A list of projects:
- Problem Statement:
We aim to increase the number of hotel orders booked through the company website by encouraging return visits from users who have previously visited the site but did not make a purchase.
- Data Collection:
We need to gather relevant data to understand user behavior. Here are some crucial data points we should consider:
User interaction data on the website (e.g., pages visited, time spent, search queries). User profiles (if available). Previous booking history. Data about the user's device and location. This data would ideally be stored and accessed through a CRM (Customer Relationship Management) system and the company's server logs.
- Data Exploration and Analysis:
We will perform exploratory data analysis (EDA) to understand patterns in the data. For example:
What are the most common user behaviors before booking a hotel? What are the common behaviors of users who visit but don't book? Are there any correlations between user characteristics and their likelihood to book a hotel?
- Modeling:
We will use machine learning algorithms to predict the likelihood of a user booking a hotel based on their behavior and profile. We may employ algorithms such as Logistic Regression, Decision Trees, or Random Forests. We can also consider more advanced techniques like XGBoost or Neural Networks.
- Building a Retargeting Strategy:
Based on the model's predictions, we will develop a strategy to retarget users who have high probabilities of booking but haven't done so yet. This could involve:
Personalized email marketing: Sending custom offers and recommendations based on user preferences. Display advertising: Showing personalized ads to these users when they visit other websites. Website personalization: Customizing the website experience for these users when they return.
- A/B Testing:
We should continuously test and optimize our retargeting strategy. We can do this by running A/B tests, where we change one variable (e.g., the email subject line) and see how it affects user behavior.
- Measurement and Evaluation:
Finally, we'll measure the effectiveness of our retargeting strategy by tracking key metrics like return rate, booking rate, and ROI. We'll also monitor the performance of our predictive model and update it as necessary.
- Implementation and Monitoring:
Once we have a retargeting strategy that works, we will roll it out to a larger audience and continuously monitor its performance to make necessary adjustments.