Data has been referred to as "the new oil" for over a decade, symbolizing its potential to fuel industries and drive innovation. While oil must be refined to become useful, raw data similarly requires significant transformation to generate actionable insights. Moving from scattered datasets to predictive, high-impact insights is far from straightforward. The journey involves combining cutting-edge technology with strategic expertise to deliver meaningful results.
This blog explores how machine learning (ML) is reshaping industries, particularly in areas like real estate, advertising, education, and e-commerce. Along the way, we'll explore a real-life example of a data-driven success story, shedding light on the strategies and challenges of creating a product that turned data into a game-changing solution.
The Rise of Data-Driven Decision Making
Every day, industries generate massive amounts of data from sources like customer interactions, operational workflows, and external market factors. But what distinguishes companies that thrive from those that struggle isn't the quantity of data they possess—it's their ability to transform that data into actionable insights.
Enter machine learning. ML models excel at uncovering patterns, predicting outcomes, and making recommendations based on historical data. These capabilities empower businesses to make smarter, faster, and more targeted decisions.
For example:
- In e-commerce, NineTwoThree AI Studio implemented a machine learning-based recommendation system for Cymbiotika, enabling personalized product suggestions based on customer preferences, purchasing behavior, and browsing history. This approach drives sales by better predicting and understanding customers' needs, enhancing customer engagement and increasing lifetime value.
- In advertising, NineTwoThree AI Studio collaborated with LaunchLabs, a company that helps ad agencies and car dealerships leverage first-party data to drive targeted traffic, to enhance the performance of lookalike audiences using machine learning.
- In real estate, NineTwoThree AI Studio has built an advanced machine learning model for DataFlik that generates prioritized lists of motivated sellers more accurately and efficiently than traditional methods, helping real estate investors target the most promising leads, reduce marketing costs, and close more deals.
- In education, NineTwoThree AI Studio has transformed the certification exam preparation process by implementing gamified learning solutions that reduce test time by 80% and provide instant feedback to students, enhancing their ability to assess their understanding and improve efficiently.
Yet, even with these advancements, implementing ML solutions isn’t always straightforward. From unstructured datasets to high costs of experimentation, the journey is fraught with challenges.
Breaking Down the Challenges
Building an ML-powered product involves more than coding and algorithms. Success depends on navigating complex challenges like data quality, resource allocation, and the integration of new tools into existing business models. Here’s a closer look:
- Data Quality is Everything
ML models are only as good as the data they’re trained on. Bad or incomplete data leads to poor predictions, wasted time, and ultimately failed projects. Organizations must invest in cleaning, organizing, and centralizing their datasets before even starting on ML. - High Costs of Implementation
Developing ML models often requires specialized teams with expertise in data science, machine learning, and software development. Startups, in particular, may struggle to afford these resources. - Proof of Concept is Crucial
Before diving into a full-scale implementation, companies need to test hypotheses and validate their models to ensure that the solution is viable and scalable. This phase requires both strategic planning and technical execution. - Balancing Performance and Usability
Even when an ML model is highly accurate, its outputs must align with the operational needs of the end-user. For instance, predictions that fluctuate wildly may be accurate but impractical if the business relies on stable results.
Case Study: Predicting Real Estate Trends with Data-Driven Insights
One standout example of leveraging machine learning to unlock actionable insights is the collaboration between DataFlik, a real estate data company and our team. This project aimed to predict housing sales and identify motivated sellers with precision, reducing costs and increasing the efficiency of real estate wholesalers.
Realtors and wholesalers often spend tens of thousands of dollars on cold calls, mailers, and research to reach potential sellers. What if they could predict which homeowners were likely to sell before listing their properties?
Armed with this question, DataFlik with NineTwoThree AI Studio to turn raw data into an advanced predictive model. Our goal was simple: reduce wasteful spending and deliver better leads to their customers.
Steps to Building a Predictive Solution
- Data Exploration and Preparation
The first step was analyzing the company's existing datasets, which included millions of properties across the U.S. Each property had over 1,700 data points, such as demographics, transaction history, and distress indicators. This required building a robust data pipeline to centralize and clean the data.
Interestingly, the company had to migrate 10 terabytes of data to a new provider during the project to improve data quality. This was an unexpected yet critical step that ensured the ML model had access to the best possible inputs. - Proof of Concept (PoC)
Instead of committing to a full-scale project immediately, the team focused on proving their hypothesis. They worked with domain experts to identify high-value data points and trained an initial model to determine whether the data could indeed predict seller behavior. - Developing the Model
Using machine learning techniques (not generative AI), the model was trained to:
- Predict which houses were likely to sell soon.
- Estimate the potential discount on deals.
- Analyze market activity for large-scale investor movements.
- Building a Customer-Focused Application
The insights generated by the ML model were packaged into reports for end-users. To make these reports more accessible and customizable, the team developed a full-stack web application. Customers could upload lists, apply custom filters, and generate reports without manual intervention—saving time and unlocking new revenue streams.
From Start to Scale: Multi-Year Transformation
Over two years, the real estate company underwent a complete transformation. What began as an outsourced project evolved into a fully staffed, scalable, and self-sufficient operation. Highlights of this journey included:
- Hiring and onboarding over 20 developers, 8 ML specialists, and other support staff.
- Training the new team on software development lifecycle (SDLC) processes.
- Transitioning the entire project in-house, ensuring the company could scale independently.
This transformation not only created a world-class ML product but also set the stage for significant fundraising opportunities.
What Can Other Industries Learn?
While the example above showcases success in real estate, the lessons apply broadly to other sectors looking to leverage machine learning:
- Invest in the Right Data
Whether you’re in retail or healthcare, your first step is to ensure you have reliable, high-quality data. Build strong data pipelines to clean, organize, and centralize this information. - Start Small with Proof of Concept
Don’t rush into building a full-scale solution without validating your hypothesis. Start small, test your model, and iterate based on the results. - Collaborate Across Teams
Combining domain expertise with technical skillsets is key to creating solutions that work in the real world. - Prioritize Usability
No matter how advanced your model is, it must align with the practical needs of your customers. Build tools that empower users with actionable insights while keeping processes simple.
Looking Ahead: The Future of Data-Driven Innovation
As ML technology continues to evolve, its potential to revolutionize industries becomes more apparent. From improving customer targeting to optimizing supply chains, the possibilities are endless. Companies that invest in data-driven solutions today will be the ones leading the charge tomorrow.
For businesses considering their own ML projects, the road may seem daunting, but the rewards are well worth the effort. With the right partnerships and a strategic approach, even the most complex challenges can be turned into opportunities for growth.
Whether it’s predicting home sales, diagnosing diseases, or recommending products, machine learning is proving to be one of the most powerful tools in the modern business toolkit. By focusing on actionable insights and end-user needs, businesses can unlock the full potential of their data—and drive meaningful transformation.
Let’s make your ML safe, reliable, and ready. Reach out to us today!
