Category: Data Analytics

Home / Category: Data Analytics

Data Analytics in Banking

Data Analytics in Banking

Banking is getting branch-less, modern, and digital rapidly. As banks compete to get an advantage over their competitors, the need to adopt data analytics becomes even more relevant. If there ever has been a sector where analytics could play a great role, it is banking. Banks have been dealing with data for decades and its scientific analysis can help them bring a huge boost in their performance, leading to an increase in the bottom line. According to a study, over 90% of the top 50 banks in the world are currently using advanced data analytics to achieve their goals. While the applications of data analytics in banking are endless, here are five of the most common applications and how they help in gaining a competitive advantage.

Customer Segmentation

Customer segmentation allows banks to differentiate their customers according to their income and other important factors to address them properly. It is crucial to differentiate customers that make the money and customers that don’t. To grow their wallet share and create more loyal customers, banking firms need to concentrate on selling the right product to the right customer. According to a study, proper segmentation can help banks increase revenue by 30% and decrease costs by 25%. Moreover, segmentation is the top area of investment in marketing and product departments in banks with over 2.5 billion in assets.

While most banks base their segmentation on basic parameters like age, gender, region, education, and so on, data-driven segmentation also takes into consideration their milestones, personal preferences, attitudes, and technological savviness. Based on this, you can market the right product, to the right customer, at the right time, and over the right medium.

Also Read: IS DATA ANALYTICS AND AI THE FUTURE OF HASSLE-FREE BANKING SERVICES? 

Fraud Prevention

Understanding the usual spending behaviour of an individual helps in raising a red flag if any scandalous event occurs. If a sudden increase in the expenditure of a cautious customer is noted, it might mean that the customer’s card was stolen and is being used by fraudsters. Data analytics has helped Americans banks prevent fraudulent transactions worth $22 Billion in 2018. Fraud prevention is so important to banks and other financial institutions that they will spend 9.3 billion for it annually.

Fraud and fraud patterns are evolving and changing more rapidly than banks can keep pace with. That is where artificial intelligence and machine learning-driven data analytics are bridging the gap by helping banking organizations detect risk, breach and fraud quickly and at the source. This early detection can stop over 60% of fraud-related losses and help the bank reissue less compromised cards to its customers.

Risk Assessment

Risk Assessment is one of the top applications of predictive analytics in banking. It is of a high priority to banks as it helps in regulating the financial activities and pricing financial investments. The financial health of a client organization can be assessed for better financing, facilitating acquisitions and mergers, and for investment purposes. Moreover, top banks like JP Morgan Chase and the U.S. Bank already use it consistently to make accurate investment-related decisions.

The traditional credit risk process is slow and labour-intensive, while data-driven predictive models provide instant results. These models use a broader range of data sources and result in a lower rate of default losses, reducing the risk of losing customers to the competitors due to a slow process. Moreover, banks can also find patterns with their customers that can lead to anticipating and mitigating an upcoming risk.

Feedback Management

Feedback management is a great example of the role of data analytics in the banking sector. Predictive analytics help banks to keep up with their relationship with the customers by offering the right products and services based on their needs. To gain a competitive advantage, banks must understand the importance of data analytics and develop strategies based on the insights from their clients’ data. The U.S. Bank has been using data analytics-driven feedback management to improve its services since 2015. It monitors over 24,000 customer reviews across over 23 websites to understand their pain points and adapt their offerings based on them.

Customers expect banks to take care of all their finance-related needs. By collecting feedback from customers, banks can effectively fine-tune their products and services to sync with the customer’s expectations. Banks that use feedback analysis and management can be faster to market compared to their competitors but also arrive there with better products.

Lifetime Value Prediction

Customer lifetime value (CLV) is a forecast of all the value a customer will bring to the bank during the course of their relationship. The importance of calculating CLV is growing rapidly, as it helps the banking organization to create and sustain a highly-beneficial relationship with a select group of customers with high lifetime value, therefore ensuring higher profits and business growth. Recent research suggests that the CLV of a customer can range from $2,000 to $4,000. Columbia Bank and National Bank of Canada regard CLV as an important factor in making business decisions.

