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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
Forecasting & Optimization

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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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
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.

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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
Next Best Action

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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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.
AI & Machine Learning

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.

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Doubling recurring revenue with embedded analytics for a B2B SaaS provider

  • Technology

Doubling recurring revenue with embedded analytics for a B2B SaaS provider

OneSix enabled the client to launch a scalable, self-service BI platform within six months, doubling ARR and empowering customers with advanced, on-demand analytics capabilities.
Data & App Engineering
Data Analytics
Snowflake
Matillion
Fivetran

Overview

Empowering a SaaS provider with self-service analytics

Our client wanted to gain a competitive advantage with their SaaS application by offering feature-rich analytics embedded into the application. Their previous platform had some basic reporting capabilities for their customers, but new requests for dashboards or reports often required long lead times and were painfully slow to build for their reporting team. Simple enhancements like adding a field to a report required a clunky ticketing system.

Our client wanted to introduce a business intelligence (BI) solution that would empower their customers to self-serve many of their data needs and free up their internal reporting team to tackle higher-value tasks. And they wanted to accomplish this in a short time frame with minimal technical coding and wanted to be able to scale the final product out to all their customers.

Our Solution

Implementing a scalable, agile BI solution with enhanced data models

Utilizing an agile methodology process, OneSix designed, built, and implemented a modern data stack with an end-to-end data warehouse, data transformation capabilities, and data visualization technologies that created opportunities for insights that were previously impossible with their previous technology.

Data models were designed to expand reporting capabilities of the customer’s source data. Initial dashboards were created to give customers a jump-start with the new BI solution and immediately start generating insights.

OneSix also partnered and led training sessions with both client team members and customers of the client to create a high rate of knowledge transfer. We also presented a new strategy for creating an additional product to sell to their customers using their new infrastructure by enabling customers to connect to secure shares of the data warehouse.

Technologies Implemented
  • Snowflake was chosen as the data warehouse due to its favorable data storage, extremely fast performance, low query calculation costs and its industry leading scalability.
  • Fivetran provided flexible data pipelines loading data into the data lake, saving time and money on data engineering development.
  • Matillion ETL was chosen as the ELT tool for its user-friendly drag-and-drop development interface, built-in SQL Server read/write compatibility, and its ability to build pipelines entirely based on table metadata.
  • Sisense was leveraged for data visualization, seamlessly embedding a fully white-labeled product into the client’s existing platform.
  • ASP.NET Core framework was used along with AWS SQS messages to maintain the synchronization of accounts, security, and pipeline configurations.

Results

Doubling ARR, exceeding targets, and accelerating time-to-market

With the customer’s new modern data platform and knowledge transfer, they were able to exceed their expected conversion rate 2-fold within three months of the launch of the new platform and doubled sales Annual Recurring Revenue (ARR) from their customers.

Following an agile methodology process enabled the organization to launch the new platform to the market in less than six months from the start of development. The new market differentiator enabled customers to self-serve all their reporting needs and accomplish exponentially more than previously possible. The client also expects to be able to retire their old reporting platform thanks to the capabilities of the new product.

Ready to unlock the full potential of data and AI?

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Establishing a competitive edge with embedded analytics for Chordline Health

  • Healthcare & Life Sciences

Establishing a competitive edge with embedded analytics for Chordline Health

OneSix empowered Chordline Health to gain a competitive edge by implementing a scalable, multi-tenant analytics solution that improved application performance, reduced operational costs, and delivered real-time insights directly within their SaaS platform.
Data & App Engineering
Data Analytics
Snowflake
Matillion
Fivetran

Overview

Enhancing SaaS competitive edge with scalable, embedded analytics

Chordline Health, a healthcare software provider, wanted to gain a competitive advantage with their SaaS application by offering feature-rich analytics embedded into the application.

Existing analytics was driven off the single-tenant operational databases using complex SQL statements that could not be reused across customers due to the different data models per customer. Any needed changes were restricted by the legacy approach that required extensive labor and testing to enhance. The application experienced severe performance issues. A scalable solution was needed to expose the trove of data for customers that could be easily integrated into their application.

