A global hotel chain was struggling to sustain interest in its flagship marketing program and required a complete overhaul of its segmentation-based campaign management
The company was looking to design hyper-personalized campaigns for over 20 million members leveraging predictive analytics and machine learning algorithms
WNS deployed an elaborate, data-driven hyper-personalization framework to build and manage end-to-end customer-centric campaigns
Gaining customer loyalty and sustaining it in the long run has become increasingly difficult in today’s business environment. As customer expectations rise to a new level, providing personalized services, communication and experience is becoming the standard norm. The next step in this dynamic landscape is hyper-personalization that can evoke long-lasting brand advocacy among customers.
Hyper-personalization requires rich customer insights and the ability to connect the dots. It starts with understanding customers’ needs, engaging in conversations, monitoring behavior, and creating products, services and content that align with their specific needs.
To create hyper-personalized marketing campaigns for over 20 million members. Past campaigns had their limitations in terms of higher cost, lower returns, reach and popularity since they were based on segmentation-based targeting. With customer interest in its flagship marketing program declining, the hotel chain required a complete overhaul of its analytical approach to campaigns.
The company was looking at transforming to a customer-centric organization, leveraging analytics and Machine Learning (ML) algorithms, to tailor offers that cater to individual preferences, improving customer engagement and driving upsell / cross-sell opportunities.
WNS deployed an elaborate, metrics-based, data-driven hyper-personalization framework to build and manage an end-to-end customer-centric campaign for the client.
The WNS solution entailed:
Devising a five-stage campaign deployment and management plan, which included:
Identifying datasets and variables to analyze customer behavior
Developing a complex analytical algorithm to tailor offers that suit individual preferences, and automating this process
Ensuring last-mile delivery of campaigns (publishing on client’s web and social channels)
Continuous monitoring and reporting of campaign performance across channels
Insights from the reports were channeled back to the personalized engine to further tailor the offers as per customers’ needs
Creating 40 predictive analytics models
Building a personalized engine to handle 60+ offers with the help of ML algorithms
Deploying big data analytics platforms such as Hive, R, SAS and Teradata
Gamifying offers leveraging artificial intelligence to strategically create hurdles / rewards. This made it challenging and exciting at the same time for customers
A universal control group that was created helped in assigning the hurdle / reward levels based on various customer attributes. A robust and well-established, data-driven process was also developed to measure the customer loyalty initiatives, programs and overall strategy.
That a strong customer analytics framework and deep domain knowledge can help increase upsell and cross-sell opportunities. The WNS solution helped improve customer engagement and delivered the following specific outcomes:
USD 450+ Million incremental revenue generated as a result of the campaign
335,000+ new member enrollments
195+ percent increase in member registration
USD 1.75+ Billion revenue generated as a result of increased loyalty program engagement
Our centralized targeting strategy now enables the client to develop customized communication across products and services.
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13 April 2021
21 November 2022
Banking & Financial Services
16 November 2022