Using CLV as a measure requires the whole organization to shift its aim from quarterly profits to establishing a long-term relationship with the customers. It doesn’t just help you track customer profitability, but also sets an upper limit to the customer acquisition costs. Therefore, a thorough CLV analysis helps the bank make accurate and calculated decisions regarding the marketing, sales & pricing for the new customers.

Premier banks like Bank of America, JP Morgan Chase, and U. S. Bank have understood the importance of data analytics in banking and have incorporated it in their business operations. SPIN Strategy offers world-class data analytics that has helped numerous banking organizations to boost their profits and gain competitive advantage. If your banking organization is planning to implement advanced predictive analytics, contact SPIN Strategy now at info@spin-strategy.com

Importance & Benefits of Predictive Analytics in the Ecommerce Industry

Importance & Benefits of Predictive Analytics in the Ecommerce Industry

Predictive analytics is a form of data analytics that helps in getting information from historical data sets to identify patterns in the data and forecast future trends and outcomes. Historically, ecommerce companies have been rather slow to adopt it, but giants like Amazon and eBay have understood the importance of predictive analytics in ecommerce and have incorporated it in their operations.

Predictive Analytics can give ecommerce companies an unparalleled insight into their customers. Before this, building and marketing ecommerce stores used to involve loads of guesswork. Now, they can easily see what is working and what is not, thereby removing any area that can pose risk. While the benefits of data analytics in ecommerce services are endless, here are a few of them:

 Better Recommendations

Recommendations are very important for any ecommerce business, but it is more important to get them right. Data analytics helps online stores make better recommendations to customers by correlating data from various sources to make a personalized recommendation. 35% of Amazon’s annual sales come from successful product recommendations made using predictive analytics.

Also Read: 13 PREDICTIVE MODELS THAT ARE TAGGED AS GAME-CHANGER FOR BUSINESS

Dynamic Pricing

The traditional approach of ‘one-size-fits-all’ is no longer applicable. With the help of advanced analytics, online stores can sell their products at the best price and make huge profits when the demand for the item is high. Amazon has experienced over 143% increase in net profit between 2016 and 2019 after adopting a data analytics-based pricing strategy.

Optimized Supply Chain

No customer wants to see the dreaded ‘Out of Stock’ label in the place of the ‘Buy Now’ button. According to research, two-thirds of customers who experience an ‘Out of Stock’ situation will choose to shop elsewhere. Top brands like Walmart.com and Amazon have invested heavily in predictive analytics to sell products that have not yet arrived in their warehouses.

Better Customer Service

Good customer service directly leads to higher customer retention and conversion, thus bringing more profit. Predictive analytics helps ecommerce companies provide better customer service by identifying issues in the delivery process, the delivery time of the goods, average response time and other factors which customer service staff can act on to reduce the chance of bad service.

The implementation of predictive analytics in ecommerce can become a huge advantage for any e-tailer. However, implementing it properly can be a huge challenge. Powered by Artificial Intelligence, SPIN Strategy provides predictive analytics solutions that help such ecommerce businesses in making strategic decisions that boost business growth. To know more, visit https://www.spinanalyticsandstrategy.com/

With the world rapidly evolving, customers have become almost equivalent to business owners dictating the shopping terms, sales funnel, marketing efforts, etc. As per their needs, customers decide:

  • How to shop
  • Where to shop, and
  • How to proceed with the transactions-online or offline

Along with adapting these changes in customer aspirations, organizations also need to redefine business operations for every industry to emerge more customer-centric and tailor-made as per customer needs.

Reality CheckCustomer-centricity is not an out-of-the-box topic for organizations, principally for those trying to set a benchmark for client satisfaction.

Before we get further, let’s get an in-depth understanding of what a Customer-Centric company is.

What is a Customer-centric company?

In business terms, being a customer-centric company means offering unprecedented customer experiences right from awareness to purchasing, and finally post-purchase stage.

Customer Centric Business Structure 2020

For a customer-centric company, it is a mandate to execute business operations that stimulates positive customer experiences, prior and post-sales conclusion. This assures to uphold the culture of repeating customers, and boosts consumer loyalty for increased profits.

