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curve (Bozovic, 2017). In our case, the outlying data points and the low R-square suggest that the logistic function might not be the best method to predict the future growth of Douyin. However, the low degree of fit does not necessarily discredit the use of logistic function in this case, as there exist several reasons that this might occur: o Data points are from different sources, and the data is from second hand sources, not from the company directly, therefore the data is not 100% accurate. o Even though the fit is not perfect, we will still assume that logistic function is a valid method to predict the future user growth for the company. From Figure 16 and Figure 17, corresponding values for initial growth rate, r, and maximum carrying capacity, K, can be derived. They are calculated and shown in Table 4 below. We consider these values as parameters for the base scenario of the Douyin’s growth. Table 4: Results from linear regression of discrete growth rate against user population of Douyin MAU DAU a Multiplication factor - b Constant - r Initial growth rate per annum b K Maximum carrying capacity -b/a -1.08*10-3 4.72*10-1 0.472 437 -2.60*10-3 5.47*10-1 0.547 210 Apart from the base growth we also considered and modeled two other growth scenarios, which we call high growth and extreme growth. Values of parameters used in these three different growth scenarios can be seen in Table 5. Table 5: Parameters used in different scenarios Data Set MAU DAU Scenario Base case High growth Extreme growth Base case High growth Extreme growth r [p.a.] 0.472 0.326 0.301 0.547 0.523 0.485 K [mn] 437 506 700 210 250 300 From Table 5 we get the following growth prediction on Douyin’s MAU and DAU. - 46 -PDF Image | Analysis of ByteDance with a close look on Douyin TikTok
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