Popular Conversation Platform Accelerates Customer Engagement with AWS
A personalized, smooth, and pleasing customer experience has been growing in significance and many companies are ahead of the race in technology and innovation to create a unique and remarkable experience.
The innovative AI based marketing solutions platform was looking to achieve goals of customer retention and engagement with Data Engineering using AWS. They began their data transformation by building a data model that acts as a solid base for enhancing their customer journey.
The growing significance of customer insights
The AI business provides marketing solutions through conversation media marketing. 50 million+ users currently use the services in 100+ languages through intelligent keyboards, animated content, transliteration, and voice to text.
Enriching conversations with a variety of language keypads, GIFs, emojis etc is the core focus of the business.
The marketing platform collects a huge amount of data that primarily comes from customer interaction and engagement through predictive keyboards and helps determine customer segment and behaviour.
The business recognized the significance of this massive volume of data in understanding their customers, focusing on user preferences, accelerating customer retention, and enhancing the customer experience. However, they were dealing with extremely heavy data and the costs for transforming & generating data insights were very high. To leverage the data for customer insights and retention, the AI service provider required the right data transformation solution.
To leverage the data for insights into customer behaviour and retention, the client required an efficient data transformation solution, which would bring the ideal balance between performance & cost.
Achieving Data transformation with AWS
- A data transformation solution with the right set of AWS components
- An efficient Data model architected on top of Data Warehouse to address data redundancy, calculating KPIs and improving dashboard and downstream processing
- The user event data was correlated with user profile data & transformed (using AWS EMR) into ~500 Business critical KPIs
- The event data was then stored on the Data Warehouse (Redshift) which acted as a data serving layer for Superset dashboards.
- AWS Step functions was used to automate and orchestrate the data flow between various Layers (RAW -> Transformed).
Data Insights Platform
Transforming customer engagement
- Share targeted content and campaigns tuned to customer behaviour thereby increasing customer engagement.
- Identify factors contributing to customer churn such as particular App versions, removal of a certain feature and so on
- Focus on popular content and relaunch campaigns
- Identify dormant or at-risk customers depending on their App Usage within last 90 days
With an efficient data transformation model, the business can embark on real time insights into customer behaviour for instant decision making, and scale easily to handle increasing data volume.
Healthcare
Big Data and Analytics
Big Data and Analytics
