## 3 Results
### Survival-Time Prediction with Early-Adopter Characteristics
Higher fluctuation of results was observed with a low number of analyzed adopted users. Detailed numerical results are presented in Table 4. It shows that analysis of characteristics of even only 10% of product adopters makes it possible to predict product assignment to a class with low or longer survival time. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Set**} & \multicolumn{8}{c}{**Number of Adopters Used for Classification**} \\ \cline{2-10}  & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline
75\% & 0.900 & 0.915 & 0.920 & 0.920 & 0.917 & 0.921 & 0.926 & 0.927 & 0.925 \\
50\% & 0.898 & 0.912 & 0.903 & 0.904 & 0.907 & 0.906 & 0.912 & 0.914 & 0.914 \\
25\% & 0.844 & 0.854 & 0.857 & 0.868 & 0.879 & 0.881 & 0.890 & 0.896 & 0.899 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of classification results with the use of Activity Factors for 25%, 50%, 75% training set and 10%, 20%,..., 90% of adopters used. Figure 11: Accuracy of classification results with the use of Activity Factors for 25%, 50%, 75% training set and 10%, 20%,..., 90% of adopters used. Apart from social activity, factor classification was performed with the use of incremental data about user activity within the system. Results are presented in Figure 12. It shows that, for 50 and 70 training, the initial accuracy of the used low-fraction data at 1-3% of used data is very high due to innovator characteristics. Additionally used data dropped accuracy to 80%. Subsequently, it grew with data acquisition. For the training set with the size of 75% of used products, the lowest accuracy was achieved after using data from 15% of adopters, and 65% accuracy for 50% of products with 15% of the adopter sample used. For all training-set sizes, accuracy continuously grew together with the increased number of adopters. Detailed numerical results for classification based on incremental usage statistics represented by Experience Factors for each user are presented in Table 5.  
Table 6 shows classification-accuracy statistics with identified user groups as innovators, then innovators together with adopters, and extended by early majority, late majority, and laggards. For Activity Factors, this shows that even using data from only innovators (2.5% of first adopters) creates the ability to assign a product to one of five adoption classes. Innovators used together with adopters delivered results above 19% for training sets with 50% and 75% size. Classification based on the 25% training dataset delivered accuracy above 18%. Further connection of the adopter group slightly improved classification, but from a practical-application perspective, it delays the time during which product survival abilities are predicted and additional adopter targeting is performed. The worst results were obtained for Experience Factors, but they were still above 80% accuracy for the training sets with 50% and 75%. Figure 12: Accuracy of classification results with the use of Experience Factors for 25%, 50%, 75% training set and 10%, 20%,..., 90% of adopters used.