Assessing customer retention strategies in mobile telecommunications
Bibliographic record
Abstract
Purpose As wireless penetration continues to increase worldwide, competitors in the mobile telecommunication industry are changing their strategies from a growth model to a value‐added one. The companies that can attract and retain customers in this highly competitive and increasingly saturated market stand poised to make considerable gains, and thus customer retention is an important field of study in this maturing market. Using the Canadian mobile phone market as an example, this work aims to study the major motivators of customer retention and their interrelationships, and assess the value that customers perceive with regard to the related advertising. Design/methodology/approach Based on a literature review and expert validation, the motivators of customer retention are divided into three dimensions and eight criteria. A systematic hybrid multiple criteria decision‐making (MCDM) method that combines the decision making trial and evaluation laboratory (DEMATEL) technique and the analytic network process (ANP) is used to examine the customer retention framework and to evaluate the promotional strategies used by various market players. Findings The interdependence relation shows that phone service quality, customer service quality, and phone plan quality are three major motivators in terms of causality with regard to brand image, customer service quality, and complaint management, while phone service quality has become a hygiene factor with regard to customer satisfaction and retention. The findings from an assessment of the promotional strategies used by the major players in the Canadian mobile telecoms industry suggest that well‐financed foreign entrants pose a risk to the major domestic carriers, and that successful promotional strategies will require strong leverage of their existing price and quality advantages. Originality/value This work adopted a hybrid MCDM approach to examine a major strategic issue in mobile telecoms, – i.e. customer retention – and demonstrated the strengths of using this method to investigate rapidly changing markets. The relative importance of the motivators of satisfaction and retention is investigated, and a strategy for customer retention in the mobile telecoms industry is provided to managers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".