Does your business is in need of it?

Business needs Customer-centricity: Why

Market hypothesize confirms that businesses that apply customer-centric strategies encounter a 55% increase in profits, year by year. For such businesses, customer-centricity is more than mere words on a paper and goes beyond employee meetings or customer surveys.

If the target is to improve the business value and be in sync with the changing trends, incorporating customer-centric strategies is the key.

As per the core principle of customer-centricity, every organization in any industry requires a robust foundation in 1) leadership and 2) strategy, and the poised use of 3) people, 4) platforms and 5) processes.

How to be a customer-centric business?

Customer-centricity commences with a company’s culture and commitment to customer’s success.

Here is how your organization can turn into a fully-fledged customer-centric business.

Customer Centric Business Strategy

1. Predict customer requirements

Anticipating the market’s future needs is a game-changing business move. To be customer-centric, it is imperative for companies to anticipate customer needs and provide helpful suggestions accordingly.

2. Compile customer feedback

Frequent and regular communication with customers is the key to successful customer-centricity. Thanks to the digital transformation of the world, there are countless encounters to accumulate customer feedback like emails, chat, In-app messages, SMS, FB messenger, etc.

3. Convenient customer support

Contacting the support team of a digitally built business is the most difficult task. Easy access to customer support builds rapport and trust, laying the foundation for long-term business relationships.

4. Deliver proactive customer service

Providing customers with added value beyond the point of purchase, like proactive customer service- resources to customers to solve minor problems independently, is the key differentiator.

5. Move beyond purchase

Only with added benefits that extend beyond the point of purchase can old customers be motivated to  buy again, and create a memorable experience.

 

Measuring customer-centric success is crucial: 

Customer Centric Business Strategy
Customer Centric Business Strategy

To measure the success of a customer-centric business, taking into account the following top three significant customer metrics is fundamental:

Net Promoter Score

Are you happy with the products/services?

NPS that focuses on unraveling customer loyalty uses the answer to define the success of customer-centricity.

Whenever a client answers to this question, the response is segregated into certain predefined criteria.

 Net Score Business

Promoters (9-10) – Such customers love the product/service and are quite likely to refer it. Customers who rate your product/service a 9 or 10 have high customer lifetime value and are indefinitely repeat customers.

Passives (7-8) – Such customers who rate your product/service a 7 or 8 are simply happy being a client, however, they are more prone to switching to a competitor in case of a better and inexpensive product.

Detractors (0-6) – Customers providing such ranking are unhappy with the product/service, and are more prone to damaging brand reputation.

Bottom line- The more Promoters a business has, the better the growth it experiences.

Churn Rate

Procuring new clients on a frequent basis puts most organizations in a quandary. Thus, more business is investing in retaining existing clients instead of chasing more fresh leads. Here’ why:

  • Procuring new clients, compared to existing clients, increases the cost up to 5 times.
  • Minimum 2% hike in customer retention is equivalent to cost-cutting by 10%.
  • On an average, companies lose around 10% of their customer base annually.

Note: Companies with a higher retention rate grow faster.

Customer Lifetime Value (CLV)

For any growing business, the customer base is one of the most valuable assets.

With CLV, the revenue collected from such clients during their customer lifetime, starting from the first purchase until the time transactions stop is measured.

With the help of CLV final calculations, it is easy to comprehend why a business should keep investing in customers.

Concluding thoughts

It is no news that businesses are reaching out to different ways to incorporate more customer-centric strategies in their approach.

Any organization that has a lack of customer data and priorities revenue over customer needs still has some catching up to do to sustain in the 21st– century business world.

Introducing the customer-centric brand culture is undoubtedly the best route for businesses to align leaders’ choices with the mindset of the team and cultivate the values. To successfully apply the customer-centric strategy, this culture must be in sync with other business strategies.

At SPIN, we maintain robust individual contact profiles and useful CLV insights and purchase likelihood tools that can help your esteemed organization to be customer-centric successfully, and market smarter.

