Data-Driven Visitor Tracking Analytical Insights and

Recommendation System

Rutuja Khedkar, Prema Sahane, Anushka Patil, Abhijit Jawkar and Sharvayu Dhemse

Dept. of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Pune, India

Keywords: Visitor Tracking, Analytical System, Recommendation System, User Experience.

Abstract: Novel Way of Visualizing Visitor Behavior: From behavior on a website to behavior in a buildingAbstract—

In the digital age, it is crucial to understand visitor behavior in websites and physical locations to improve

user experience and optimize business strategy. We present a Visitor Tracking, Analytical, and

Recommendation System (VTARS) able to log, process and produce insights about visitor interactions.

VTARS uses cutting-edge tracking technologies to record the movements, preferences, and activities of

individual visitors across multiple touchpoints. It aggregates data from different sources, such as web

analytics, location-based services, IoT devices to create holistic visitor profiles. VTARS captures visitor

insights using machine learning algorithms and statistics, tracking visitor interaction data like browsing,

purchases, and engagement frequency. This information is then synthesized, resulting in an interactive report

and visualizations to give stakeholders a better idea of who their visitors are and what things interest them. It

functions by anticipating the specific needs that users may have during their engagement and suggesting

relevant content, products, or services that users may be interested in based on their previous interactions and

behaviors, with the purpose of improving user satisfaction and conversion rates. Iterative learning improves

recommendations over time, as they adapt to changing patterns in visitor behavior and preferences.

1 INTRODUCTION

In this digital age, many organizations try to

understand what their visitors do to improve users

experience and make strategic decisions. To meet

these goals, proper visitor tracking and analysis have

become essential elements. The Visitor Tracking,

Analytical and Recommendation System (VTARS) is

developed as a powerful platform for observing

visitors online and physical places and analyzing

their behaviours.

VTARS employs sophisticated tracking

techniques to monitor individual user behavior and

activity. The system creates detailed visitor profiles

by combining data from a variety of sources,

including web analytics, location-based services, and

Internet of Things (IoT) sensors. This information is

condensed in profiles, which provide a

comprehensive picture of user habits, such as what

kind of online content they are consuming, how much

they are buying, and how often they engage with

platforms.

Using extensive machine learning algorithms and

statistical methods, VTARS identifies trends and

patterns in visitor behavior. Through the system's in-

built analytics feature, stakeholders have access to

on-demand reports and visualizations to gain insights

into visitor demographics, interests and engagement

levels. Such data-drivenness allows businesses to

make sound decisions that lead to an elevated user

experience and increased productivity.

VTARS is designed with a recommendation

engine that analyzes user behavior and preferences to

suggest content collaboratively and based on content

features. VTARS seeks to enhance user satisfaction

and drive conversion rates by providing personalized

recommendations for content, products, or services

tailored to users' unique preferences and previous

interactions. This information, along with real-time

updates, helps the system to learn over time, making

it an integral part of the visitor planning experience.

Finally, VTARS is a complete package for any

business looking to capitalize on their visitors and

make properly justified decisions. VTARS also

improves operational efficiency and encourages

Khedkar, R., Sahane, P., Patil, A., Jawkar, A. and Dhemse, S.

Data-Driven Visitor Tracking Analytical Insights and Recommendation System.

DOI: 10.5220/0013640700004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 3, pages 705-711

ISBN: 978-989-758-763-4

705

better customer engagement and loyalty in an

increasingly competitive setting.

2 LITERATURE SURVEY

They have come up with a low-cost indoor

navigation and tracking system based on Wi-Fi RSSI

(Received Signal Strength Indicator) values.

HandyMap used a Fingerprint map and a k-Nearest

Neighbor (k-NN) algorithm for user location

estimation and Dijkstra's algorithm for finding the

shortest path. It is based on an Android mobile device,

integrated with a server on a Raspberry Pi 4B for data

processing and realtime tracking via a webapp. A

maximum accuracy of 78% was reached with an

average distance error of 0.86 meters and a maximum

distance error of less than 3 meters. They also achieve

optimal performance equipped with 8 routers and a

sampling time of 3 seconds result in user navigation

and tracking of individuals' movements inside a

building by the system. (Ryan, et al. , 2020), (Russo,

et al. , 2010)

