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Leveraging Data Science and Generative AI for Customer Relationship Management 2.0


One Minute with Ruble Joseph

VP - Global Data Science Consulting at eClerx

While customer acquisition and building a foundational customer base are important, to maintain end-to-end CRM efficiency it takes driving activation, engagement, retention, and loyalty.

In this one-minute video, Ruble Joseph, VP of Global Data Science Consulting at eClerx Digital, highlights the various key elements that contribute to a sustainable and robust CRM practice and where investments need to be focused to drive growth.




Transcription of the Video

The rat race across all industries starts with customer acquisition and building a strong customer base. But, once acquired, the entire life cycle from activation to engagement, retention, and loyalty become equally important to drive sustained CRM efficiency.

Across these life cycle stages, personalization to enable superior customer experiences is driving significant customer lifetime value, per transaction value, and experience metrics as well.

Richer consumer data and insights layered with advanced data science models like lead scoring, customer profiling, propensity modeling, next-best-action, and cross-sell recommender systems have made CRM much more capable, intelligent, timely, and efficient, as well.

The ability to advance customers across life cycle stages as well as optimize individual journeys from reach, engage, and buy has improved significantly as well.

The CRM platforms have become much more capable, and the learning feedback loops have become shorter due to data science, machine learning, and AI. We have delivered impact on immediate metrics like conversion rate and average order value by 8 to 10 percent across thousands of transactions.

At the same time, the bigger impact is achieved by retaining and growing customer lifetime value that leads to net new incremental revenue by 13 to 15 percent across thousands of customer bases which are high potential.