MAIA is an advanced system for creating a comprehensive customer profile in a brick-and-mortar store (offline), examining their behavior and factors that influence purchasing decisions. Conducts such analysis, enables us to create personalized marketing offers for both individual customers and people belonging to different target groups.
This system comprises a group of cameras and an edge gateway management system that analyzes customer behavior in real-time within a physical store, using data from the cameras to create customer profiles.
MAIA expands existing solutions for counting people, providing the means to understand patterns of pedestrian traffic in the store. With pedestrian traffic and conversion, you can increase sales by identifying top-performing stores and learning best practices that can be quickly replicated in low-performing stores.
Analyzing the distribution of traffic throughout the day will make it easier to manage employees and supervise the proper functioning of the store, and identifying peak and off-peak hours will allow for effective decision-making. For example, if you determine that customers primarily come to your store between 2 and 4 pm, you will ensure that all shelves are stocked and that you have sufficient staff to serve customers.
We make it easy to discover certain repeating patterns, such as how customers move around the store, and therefore, what the hotspots and cold areas are, as well as identifying bottlenecks.
With detailed statistics of customer behavior and heat maps, it’s easier to identify popular areas throughout the store, enabling category managers to make effective decisions regarding product placement and category distribution.
There are many applications of heat maps, including:
“How many customers enter my store, and with what frequency? What are the peak hours throughout the store, as well as in individual departments of the store?” – You will get the answers necessary to improve sales results.
Considering the diversity of traffic patterns and natural hotspots generated by shoppers, as a retailer, you can easily adjust store planograms. By changing the layout, you can analyze how it changes over time and translates into an increase in the shopping basket, thus optimizing solutions that allow you to achieve your goals.
“Which parts of your store are visited by customers and which are ignored? How can you better utilize these traffic patterns for your business? How effective are the displays?” – Thanks to our analytics, we have answers to all of these questions.
One of the key challenges for modern retailers is finding new ways to engage shoppers during their in-store visits, providing meaningful yet personalized experiences to increase conversion and loyalty. This is a crucial component for increasing sales and basket size.
Analyze changes in foot traffic, flow patterns, main directions or time spent by customers both in the store and at the category level.
When it comes to analyzing customer flow, it is very useful for category managers. It is worth examining how in-store promotion translates into customer flow, as these indicators reveal information about incoming and outgoing traffic, their direction, and show how the purchasing process can change.
In addition, we are able to determine where customers come from and where they go after visiting the promotion, allowing category managers to consistently adjust their strategy to specific customer needs.
For store managers, it is important to know how much time customers spend in their stores in order to adjust their strategies to what is actually happening. It is possible to learn the average duration of a shopping journey, as well as the average time customers spend in each section.
When a customer spends more time than estimated in a certain category, we consider them engaged. We know that the more time customers spend in the store, the more money they spend, so analyzing customer flow and measuring the effectiveness of sales promotion will help achieve the goal of customers spending as much time in the store as possible.
Although shoppers usually have a specific mission when they go to stores, we will help you maximize their visit and attract their attention to purchase other products as well. At this point, cross-selling strategies play an important role in achieving this goal. In addition, these strategies are also essential when it comes to retargeting missed conversions that did not occur in a particular category.
Although we may not always realize it, there are many relationships between categories, so identifying these relationships is necessary to effectively develop any cross-selling strategies. The correlation matrix shows the likelihood that a buyer will engage in a particular category after being engaged in another category during the same trip. Therefore, it reveals information about popular sections/areas and their connections to other categories.
Often, products are displayed in multiple locations, and their availability and visibility attract customers, but this may not be enough for all categories, so maintaining control over them will help identify areas that need improvement.
How, where, and for how long your staff interacts with shoppers in the store helps your stores achieve their goals.
Understanding the interactions between your sales staff and store customers helps your stores achieve their goals. Understanding your staff’s paths throughout the store and their interactions with customers is a key component in providing the best quality of service. Knowing where customers are spending time in the store compared to where sales staff are located can provide crucial insights into which areas are underserved and how to optimize store operations. By measuring category performance, foot traffic, and other metrics, you can identify ideal locations for new displays and accurately assess their impact on sales performance.
“You can only manage what you can measure.” This well-known principle also applies to customer flow analysis.
Whether you want to count only the customers entering the store or combine this data with other information, our statistical tools provide impressive on-demand or fully automated reports. From “opportunity to see” (OTS) to time spent analytics and every form of interaction.
In retail, it is convenient to compare data from different periods to assess business evolution, as it provides accurate information on areas for improvement. In addition, it is also important to conduct comparative tests in similar stores, by region, format, or store type. This way, retailers will be able to determine whether their results are below or above average and whether action needs to be taken in specific areas.
We also record the length of time customers spend in a specific product category. Like all other data, research results are entered into the overall database, which enables further, crucial evaluation of customer behavior through correlational aspects.
Based on person detection technology, we determine the demographic profiles of customers, such as gender or age groups, but also optionally mood profiles (positive/negative, good mood, etc.).
We provide tools that allow you to learn about preferred times of day, hours, or days of the week for different age groups. You can verify whether people who come alone spend more time shopping than those who come in groups. You will discover how comparably younger and older people behave. This tool enables you to identify differences in the behavior of buyers and non-buyers and determine whether they are similar in each product category.
The MAIA Suspect Alert system uses AI and computer vision to monitor activity in the store and detect potential retail fraud.
Suspect Alert records and analyzes images captured by monitoring cameras in the store. Thanks to AI algorithms, the system processes video images from cameras very quickly and is then able to provide real-time full visual documentation based on them, allowing for appropriate actions to be taken to address typical problems faced by companies.
Overall, the use of advanced M5 Technology enables companies and organizations to more effectively detect irregularities and prevent theft and other security breaches.”
The system utilizes proprietary technologies such as biometric identification for large populations, event linking between cameras to understand movement in the store and behavior analysis.
With machine learning, it can detect atypical movements. For example, a suspicious gesture in a clothing pocket is detected in real-time, and artificial intelligence warns you through the application. The solution can be managed through one interface: our solution.`
The technology and system can be used to improve customer satisfaction, optimize merchandising, and streamline operations.
MAIA can identify everything from the number of customers at a given time or day, wait time in line, and shopping habits by demographic and location data.