~In the project, we used a technology called "Light GBM." Light GBM is a powerful and efficient machine-learning framework for making predictions and solving data-related challenges. It's a tool that can analyze data and make predictions about which members are most likely to engage with emails. This technology helped us sort and rank members based on their likelihood to open and click on emails, allowing us to target our marketing efforts more effectively.
Technologies Utilized:
- Light GBM: A powerful and efficient machine learning framework for data analysis and predictions.
- Data Collection Tools: Various tools and methods to gather crucial member data.
- Feature Engineering: Techniques for crafting features to understand member behavior.
- Predictive Modeling: Developing a predictive model to identify likely email clickers.
- Decile Ranking: Segmenting members based on predictions.
Introduction:
In the fast-paced world of digital marketing, two things are super important: keeping customers engaged and not spending more time. Our exciting project is all about using some tech magic to make these things happen. We're focusing on emails, a big part of how companies talk to customers, and we want to change how they do it. We're using something called "propensity scores" to help marketers target the right people. This new way not only boosts engagement but also saves money by not bothering people who aren't interested.
Opportunity:
In the big world of digital marketing, getting people excited and not wasting cash is a big deal. So, we're all about using science and numbers to send better emails. We're ranking people based on how likely they are to open and click on emails. This means we can put more effort into the folks who really want to hear from us, which makes them happy and saves money.
Proposed Solution:
Our secret weapon is a fancy computer program called Light GBM. This program looks at lots of things like who you are, what you've done before, and what you like. It then figures out how likely you are to click an email in the next two weeks.
How We Did It:
- Gathering Data: We collected all sorts of info about people, like their age, health info, and what they've done in the past six months. This data helped our computer program do its thing.
- Making the Program Smarter: We added more info like what kind of emails you like, if you usually open them, and if you click on the links inside.
- The Magic Model: Our computer program, Light GBM, took all that info and ranked people from most likely to least likely to open an email in the next two weeks.
- Dividing People Up: We put people into groups based on their rankings. The top group is where we put all our attention because they're the most likely to click an email.
Technology Utilized:
In this project, we harnessed the power of Light GBM technology to drive our data-driven approach. Light GBM was instrumental in:
- Data Collection: Gathering crucial member data.
- Feature Engineering: Crafting features to comprehend member behavior.
- Model Building: Developing a predictive model to identify likely email clickers.
- Decile Ranking: Segmenting members based on predictions.
This strategic use of Light GBM allowed us to optimize our marketing campaigns by targeting the most engaged members, ultimately leading to improved engagement and cost reduction.
Results and Benefits:
More People Clicked: By focusing on the top group, we got three times more people to click on our emails. This means more people got what they wanted.
- Saving Money: By not bothering people who weren't interested, we saved a lot of money. This is because we only spent time on the folks who really wanted to hear from us.
- Better Investment: Focusing on the people who really liked our emails made our marketing efforts more successful with the help of our Digital Transformation services.
How We Measured Success:
We looked at numbers to see how well our plan worked. We saw that the top group was three times more likely to click our emails. Also, our program was good at predicting who would click, with a score of 72.9%. That's like getting an A+ in school.
Conclusion:
We used some cool technology and a program called Light GBM to solve our marketing problem. By focusing on the people most likely to engage, we saved money and made our marketing efforts better. This shows that using tech to make marketing decisions can really change the game and get you better results.
