Outstanding IT features
Footfall Tracker
Footfall data, also known as foot traffic, mobility data, or sometimes called “people counting,” is an increasingly powerful source of information for leaders in retail, real estate, investing, and marketing. What makes this data so valuable to business leaders is that it’s a holistic way of tracking patterns of behavior based on location, time, demographics, and other metrics, understanding the patterns behind how, where, and when people move is critical for decision makers across sectors.
Equipped with reliable footfall analytics, real estate and investment executives can glean predictive insights for finding successful investment opportunities. Marketers can plan and execute location-based marketing campaigns to reach highly targeted audiences. Retailers can better understand where to open their next franchise by using data gathered from mobile devices, in-store hardware, and WiFi networks to make educated decisions. In fact, footfall data has evolved into the most reliable indicator for the retail sector at a time when margins are contracting.
Below are some top use cases for footfall data by industries:
Frequently asked questions
What is footfall data?
Footfall data, also referred to as mobility data or foot traffic data, shows how people engage with Points of Interest (POIs) in the real world.
Footfall data is sometimes called “people counting,” and although tracking the total number of people in a given space is a critical part of footfall data, that’s only the surface. What makes this data so valuable to business leaders is that it’s a holistic way of tracking patterns of behavior based on location, time, demographics, and other metrics.
Often footfall data is integrated with other sources of data, such as demographics or market trends, for more robust insights. It is also widely used to track visits to a site and discover where visitors are coming from (trade area). Footfall analytics have become a powerful force in marketing campaigns, real estate investing, and retail operations.
Where does footfall data come from?
Various sources, including in-store sensors and geolocation hits from mobile devices, provide the raw data on foot traffic. To provide their clients with more targeted insights, foot traffic analytics firms might concentrate on just one of these data sources or, in some cases, combine several sources for broader, deeper insights.
How can footfall data help with my business?
Retailers and investors want data-based proof to guide their operational decisions. But raw data alone is not actionable. Foot traffic analytics companies like Unacast can give businesses a holistic picture of customer movement by interpreting mobility data—making it meaningful for decision-makers. The data that is most helpful to retailers tends to be:
- The number of consumers visiting your business
- The percentage of people in the area that visit your business, known as the capture rate
- The patterns for when people visit your business (hourly, daily, weekly, monthly)
- The amount of time people spend visiting your business
- Do people come back and stay loyal to your business?
- The demographics of consumers such as age, gender, income group and education level
Retailers in the real world benefit significantly from foot traffic data since it gives them a comprehensive understanding of consumers and ultimately teaches them how to get more customers into their stores. Analytics solutions give users the same degree of insight as their online equivalents, giving them the information they need to increase productivity.
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Outstanding IT features
Customer Demographic
Identification
Customer demographics are categories of consumer populations that are useful to a business for purposes, such as marketing and product design. The term also refers to the study of such categories in a business context. An endless number of variables can be used to categorize customers and potential customers. Some examples of the most common customer demographics for business purposes include the following:
- Age
- Gender
- ethnicity
- Geographic Location
- Household Income Level
- Level of Education
- Marital Status
- Home Ownership
- Occupation
- Employment Status
- Number of Children in Household
- Hobbies
Frequently asked questions
What is customer demographic identification?
Customer demographics are categories of consumer groups used for business purposes. The most commonly used categories include age, gender, race/ethnicity, household income, education level, marital status, and geographical location.
What is customer segmentation?
Demographics and demographic segmentation are a key element of customer segmentation, which involves profiles that are based on grouping people together who have similar demographic characteristics. Targeting specific groups of customers enables more efficient allocation of marketing resources and increases the opportunities for cross-selling and up-selling.
Customer segmentation enables more personalized interaction between a business and its customer base. It improves customer service and fosters customer loyalty and customer retention. Appropriately targeted marketing campaigns are typically more relevant to the customer's needs and more welcome.
How can analyzing customer demographics helps with your business?
Besides being used to improve marketing and customer service, a business might study consumer demographics to get a better understanding of who is using a product or other resource. For example, demographic information helps delineate the characteristics of a population in a particular neighborhood or the audience of a website. This approach is used to create demographic profiles of individual customers as well as groups of target customers.
Virtually any organization trying to sell products and services to a target audience uses market research to gather demographic data, market segmentation data and related statistical information to formulate its marketing strategy. This specific demographic information is essential when preparing messaging or promotional content to ensure that it reaches its target market and addresses specific metrics, such as age group and ethnic group.
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We provide only the best product and services to our customer because we believe happy customers made a successful business.
Ng Boon An
Director @ Company
Outstanding IT features
Behaviour and Pattern Analysis
Behavior analysis in the context of video surveillance is the process of studying and interpreting the actions, interactions and behavioral patterns of people and objects that are captured by camera systems. Using machine learning and data analysis techniques, the behavior analysis allows to better understand what is happening in the recordings, how people engage with your business, what part are they engaging with, how much time they spend doing it and automatically detect unusual or suspicious situations.
Frequently asked questions
What is behavior analysis in the context of video surveillance?
Behavior analysis in the context of video surveillance is the process of studying and interpreting the actions, interactions and behavioral patterns of people and objects that are captured by camera systems. Using machine learning and data analysis techniques, the behavior analysis allows to better understand what is happening in the recordings, and automatically detect unusual or suspicious situations.
What technologies are used in behavior analysis?
Technologies used in behavior analysis include machine learning, particularly deep learning and neural networks, image and video analysis, and various signal processing methods. Today’s analytics systems also use cloud computing technologies to improve performance and scalability.
What are the popular methods of behavior analysis in video surveillance?
Popular behavior analysis methods in video surveillance include object tracking, anomaly detection, facial recognition, motion analysis and threat assessment. Machine learning, particularly deep learning, plays a key role in the development of these methods, enabling the identification of subtle patterns of behavior that can be difficult for humans to see.
How does machine learning affect behavior analysis?
Machine learning allows for the automation of behavior analysis through the use of algorithms capable of learning from data. This allows video surveillance systems to improve on their own, increasing their effectiveness in identifying threats and providing more detailed information about the behavior of the people being watched.
In which sectors is video surveillance behavior analysis most useful?
Behavioral analysis in video surveillance is useful in many sectors, such as public safety, transportation, retail, industry and sports. By identifying suspicious behavior, analytics can help in crime prevention, traffic management, store optimization or sports performance analysis.
How can behavior and pattern analysis helps with my business?
There are too many benefits of analyzing the bahavior and pattern and it would be troublesome to go into details of them one by one. But below is a summary list of the main benefits it brings to a business.
- Understanding Customer Behavior
- Personalizing the Customer Experience
- Improving User Experience (UX)
- Increasing Customer Loyalty
- Enhancing Sales Conversion Rates
- Boosting Revenue
- Identifying Trends and Patterns
- Refining Marketing Strategies
- Improving Product Development
- Competing with Industry Leaders
Overall, by using data and insights to inform decisions and continuously improving customer's engagement, businesses can build long-term success and stay ahead of the competition.
Do you still have questions for us? Contact us now!
We provide only the best product and services to our customer because we believe happy customers made a successful business.
Ng Boon An
Director @ Company
Other services
Professional Audio System
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