Visit today: https://www.spinanalyticsandstrategy.com/

 

In an era of machines and Artificial Intelligence, the traditional form of banking has taken the back seat, when compared with tech-savvy Fintech players of the industry who are keen to adapt latest technologies to keep up.

As per market experts, it goes without saying that AI will empower Banking Services, redefining operations by innovating products and services beyond the conventional norm. This eventually perks up customer experiences. Leveraging advanced technologies, human workers will be replaced with sophisticated algorithms.

Such a competitive edge can only be achieved by banking corporations by embracing AI and weaving it into strategic business decisions for maximum profits. 

AI can double Business Profits

AI professionals quote; there are three primary ways the introduction of AI in the Banking Sector can shoot business profits.

Here they go:

  • Enhancing contemporary Profits and Loss levers
  • Identifying new growth patterns
  • Delivering the Digital bank

But the question is: How AI can achieve this objective? The answer to it is by:

  • Profiling and Customer Segmentation– Being aware of the financial profile of all the customers helps banks to jack up the expenditure and income for next month, and maximize revenue.
  • Customer Spending Pattern Analysis- Typically, banks have adequate data about a customer’s flow of income every month, net savings and utility expenses. With the help of this model, banks can conduct a risk assessment, loan screening, cross selling of financial products, and mortgage evaluation.
  • Evaluating Creditworthiness- Determining whether an individual is likely to be a defaulter and estimating the amount that can be offered to him/her.
  • Transaction Channel Identification- With the help of AI, banks can determine if a customer is likely to keep or withdraw money on payday. The latter customers can be pitched for short-term investments.

All the above-mentioned objectives have one thing in common- SPIN Strategy AI Models.

SPIN’s AI models are banking Operation-Focused

At SPIN, we provide a range of AI and ML learning tools for a host of operational applications for financial institutions. Such tools include:

1. Risk assessment, compliance & reporting 

Comprehends the spending pattern and analyzes previous credit history to assess the risk of issuing a loan to a customer.

2. Sentiment Analysis: 

Customer view and market rumors play a significant role in business, and with NLP it is easy to comprehend customer sentiment and its impact on the enterprise.

3. Propensity Modeling

Uses historical data to make business predictions by directing consumers to the right messages and website locations.

If that’s not all, here are a few factors to conclude the fact that SPIN  Models are a boon for the banking sector.

Read More for Related Blogs: 

SPIN for Banking Services: Here is why

  1. Communication: We keep our customers in the loop with necessary and crisp data, perfecting the art of delivering the right solution to the right clients at the right time.
  2. Minimal expenses: No fluctuating invoice rates for our clients. We maintain a simple flat monthly rate.
  3. Rate: Simple monthly pricing for your business requirements at easy rates.
  4. Experience:  Maintaining optimum performance standards, our experts use AI-models for Data Integration, Conversion Optimization, Data Analysis, etc.
  5. Data Integration: We combine data from multiple digital marketing platforms to prepare customized reports to generate a holistic understanding of your brand’s ROI and growth.
  6. Predict Churn: Using AI and ML, SPIN identifies segments of the customer base who are about to leave you for your competitor brand.

The result of using SPIN AI Models for Banking Decisions- Improved decision making for credit and loans.

Here is a real-life scenario- Case Study

The Client-  The client offers financial services and technical support in a bid to increase productivity, boost innovation, and add economic integration. 

The Issue– The bank officials have diverse knowledge on various topics, and have shaped themselves as SMEs of certain topics. They share their knowledge from different geographical locations. The client wanted a single integrated knowledge management platform for individuals to reach out to these SMEs for a certain project in hand.

The Solution- SPIN worked with the client to deploy a cognitive computing application that would include a virtual agent to comprehend Natural Language request inputs and utilizes cognitive deductions to answer the queries.

In a bid to simulate human-like conversations between the systems and the users, SPIN leveraged Advanced Text Analytics including entity extraction, keyword extraction, emotion analysis, sentiment analysis, etc. 

The Result- The bank now has the faith that productivity across the globe will hit the roof due to the easy availability of SMEs. This will boost operational efficiency by 30%-40% and expand cross-broader collaboration and employee management too.

