The Basics of Measuring Conversion Rates

                                                                                                                    (Shrestha,2018)
Metrics Measurements in Web Analytics
The basics of measuring Conversion Rates
 





(Cutler, 2014)



  The Basics of Measuring Conversion Rates


Conversion Rate

(Imagex, 2016)

The Conversion Rate Defined


In the field of Web Metrics, Conversion occurs when a visitor of the website completes a tangible action.  An example would be a purchase made by a customer, the completion of a form, or a specific task.  The Conversion Rate is determined by the total number of visitors or customers completing an action that is being tracked by a Web Metrics tool.  The Conversion Rate can be defined as the percentage of all Visitors including Unique Visitors who completed a task or purchase while visiting the website.  The Conversion Rate formula is CR = the number of conversions divided by the number of total clicks (Cutler, 2014).

Additionally, according to Avinash Kaushik, “Conversion rate, in percentage, equals Outcomes divided by Unique Visitors during a particular time-period (Kaushik, 2018).” 

Outcomes

Outcomes in Kaushik’s equation are the total orders or actions taken based on the purpose or the reason the website exists.  For websites that are not marketing driven, the total number of Conversions can be based on the Visitors who completed a task.  Further, it could be a number of Unique Visitors who addressed a FAQ, answer, or a knowledge-based question.

Unique Visitors 

There is much discussion around the topic of Unique Visitors.  Some experts believe that Total Visitors should be placed in the equation, while others believe Unique Visitors should be placed in the equation of the metric.  Depending upon which one you prefer, it is advised to be consistent in tallying the data.  Kaushik’s view on using the Unique Visitor arises from his belief that not every session provides an opportunity to take an action like submitting an order. 
Kaushik is a practitioner of ecommerce and when it comes to shopping, his rationale for using the Unique Visitor in his equation is that visitors may come to glance at a website, view a different site to read reviews, come back to the original site to review the benefits, and then take some time to contemplate and make a final actionable decision.  There could be some additional steps to the process of making a final actionable decision.
By using the Unique Visitor as part of the Conversion equation, it gives the researcher a read on what is really happening on a marketing-focused website, giving credit to the prior sessions of the Unique Visitor. 
The Unique Visitor is identified by and measured using a persistent cookie that Kaushik calls ‘Shopper_id.’  It is noted that the cookie is unreliable.  However, at this time, it is the best the Conversion Metric available (Kaushik, 2018).

Time Period 

The last part of the Conversion equation focuses on measuring a time period.  For example, if the researcher is trying to determine a Weekly Conversion Rate, he or she would calculate the sum of the orders placed during that ‘Time Period’ along with the sum of the unique ‘shopper_id’s.’  It is not recommended to calculate the ‘Time Period’ by adding daily Unique Visitors to get a total for the month or a week (Kaushik, 2018).

Rationale for Measuring the Conversion Rate 

The Conversion rate is very important and useful regardless whether the goal of the website is to sell products, complete forms, or other related actions (Cutler, 2014). 

Conversion Rate Example 

An example of using Web Analytics for Conversion Optimization is a hotel website.  A hotel has a goal that visitors reserve a room on its’ site using their credit card.  Using a Conversion analytics tool, each step in the process to the point of a customer booking a room can be measured:
1.      The visitor performing a search for hotels in the specific area
2.      The visitor checking prices and various amenities available at the hotels
3.      The visitor Selecting the hotel and going through the checkout process
4.      The visitor entering his or her payment information and the finalization of the reservation
By analyzing the Conversion Rate of the visitors who progress to the final step, the hotel can effectively determine what is effectively working on each page and what isn’t.  Once that is determined steps can be taken to make the process more user friendly or efficient and thus improve on the goal of financial performance (Stokes & Blake, 2012). 

References


Shrestha, I. (2018, January 21). Web Analytics. Retrieved from http://ishworshrestha.com.np/service/376-Web-Analytics.html

Cutler, K. (2014, June 11). Web Analytics: KPIs – Understanding Conversion Rate. Retrieved from https://www.google.com/search?q=web analytics conversion rate graphic&source=lnms&tbm=isch&sa=X&ved=0ahUKEwj85bTU8enYAhVRL6wKHT-rA9IQ_AUICigB&biw=1600&bih=720#imgrc=o0t1ulnts6Y9uM:

Cutler, K. (2014, June 11). Web Analytics: KPIs – Understanding Conversion Rate. Retrieved from https://konacompany.com/web-analytics-kpis-understanding-conversion-rate/

Imagex. (2016, July 21). DON’T LET BAD CONVERSION RATES GET YOU DOWN - HERE’S 5 WAYS. Retrieved from http://imagexmedia.com/blog/2016/07/don%E2%80%99t-let-bad-conversion-rates-get-you-down-here%E2%80%99s-5-ways

Kaushik, A. (2018). Excellent Analytics Tip#5: Conversion Rate Basics & Best Practices. Retrieved from https://www.kaushik.net/avinash/excellent-analytics-tip5-conversion-rate-basics-best-practices/

Vivocha. (2011, November 4). Conversion funnel optimization. Retrieved from https://www.vivocha.com/christmas-coming-six-quick-tips-conversion-funnel-optimization/

Stokes, R., & Blake, S. (2012). EMarketing: the essential guide to online marketing. Washington, D.C.: Saylor.org.


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