The gang made a bi-directional individual counter

the usage of an Arduino Uno microcontroller and IR

sensors. The primary sensors used were infrared (IR)

sensors what saved the results; the Arduino Uno as a

signal controller and a visitor display on an LCD. All

of the system software was designed on the Arduino

IDE and validate through Proteus simulation. For

hardware, sensors were interfaced with Arduino, the

custom PCB for placement and assembly and

enclosure for handling. The result was a dependable

visitor counter that showed up-to-the-minute

information on how many people were coming in and

out of a designated space. This system proved to be

strong and affordable, making it useful for managing

crowds and monitoring traffic.

http://dx.doi.org/10.2139/ssrn.4443869 (Erlina, and,

Fikri, 2023), (Singh, et al. , 2023)

This study looked into how visitors move around

the Fort Larned National Historic Site by using GPS

Visitor Tracking (GVT). Visitors were given GPS

devices, allowing researchers to track where and

when people traveled around the site. They created

maps that show where visitors went most often. The

results showed that many people tended to move in a

clockwise direction, starting at a key attraction and

heading towards the visitor center. The maps also

highlighted areas that didn’t see as many visitors,

giving site managers ideas on how to adjust their

outreach and spread out the flow of guests. This study

indicates that GVT can help improve the visitor

experience by pinpointing busy spots and finding

ways to draw attention to the quieter areas. Overall,

this method can enhance how heritage sites engage

with people and tell their stories better. (Wu, et al. ,

2021), (Choi, et al. , 2013)

This project outlined in the document combined

the YOLO algorithm, Raspberry Pi, and other

hardware like webcams and speakers to set up a

system that detects visitors in small retail stores. The

YOLOv4-tiny model was trained to spot people and

reached an impressive mAP of 89.21%. It can tell the

difference between customers and possible

shoplifters by analyzing their movements, alerting the

store owner via Telegram. This system not only

enhanced store security but also increased customer

interaction, all while being affordable and simple to

implement, making it perfect for small businesses.

(Khedkar and Tandle, 2019), (Korade, et al. , 2024)

In another part of the research, advanced tracking

methods were used, mainly relying on GPS to collect

data about how visitors moved throughout a theme

park. This approach helped in examining how people

behave and move in a controlled setting. Although the

initial tracking was limited, it revealed the importance

of gathering data for managing attractions effectively.

The outcomes pointed out the need to tackle various

challenges in future studies, especially in less

controlled spaces like semi-open and outdoor tourist

sites. The research also highlighted the possibility of

integrating mobile tracking technologies alongside

GPS to deepen the understanding of visitor behavior

in broader contexts. Furthermore, privacy concerns

and the logistics of managing devices in open areas

were identified as challenges. In the end, the study

showed that advanced visitor tracking could greatly

improve how attractions and destinations perform,

opening up possibilities for future research that might

expand beyond theme parks to various tourist spots

and urban locations. (Ayed, et al. , 2019), (Korade, et

al. , 2024)

The researchers used a range of wireless

communication techniques for location tracking,

particularly focusing on ZigBee technology. Their

main strategies included Time of Arrival, Received

Signal Strength, Angle of Arrival, Time Difference of

Arrival, and Time of Flight to figure out where

visitors were located. The setup was designed to track

visitors by giving each a unique transmitter node that

wirelessly communicated with reference nodes

placed at intervals along their route. As visitors got

closer to these reference nodes, the system updated

their location and estimated arrival time based on the

signal strength from the nodes. A tree routing method,

based on the ZigBee standard, ensured smooth data

transfer from sensor nodes to a central hub,

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maintaining their relationships during tracking. As a

result, the project boasted improved accuracy in

pinpointing visitors' locations, checking their paths,

and providing estimated arrival times, all of which

enhanced the visitor experience in places like

university campuses and factories. (Ching, 2019),

(Joshi, 2023)

The researchers adopted various techniques to

meet their goals. They conducted thorough data

analysis to reveal patterns and trends that guided their

decisions. A key focus was on developing predictive

models, allowing them to forecast outcomes based on

past data. They also used simulation methods to

explore different scenarios and evaluate how various

factors affected outcomes. Optimization techniques

helped refine model performance for better resource

use. They put robust validation processes in place to

ensure the results were accurate and dependable. The

project's outcomes were impressive; it improved

prediction accuracy, which was a central aim, and

increased process efficiency, leading to quicker

decision-making. The results provided a solid

understanding of the data landscape, offering

valuable ideas for future research. Moreover, the

models created were not only effective but also

scalable, indicating they could be applied more

widely. Overall, the project showcased the successful

blending of different techniques, leading to

meaningful results that could benefit various fields.

