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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.

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Achieving a 31% revenue increase by optimizing ad pricing for Dictionary.com

  • Technology

Achieving a 31% revenue increase by optimizing ad pricing for Dictionary.com

OneSix developed a machine learning platform for Dictionary.com to optimize ad pricing across segments, achieving a 31% revenue increase in one segment through dynamic, data-driven strategies worldwide.
AI & Machine Learning
AI-Driven Marketing

Overview

Optimizing ad pricing at scale for global reach

Dictionary.com, the first and largest digitally-native English language dictionary, serves tens of millions of users worldwide every month. With users accessing millions of definitions, synonyms, audio pronunciations, translations, and spelling help, Dictionary.com generates an immense amount of data daily. To optimize ad pricing at this scale, they needed a sophisticated solution that could leverage impression- and bid-level data, while being adaptive to the fast-evolving online advertising landscape.

Our Solution

Building a machine learning platform for ad floor optimization

OneSix partnered with Dictionary.com to create a powerful ad floor optimization platform powered by Apache Spark. This platform not only supported day-to-day production algorithms but also provided Dictionary.com’s in-house data science team the flexibility to prototype new algorithms and iterate on optimization strategies. 

Additionally, we developed a custom dashboarding tool to visualize key metrics and trends for each segment, equipping ad-yield managers with accurate market snapshots and optimal pricing recommendations.

Results

Significant revenue increase through optimized ad strategies

The implementation of the new ad floor optimization strategy led to a significant impact. In one experiment, Dictionary.com experienced a 31% increase in revenue in a targeted segment, demonstrating the effectiveness of the new optimization platform over previous strategies. This success has set a strong foundation for ongoing improvements in ad revenue and pricing strategies.

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Maximizing casino player profitability with a one-to-one marketing engine

  • Travel & Hospitality

Maximizing casino player profitability with a one-to-one marketing engine

By implementing a one-to-one marketing engine with real-time personalization, OneSix helped a casino gaming provider enhance player experience and boost profitability through tailored offers.
AI & Machine Learning
AI-Driven Marketing
Snowflake

Overview

Revolutionizing loyalty marketing in casino gaming

Casino gaming is a high-energy industry that continually seeks to use state-of-the-art technology to improve the player experience. However, most loyalty marketing programs in the industry still follow traditional, tier-based direct marketing approaches. Using this model, casino patrons are evaluated based on monthly spending data, then categorized into low- to high-value tiers. Promotions like free play, restaurant discounts, and hotel perks are allocated by tier, with players receiving updates via direct mail or email.

This legacy system has significant drawbacks. Promotions are distributed only once a month, resulting in outdated information by the time offers reach players. Additionally, the process is labor-intensive and static, failing to respond to real-time changes in player behavior. Since players are grouped into broad value categories, the traditional approach also lacks personalization, preventing casinos from crafting customized offers that reflect individual preferences and engagement patterns.

In response to these limitations, our client, a leading casino gaming provider, partnered with OneSix to develop a solution capable of engaging players individually. The goal was to build a dynamic marketing engine that would automatically learn from players’ behaviors both online and in-casino, creating a personalized, real-time experience for each player.

Our Solution

Building a one-to-one marketing engine for personalized engagement

OneSix designed and implemented a one-to-one marketing engine powered by a distributed reinforcement learning platform, tailored specifically for the gaming industry’s needs. Recognizing that each player’s engagement is driven by unique behaviors, we developed a platform capable of continuously learning and adapting to individual player activity. This multi-channel “next best action” engine integrates data from multiple sources to generate a comprehensive, real-time view of each player.

The system leverages this data to make intelligent, individualized decisions, including when and how to communicate with each player. The engine streams out optimal actions that maximize engagement, offering personalized incentives and communication across various channels, such as email, direct mail, online platforms, in-app messages, and SMS. This approach allows the casino to engage each player based on their current behavior, creating a highly responsive, individualized experience that feels more relevant and timely than traditional loyalty programs.

In addition to building this powerful engagement engine, OneSix ensured that the platform would continue learning over time. Our solution includes a feedback loop that adjusts strategies based on player response, optimizing interactions to align with evolving preferences. This adaptive approach allows the casino to nurture player relationships effectively, combining the best of real-time analytics with advanced AI-driven marketing capabilities.

Results

Enhanced player experience and increased profitability

The deployment of this one-to-one marketing engine has transformed the client’s approach to player engagement, providing a more immediate and personalized experience for casino patrons. Players now receive offers tailored to their individual habits and preferences, and these offers are refreshed in near-real-time as new player data is collected.

Experimental testing of the platform has demonstrated substantial improvements in player profitability, as well as enhanced engagement and loyalty among both new players and those migrated from traditional loyalty programs. By responding to shifts in player activity and delivering customized offers across multiple channels, the casino has seen a marked increase in player satisfaction and profitability.

$450M

Annual player reinvestment

10%

Increase in player visits

6%

Increase in player profitability

Ready to unlock the full potential of data and AI?

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Scaling multi-touch attribution to optimize pharmaceutical marketing impact

  • Healthcare & Life Sciences

Scaling multi-touch attribution to optimize pharmaceutical marketing impact

OneSix developed a scalable multi-touch attribution solution for a biopharmaceutical company, enabling precise measurement of marketing impact across channels, optimizing budget allocation, and accelerating data-driven insights for increased healthcare provider engagement.
AI & Machine Learning
AI-Driven Marketing

Overview

Improving multi-touch attribution for targeted biopharma marketing

A leading biopharmaceutical company, known for its breakthroughs in innovative treatments, sought to improve its understanding of multi-channel marketing impacts on healthcare providers (HCPs), specifically in driving new-to-brand prescriptions (NBRx). With a vast marketing ecosystem, the company employed multiple touchpoints—including email, digital ads, and in-person events—to reach providers across various stages of the decision journey.

