Online Advertising Strategies to Effectivly Market a Business School
Notice bibliographique
Résumé
Advertising has always played an important role in creating visibility for educational institutions. In today’s time, digital marketing is the sought-after mode as there has been a significant shift from offline to online advertising. With the evolving times, flexibility and convenience take significant importance and it is critical for educational institutions to shift gears and adapt to the new formats. In order to stay relevant and have a competitive advantage, digital advertising helps higher educational institutions go that extra mile in engaging with their potential customers. It also helps in building awareness and attract good quality of students. In the world of digital advertising, ‘Google Advertisement’ is an online advertising platform developed by Google, where advertisers bid to display brief advertisements, service offerings, product listings, or videos to web users. It can place advertisements both in the results of search engines like Google Search and on non-search websites, mobile apps, and videos. Google AdWords offers the most pragmatic solutions and tools to all strategic issues of digital advertising. Click Through Ratio (CTR) stands out as the most significant index of reflecting its influence and impact. Amongst the array of choices, the right strategy requires an academic and strategic backing. The objective of this paper is to assess on the impact of Google Adwords is used in digital advertising campaigns promoting business schools in specific. This research concentrates on CTR as a measure of the campaign’s effectiveness. This paper try’s to understand CTR in the context related to the type of content embedded in these digital advertisements; the structure of this content; and hence identify and suggest new strategies. This paper identifies and proposes the right online advertising strategy that can be used by a Business School (B School).Purposive/non-probabilistic sampling was carried out to choose the specific of Business Schools (B-schools) for this study. The business schools selected were based on the National Institution Ranking Framework (NIRF) 2018 of the Indian Human Resource Development. The data was analyzed using to the Social Sciences Statistical Suite (SPSS). There was only access to publicly available and publicly displayed advertisement with no access to user profile data. CTR was utilized to measure total and proportional engagement. The advertisements were then categorized based on their content and analyzed through a one-way ANOVA test. For the purpose of an operationalizing, CTR was utilized as defined by Pak et. al. (2018): “A ratio showing how often people who see your advertisement end up clicking it.” The main components analyzed are the characteristics of an effective advertisement appearing on the digital platform measured through its Click Through Ratio. One-way ANOVA has been conducted to assess the Click Through Ratio of advertisement segregated in twenty categories based on their format, content and time of appearance. The analysis reflects that Click Through Ratio differs for different format of advertisements, the information that they contain and for the time and day that they appear. Strategies based on these findings are suggested along with discussion, limitations and further scope of research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,013 | 0,009 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,062 | 0,027 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».