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Conversion Rate Opimization

Conversion Rate Optimization Definition

Conversion rate optimization (CRO) is the technique for enhancing the number of guests to a website who then change to become customers on that webpage.

A Little More on What is Conversion Rate Optimization

Website optimization came about as a result of the urge of e-commerce traders to enhance the performance of their website during the .com season. As competition grew in the early 2000s, a lot of awareness on the uses of the website was created by those promoting the internet. They also developed ways of improving the user experience during the website usage.

In 2004, internet marketers were enabled by new tools to test different content and web designs with a motive of finding out which layouts, offers, and images perform best. This enhancement was realized in 2007 when free Google Website Optimizer was introduced. Nowadays, conversion and optimization are critical components of digital marketing campaigns. A survey carried out amongst internet promoters in 2014 revealed that 59% of participants believed that CRO was critical in their marketing strategy.

CRO shares many features with direct response marketing which emphasizes testing, tracking, and continuous improvement. This direct response marketing became popular in the early 20th Century and was supported by the establishment of groups like the Direct Marketing Association in 1917.

Direct Response marketers, just like the present day conversation rate optimization also engage in A/B split testing, print campaigns, tracking of response, and testing of the audience to optimize radio and mail.

Statistical significance

Marketers normally discover inconsistencies in how customers act when marketers study campaign ads. The response rate in online marketing significantly reduces in terms of segments, offers and from hour to hour.

This aspect can be attributed to the challenges people face in distinguishing real effects from chance events. By use of haystack process marketers are restricted to studying and making conclusions from small samples of data. Psychologists (led by Amos Tversky and Daniel Kahneman) have come up with methods to establish why poor decisions are made when dealing with small samples. In addition to this, statistical methods can be used to study huge samples and taming the need to observe trends where none exist.

These procedures of optimizing conversion are then applied practically in a real-time environment. The gathering of real-time data and continuous messaging enhances the impact and efficiency of campaigning online.

It is never enough to attain results that are statistically sound because those responsible for optimizing conversion should always make sure that the size of their sample explains major variables. For instance, a test may seem to be significant statistically before seasonal aspects (day of the week, time of the day, time of the year) have been considered in the sample data. One distinction may reflect in one season compared to others hence misguiding the outcome.

It is also critical to comprehend how different factors influence tests and findings. Various user factors such as device location, type, new vs. returning visitor will react separately to individual variation. Analysis of outcomes without considering various aspects can significantly enhance one segment; or many factors can eliminate bad outcomes for a different segment. For instance, the uplift in the conversion rate of a desktop could result to a reduced conversion rate on telephone equipment. In such a case, it is only the desktop test that should be deemed winning.


Conversion rate optimization focuses on enhancing the number of those who visit websites and what they do there by procedurally testing distinct kinds of a process or page. By doing this, organizations can realize high sales without having to invest a lot of money on website traffic thus enhancing their marketing profits on the investment.

The conversion rate is further referred to as the number of newcomers who fulfill an objective as set by the owner of the site. Some testing procedures like A/B testing, allows one to control and tell those contents that change site visitors into real customers.

There exist many ways of conversion optimization with two leading major schools of thought taking center stage for the last few years. One school mostly focuses on establishing how best to enhance campaign, website or page conversion levels. The second school gives attention to the pretesting stage of conversion optimization. In this second school, the firm responsible for optimization invests a good time comprehending the target customers and designs specific information that pleases that specific group. After this is when it will come up with testing methods to enhance the rates of conversion.

