Advanced Analytics Project ANLY-FPX5510 Advanced Business Analytics Executive Team Places Analytics Department on 90-Day Probation: Business Strategy at Stake
In recent months, the executive team has been making significant business decisions based on the outcomes provided by the analytics department. These decisions, however, have yielded mixed results. While some moves have proven successful, others have resulted in considerable financial losses for the organization. Consequently, the analytics team has been placed on a 90-day probationary period, after which the company’s leadership will determine whether the group will continue or be disbanded.
The head of the marketing department has been tasked with overseeing the analytics group’s performance throughout this period. All of the team’s work will undergo rigorous review before submission to the executives for final decisions.
Business Problem: Evaluating Revenue through A/B Testing
One of the methods chosen to assess business performance involves using A/B testing to compare the revenue generated per visitor from the same banner ad displayed on two different websites—Yahoo and CNN. The objective is to determine which platform drives higher revenue from the company’s advertisements. This approach aims to streamline advertising efforts, potentially reducing the number of sites the company needs to manage without involving a third-party service provider.
In this A/B test, two hypotheses were examined. The null hypothesis suggested that the average purchase price between Yahoo and CNN was the same. The alternative hypothesis proposed that the purchase amounts differed significantly between the two platforms. Upon completing the test, a significant variance in customer purchase behavior was found, showing that users on Yahoo responded more favorably to the ad than those on CNN.
Testing Methodology: Applying Hypothesis Testing to Business Strategy
The chosen testing method aligns closely with the broader business problem. Just as A/B testing was used to measure ad performance across two websites, a similar approach will be applied to evaluate the outcomes of the analytics team during the probation period. The goal is to determine the team’s value based on their ability to deliver accurate and actionable insights.
Throughout the 90-day probationary period, the analytics team will be assessed using a structured A/B testing framework. The marketing department head will monitor their output, generating two hypotheses. The null hypothesis posits that the team will deliver satisfactory results, with their work passing review and being approved by the marketing manager. The alternative hypothesis suggests that the analytics department will produce unsatisfactory results, potentially leading to the team’s dissolution.
A/B Testing: A Proven Marketing Experiment
A/B testing is a powerful marketing experiment where the audience is split to test multiple variations of a campaign, helping identify which version performs better. For instance, one segment of the audience may see version A of a marketing asset, while another sees version B. The results of this experiment determine which content generates the most favorable response.
To run an effective A/B test, two distinct versions of the same content must be developed, with only one variable changed between them. These versions are then shown to two similarly sized groups of the target audience. The performance data from both versions is collected and analyzed to see which one yielded better results (Kolowich, 2019).
Before launching an A/B test, it is crucial to conduct research on the current performance of the variables being tested. In the case of the business problem at hand, research would focus on the accuracy, reliability, and quality of previous analytics reports submitted to the executives. Based on this research, hypotheses regarding future performance would be established and tested.
Requirements for Implementing A/B Testing in Business
Once the research and hypotheses are established, the next step is to create a variation of the control version and conduct the A/B test. The goal is to identify which version performs better—either the existing process or a modified approach. Running the A/B test during the 90-day probationary period will help determine if the analytics team can contribute positively to the company’s strategic goals.
However, some potential issues could arise during A/B testing, including biased sampling and length pollution. Biased sampling occurs when a test sample does not accurately represent the entire population, potentially leading to misleading results. In the context of website testing, factors such as ad campaigns or newsletter schedules could influence conversion rates, skewing the results (Saleh, 2019).
Length pollution is another potential issue, which arises when an A/B test is stopped prematurely, resulting in in