## 2 Methodology  
### Research Questions and Study Design  
The presented study assumes the ability of virtual-product survival prediction with user attributes, especially those interested in the product at different stages of the product lifecycle. This research is based on the conceptual framework presented in Figure 1. A set of virtual products, Pi, was introduced to the audience of a social platform. Behaviors related to user engagement and products usage were collected. The node position within the example social network is represented by node size. Small circles were used for low degree nodes with one connection through medium sizes up to biggest ones for nodes with four connections. In general, user characteristics can represent various attributes related to network centrality and activity within the system like communication frequency and intensity of platform usage. They create parameters space with m distinguished variables assigned to each user in the form of vector \(V=[V_{1},V_{2},...,V_{m}]\). Users adopted to each product can be divided into five adoption groups with 2.5% of users interested in product distinguished as innovators, next 13.5% classified as early adopters, 34% as early majority, 34% of late majority, and users adopting to product at the end (laggards) as 16% of all adopters. One of the research questions is whether innovator and early-adopter characteristics can affect product lifespan. It would be possible to identify the characteristics of initial users and, in cases of low-performance prediction, the interest of users with central network positions could be increased by  
Figure 1: **(I)** Analytical system integration with the platform with the ability to detect the characteristics of users engaged in a new product, and the stages when adoption takes place; **(II)** product classification according to survival time and audience characteristics; **(III)** monitoring the performance of new products, predicting their usage, and additional audience targeting. sample delivery, trial accounts, or other incentives. As a result, followers and late adopters would be influenced and motivated for new-product usage. Three exemplary possible scenarios are presented. In Scenario 1, Product P1 is introduced, and two users, namely, \(UP_{1,1}\) and \(UP_{1,2}\), with high positions represented by red nodes adopt the product as innovators. They are followed by users adopted at several stages of the product lifecycle, and it is considered a successful product launch, boosted by top-user engagement. The campaign is characterized by high dynamics \(D_{1}\) and long product lifespan \(L_{1}\). Scenario 2 for Product P2 assumes three innovators, \(UP_{2,1},UP_{2,2}\), and \(UP_{2,3}\), characterized by medium metrics within a social network, and this is represented by orange and blue nodes. They build the interest of other users in the launched product, and overall campaign evaluation results in medium dynamics \(D_{2}\) and product lifespan \(L_{2}\). Scenario 3 assigned to Product P3 is based on the interest of innovators with the lowest network positions. It results in dynamics \(D_{3}\) and lifespan \(L_{3}\). In an analytical system, historical product data are used to analyze the influence of user characteristics, especially innovators and early adopters, on the product's lifespan and engagement among other users. This is based on three stages of data processing. In Stage (I), the characteristics of adopters from all groups are measured. In Stage (II), classification is performed to build class descriptors of users who are characteristic for a product with different survival time. Results are used to build a knowledge base and rules set for further use within the system and future product evaluation. In the next stage, new product Px is launched and introduced to the system. Innovators and early adopters were monitored, and prediction of the product lifespan was performed.