ENHANCING CUSTOMER RETENTION THROUGH AI-ENABLED CRM SOLUTIONS A MARKETING ANALYTICS PAPER OF GENPACT
Bibliographic record
Abstract
This paper delves at the ways in which Genpact, an AI-driven CRM platform, may maximize client retention through the application of advanced marketing analytics. This paper looks at how customer relationship management systems can use artificial intelligence, machine learning, and predictive analytics to better understand customer tastes and habits. Businesses can instantly analyze massive volumes of structured and unstructured customer data with the help of AI-powered CRM solutions. Customer journey visualization, sentiment suggestions, and sentiment analysis all contribute to higher engagement, according to the paper. By identifying potentially vulnerable customers, predictive models pave the way for proactive retention strategies. The importance of making decisions based on data in building long-term relationships with customers is emphasized by the research. The use of chatbots and intelligent procedures automates client contacts, which improves service quality and response speed. The ability of AI to conduct segmentation analyses and deliver tailored marketing is the focus of this research. If demand projections are accurate, customer satisfaction goes up. The findings show that customer relationship management systems with AI capabilities allow for consistent and personalized interactions with clients across different platforms. In order to improve marketing strategies, the paper highlights the significance of insights produced by analytics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".