Although they had a proof-of-concept model for multi-touch attribution (MTA), it needed to be scaled and fine-tuned to operate effectively in a production environment. Additionally, the company needed a parameterized solution capable of segmenting MTA results by brand, franchise, and indication. The ultimate objective was to develop a robust and flexible MTA model that could accurately attribute marketing impact and optimize budget allocation to maximize engagement with HCPs.

Effective multi-channel marketing in the pharmaceutical industry is challenging, as each channel and publisher varies in its reach, engagement, and effectiveness. Unlike a single-channel approach, multi-touch attribution must capture how touchpoints interact within complex user journeys. An ideal solution would involve controlled experiments to precisely isolate channel impacts; however, the cost and frequency requirements of such experiments make them impractical for real-world applications. The client needed a more scalable approach that leveraged existing data to measure past performance and generate actionable insights for future marketing decisions.

Our Solution

Designing a scalable and adaptive MTA pipeline

OneSix built a highly parameterized, unit-tested Python package to perform MTA on the client’s diverse marketing initiatives, focusing on measuring individual touchpoint effectiveness across brands and indications. The model’s core function was to predict the probability of an NBRx occurring, based on a combination of control and independent variables derived from the various marketing channels. To further refine the model, OneSix introduced an advanced explainer model that could assign a partial contribution to each control and independent variable, providing a breakdown of the factors driving NBRx outcomes.

The MTA model was designed to address key technical challenges, including calibration to adjust for the sigmoid distortion often seen in probability densities from predictive models. This adjustment was achieved through a custom calibration scheme, which corrected probability distortions to ensure that all variables received a positive partial contribution. The parameterized structure of the model allowed users to modify factors such as study period lengths, feature sets, and segment parameters (e.g., brand or indication) with ease. The package was controlled by a single configuration file, providing a centralized interface for rapid experimentation and model adjustments via a command-line interface.

As a result, the pipeline offered flexibility for experimentation across different market baskets and feature combinations, empowering the client’s data science team to iterate quickly and test various configurations. By providing a modular, scalable, and flexible solution, OneSix’s MTA model allowed for high adaptability, enabling the client to execute MTA analyses on demand and derive actionable insights at a pace previously not possible.

Results

Accelerated data-driven insights and improved marketing allocation

The implementation of this comprehensive MTA pipeline enabled the client to gain a deeper understanding of how different marketing touchpoints contributed to NBRx conversions and overall engagement with HCPs. With OneSix’s solution in place, the company was able to assess the individual and combined impacts of each marketing channel, allowing them to identify high-performing channels and optimize spend allocation with confidence. By analyzing the contributions of different touchpoints within the customer journey, the company could now tailor its marketing strategies to maximize engagement and ROI on specific channels.

The streamlined configuration and command-line interface allowed the client’s data science team to rapidly test hypotheses and iterate on model features, reducing the research cycle and enhancing their agility in responding to market dynamics. Continued collaboration with OneSix provided the company with regular updates and enhancements to the MTA model, enabling ongoing improvements and refinements to its methodology. As a result, the biopharmaceutical company was able to achieve a more precise, data-driven approach to marketing attribution, laying a scalable foundation for sustainable growth and optimized channel investment across its brands and franchises.

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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?

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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?

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Leveraging AI to increase provider engagement for a top pharmaceutical company

  • Healthcare & Life Sciences

Leveraging AI to increase provider engagement for a top pharmaceutical company

OneSix developed an AI-driven, multi-channel outreach engine for a global pharmaceutical client, enabling automated, personalized provider engagement across brand portfolios with scalable, data-informed insights.
AI & Machine Learning
AI-Driven Marketing

Overview

Leveraging AI for a coordinated, personalized marketing strategy

Our client, a top 5 global pharmaceutical company, sought to build and deploy an AI-powered multi-channel outreach engine to coordinate provider engagement across multiple brand portfolios. Their manual engagement process lacked the ability to personalize outreach at the individual provider level, relying instead on aggregate customer segments. This approach created substantial inefficiencies, limited scalability, and missed opportunities to tailor interactions based on individual provider traits and behaviors.

Our Solution

Developing an AI-driven multi-channel outreach engine

OneSix built a custom AI solution leveraging enterprise reinforcement learning technology to automate and optimize provider engagement. Using real-time data on provider traits, behaviors, and patient populations, the solution integrates multi-brand and multi-channel outreach into a single system. The platform, powered by Strong RL, Apache Spark, and Tensorflow, is designed to scale within the client’s on-premise infrastructure to accommodate vast data volumes and diverse brand requirements.

Results

Enhanced efficiency and personalized engagement at scale

With this AI-driven outreach engine, our client achieved a highly efficient, data-driven provider engagement strategy, enabling coordinated, personalized interactions across brands and channels. The system improved engagement outcomes by predicting provider response probabilities and suggesting targeted interventions. As a result, the pharmaceutical company can engage providers in a more tailored, impactful way, leveraging individual insights at scale and significantly reducing manual efforts.

Ready to unlock the full potential of data and AI?

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