An Emerging Trend in Retailing: Innovative Use of Gift Cards
Bibliographic record
Abstract
ABSTRACT Over the last two decades, an increasing number of shoppers have started using gift cards for their retail purchases. In response to this emerging trend in shopping, and merchants are offering unique gift cards to accommodate a variety of customers' needs and wants. While some retailers offer quite plain gift cards, others, such as Target, have created colorful, multipurpose gift cards. These newly designed gift cards are three-dimensional, voice recordable, reloadable and online-redeemable. Other special features include graphics, holograms, scents, mood sensors, textured or glittered finishes, personal statements, and pictures. Some of these gift cards can be used as toys, including finger puppets and games, or worn as ornaments. Retailers are using these increasingly popular cards not only to increase sales, but also to communicate their marketing mix (product, place, promotion, and price). The purpose of this study is to analyze the physical characteristics of gift cards issued by entrepreneurial retailers to help other retailers better design the next generation of gift cards. INTRODUCTION An emerging trend in the marketplace, cards are a saving grace to holiday shoppers struggling to find that perfect (Canadian Business 2006/2007, p. 17). Americans now use than 840 million credit cards and annually charge one-trillion dollars, which is than what they spend in cash (Toffler and Toffler 2006, p.278). Indeed, besides the use of credit cards, Gift cards actually began as paper gift certificates; but during the 1990s evolved into plastic cards with magnetic strips (Hudson 2005). In the late 1990s, major retailers initiated closed-loop or retailer-specific gift cards (Home 2007). Then major credit-card companies followed suit (Acohido and Swartz 2007) by issuing open-loop or network-branded gift cards (e.g., Visa gift card or Master Card gift card) (Home 2007; Fest 2010). Gaining popularity as holiday gifts, gift cards were ranked as the second-most-popular item after clothing in 2005 (Yang and Lewis 2006). Now, nearly three-fourths of consumers in the United States either purchase or receive at least one gift card annually (Promo 2006). Consumers spent $100 billion on gift cards in 2010, up 22 percent from $82 billion in 2006 (Acohido and Swartz 2007; Byrnes 2008; Steiner 2011) in contrast to $45 billion in 2003 (Harris 2005). An estimated 5.1 billion merchant gift cards (issued by retailers) and bank gift cards (issued by Visa, Master Card and American Express) are used worldwide (Acohido and Swartz 2007). With their growing popularity, gift cards are becoming personalized. For example, with increasing shopping options, including self and home improvement, gasoline, air travel, and tattoos, gift cards are providing consumers opportunities to give more personalized presents (Petrecca 2006; p. IB). In addition, the cards' features are becoming personalized. For example, Wal-Mart allows consumers to put their photo or a text on gift cards (Jacobson 2005). Visa also provides options to personalize Visa gift cards with personal photos or stock images and engraved messages by visiting GiftCardLab.com at a cost of $5.95 per card (Edwards 2007). Furthermore, the widespread use of smart phones and iPads will likely increase the number and variety of digital gift cards (virtual gift cards) (Murphy 2010). As consumers start using digital gift cards, retailers will probably provide even personalization options and use social media to promote and sell gift cards (Murphy 2010). Gift cards benefit not only the gift givers and recipients but also, most importantly, the merchants. For example, most shoppers tend to spend when they are given gift cards (Shambora 2010). Therefore, revenue is generated not only by consumers purchasing the cards but also, in turn, by recipients who spend than the gift card's face value (Home 2007). …
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".