To conclude with

We are more than happy to help Banking Organizations with AI and Data Analytics services. Guaranteeing a solution that caters to clients’ needs and budget, at SPIN we ensure to provide excellent value for businesses. 

For more info, visit: https://www.spinanalyticsandstrategy.com/

Read More for Related Blogs: 

In the true sense, Farming is by far one of the oldest lines of work in the world.

But, with the passage of millennia, Humanity has come a long way and so did agriculture. From conventional methods to grow crops to the usage of AI in Agriculture, Humanity has indeed taken a big leap.But with land getting in short supply and population growing by leaps and bounds, using creative methods to produce crops and boost productivity in limited space has become the need of the hour. 

Change has stepped in. And this can be testified by the fact that the worldwide agriculture industry which is roughly estimated to be around $5 trillion, is stepping in the shoes of other sectors, shifting to what is known as Precision Farming

For instance, adopting AI technologies to reap healthy crops, monitor soil, control pests, accumulate data for farmers etc. and eventually perk up a number of agriculture-related errands in the food supply chain.

Artificial Intelligence in Farming

Digital Agriculture: Farmers are using AI to increase crop yields

Artificial intelligence holds the promise of driving an agricultural revolution at a time when the world must produce more food using fewer resources.

Artificial Intelligence has various applications in agriculture ranging from rural automatons, facial acknowledgment, computerized water system frameworks, and driver less tractors. These applications are done in relationship with an alternate sort of sensors, GPS frameworks, radars, and other cutting edge contraptions dependent on AI.

Innovative progressions and the modernization of GPS are making ranchers and the agriculture specialist co-ops anticipate that additional upgrades will increase the profitability.Increasing adoption of the mechanical technology and IoT gadgets in agriculture is additionally assessed to drive the AI in agriculture.

Agriculture is slowly becoming digital and AI in agriculture is emerging in three major categories, (i) agricultural robotics, (ii) soil and crop monitoring, and (iii) predictive analytics.

Agricultural Robots – Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

Crop and Soil Monitoring – Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health.

Predictive Analytics – Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.AI With Agriculture Farming

How Analytics and AI steps in actually

Intelligent farming practices that have eventually transformed into knowledge-based agriculture, increases production levels and product quality to significant numbers. 

With trained professionals in this art, companies like SPIN Strategy extracts insights from numerous data sources that are integrated into an Advanced Big Data Framework with data analysis decision-making, and automated data recording. 

Result- Customized data for better plant health.

At SPIN, with the combination of smart farming and AI, we assemble, analyze, and digitize massive amounts of data to aid farmers to optimize their production systems. And, that’s why we like to be termed as the Farmer’s Little Hand.

With the use of technology, we:

  • Determine the ripeness of the crop
  • Help farmers preserve water
  • Customize production

Let’s dig deep to understand how ML and AI make a difference in Smart Farming using IoT.

Role of Artificial Intelligence in Agriculture

The agricultural industry is just like any other industry beginning to show interest in implementing the best-in-class technologies to save on resources and create more efficient processes. Agriculture is responsible for the survival of human beings, and the industry has made steady technological improvements in the last few years.

IoT in Smart Farming

How SPIN’s ML and AI programs make the difference in Agriculture: 

Machine Learning in Farming: 

Provides faster and precise results by evaluating the Leaf Vein Morphology that has more data about the leaf properties.

Artificial Intelligence in Farming: 

Uses algorithms and previous field data to determine crop performance in different environments, and builds a Probability Model to forecast the genes beneficial for the plant.

Here is a detailed overview, how SPIN’s AI programs turn the tables for agriculture:

1 . Water Management:

An AI-based application that can be connected with more successful use of irrigation systems and forecasting of daily dew point temperature, which is the base to determine any weather phenomena and analyze evaporation and transpiration.

Water Management with AI

2. Yield Prediction: 

Moving beyond the traditional prediction of historical data, SPIN incorporates computer vision technologies to supply data on the go and conducts a thorough multidimensional analysis of weather, crops, economic conditions, etc. to reap the maximum benefit of the yield for farmers and the population.

