Online Advertising Strategies to Effectivly Market a Business School
Bibliographic record
Abstract
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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.027 |
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".