Intelligent Social Listening
Case Studies
Case Studies
Intelligent social listening is the next evolution in online brand management
and market analysis. It goes beyond merely tracking and counting keywords,
hashtags, or mentions across digital platforms. Instead, it leverages
sophisticated algorithms and artificial intelligence to dive deeply
into online conversations, extracting meaningful insights, understanding sentiments,
and uncovering emerging trends from the vast ocean of social media interactions,
customer reviews, forums discussions, and beyond.
Use the power of intelligent social listening to align your business strategies
with real-world feedback and perceptions.
Strategic Brand Positioning
Navigate through the noise
of the digital world, identifying what truly resonates with your audience, and
position your brand strategically in the competitive landscape.
Enhanced Customer Experience
Engage with customers more effectively, addressing their needs and concerns in real-time, and enhancing their overall experience with your brand.
Informed Product Development
Utilize the rich insights gathered to inform and refine product development processes, ensuring that your offerings meet customer expectations and industry demands.
Leveraging AI in Intelligent Social Listening
In a world where digital conversations are pivotal, Intelligent Social Listening,
bolstered by AI, emerges as a crucial asset for businesses aiming to flourish by
staying attuned to market dynamics and customer sentiments.
Data Analysis
AI delves into massive sets of data, identifying patterns, sentiments, and trends that are essential for making informed business decisions.
Predictive Analytics
Utilizing AI, predict upcoming market trends, customer behaviors, and potential opportunities or threats, enabling proactive strategy formulation.
Automated Reporting
AI automates the reporting process, providing consistent, accurate, and easily digestible reports, allowing you to focus more on strategy implementation and less on data crunching.
Case study: Using the Power of AI to Find and Respond to Blood Donor Searches on Social Media
Background
According to the World Health Organization (WHO), the rates of blood donation
vary significantly across different economic sectors, with high-income countries
boasting a donation rate of 31.5 per 1000 people, compared to just 5.0 in
low-income countries. Social media platforms such as Facebook, Twitter,
and Instagram have become prevalent forums for individuals and organizations,
including health care facilities and blood centers, to solicit blood donations.
However, a substantial number of these requests unfortunately go unnoticed.
Objective
This study aims to optimize the recruitment of blood donors by leveraging
social media for DonorUA nonprofit organization. The real-time analysis of
donation requests across various platforms can offer invaluable insights,
enabling organizations like the Red Cross and WHO to respond promptly and
efficiently within specific regions or cities. Moreover, the historical
data accrued over time can facilitate predictive analysis to anticipate
and mitigate potential shortages in blood supply.
Methodology
We have engineered an application on Microsoft Azure to meticulously
monitor and analyze blood donation requests on social media. The initial
phase of the project utilizes YouScan, a sophisticated social listening
tool, to identify and extract relevant posts from Twitter. Posts are
filtered based on specific keywords and phrases such as "blood donors
required" and "blood donors needed".
Implementation
Our system is equipped with a robust classification model that discerns
actual donor requests from unrelated posts. Non-pertinent posts are
systematically excluded from the dataset. Additionally, we have integrated
the Language Understanding service from Microsoft (that was shifted to
ChatGPT from Azure OpenAI Service) to enhance the extraction of meaningful
and precise information from the collected data. This service facilitates the
identification of key details such as the location of blood centers, the
specific blood type and Rh factor required, the quantity of blood units needed
and pertinent contact information.
Conclusion
This innovative approach aims to revolutionize blood donation recruitment
strategies by harnessing the power of social media analytics, facilitating
a more responsive and effective blood donation system. Through meticulous
data analysis, our model aspires to bolster the efforts of NGOs and global
health organizations in securing a consistent and reliable blood supply.