In another study, the researchers looked closely at

a visitor management software system using Grid

View. This approach allowed them to thoroughly

assess how the software affected visitor management

processes by gathering both qualitative and

quantitative data. They collected primary data

through interviews with key people, such as security

staff, front desk employees, and visitors, while also

observing the visitor management process directly.

They supplemented this with secondary data from

company documents, visitor management policies,

and logs. The study found significant improvements

in multiple areas. The introduction of the visitor

management software using Grid View made the

system more efficient, user-friendly, and secure. It

streamlined check-in and check-out processes,

reduced data entry mistakes, and improved data

consistency. (Kumbhar, et al. , 2023)

The authors used advanced techniques to

recognize suspicious behavior based on facial

features. They combined High-order Joint Derivative

Local Binary Pattern, Local Binary Pattern

histogram, and Support Vector Machine algorithms

to extract expressions, especially focusing on fear,

achieving about 69.3% accuracy. They also applied

techniques like band-pass filtering and Eulerian and

Lagrangian transforms to analyze frequency signals

from video data to estimate heart rates linked to

feelings of fear. The system was trained using the

CK+ dataset and tested on online videos, showing a

true recognition rate of 88.89% for identifying fear,

even though it struggled to meet real-time processing

needs. Overall, the results showed that this method

exceeded traditional approaches in accuracy and heart

rate estimation, while also being efficient when run

on a Raspberry Pi 3. The project successfully

illustrated the possibility of using facial features to

detect suspicious behavior, especially focusing on

emotional recognition. (Gawade, et al. , 2020)

Lastly, the team employed various techniques,

including the YOLO model for real-time people

detection and the Particle Swarm Optimization

algorithm for tracking individuals. The system was

created to allow smooth tracking as someone moved

out of one camera's view and into another, achieved

through an inter-camera hand-off protocol. To assess

tracking quality, the researchers introduced the

Motion Smoothness metric. Their tests, which

included tracking two individuals with three cameras,

showed solid and smooth tracking, with most errors

kept below 30 pixels and only 0.15% of frames

experiencing significant discrepancies. (Sahane, et al.

, 2024)

3 METHODOLOGY

The application is supported by a technical

implementation of backend storage, APIs, and system

architecture that allows easy usage and GPS tracking

by the user. The website is visited by users and they

are asked to give some basic information like name,

place, age and if we can track their location. Virtual

boundaries are configured using geofencing

technology to track visitor desire within the park.

With data trucked to a processing and storage, tools

like Apache Flink, Apache Spark, and PyTorch are

used to process, clean, and sort noisy data.

Furthermore, behavioral analysis is performed using

Tableau, Power BI, and Apache Spark, a tool to gain

insights about visitors path, preferences, etc. Clues

include paths taken, areas visited, sessions duration.

Tools like Mapbox, QGIS, and Qlik Sense create

entire reports and maps, which provide actionable

insights for improving visitor experience and park

management.

Data-Driven Visitor Tracking Analytical Insights and Recommendation System

707

Figure 1: Proposed Method Architecture

3.1 Analytical System

3.1.1 Data collection

Learn more about VTARS VTARS first collects data

from different sources, so it provides a holistic

perspective of visitor interactions. Web analytics are

an essential part of the process, as they record data on

how a website is being used including page views,

click-through rates, average session times and

navigation paths. Furthermore, the system also

employs location-based services to track the

movements of individuals in the physical world,

measuring foot traffic, movement patterns, and dwell

times within the space. By incorporating IoT-

Augmented Visitor Tracking and Recording

Systems (VTARS), organizations can capture and

analyze interaction events with physical objects and

environments, allowing them to collect data from the

digital world that enhances their view of interactions

across the visitor journey or lifecycle. For location we

use Google Maps API, Leaflet.js (already in use), or

OpenStreetMap.

3.1.2 Data Analysis

So in essence VTARS is all about crunching

significant amounts of data and providing the

business value to users. Descriptive Analytics

Descriptive analytics summarizes historical data,

giving a comprehensive view of visitor behaviors

over time such as average duration of visit, most

visited pages, and common paths taken. VTARS

uses predictive analytics that helps with trending of

visitors, which goes beyond descriptive analysis,

utilizing machine learning algorithms to predict

future visitor behaviors and trends. This predictive

ability allows companies to meet visitor expectations

and take preemptive action. It also employs

behavioral segmentation to categorize visitors with

similar traits and behaviors, enabling more precise

strategies for marketing and engagement. We use

Logistic Regression, Decision Trees, Random Forest

for analysis of data.

