Please Assist
1 Wk 5 -Apply Operations Plan Deanna Nelson University of Phoenix OPS/575 Matthew Church 03/04/3024 2 Wk 5 -Apply Operations Plan The company of focus in this assessment is Google and the quantitative elements selected for analysis are the search engine market share and advertising revenue. The data that has been collected for these two metrics are from the year 2014 to 2023. The forecast method selected is the moving average method. Notably, applying the moving average method highlighted discrepancies between the forecasted values and actual outcomes. Specifically, in the case of advertising revenue, the two-year moving average consistently resulted in forecasted values that were lower than the actual revenue for the subsequent year. Search engine market share forecast where more stable, indicative of the dominance that Google search engine has in the industry.Graph 1: Search Engine Market Share2014-02 2014-10 2015-06 2016-02 2016-10 2017-06 2018-02 2018-10 2019-06 2020-02 2020-10 2021-06 2022-02 2022-10 2023-06 0 10 20 30 40 50 60 70 80 90 100 Search Engine Market Share Worldwide 2014 - 2023 Google bing Yahoo! Baidu YANDEX YANDEX RU DuckDuckGo Ask Jeeves Naver Sogou Haosou Shenma AOL Mail.ru Seznam Ecosia MSN CocCoc Daum Webcrawler t-online Other Note: Data adapted from Statcounter. (2024). https://gs.statcounter.com/search-engine-market- share#monthly-201402-202402 There are operational decision making implications based on these findings. Notably, innacurate forecasting, especially of revenue, will lead to poor resource allocation and negatively impact the marketing and product pricing strategies. In this intance, the advertising revenue forecast was consistently understimating the actual revenue. In a real world scenario this would 3 make Google miss out on investment opportunities and expanding in offerings. In a similar vein, if the market share forecasts are off, Google will fail to anticipate shifts in consumer preferences. Consequently, their decisions will be based on flawed assuptions, the result is a loss of market share.
Historical market share data ensures that companies can identify trends, seasonality, and patterns. From the data obtained, it is clear that market share trends usually fluctuate overtime, even though for Google this is usually represent as a drop in negligable percentage points, this may be due to preferences, or competitive dynamics. In addition, there are seasonal variations in search engine usage that mean some periods will have higher activiting levels more than others.
Google depends on this information for operational decision making as it enables them to anticipate changes and adapt to their market environment.
Graph 2: Google Ad Revenue 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 0 50 100 150 200 250 59.62 67.39 79.38 95.58 116.46 134.81 146.92 209.49 224.47 237.86 Google Ad Revenue 2014 -2023 in Billions of Dollars Note: Data adapted from Johnson, J. (2024). https://www.statista.com/statistics/266249/advertising-revenue-of-google/ 4 Google must thoroughly assess any forecasting method that it uses and consider possible alternatives that are capable at more accurately capturing market dynamics. The company should begin exploring advanced forecasting methods which can account for trends, seasonality, and other underlying patterns in the data. Furthermore, it is necessary for google to use additional data sources so that the forecasts can be more accurate. There should also be continuous monitoring and improvement of the forecasting processes. 5 References Johnson, J. (2024). Google: ad revenue 2001-2023 . Statista; Statista.
https://www.statista.com/statistics/266249/advertising-revenue-of-google/ Statcounter. (2024). Search Engine Market Share Worldwide . StatCounter Global Stats.
https://gs.statcounter.com/search-engine-market-share#monthly-201402-202402
