Competitive Edge Using Big Data Analytics to Improve Customer Relationship Management
Bibliographic record
Abstract
In order to handle e-commerce data for customer behaviour analysis, bigdata analytics is a developing technology that shows promise. The ability to analyse heterogeneous data in native format and at a comparatively low cost is one of big data's advantages. Data is being produced in various structures, or even unstructured, due to the rise of social media and personal digital assistants. The amount of processing time can be significantly reduced by using big data technologies, even though the data is enormous, by performing parallel processing. Metrics like consumer loyalty, affinity, transaction value, and likelihood of purchase can all be projected by machine learning algorithms. In addition to helping the shop adjust business strategy, this also helps them add inventory and run marketing. In this thesis study, two models are proposed: the Enhanced Model for Customer Behavior and Purchase Analysis and the Mouse Movement Pattern based Analysis of Customer Behavior (CBA-MMP). Two methods are used to classify the customer: the Decision Tree algorithm and Multi-Layer Neural Networks (MLNN). Decision trees employ metrics including gender, age, day segment, special occasion, total time spent on-site, and purchasing done or not done. Additionally, lowering processing times and increasing accuracy are the goals. Processing the input with a collection of hidden layers and an output layer is done using a multi-layer neural network. Every layer node has an activation function attached to it. Based on a comparison investigation, the suggested model yields a high accuracy rate with a reduced processing time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".