Yield Production

3. Crop Quality:

The precise detection and categorization of the crop quality can shoot up the product price and cut down waste. Compared to human counterparts, machines avoid meaningless data to determine the quality of the crops and any possible anomalies.

Soil Management

4. Disease Detection:

At SPIN, we evaluate field images with Conventional Neural Networks to classify pests and diseases, track agro-technical activities, and gather data. To be more efficient, this approach needs more pesticides that lead to huge environmental expenses. ML is used as a general agriculture management to determine diseases and cut those costs.

Disease Detection

5. Monitoring Crop’s health: 

Hyper spectral imaging, together with sensing techniques and 3D laser scanning are vital to establish crop metrics across the land. SPIN crop health monitoring agent has the potential to change farmland monitoring by farmers and can significantly cut down on the effort. 

Monitoring crop’s health

How SPIN Contributes to Smart Farming

Confirmation and extensive testing of emerging AI applications in the Agriculture sector is estimated to be quite vital, since agriculture is affected by environmental factors that cannot be tamed, unlike other sectors where the risk is easy to predict.

At SPIN, we ensure a steady adoption of AI in agriculture with the help of Image Sensor Technology that helps in:

  • Real-time monitoring, analysis, and control of pest & disease
  • Pollination, Phrenology, Fertilization, Irrigation
  • Pollination, Phrenology, Fertilization, and Agri-Technical activities
  • Monitor and forecast yield performance in real-time to optimize results
  • Using Support Vector Machines to predict yield and crop quality
  • Using Artificial Neural Networks for crop management and weed detection

Scenario

Issue-One of our clients, a Colorado-based organization, wanted a preventive measure for defective crops, and optimize the potential for healthy crop production.

Solution– The trained AI professionals at SPIN conducted a comprehensive Soil Analysis and developed a system that will use Machine Learning to deliver clients with an idea of the soil’s strength and weakness. This way defective crop production could be prevented to a significant degree.

To conclude with

Artificial Intelligence and Farming have the potential to pave the way for an agricultural revolution, especially when the world needs more food production with limited resources.

As per the UN Food and Agriculture Organization, the population will hit the roof by 2 billion by 2050. However, experts are of the notion that only 4% of the additional land will fall under cultivation category. In tune with this, the use of the latest technology to do smart farming still takes the front seat.

AI-Powered solution will enable farmers to do more with limited resources and produce the finest quality of crops that amazes even the producer.

Need professional guidance to reap the benefits of using AI in farming? Visit SPIN Strategy today: https://www.spinanalyticsandstrategy.com/

In a competitive era, when every industry is trying to reach the pinnacle of success, the healthcare industry too is picking up the pace with enhanced and exceptional Patient Experience

Be it the hospitals, nursing homes, physician practices or healthcare systems, all are laying special emphasis on Patient Satisfaction. Period.

Only healthcare facilities equipped with improved functionality and better convenience, providing whatever modern consumers look forward to, are emerging as the true leaders in the healthcare domain. 

Since patients these days are far more particular than before and have ample options to choose from, it is imperative for healthcare services to be proactive in roping in and hanging on to patients.

And Sentiment Analysis is one of the best ways to do it. 

To start of with Sentiment Analysis

Have you ever wondered what the feedback of your patients is once they leave your medical facility? 

Enters, SPIN NuInsights Model, with its Sentiment Analysis Application that can gather authentic patient feedback and transform them into reports and intuitive charts to help you draw a conclusion where the medical services are lacking or pleasing.

In tune with it, examining trends for different human emotions like sadness, anger, joy, disappointment, etc. by NuInsights Sentiment Analysis Model enlightens a healthcare provider about the present status of patient care services and the areas of improvement.

To cut the long story short, we want to endow healthcare providers with the tool and cutting-edge analytics (known as Sentiment Analysis application) that can nourish and boost their relationship with patients.

So what is Patient Sentiment Analysis?

Simply put, the process of evaluating and analyzing feedback from the patient’s experience (typically based on emotions at the time) is termed as Sentiment Analysis.