3.1.3 Pattern Recognition

I figured out VTARS is an edge based component for

doing pattern recognition over visitors behaviour and

for use as a decision making engine. As categories of

products and content trends resurface, the system is

tuned to catch them early. Identifying trends early

allows businesses to adjust their strategies and make

the most of new opportunities Identifying trends

early, allows businesses to adjust their strategies and

takes advantage of the new opportunities. VTARS

not only can identify trends, but also helps detect

anomalies. These functionalities help in spotting

anomalous visitor behaviour that deviates from

established behaviour patterns, which may signify

potential customer experience issues or upswings in

demand for a new product or service here we use

Visualize high-density visitor areas using tools like

d3.js or heatmap.js.

3.1.4 Visualization and Reporting

VTARS also provides strong visualization and

reporting capabilities to make insights from data

analysis easy to access and use. Let’s explore a

completely different way of visualising your insights,

real-time dashboards! Reports can be tailored to suit

the business requirements outlining key findings and

recommendations. Custom reports are produced,

showcasing the most relevant findings and

recommendations in a manner customized to

individual business needs. It can also use visual tools,

like heatmaps and flow charts, to graphically

represent where visitors are moving or where they are

interacting, whether online or IRL. Visualizations

allow businesses to understand complex data at

glance and take informed decisions in a timely

manner.

3.1.5 Security and Privacy

In brute where data privacy is emphasised, VTARS

focuses on data security and privacy compliance.

Visitor data is protected from unauthorized access

and breaches because of the system's string security

measures. Such capabilities are encryption, secure

data storage and access control mechanisms.

Moreover,VTARS complies with data privacy

regulations and standards like the General Data

Protection Regulation (GDPR) and the California

Consumer Privacy Act (CCPA), allowing businesses

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to use the system with confidence, knowing that

visitor data is handled responsibly and in compliance

with legal requirements. We use TLS/SSL for

security and Privacy purpose

3.2 RECOMMENDATION SYSTEM

3.2.1 Recommendation Engine

A core component of VTARS is a powerful

recommendation engine that serves personalized

content, products, or services to visitors. The system

is based on two central algorithms namely

collaborative filtering and content-based filtering.

Collaborative filtering makes recommendations

based on the behavior of users that are like the given

user, whereas content-based filtering recommends

items based on the features of the items or content

that the user has interacted with in the past.

Collaborative filtering recommends items based on

the actions of its similar users while the content-based

filtering suggests items according to the features of

the items or contents from which a user interacted

before. To increase the accuracy and relevance of its

recommendations, VTARS employs a combination of

hybrid model and content-based approaches, ensuring

that the users receive personalized suggestions that

match their interests.

3.2.2 Data Input for Recommendations

The recommendation system is driven by multiple

data inputs which help to make accurate

recommendations. One type of data source is the rich

user profiles generated (through tracking and

analysis) that enable identification of the users and

their cohorts by their preferences, behaviors and

previous interactions. Additionally, VTARS takes

into account contextual information, like time of day,

user location, and device type, to further tailor

recommendations. VTARS also incorporates

contextual data, such as the time of day, user location,

and device type, to further refine recommendations.

For instance, a visitor’s preferences may change

based on their location (in-store vs. online) or the

device they are using (desktop vs. mobile), and

VTARS adapts its recommendations to suit these

contexts, providing a more relevant and timely

experience for the user.

3.2.3 Recommendation Filtering

VTARS uses two primary filtering methods:

collaborative filtering and content-based filtering. It

uses commonalities for the behavior and tastes of

other users to highlight trends to recommend items

that these users have initiated or bought, named

collaborative filtering. Content-based filtering, on the

other hand, recommends items based on their

similarity to items the user has already interacted

with, relying on properties or characteristics of the

items, like categories or features. We use

Collaborative Filtering (Matrix Factorization),

Content-Based Filtering for recommendation

filtering.

3.2.4 Real-time Processing

VTARS makes sure that its suggestions are always

fresh and relevant by using real-time processing.

