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Quality Score (Web Marketing) Definition
Quality score is a rating for how Google measures the usefulness or relevance of your Pay-Per-Click advertisement on Google’s search platform. Generally, the usefulness and relevance of your ad to those searching a particular term in Google is determined by examining the keywords used and the searcher’s reaction to your ads. If the reaction is positive (I.e., the searcher clicks the advertisement) then it was successful.
A Little More on Google’s Quality Score for an Advertisement
The quality of an add is measured by looking at several factors:
- How closely is your ad content (or text) related to the searched topic?
- What percentage of individuals seeing your ad clicked on it (this is the CTR or click-through rate)?
- What is the quality and relevance of the landing page to which the ad leads?
The most important factor for determining the quality score is generally the click-through-rate. If lots of people seeing your ad click on it, then Google makes money and the advertiser is happy with the result. The CTR is thus used by Google to determine the appropriate cost for displaying the ad. This is expressed as the Cost Per Click, or amount the advertiser must pay for each click on the ad. Th higher the bid price and the relevance of the ad to search terms (some combination of the two) will result in where and when Google displays your ad individuals employing the Google search engine.
What these factors are all import in determining the Quality Score, Google does not make public its exact algorithm. Nonetheless, marketers work diligently to improve the quality score of an advertisement, as this yields better return on investment (invested marketing dollars). More specifically, it results in more conversions (meeting your value position, such as sales o customer/client acquisition) for the advertisement.
Below are some popular methods generally used to increase an advertisement quality score.
- Keywords – Identifying the relevant keywords that customers use when searching for topics relevant to your ad or value offering. These keywords need to be integrated into the test of your add. They also need to be grouped or organized appropriately.
- Landing Page – An ad ultimately leads to a landing page where a customer will be confronted with the opportunity to take part in the value proposition that your business offers. You can improve your quality of your ad by making certain the landing page for the ad meets the expectations of those clicking on it.
Both of these things improve the relevance of your ad to the individual searching on Google’ search engine. A poorly planned ad campaign will employ irrelevant keywords or the keywords used will not relate well to the actual value proposition being offered by the advertiser to individuals visiting the website.
References for Quality Score
Academic Research on Quality Score (QS)
- Fingerprint quality and validity analysis, Lim, E., Jiang, X., & Yau, W. (2002). Fingerprint quality and validity analysis. In Image Processing. 2002. Proceedings. 2002 International Conference on (Vol. 1, pp. I-I). IEEE. This paper presents various methods used to estimate the quality of fingerprint images and utilizes orientation certainty to validate the localized texture pattern of these images and analyzes the ridge to valley structure to identify the invalid images. It proposes an algorithm and quality benchmark to be used. A monotonic relationship is identified and it suggests that this algorithm is realistic in identifying low quality and invalid images.
- Fingerprint quality indices for predicting authentication performance, Chen, Y., Dass, S. C., & Jain, A. K. (2005, July). In International Conference on Audio-and Video-Based Biometric Person Authentication (pp. 160-170). Springer, Berlin, Heidelberg. This article presents two new quality indices for fingerprint images. The first index is used to determine the energy concentration in the frequency domain as a global feature while the second one is used to calculate the spatial coherence in local regions. The article then provides a framework to evaluate and compare quality indices using their ability to predict the system performance in image enhancement, feature extraction, and matching.
- Development and utility of Q-score for characterizing cotton fiber quality, Bourland, F. M., Hogan, R., Jones, D. C., & Barnes, E. (2010). J. Cotton Sci, 14, 53-63. The purpose of this study is to explain the logic and calculation of a numerical index (Q-score), evaluate the Q-score’s relationship to loan value, and also to describe the use of Q-score in cotton breeding and testing of the cultivar. Computing the Q-score is done in two steps, the first is normalizing the fiber properties from 0 to 1 and the second is to combine these values through quality-weighting factors which are based on input from textile processing experts.
