Impact of AI on Customer Experience in E-commerce in Egypt
Bibliographic record
Abstract
In a world shifting from being product centric to become customer centric, customers are at the center of attention for each company since they are the key to any desired success and profitability. The e-commerce industry is exponentially growing with rising number of customers shifting to the online shopping experience as opposed to the physical one, causing challenges for businesses to sustain their competitiveness while addressing the diversified tastes of their increasing customer base. With the change in consumer behavior, offering a unique customer experience tailored according to each customer requirements and needs can be an added value & a main differentiator in a rapidly growing and competitive market in order to retain current consumers as well as attracting new ones. Shoppers moving to the virtual experience are looking for convenience which differs in perspective according to each one and this requires understanding of their decision-making drivers and purchase motivators that varies from one customer to another that entail the need for personalization. Artificial intelligence allows businesses to have deep understanding of clients’ requirements providing insights derived from different resources like historical data & customers behavior providing accordingly personalized experiences realizing the desired convenience. This paper can assist businesses to comprehend the impact of AI on the customer experience in e-commerce industry & how it influences the personalization efforts an organization use in its process as well as the convenience with focus on Egypt as a country and for population of age ranging from 15-59 years old.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".