## 3 Results
### Descriptive Statistics
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**7 Days**} & \multicolumn{2}{c}{**14 Days**} & \multicolumn{2}{c}{**1 Month**} & \multicolumn{2}{c}{**2 Months**} & \multicolumn{2}{c}{**3 Months**} \\ \cline{2-11}  & **Wald** & **p** & **Wald** & **p** & **Wald** & **p** & **Wald** & **p** & **Wald** & **p** \\ \hline CA & 24.90 & \textless{}0.01 & 28.90 & \textless{}0.01 & 20.16 & \textless{}0.01 & 23.73 & \textless{}0.01 & 21.61 & \textless{}0.01 \\ SD & 4.99 & 0.03 & 2.82 & 0.09 & 6.09 & \textless{}0.01 & 4.11 & 0.04 & 4.49 & 0.03 \\ CP & 0.51 & 0.47 & 0.08 & 0.78 & 2.0  
The first group of predictors for AG1 showed significance for CA and AA. Average predictor AA was characterized by the strongest impact. For AG2, the case was definitely different. Four of the five predictors, i.e., CA and CP to AA, were significant. The only predictor that did not have statistical significance was average predictor SD. The impact forces of the predictors, especially in SP, were characterized by a strong accent. For AG3 and AG4, regression analysis showed similar significance to AG2 also for four predictors, but in these cases, lack of predictor significance in relation to dependent variables was shown by the mean of AA variables. In both cases, the CA variable strongly affected G5 results, where things were quite different. Statistical significance was only demonstrated in three cases: CA, CP, and SP. The second group of predictors that affect the dependent variable also showed variability. In AG1, one of the four predictors was statistically significant, namely, FR_out (0.03). The situation looked completely different for AG2. Here, we can clearly see the strength of joining two classes. Significance statistics showed a positive result for up to three predictors, i.e., MSG_out, FR_in, and FR_out. In the case of AG3, AG4, and AG5, statistical significance was shown by 100% of predictors from FR_out, being the one that acts the strongest on the dependent variable. The next part of analysis was based on an intergroup comparison of user characteristics between products with different survival time. In order to compare individual lifecycles with the Activity and Experience factors, we used the Mann-Whitney U Test. Analysis was presented in four perspectives: analysis of individual user classes, analysis of aggregated user classes based on activity-factor analysis of individual user classes, and analysis of aggregated user classes based on Experience Factors. Periods that we compared with each other are visible in Table A1. By starting division-variable predictor analysis for innovators, we can see the lack of significance of parameters at the first comparison period. In the next two, we can see that predictor CA was significant, which indicates that the periods significantly differed from predictor CA. In the last pair of compared periods, predictors CA, SD, and SP showed the largest differences. Statistics for innovators show us a tendency for the comparative period to be smaller, in this case, two to three months, so more predictors influenced the differences. Analyzing the four other user classes, we see the opposite relationship. Starting with early adopters, where the differences could be seen in the four predictors in the first two pairs, in the next two the number of differences decreased. In the cases of early-majority, late-majority, and laggard users, significance statistics that point to differences are slowly blurred, as in the case of laggards, where in the last group of period comparisons we see the lack of significance of the given predictor data, which indicates low differences. Based on the aggregated user classes, we can see that the first combination of innovators and early adopters positively affects predictor significance, and this indicates large differences for most of the analyzed pairs (from three to four strongly affecting differences).