Have you ever been frustrated trying to make a decision with your friends or family?
Imagine this: You’re trying to plan a vacation, set up a fantastic night out, or pick a gift for someone special. Everyone’s together, sharing their ideas, and the energy is high. But you’ve reached the point where you need to make a choice. All that excitement and chatter turn into a confusing mix of different opinions and preferences, creating a bit of a decision-making mess. It’s like having too many people involved in the decision, and things can get a bit chaotic.
The dynamics of group decision-making are far from straightforward. Balancing diverse opinions, navigating potential conflicts, and reaching a consensus can often feel like an intricate puzzle.
Don’t worry; you’re not the only one facing these challenges. In the world of marketing products and creating experiences, these situations are as common as using emojis in text messages. We’ve all been in such spots, dealing with frustration, uncertainty, and the occasional temptation to leave things to chance. So, what if we could offer a better way to handle these situations?
By using a virtual AI assistant (a group recommender system) for online shops that presents swipecards with relevant questions or images to each person in the group, it becomes possible to make a decision based on the wishes of the entire group rather than the person who shouts the loudest or argues the best.
What is a group recommender system?
A group recommendation is a decision support system that presents personalized recommendations based on the answers given. The suggestions are based on the answers from the users in a group to help make a decision.
An experience or product suggestion customized to the wants and needs of the whole group.
This results in a joint decision where everyone feels heard and with a solution that is relevant to the group. But in addition to the group experiencing a personalized recommendation, the webshop will achieve increased conversion and insight into new trends and needs. In other words, you will gain insight into group preferences.
When to use group recommendation?
Group recommendation systems are relevant in various situations where multiple individuals are involved in the decision-making process, and personalized suggestions can enhance the collective experience. Group members often bring different backgrounds, experiences, and viewpoints to the table. These diverse perspectives can lead to conflicting opinions and make it difficult to arrive at a consensus.
Furthermore, making a decision in a group can often involve processing a lot of information and considering numerous factors. This cognitive load can overwhelm individuals, making it harder to reach a decision. Despite these challenges, group decision-making can also have significant advantages. It can lead to better-informed decisions, harness collective intelligence, and promote diverse thinking.
In addition, it should also be mentioned that group swipe is not only a benefit for users. Group recommendation is also useful in several different markets and customer types, ranging from e-commerce, where visitors typically don’t know what suits their preferences, to the experiences industry, where visitors need inspiration before making a purchase decision.
Online travel sites: When planning group trips or vacations, group recommendation systems can suggest destinations, hotels, activities, and restaurants that appeal to the diverse interests and preferences of the group members.
Entertainment: Recommending movies, TV shows, or music playlists for a family, a group of friends, or a couple sharing a streaming subscription is an everyday use case. Group recommendations can make the entertainment selection process more enjoyable and convenient.
E-commerce: Suggesting products or services to a family or a group of shoppers who are making a purchase decision together. An example could be couples shopping for adult toys together. They swipe separately but only see jointly recommended products.
How we work with group recommendations?
The method used today for recommendations is typically based on cookies and especially logins and, thereby, individual data, where you usually would track the user’s visitor behavior, such as page views, click-through rates, and time spent on the website, for recommendations. This approach often creates the same “echo” of product recommendations due to feedback loops.
Roccai’s swipe module is instead based on the result of the questions or image swipe cards that the AI model has set up. This gives Roccai relevant Zero-Party Data from the visitors and direct knowledge of their needs. Thus creating a virtual AI shop assistant or inspirational journey that increases both conversion and customer insights. The group recommendation model can handle inputs from different individuals at the same time, making it possible to not only offer a solution that can deliver recommendations individually but also to groups.
But, group recommendations can be challenging because they need to take into account the diverse preferences of multiple individuals within the group. Group dynamics can influence the decision-making. Power struggles, dominant personalities, and social pressures within the group can bias decisions or prevent members from freely expressing their opinions.
It is, therefore, essential to consider how the balancing of users’ different choices is handled, as this can be pretty tricky since there are several ways to handle the data.
