonate with each segment.

Results

Increased revenue and customer engagement

The implementation of a highly targeted, personalized marketing platform delivered significant business impact for the client. The integrated approach of automation, real-time data analytics, and strategic segmentation resulted in millions of dollars in incremental revenue. Customer engagement improved substantially, with personalized messaging leading to higher rates of acquisition, retention, and loyalty.

The project established an evergreen marketing framework that will continue to serve the client well into the future. Insights and best practices gained from this engagement are now embedded across the organization, providing a scalable foundation for future personalization efforts. The client now possesses the tools to continuously adapt to evolving customer preferences and market trends, positioning them for sustained growth and competitive advantage.

Ready to unlock the full potential of data and AI?

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

Contact Us

Optimizing restaurant inventory using demand forecasting for Relish Works

  • Travel & Hospitality

Optimizing restaurant inventory using demand forecasting for Relish Works

OneSix developed a machine learning platform for a food distributor, enabling restaurants to optimize ingredient ordering, reduce waste, and enhance profitability through data-driven inventory management.
Data Science
AI & Machine Learning
Forecasting & Prediction

Overview

Maximizing restaurant profitability by optimizing ingredient ordering

A leading food distributor sought OneSix’s expertise to create a data-driven platform that could support restaurants in managing ingredient inventory more efficiently. While many restaurants relied on manual processes and intuition to determine restocking needs, the client wanted to introduce a solution that would allow for optimal ordering strategies to balance the risks of missed demand and food wastage.

Our Solution

Building an ML platform for dynamic ingredient forecasting

OneSix developed a machine learning-powered tool to generate ingredient-level forecasts based on diners’ orders, point-of-sale data, and menu items. The platform used these forecasts to predict future inventory needs, providing optimal restocking recommendations.

By factoring in each ingredient’s shelf life, seasonality, demand patterns, and role in various menu items, the platform automated order generation, allowing restaurant operators to submit optimized orders with just two clicks. Additionally, the tool aggregated ingredient and dish-level trends, offering insights into seasonal shifts and evolving consumer preferences to further guide menu planning.

Results

Enhanced profitability through data-driven inventory management

The machine learning solution enabled restaurants to make informed restocking decisions, minimizing waste while ensuring ingredient availability for high-margin items. By automating inventory management, the platform significantly reduced manual efforts and improved restaurant profitability. The aggregated trend insights also empowered operators to adapt to seasonal preferences and make data-backed decisions in menu engineering, providing ongoing value to restaurant businesses.

“OneSix partnered with us to design a new, machine learning-powered tool for restaurant operators to manage their business. They guided us through the process of framing the problem and determining what was possible with state-of-the-art ML/AI. The solution they developed transformed a key operational challenge into an automated solution that drives new value for restaurants.”

Mandy Tahvonen, Managing Director

Ready to unlock the full potential of data and AI?

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

Contact Us

Keeping children safe online with machine learning

  • Technology

Keeping children safe online with machine learning

OneSix developed a scalable, adaptive machine learning platform to detect abusive content in real time, enabling a tech company to enhance online safety for children while continuously adapting to their evolving communication styles.
Data Science
AI & Machine Learning
AI Agents & Chatbots
Forecasting & Prediction

Overview

Protecting children from online abuse

As social media use continues to grow, children are increasingly exposed to potentially harmful content. OneSix partnered with a fast-growing technology company to address this challenge, aiming to monitor and detect abusive content effectively while respecting the unique style and nuances of children’s online communication. This required developing a solution that could handle the vast scale of daily content, including millions of interactions, and adapt to the evolving ways children communicate online.

Our Solution

Designing a scalable, adaptive machine learning platform

OneSix built a custom, end-to-end platform with multiple machine learning models specifically trained to recognize abusive language, emojis, and media in children’s online communication. The solution included independent autoscaling pipelines for triaging content types, analyzing web and media content, and processing natural language text to detect abuse as it occurs.

To adapt to the unique and changing nature of children’s online language, the platform included a real-time feedback loop for continuous model updates. This loop enabled strategic selection of content for expert annotation, feeding those insights back into model training to keep pace with evolving trends. Additionally, a suite of performance monitoring tools was developed to track model accuracy and responsiveness, ensuring both effective monitoring and high-quality data for ongoing improvements.

Results

Enhanced online safety through dynamic abusive content detection

The solution has enabled near real-time identification of abusive content across millions of daily social media interactions, providing a powerful safeguard against cyberbullying and harmful language exposure. The adaptive model and feedback loop ensure that the platform remains effective as communication styles shift, delivering timely and accurate detection to keep children safe online. The collaboration with OneSix has equipped the client with a robust, scalable system that continuously learns and improves, meeting the demands of online safety in an ever-changing digital landscape.

Ready to unlock the full potential of data and AI?

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

Contact Us