Segmentation and Evidence of Relationship-Building Activities in Life Insurance Companies’ Websites
Bibliographic record
Abstract
Companies that offer online insurance services have their work cut out for them. Not only must they search for loyal customers, but also for free advocates who can influence their friends to purchase an insurance policy online from them. In terms of time, cost and effort, attempting to attract the whole market is just a waste. As the best customer services with all insurance coverage for too much insurance policies is just vain. This is where the concept of target marketing comes in, as the most important factor influencing market share of an online insurance company is targeting an accurate market segment. Market segmentation is defined as a marketing strategy that involves dividing a broad target market into subsets of consumers who have common needs and priorities, and then designing and implementing strategies to target them. Market segmentation strategies may be used to identify the target customers, and provide supporting data for positioning to achieve a marketing plan objective. Businesses may develop product differentiation strategies, or an undifferentiated approach, involving specific products or product lines depending on the specific demand and attributes of the target segment (Kotler, 2003). Then again, it’s not enough for a company to market its product to its customers. It has to keep and maintain that customer’s loyalty through the provision of excellent services and other relationship-building activities. Our project aims to study these two aspects of online marketing by analysing the websites of three well-known insurance companies (www.metlife.com (US) www.standardlife.co.uk (UK), www.william-russell.com (UK). This paper will study the websites of the three selected insurance companies, which use similar E-Business models but different market segmentation, and examine how they intend to meet the needs of the different customer segments, and the extent to which they have managed to build customer relationship activities to maintain customer satisfaction through their website designs. Thus the study deals with these issues, not only from a customer perspective, but also from a business perspective. The findings present the effectiveness of the MetLife site which has offered various features to woo the retirement sector, whereas Standard Life has concentrated on the corporate client with regard to healthcare. William Russell is particularly focused on the expatriate client and is equally savvy for policy holders as it is with new customers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".