Our Solution

Implementing an automated data framework

OneSix designed an end-to-end automated framework to load multi-model databases per customer into a data lake and transformed that into a multi-tenant data warehouse addressing Cases, Authorizations, To-do lists, and Compliance threshold reporting.

All new tools and platforms were introduced that would help automate new customer loads by using a simple control table. A live connection to the data warehouse using the powerful Snowflake engine served embedded analytics using Sisense visualization. The complex security model was simplified via two security tables that allowed bi-directional integration between Sisense and the SaaS application.

Technologies Implemented
  • Snowflake was leveraged to create a highly secure, scalable data lake and multi-tenant data warehouse environment.
  • Fivetran provided dynamic data pipelines per customer’s unique data model, loading data into the data lake, saving time and money on data engineering development​.
  • Matillion was the core data transformation technology used to integrate and cleanse single-tenant data to load in the multi-tenant data warehouse in preparation for visual analytics.
  • Sisense was leveraged for data visualization, seamlessly embedding a fully white-labeled product into the client’s existing platform.

Results

Boosted application performance and reduced labor costs

The implementation of the Embedded Analytics solution brought about significant positive changes for the SaaS provider:

  • Improved SaaS Application Performance: By driving analytics from a separate database, the SaaS provider effectively segregated the workload. This separation reduced contention for resources on their main application, resulting in improved performance and responsiveness for end-users.
  • Reduced Labor Time and Costs: The adoption of a single warehouse and transformation architecture streamlined data management across various application versions. This unified approach significantly reduced the laborious task of managing multiple data repositories and schemas, leading to considerable time and cost savings.
  • Achieved Competitive Edge: By integrating feature-rich analytics directly into their SaaS application, the company gained a competitive advantage by providing real-time, insightful data to users without requiring external tools or platforms. This integration streamlined user experience and differentiated their offering in the market, enhancing user engagement and retention.

Ready to unlock the full potential of data and AI?

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Creating a new revenue stream with embedded BI for a B2B SaaS platform

  • Technology

Creating a new revenue stream with embedded BI for a B2B SaaS platform

OneSix transformed a B2B SaaS provider’s reporting capabilities by implementing a modern data stack and embedding BI, enabling a new revenue stream, reducing tool costs, and enhancing client data access and insights.
Data & App Engineering
Data Analytics
Snowflake
AWS
Sigma

Overview

Transforming reporting capabilities for a streamlined client experience

Our client wanted to revamp their existing reporting and create a new advanced reporting revenue stream by embedding a BI solution into their web application.

Their previous platform was developed in-house and difficult to support. Additional requests for data and reports had to be supported with another tool and consumed much of the customer support team’s time. Our client wanted to retire both reporting tools and provide clients with better insights into their data by allowing direct database access to incorporate into existing workflows and reporting solutions.

Our Solution

Building a modern data stack with embedded BI

OneSix built new custom data pipelines to efficiently move the large volume of their production data into the cloud, in order to create a modern data stack that could be levered with their new technologies.

Sigma was seamlessly embedded within the existing web app, and existing reports were sunset to use the new reporting capabilities. New data sets were also created to increase accountability and decrease development time for new custom reporting requests in the future.

Data sharing within Snowflake was implemented to give clients direct access to their data without the need for reports to the ServiceCore team and the use of another tool. This enables their clients the full flexibility to use and manipulate the data as they see fit.

Technologies Implemented
  • Snowflake was chosen as the data warehouse due to its favorable data storage, extremely fast performance, low query calculation costs and its industry leading scalability.
  • AWS, Snowflake and Terraform were used to create an in-house ETL process to limit costs and provide a workflow that could fit into existing Dev/Ops procedures.
  • Sigma was leveraged for data visualization, seamlessly embedding a fully white-labeled product into the client’s existing platform

Results

New revenue stream, reduced tool overhead, and simplified reporting

With the customer’s new modern data platform, they were able to generate a new tiered revenue stream that fits the needs and market size of their clients with a handful of clients already using the direct database access.

The customer was able to retire existing tools and licensing fees and the reporting stack was simplified. The data stack created will continue to be further leveraged in the future for more internal business reporting needs.

Ready to unlock the full potential of data and AI?

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

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