Unraveling of opinions, emotions, and reactions of patients based on the practice they just experienced is one of the biggest Sentiment Analysis benefits.

Add to it, such a form of analysis empowers healthcare caregivers with a competitive edge, revamping their service based on the feedback shared.

Furthermore, SPIN’s Sentiment Analysis Application can also be used to observe the texts or comments by patients and their overall experience in the facility, shared on Twitter, Facebook,LinkedIn and other popular online platforms.

The Significance of Patient Sentiment Analysis for Healthcare firms

It enables caregivers to evaluate the genuine notion of patients for them, figure out the gaps in patient experience, and employ corrective measures on time.

Here are a few pointers to support the above statement:

1. Accumulating data on patient experience

Patient satisfaction is the key for healthcare organizations to prosper and Nu Insights’ Sentiment Analysis Application excels in it. 

It analyzes the data accumulated from multiple common complaints or compliments, segment the data based on doctors and other parameters to spot the opportunities for patient care improvement.

2  Measuring performance 

Such analysis takes into consideration the emotions of patients to derive a conclusion about the performance of a medical facility. 

Such analysis classifies patient comments into various categories like places, processes, people, etc., and then gives them scores to uncover what patients actually feel about the facility services.

For instance, Nu Insights Sentiment Analysis model analyses the frequency of words used by the patients and selects only the most used words left in the comments. 

Here are a few examples:

  • We waited in the queue for a long time (Negative feedback)
  • Good staff and less waiting time (Positive)
  • Nice services but certain aspects need to improve(Neutral)

Based on the above such comments from patients, the Nu Insights Model determines whether the healthcare practice has any scope for improvement or it is going downhill.

Benefits

  • Insights into the performance of a healthcare facility are one of the biggest benefits of Sentiment Analysis. The evaluation of collected data gives a clear picture of what patients actually expect from your practice, how are your employees seen, and other aspects of the management.
  • Generating authentic and quantifiable data regarding the staff about their performance motivates employees to do better in the future. In case of negative comments, the gaps can be fulfilled by the staff. The bottom line is, as long as employees are motivated and get proper feedback for their hard work, patient care is bound to improve.

How Nu Insights Uses ML for Sentiment Analysis

Nu Insights’ Sentiment Analysis model can give you a clear idea of what patients are thinking about your practice by gathering feedback from various locations and using ML to analyze and use the data to make charts and reports.

Here is an example, how our Nu Insights’ Sentiment Analysis model applies to healthcare business scenarios.

A leading hospital chain in the US witnessed declining patient satisfaction to a significant level and wanted to incorporate the latest ML and Analytics trends to comprehend what opinions patients had about the facility. 

In this regard, effectively using Advanced Analytics to understand the root cause of the issue and creating a mechanism to track the same in a bid to timely correct customer satisfaction was the primary approach of SPIN.

 Scenario

With multiple facilities operating under the hospital chain, proper tracking and standardization of its operation became quite challenging and difficult. With an alarming fall of patient satisfaction leading to higher attrition, management decided to get to the bottom of the issue and address it.

Challenges

  • Removing unstructured data from different platforms
  • Same comments on different platforms
  • Determining fake reviews

Solution

Patient comments and reviews shared across different platforms like social media, review forms, blogs hospital websites were accumulated and were filtered for clarity, authenticity, and relevance. Then these comments were subjected to NLP for further filtration.

Results

The gaps that required quick attention were spotted and quick changes were applied in the operations, leading to enhanced customer satisfaction. Not only best practices for business were determined and standardized, the weekly tracking of the facility operations was also streamlined.

To improve healthcare, Sentiment Analysis is needed

Patient satisfaction takes the front seat when it comes to measuring the achievement of your healthcare practice and is always the primary focal point of any marketing campaign.

For marketing campaigns to prosper and reap necessary results, physicians, health centers and healthcare facilities can use Sentiment Analysis to take their clinical services to the next level, by analyzing the exact expectation of patients.

At SPIN, our healthcare marketing strategies are integrated with programs that boost patient experience significantly.

After all, it is a satisfied patient eventually lead to an increase in revenue.