References for Conversion Rate Optimization

Academic Research onConversion Rate Optimization

  • From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics, Chaffey, D., & Patron, M. (2012). Journal of Direct, Data and Digital Marketing Practice, 14(1), 30-45. This paper is concerned with how to optimize digital marketing and enhance the commercial value of digital analytics. The author here looks into the need for firms to put into use web analytics so as to enhance the performance of digital marketing. In addition, the article narrates ways that can be utilized to develop a program for optimization of digital marketing.
  • More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates, Ludwig, S., De Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., &Pfann, G. (2013). Journal of Marketing, 77(1), 87-103. This paper is concerned with how customer reviews influence potential online customers to make buying decisions. The author uses text mining to obtain changes that are obtained from customer book reviews in Amazon.com. The finding reveals that good content in customer reviews have little impact on continuous addition in conversion rate. In addition to this, positive perceptions from those interested in the product lead to an increase in conversion rate. These findings imply that managers should point out and promote categories of products with a number of powerful reviews. Besides this, they should infuse their modes of reviews to such relevant products.
  • Affiliate marketing and its impact on e-commerce, Duffy, D. L. (2005). Journal of Consumer Marketing, 22(3), 161-163. In this article, the author is concerned with the influence of affiliate marketing on electronic commerce.
  • Search engine marketing: Transforming search engines into hotel distribution channels, Paraskevas, A., Katsogridakis, I., Law, R., &Buhalis, D. (2011). Cornell Hospitality Quarterly, 52(2), 200-208. This section is concerned with measures that can be taken to optimize WebPages for marketing search engines. The author reveals that a good design of the website enhances the search engine’s web and that appropriate keywords encourage search engine ranking to be optimum. The study dwells into various aspects and variables that take part in SEM then give strategies that hotel promoters can use to fulfill those objectives. It goes ahead to propose a four-stage framework for the development of the SEM strategy namely; planning, control, implementation and analysis
  • Experience curves and dynamic demand models: Implications for optimal pricing strategies, Dolan, R. J., &Jeuland, A. P. (1981). The Journal of Marketing, 52-62. This article proves that supply and demand aspects become unstable with time which has impacts on the pricing. In general, the author gives us a general methodology that can be used to set up optimal strategies of pricing over a particular life cycle of a product.
  • Integrating science into web design: consumer-driven web site optimization, Gofman, A., Moskowitz, H. R., & Mets, T. (2009). Journal of Consumer Marketing, 26(4), 286-298. In this article, the author is concerned with how science can be integrated into the web design so as to optimize the consumer-based web site.
  • Consumer-driven multivariate landing page optimization: overview, issues and outlook, Gofman, A. (2007). The IPSI BgD Transactions on Internet Research, 3(2), 7-9. This paper is investigating how consumer-driven multivariate landing page can undergo optimization.
  • Estimating conversion rate in display advertising from past erformance data, Lee, K. C., Orten, B., Dasdan, A., & Li, W. (2012, August). In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 768-776). ACM.  In this article the writer is concerned with how conversion rate can be estimated by utilizing observations of the past performance of the user, publisher, and advertiser. To be specific, the conversion event is modeled around different levels with distinct names which predict these parameters individually. The model then tries to propose how to merge these distinct estimators by use of logistic regression to accurately point out conversion events. Finally, the author also goes ahead to talk about practical aspects like missing data, data imbalance, and calibration of output possibility which makes this estimation challenge even harder while fastening implementation of the conversion approach
  • Taking the measure of e-marketing success, Cotter, S. (2002). Journal of Business Strategy, 23(2), 30-37. The author in this section is discussing how electronic marketing has been a success.
  • The Atlas rank report: How search engine rank impacts traffic, Brooks, N. (2004). Insights, Atlas Institute Digital Marketing. This paper uses Atlas rank report as the case study to find out how online traffic is influenced by the search engine rank.
  • Conversion rate based bid adjustment for sponsored search, Rey, B., & Kannan, A. (2010, April). In Proceedings of the 19th international conference on World wide web (pp. 1173-1174). ACM. This article is concerned with how advertisers make use of sponsored search to cause traffic to their web at a conversion rate and cost per conversion that gives them value. The author in this paper tries to suggest how fairness of every keyword’s bidding price can be improved within the AM (Advanced Matching) bundle. The author narrates how to quantify the advertiser’s rates of conversion despite the adjustments in the aspect of AM. In addition to this, the article goes ahead to propose ways through which we can adapt keyword bid prices so as to reflect their value with the marketer/promoter hence promoting the auction, the advertiser return, and user satisfaction.

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