Your AI powered learning assistant

What Does a Data Scientist Actually Do?

Intro to the story

00:00:00

Audimax, an audiobook platform offering a free trial with up to 30 minutes of daily content, seeks to boost its conversion of free users to paid subscribers using machine learning. A request emerges to develop a model for this purpose, but the underlying problem is identified as ambiguous and not well defined. The narrative underscores the importance of precisely articulating business challenges so that data scientists can transform vague objectives into targeted, effective solutions.

Talking to stakeholders

00:00:36

Exploring multiple strategies to improve free-to-paid conversion rates led to the gathering of diverse insights from within the company. In-depth discussions with product, marketing, and finance teams merged technical expertise with market understanding. Detailed evaluations combined core product knowledge with strategic business insights to form a unified view. This approach emphasizes the power of collaborative analysis in driving conversion improvements.

Reformulating the problem

00:00:51

Reformulating the problem enables sharper questioning and fosters effective collaboration among teams. Greta and Ken redefined the challenge by using benchmark data which revealed that the company lags behind its peers. Their analysis shows that Audimax's free-to-paid conversion rate must increase from 5% to the industry standard of 20%. The findings underscore the considerable effort and expert input required for successful data science initiatives.

Simpler solution

00:01:15

Healthy skepticism drives the evaluation of new requests, urging one to assess if a complex machine learning model is truly necessary. Instead of building a sophisticated model immediately, simpler approaches should be explored first. If a basic solution produces nearly equivalent results, it is the preferred choice. This mindset promotes efficient problem-solving by avoiding unnecessary complexity.

Formulating a hypothesis and A/B testing

00:01:41

A hypothesis proposed that offering a 50% discount to new free users within five days would significantly increase Audimax’s conversion rate and double monthly revenue. An A/B test was implemented by splitting new customers into two groups, carefully controlling factors such as sample size, test duration, and statistical power. The promotional group experienced a 50% increase in revenue and an improved conversion rate from 5% to 10%, while the control group stayed unchanged.

Exploratory data analysis (EDA)

00:02:58

Recognizing the need to enhance conversion rates, a thorough analysis of customer behavior was undertaken. Over several weeks, the database was meticulously reviewed, variables were understood, and tables were cleaned before exploring correlations and visualizing data patterns. The analysis revealed that free-plan users engaging with the product for at least 60 minutes within their first three days were four times more likely to become paid customers, offering a clear strategic opportunity.

Second A/B test

00:03:46

Free users were granted unlimited premium content and exclusive features for 24 hours after registration as part of an A/B test aimed at increasing engagement and conversion. The encouraging test results led to rolling out the changes to all free users, raising the free-to-paid conversion ratio to 15%, close to the 20% industry benchmark. Simpler solutions played a significant role in achieving this notable improvement.

ML Brainstorming

00:04:24

Quick fixes proved insufficient, driving the shift toward a sophisticated, machine-learning approach. Persistent product discussions revealed a divide in user perceptions of the base price, with some finding it too high and others not. The challenge of differentiating these user segments underscored the need for a targeted solution, leading to the insight that an ML algorithm could boost conversion rates and revenue.

Machine learning mechanics

00:04:50

A predictive model was developed by gathering extensive data and applying rigorous preprocessing steps that included handling abnormalities, compensating for missing values, and normalizing data within defined date ranges. The process ensured the dataset was robust and aligned with the specific requirements of forecasting discount amounts necessary for client conversion. Determining the appropriate volume of high-quality data set the foundation for accurate predictions. Meticulous feature engineering then transformed this curated data into optimized inputs for the machine learning model.

ML Fine-tuning

00:05:16

Greta spent a month evaluating multiple machine learning algorithms to determine the best fit for her dataset using metrics like accuracy, precision, and recall. She selected the most effective model and meticulously refined its hyperparameters to enhance its performance. The model's validation produced promising results, reinforcing her enthusiasm for deploying the improved system.

ML Engineering

00:05:41

Audimax, a small firm without a dedicated ML ops engineer, needed a production-ready machine learning model. Greta collaborated with developers to address this gap, engineering a solution that brought the model into production. She developed a REST API to enable seamless integration, showcasing resourcefulness and teamwork in overcoming operational constraints.

ML A/B test

00:05:58

ML models deliver predictions swiftly within the live application, ensuring a smooth user experience without interruptions. The system’s responsiveness allows for real-time outcomes that do not compromise interface performance. Rigorous A/B testing confirms that these prediction results remain consistent and accurate in a live setting. This approach validates the model’s effectiveness while preserving user satisfaction.

Conclusion

00:06:05

Significant financial improvements emerged within months, driven by an optimized strategy that converted users from free to paid services. The refined process underscores how targeted data science techniques can revitalize business performance and boost revenue. This narrative illustrates the transformative potential of data science in overcoming operational challenges and catalyzing growth. The success story serves as a reminder to harness analytical skills to drive real-world business improvements.