Social media analytics involves collecting and analyzing data from social networks to improve business decisions. It can help businesses spot consumer trends, improve the success of marketing efforts, and conduct competitive analysis. Key terms related to social media analytics include social media intelligence, social media listening, social media monitoring, and social competitive analysis. Web analytics involves measuring, collecting, analyzing and reporting internet data to understand and optimize website usage. It is important for understanding customer relationships and trends, assessing marketing campaign effectiveness, and improving the customer experience. Challenges include high data volume growth, unstructured social media data integration, and lack of single analysis tools. Healthcare analytics involves analyzing industry data to predict trends, improve outreach, and manage disease spread

1. 1. E-Content: Web & Social media analytics:
https://youtu.be/frC74NvVSVw?t=27
Web & Social media analytics:
Social mediaanalyticsisthe processof collectingandanalyzing audience data shared on social
networks to improve anorganization'sstrategicbusinessdecisions.
Social mediacanbenefitbusinessesbyenablingmarketerstospottrendsinconsumerbehaviorthatare
relevanttoa business'sindustryandcan influence the successof marketingefforts.
Social MediaIntelligenceis the closestterm-cousintosocial mediaanalytics.Social intelligence
representsthe stackof technologysolutionsandmethodsusedtomonitorsocial media,includingsocial
conversationsandemergingtrends.
Social MediaListening is one of the termsmostoftenconfusedwithsocial mediaanalytics.Butsocial
listeningappliestoone specificaspectof social mediaanalytics:Learningaboutyouraudience.
Social Media Monitoring is the second term most often confused for social media analytics. It’s
also thought to be synonymous with social listening, but the two are very different.
Social CompetitiveAnalysis isthe processof investigatingcompetitorsof yourbrandand their
audience.Because social mediaissucha transparentmedium, socialmedia analyticstoolscanbe
appliedtobrandsbeyondyourown.
What are the use cases of social media analytics?
Social media analysis platforms can track and analyze a range of data and interactions used in a
variety of social media marketing use cases.
Measure the ROI of social media marketing efforts
The main goal for any social media post, like, retweet or share is ROI.
To determine social media ROI, marketers must first determine an initial benchmark and then
have a way to measure key performance indicators (KPIs) against that benchmark over time.
When efforts aren't working well, analysis of those metrics will reveal tweaks marketers can
make to improve the performance of the campaign and overall ROI.
Improve strategic decision-making
Social media analytics can improve a marketing team's ability to understand what social media
strategies are working and which ones aren't as effective.
Track the efficiency of marketing teams

2. Most organizations strive to streamline workflows and enable team members to be more
productive. A lesser known, but still important, feature of social media analytics is its ability to
improve efficiency with your marketing team.
What is Web analytics
What is web analytics?
Definition: Web analytics is the measurement, collection, analysis, and reporting of Internet data
for the purposes of understanding and optimizing Web usage. (Source: Web Analytics
Association)
Why is it important?
 Today, Web interactions between commercial businesses and their customers take place
via e-commerce stores, customer service sites, interactive real-time chat, e-mail, and
social media streams. These interactions are as important, if not more so, for a business’s
growth as customer touches through traditional voice and bricks-and-mortar channels
 Web data integrated with other channels provides a better picture of the customer–
business relationship and helps in identifying customer trends
 It is also useful in assessing the effectiveness of marketing campaigns and optimizing
marketing spend
 And it improves the customer experience through faster service, thereby driving business
growth and enhancing reputation
What are the challenges?
 Data volume growth is accelerating, making it cumbersome to capture and analyze Web
data
 Unstructured social media data growth compounds the challenge, particularly as it must
be integrated with enterprise structured data
 Multiple Web interaction platforms (PC, smartphone, tablet) further add to data capture
and integration challenges
 Location and other smartphone sensor-based feeds also increase the complexity of
continuous/real-time data capture
 There is no single tool available to capture and analyze all types of data

3. 2. E-Content: Healthcare Analytics:https://youtu.be/3W9_BfbNkSs?t=40
What Is Healthcare Analytics?
Healthcare analytics is the process of analyzing current and historical industry data to predict
trends, improve outreach, and even better manage the spread of diseases. The field covers a
broad range of businesses and offers insights on both the macro and micro level. It can reveal
paths to improvement in patient care quality, clinical data, diagnosis, and business management.
Benefits of Healthcare Data Analytics
Improve quality of clinical care by increasing healthcare organizations’ access to patient data and
allowing health systems to unlock important patterns and trends around diagnosis, treatment, and
continued care.
 Access actionable insights that inform future interactions with patients, consumers, and
populations.
 Inform population health initiatives and allow public health organizations to better
manage the spread of disease, predict outbreaks, and allocate health resources to
communities in need.
 Uncover the root cause of consumer response – or lack of response – to outreach and
create personalized campaigns to improve patient engagement.
 Gain market share by uncovering what type of care your consumers want and where
healthcare delivery can be optimized.
 Personalize the patient journey via informed contact center agents or even automatically
via chatbots.
In health care, all four types can be used:
 Descriptive analytics can be used to determine how contagious a virus is by examining
the rate of positive tests in a specific population over time.
 Diagnostic analytics can be used to diagnose a patient with a particular illness or injury
based on the symptoms they’re experiencing.
 Predictive analytics can be used to forecast the spread of a seasonal disease by
examining case data from previous years.
 Prescriptive analytics can be used to assess a patient’s pre-existing conditions,
determine their risk for developing future conditions, and implement specific
preventative treatment plans with that risk in mind.

4. Applications of Data Analytics in Health Care
1. Evaluating and Developing Practitioners
Data gatheredfrompatientsregardingtheirexperienceswithmedical practitionerscanbe analyzedto
reveal areasforimprovement.
2. Detecting Anomalies in Scans
Another way to leverage data analytics in health care is with machine-learning algorithms. When
used correctly, algorithms can analyze data more quickly and efficiently than humans.
3. Predicting Outbreaks
Data analytics can also predict trends in the spread of illness, allowing doctors’ offices, hospitals,
schools, and individuals to adequately prepare.