## 3 Results
### Survival-Time Prediction with Early-Adopter Characteristics  
For survival-time, a prediction dataset containing the usage statistics of 195 newly introduced products was used. Usage statistics for each product are defined by product and user identifiers, and a timestamp representing time when the product (in this case, the avatar) was used by a specific user. For each product, only the first usage per user was taken into account. For each product, data were collected from a newly added product starting from product launch until last product usage. For each analysis, two sets of variables were used based on the User Activity and User Experience factors presented earlier in Table 1.  
In Figure 7, we see a high increase in the CP variable for products with seven-day survival with simultaneous small CA values. In other periods, we see density with slight deviations, as in the case of the three-month period, where we see growth in the CA variable; in a 14-day survival period, an increase in the SD variable was observed. As in the previous chart, Figure 8 shows a clear division into survival-period groups. Within seven days, an increase in the AA ratio with a simultaneous drop in SD was visible, which may indicate a drop in interest from users with low SD. In the remaining periods, we can see in Figure 9 a clear decrease in the SD index with a simultaneous increase in CA; this showed that the more users communicate with others, the less likely it is for the product to be accepted. In the case of this chart, we can clearly see that the fewer users log in, fewer messages are sent to others, and fewer sent to the circle of potential friends. There is a clear decline from that period to the next. In the case of the last graph, Figure 10, we can see density against the FR_out indicator at initial values oscillating at 150-250. Here, however, we also see a decline from period to period. In the initial period, the MSG_in indicator is small, but increases with survival time. However, the last period (three months) oscillates near the first period, which indicates a lower number of messages sent by the users adopting products in that group. Results from all Experience Factors and Activity Factors are presented in Figures A1 and A2 within Appendix A.  
Figure 6: The Kaplan-Meier survival model for one group of divisional and non-divisional variables over a period of one month. Figure 8: Dependence of objects in classes from CA, SD and AA variables. Figure 7: Dependence of objects in classes from CA, SD and CP variables. Figure 10: Dependence of objects in classes from FR_out, MSG_in and MSG_out variables. Figure 9: Dependence of objects in classes from U_log, FR_in and MSG_in variables. Another stage investigates how the number of analyzed adopted users from 1% to 100% and their characteristics affect classification accuracy for the prediction of product lifetime and survival-class assignment (one week, two weeks, one month, two months, three months). The selection of observations to the training dataset was randomly performed; therefore, to stabilize the results, we repeated and averaged classification one hundred times for each dataset measure to obtain accurate information. The experiment was carried out in three training-dataset sizes: 25%, 50%, and 75%. Classification and the decision-tree model were implemented with the help of the scikit-learn machine-learning library for the programming language Python. Classification was performed and, in the first stage, user-activity factors were used. Results are presented in Figure 11. They show high classification accuracy achieved for the training set based on 50% and 75% of the analyzed products. Accuracy at a level higher than 90% is achieved with less than 20% of product-usage statistics with activity factors taken into account. The training set based on 25% of the products delivered low accuracy, with a percentage of adopters lower than 60%, but it reached 90% when 70% of data were used for each product.