When users engage with the system, their information

is processed right away, which means the

recommendations can change instantly based on

what’s happening. This feature keeps users up to date

with the most relevant suggestions, improving their

experience. Plus, VTARS is designed to grow, so the

recommendation engine can manage a lot of data and

users without slowing down, making it a good fit for

businesses of all sizes.

3.2.5 User Feedback Integration

One important thing about VTARS is how it takes

user feedback to make its recommendations better. It

gathers feedback in two ways: users can give ratings

and write reviews, which is called explicit feedback,

while implicit feedback comes from things like how

often people click on items, how long they stay on a

page, and how frequently they interact. This

information helps VTARS fine-tune its

recommendations. By looking at this feedback

regularly, VTARS changes its suggestions to match

what users like more closely, making the

recommendations feel more personal and useful as

time goes on.

Table 1: Recommendation System

Recommend

ation Syste

Accuracy. 88% Percentage of correct

recommendations

Precision 91% Proportion of relevant

recommendations

among all

recommendations.

Clustering

Accuracy

93% Effectiveness in

segmenting visitors into

meanin

ful

rou

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709

4 USER EXPERIENCE

4.1 Tailored User Journeys

VTARS helps you create personalized user journeys

by customizing interactions according to the

individual visitor data. The system captures user

actions through several touchpoints, online as well as

offline, and utilizes these data to personalize the

content, products, and services that are shown to

each visitor. VTARS uses this history to ensure each

user presents an experience that is tailored to their

unique preferences and needs. VTARS tailors the

user journey to their interests so that whether the

visitor is browsing a website, shopping in-store, or

using a mobile app, they feel satisfied and are

encouraged to come back and engage again.

4.2 Real-time Interaction

VTARS improves user experience through real-time

interaction. VTARS continues to process user

interactions with a website or app and make instant

updates to the content or offers immediately. If a

visitor shows interest in a particular product

category, VTARS is capable of instantly

recommending similar products or providing

discounts, bringing interactivity and personalization

to an all-time high. Being capable of in-the-moment

actions, enhancing user experience and boosting

conversion rates as their needs are catered to

instantly.

4.3 Proactive Support and Guidance

Finally, VTARS enhances user experience through

its proactive support and guidance throughout the

user journey. For example, if the AI system notices

that visitors are spending a lot of time on the FAQ

page, it can predict that they may needed assistance

and can prompt visitors to live-chat with a

representative, show visitors the relevant tips, guide

users through procedures steps etc. Educating visitors

in this way proactively ensures that they get the

assistance they require before problems occur,

minimizing frustration and improving the overall

experience. VTARS fosters a better and more user-

friendly environment by making interactions

seamless and fluid.

4.4 Feedback and Continuous

Improvement

User experience is a continuous improvement

process, driven by feedback loops, across and through

VTARS. The system gathers and interprets

information from users, either in explicit form such as

surveys and ratings or in the form of implicit signals

such as engagement metrics and behavioral data. The

feedback received is to identify areas for

improvement and make informed adjustments to

optimize the user experience. VTARS improves and

enhances the way people interact with any platform

by creating smooth, fluid and easy interactions.

Feedback and Continuous Improvement User

experience is a continuous improvement cycle, fed in

and across and through VTARS by feedback loops.

The system collects and processes data from users,

both in explicit forms (surveys, ratings) and implicit

signals (engagement, behavioral data, etc.).

5 CONCLUSIONS

In a world increasingly driven by data, the confluence

of visitor tracking, analytics, and recommendation

systems represents a powerful toolset for shaping the

future of digital interactions. These systems offer the

potential to understand users on a granular level,

enabling the creation of personalized experiences that

resonate with individual needs and preferences. From

e-commerce to content delivery, the ability to predict

and respond to user behavior in real time is

transforming industries and redefining engagement

strategies.

However, as we push the boundaries of what is

possible, we must remain vigilant about the ethical

implications of our work. The fine line between

personalization and privacy must be carefully

navigated. Transparency in data collection practices,

user consent, and robust security measures are not just

legal obligations but moral imperatives. Ensuring that

users feel safe and respected in their digital

environments will be key to the long-term success of

these technologies.

As we look ahead, the potential for visitor

tracking and recommendation systems to create more

meaningful, efficient, and enjoyable user experiences

is immense. The challenge lies in harnessing this

potential responsibly, balancing innovation with the

need to protect and empower users. With thoughtful

design and ethical considerations at the forefront,

these systems will not only enhance business

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outcomes but also contribute to a more connected and

user-centric digital landscape

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