About the Client
Our client has a leasing business across the United States, providing various retail products like clothing, gadgets, electronic home appliances, and other essential healthcare/beauty needs to customers.
The Problem
- The client had a legacy architecture with separate systems for maintaining merchant and customer information respectively and it made consolidation of the data very difficult.
- The system had multiple customer identifiers and this redundancy made it even more complicated to identify the right customers and their products of interest and thus failed to recommend the right product to the right customer.
- With massive data growth and maintaining the legacy system, real-time reporting and data consolidation were not possible.
- The client had customers from different backgrounds, and they found it extremely difficult to retain the existing customers and onboard new ones. The reasons varied from budget issues to rejected leasing opportunities.
- Due to the gaps in their marketing approaches, they also could not reach the target customers.
- The poor target marketing practices led to low customer onboarding rates and low lead generation opportunities.
- As a result, the client was unable to analyze the metrics and understand each customer’s journey through the customer journey platform, build a personalized user experience based on the pre-purchase consumption, develop effective marketing strategies for targeted campaigns and drive suitable action.
.
Continue Reading
The Solution
We built a custom event-based mobile application that transferred event-level customer information to the purpose-built PostgreSQL database. The data from the PostgreSQL database was then consumed using a custom-built API that produced JSON events. To flatten the JSON data, we leveraged Azure Synapse and extracted the most recent customer record of each session from the database. Customer data was cleansed to standardized values relating to City, State, and Age Groups.
We also identified the products that the customers showed interest in based on each session mapping before it lapsed. The customers were filtered and segmented into different categories based on Age, Gender, Income Category, Historical Significance, Product Interest, and Lease Threshold Status.
To monitor the Lead generation results and evaluate campaign performances, we leveraged Power BI to create business intelligence dashboards and focused on identifying high-value leads coming from different segments of audiences (Age, Location, Payment frequency, and gender). These Leads were broken down further to assess and identify detailed information about each customer with the number of leases available, product interest, and potential approval amounts.
We implemented a marketing dashboard for targeting the customers who showed interest in marketing campaigns and evaluated the response through bounce rates, clicks, and repeated visits. This enabled target marketing and helped the client’s sales team to reach the target customers specifically.
For enabling effective targeted marketing campaigns, the segmented customer information was exported as CSV to Salesforce through a common FTP server. Various campaigns targeting the customers were run and the results were sent back to the Azure Data platform through the aforementioned FTP server. With this two-way sync downstream Salesforce, the client was able to get insights on recommendations with more personalized notifications for the customers, products the customers showed interest in, and suggestions for leasing discounts & coupons. Further, these details with highly personalized notifications with relevant offers and discount coupons were consumed by the front-end application and displayed to customers through the UI of the app. The end users received notifications for this.
Business Impact
- A complete end-to-end digital transformation led to 3x revenue growth
- Modernizing the custom application enabled fast and easy customer onboarding with personalized targeted campaigns at a granular level.
- Identify potential prospects and provide relevant recommendations
- Run campaigns and target the customers specifically
- Determine the stages of the customer’s journey and build a personalized user experience based on the journey map.
- Make more data-driven decisions and establish concrete marketing strategies using the most effective approaches.