Developed by a team of former Amazon employees, this new app is designed to provide the ideal personal shopping experience. The app is called 'Mona' and it uses a blend of personalization, aggregation and simplification to redefine our retail consumption.
The app works by using data intelligence to provide hyper-personal shopping assistance. The app asks consumers to input their personal style, favorite brands and optimal budget, in order to generate personalized suggestions. Mona will not only provide style recommendations, but the app will also track products that you may need to replace. Mona even analyzes your purchasing habits over time in order yo provide more specific information.
Unlike other shopping apps that adopt a one-size-fits-all approach, Mona relies on data intelligence to provide a more personal shopping experience.
Personalized Shopping Apps
This App Uses Aggregation to Create a Personal Shopping Experience
Trend Themes
1. Data Intelligence - Disruptive innovation opportunity: Develop advanced algorithms to analyze consumer data and provide personalized shopping experiences.
2. Hyper-personalization - Disruptive innovation opportunity: Create apps that gather user preferences to deliver highly tailored shopping recommendations and suggestions.
3. Simplification - Disruptive innovation opportunity: Streamline the shopping experience by aggregating products, brands, and styles to make shopping more convenient and efficient.
Industry Implications
1. Retail - Disruptive innovation opportunity: Incorporate personalized shopping technology to enhance customer satisfaction and increase sales.
2. E-commerce - Disruptive innovation opportunity: Develop personalized shopping apps that combine data intelligence and aggregation to revolutionize online retail.
3. Consumer Goods - Disruptive innovation opportunity: Utilize data intelligence and simplification techniques to enhance the shopping experience and provide customized product recommendations.