- Return on quality improvements in search engine marketing, Nabout, N. A., & Skiera, B. (2012). Journal of Interactive Marketing, 26(3), 141-154. This paper uses a descriptive study to prove that quality improvements possess complex effects with unclear returns. It suggests that quality improvements result in greater weighted bids which reduce the prices if the advertisement ranking is not improved. A decomposition method, however, can be utilized to untwine these effects and explain them on marketing costs and profits of search engines. The paper then presents results which show that the advertisers benefit when they lower their bids after advertising quality is improved.
- An image quality assessment metric based on non-shift edge, Xue, W., & Mou, X. (2011, September). In Image Processing (ICIP), 2011 18th IEEE International Conference on (pp. 3309-3312). IEEE. This article presents an original metric to assess image quality using the Non-shift Edge (rNSE) ratio, whose simplicity is based on its succinctness and effectiveness. It filters an image through the LOG operator and identifies the edge points as the zero-crossings of the filtered image. The rNSE performs similarly with other comparable well-designed metrics.
- Scalable image quality assessment based on structural vectors, Narwaria, M., & Lin, W. (2009, October). In Multimedia Signal Processing, 2009. MMSP’09. IEEE International Workshop on (pp. 1-6). IEEE. Since Human Visual System (HVS) is complicated and challenging to model, this article suggests the use of singular vectors derived from Singular Value Decomposition as adequate structuring elements in images to quantify the loss in the structural information contained in images. This suggested metric has been certified on three databases and found to exceed the performance of current image quality metrics in literature.
- Human observer confidence in image quality assessment, Engelke, U., Maeder, A., & Zepernick, H. J. (2012). Signal Processing: Image Communication, 27(9), 935 -947. This paper considers observer confidence as a factor that impacts variations in quality ratings. It analyzes the results from a study carried out to identify the association between observer confidence and judgment of image quality. It also creates models for forecasting the mean observer confidence as a complementary measure to the extensively used mean opinion scores. This paper concludes that a strong interrelation between quality perception and confidence exists and this leads to the development of predictive models of high accuracy.
- Measuring Fingerprint Image Quality Using the Fourier Spectrum, Li, Q. R., & Xie, M. (2007). Journal of Electronic Science and Technology, 5(3), 264-267. This article suggests a method to measure the fingerprint image quality using the Fourier spectrum. This method searches for the band frequency that corresponds to the ridge global average period and then calculates the quality score of the fingerprint image through measuring the relative magnitude of the band frequency components. This method has been proved through experiments to have excellent performance.
- Quality scoring–A tool for sensory evaluation of cheese?, Kraggerud, H., Solem, S., & Abrahamsen, R. K. (2012). Food quality and preference, 26(2), 221-230. This article has a purpose of evaluating the relevance of data derived from the quality scoring methodology of ISO/IDF which is done by expert assessors for the sensory quality control of cheese. It compares quality scoring with sensory quantitative descriptive data derived from a trained panel and consumer preference data. This article concludes that quality scoring is essential and a holistic sensory quality measure.
- A case study in applying software certification model by product quality approach, Yahaya, J. H., Deraman, A., & R Hamdan, A. (2007, June). Proceedings of the International Conference on Electrical Engineering and Informatics. This paper presents a case study that uses and evaluates the significant aspects of a method created for software product assessment certification process. This model utilizes a pragmatic quality model as the benchmark for the assessment, weighted scoring method, repository, and certification representation method. It removes bias assessment and unfairness evaluation and ensures that data is secure and confidential.
- The correlation between perceived internal audit quality and defined corporate governance soundness, Barac, K., & Van Staden, M. (2009). African Journal of Business Management, 3(13), 946-958. This study examines whether a correlation is present between the perceived quality of an internal audit function and the effectiveness of a corporate governance structure using a sample of 30 big South African companies. However, no correlation is found to exist, and these findings lead to doubts regarding the role of internal audit as a corporate governance mechanism in an organization and show that in-depth research needs to be done on the appropriateness of internal audit as a corporate governance mechanism.