Reading Time: 3 Minutes ReadThis is our story of developing a personalized ancillary recommendation system based on Google Analytics 360 Suite, Google BigQuery, and artificial intelligence (AI) platform.
The client for ancillary recommendationThe leading Central and Eastern European low-cost airline, as a value-driven carrier, focuses on innovation at every stage of the customer journey. Its goal is to provide affordable services with great user experience for its customers. To successfully represent its core values, the must be able to cater to the needs of its customers and provide personalized recommendations for ancillary products, such as seats and priority boarding. This is how Aliz came into the picture. Having previously worked with the airline, they knew Aliz would be able to help them realize this vision.
The challenge of ancillary recommendationAfter some very informative meetings to discuss the client’s needs in detail, we decided to develop a personalized ancillary recommendation system on Google Analytics 360 Suite, Google BigQuery, and AI platform. The client had been carrying out pre-departure email campaigns but as these campaigns hadn’t met their expectations, they wanted to improve them with personalized messages and recommendations. They’d also been using Google Analytics 360 Suite (Analytics 360) to record user interactions on their online platforms; however, they hadn’t yet made use of the stored data.
The Problem: How to recommend the right offers like ancillary to the right customersBased on our client’s business factors, the challenges we faced were many.
- We had to extract useful features from Google Analytics 360.
- We had to find the right offers (ancillaries) to the right customers.
- Then, we needed to identify customer segments to obtain effective target groups for marketing campaigns.
- We also had to design personalized messages by utilizing both the recommendations as well as the customer segment unique preferences was a must.
- Lastly, we had to build a data pipeline and integrate with their marketing platform.
- Prove that personalized communication would deliver commercial uplift.
- Test Google Cloud for easier scalability/higher availability.
- Prove that Google Cloud could offer a better time-to-market.
The SolutionWe developed a personalized ancillary recommendation system on Google Analytics 360 Suite, Google BigQuery, and AI platform. The project took 3.5 months to complete with A/B testing, with an overall engineering effort of 77MDs.
We implemented a solution that utilizes the data collected in Google Analytics in Google BigQuery and built Machine Learning models on top of it.Since Google Analytics 360 data already resided in Google BigQuery, the extract, transform, load (ETL) pipeline for obtaining useful features was also designed and implemented in BigQuery. Because GA 360 data is not structured to have the features directly, it took several transformation steps to extract.
- Context data
- Historical user behavior (purchase and search)
- Historical flight data
- A recommendation engine consisting of four models for Seat/Priority and One-Way/Return combinations.
- Customer segments based on purchase and search history: Travel Lovers, Family Travelers, First Time Travelers, Business Travelers, and One-Way Travelers.
- E-mail open rate
- Ancillary sales