In the online world, where the amount of options available on the internet is totally overwhelming for users, we have long seen the need to filter, prioritize and deliver the most relevant information to surfers in the most efficient way to alleviate the problem of information overload.
On the other hand, many retail executives and owners are beginning to embrace the possibility of using their ability to analyze data collected in physical spaces to gain an unparalleled competitive advantage by improving the shopping experience for their visitors.
How is this possible, and are we ready to take advantage of off data to improve retail decisions?
The answer is YES.
In most cases, we business people greatly underestimate the advantage of data in decision-making.
Through recommender systems and engines we solve the problem of searching and filtering through a large volume of dynamically generated information to provide users with personalized content and services in real time.
In this article we will explore the potential and characteristics of predictive algorithms in recommendation engines in order to serve as a compass for business decision making and optimisation of the shopping experience in physical shops.
Intelligent retail recommendation system
We at Ladorian have often talked about how technological innovation has changed the rules of the game when it comes to data analysis and business decision making.
Advanced algorithms and software systems greatly reduce analysis time, giving companies the ability to make much faster decisions that help increase revenue, reduce costs and improve user experience.
In this respect, physical retailers, who once led the way through various in-store customer tracking actions such as loyalty cards or coupons, are now lagging behind their online competitors.
However, in recent years we have been seeing a shift in trend as more and more retailers implement technologies ranging from simple sensors to advanced emotion detection systems, even allowing them to personalize pricing and shopping experiences per customer.
Want to find out how to leverage data from the offline world to optimize decision making in your business?
Leveraging technology to increase sales in physical spaces
Entrepreneurs have always considered technology as a process provider in physical shops, but in some cases they have not yet assimilated the true potential of off data processing to understand and interact with users in real time.
Recommendation engines, based on powerful algorithms developed from artificial intelligence, are able to analyze, predict users’ needs (making use of data) and recommend proposals for products and services of interest to them in real time.
In short, by processing data collected at the point of sale and using AI, algorithms and other tools, we analyze consumer behavior and process it to design personalized communication strategies in real time.
Real-time communication through powerful recommendation algorithms
By capturing the attention of consumers at the point of sale, we can leverage competitive advantage through personalized, relevant and meaningful messages.
To achieve this, Ladorian proposes to contextualize data and create customer profiles in real time in order to impact only the most relevant target audience, reducing advertising saturation and improving the shopping experience.
Ladorian’s Prime Time Algorithm allows us to lead this digital revolution project that will mark a before and after in the retail sector.
In short, new agile, personalized, unified and scalable data management solutions that offer real answers to these new needs.
Are you ready to optimize your company’s communication and capture the attention of your consumers to take advantage of the sales opportunities generated in your business?
Contact our sales team and request a free commercial consultancy.