Data science is a tool that has been applied to many problems in the modern workplace. Thanks to faster computing and cheaper storage we have been able to predict and calculate outcomes that would have taken several times more human hours to process. Insurance claims analysts can now utilize algorithms to help detect fraudulent behavior, retail salespeople can better tailor your experience both online and in store all thanks to data science. We have combined a few examples of real life projects we have worked on as well as a few other ideas we know other teams are working on to help inspire your team. Let us know if you need help figuring out your next data science project!
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Predicting the Best Retail Location
One of the true factors of business success is “Location, Location, Location”. You have probably seen this to be true when you see a spot that always has a new restaurant or store. For some reason, it just will never succeed. This forces businesses to think long and hard about where is the best location for their business. The answer is where your customers are when they think about your product. But where is that?
This example is actually being taken on by a few companies. One example is Buxtonco. Buxtonco is answering where should you open your next business with data! Their site exclaims:
“That any retailer can achieve greater success and growth by understanding their customer and that there is a science behind identifying who that customer is, where potential customers live, and which customers are the most valuable”
The concept is brilliant. Think Facebook geo-fencing in real life. By looking for where your customers may spend their time, and what they might be doing in certain locations the technology can help determine where it would be best to open your next business. Whether that be a coffee shop or a dress store. Data science and machine learning can occasionally seem limited to the internet. However, information provides power both online and in real life.
Predicting why patients are being readmitted
Being able to predict patient readmission can help hospitals reduce their costs as well as increase population health. Knowing who is likely to be readmitted can also help data scientist find the “why” behind specific populations being readmitted. This is not just important because of public health but also because the affordable care act reduces the amount of medicaid for claims when readmission occur prior to 30 days.
Hospitals around the country are melding multiple data sources beyond just typical claims data to get insight into what is causing readmission. One of the common approaches is researching ties between readmission and socioeconomic data points like income, addresses, crime rates, and air pollution.
Similar to the way marketers are targeting customers using machine learning and product recommendation systems that factor socioeconomic data points to tell how to sell to a customer. Hospitals are trying to better tailor their care to help their patients based off of how other similar patients have responded in the past.
Even a phone call at the right time after an operation has been shown to reduce the amount of readmission that occurs. Sometimes the reason patients are readmitted can have nothing to do with how the doctors treated them in the hospital but instead it could be that the patient didn’t understand how to take their medication, or they didn’t have anyone at their house to help take care of them. Thus, being able to figure out the why behind the readmission can in turn fix it. Once policy makers understand the why, it is much easier to develop better practices to approach each patient.
Detecting insurance fraud
Insurance fraud costs companies and the consumers (who are subjected to higher rates) tens of billions of dollars a year. To add to the problem, attempting to prove claims are fraudulent can in turn costs the companies more than the original cost of the claim itself.
This is why many companies have been turning to machine learning and predictive models to detect fraud. This helps pinpoint more claims that should be researched by human auditors. This method doesn’t just reduce the costs of human hours, it also increases the opportunity to reclaim stolen dollars from fraudulent claims.
Once you have a fine tuned algorithm, the accuracy and rate at which your team processes fraudulent claims will increase dramatically.
Brick And Mortar Stores Predicting Product Needs and Prices Live As You Walk Into The Store
The concept of targeting a price for a specific customer is a tried and true method that many companies have implemented(even before we coined the term “data scientists”). If a salesman thought you were wearing an expensive suit, then they might offer you the same car they sold earlier that day at a higher price. In the same way, now the computer can quantify the best price to encourage a customer to make the decision to buy while also maximizing profits(Like Orbitz Did In 2012 For Mac Users “Oh, you like spending $1200 on your computer…well here is your plane ticket + a $100 upcharge”).
This isn’t even limited to e-commerce! Image if in life retail stores actually start using previous purchase history as soon as a customer walks into the door(like in the Minority Report).
Perhaps it’s a Men’s Warehouse or a Macy’s, pick your store. They could meld that data with other information like your LinkedIn profile and Glassdoor salary estimates. Now they will know how much money you make and your buying habits, maybe even some notes from the previous salesman or saleswoman. All of this combined would allow them to better tailor an experience for you and other customers like you.
For customers who enjoy buying clothes and other products in person this could help provide a major competitive advantage for Men’s Warehouse or other similar companies that already have a tendency to focus on the experience not just the sale(who knows, maybe that is why their stock has doubled in the last 6 months…probably not). Plus, then companies can better plan which sales person to partner with which customer. Maybe they can predict that a customer will respond better to the hard sell vs. the softer approach. All of this paired with a human could massively increase sales and customer satisfaction.
Managing IT service desks is a balance of having enough tech support professionals to minimize wait time and keep customer satisfaction at a high and keeping costs low by not having too many people working at one time.
Detecting Who To Call Fundraisers
As someone who has managed a fund-raiser, automation only takes things so far when it comes to donors. Certain donors may respond better to custom emails, or slightly different worded messages, maybe they respond better to a phone call. This is where data science and targeted messages and approaches can help.
Marketing departments are already implementing techniques like A/B testing to their websites and emails to help convince customers to buy a product. The concept of finding the right donors isn’t really different at all.
The key is to start collecting data and managing it efficiently. We have been talking to a few non-profits, and although this use case is a possibility, most of them don’t have the data stored in any form of data storage besides excel, or a basic data base. This makes it difficult to pull out these insights. This is why step one is to creating a data system that will provide insights in the future.
Predicting When A Patient Needs Behavioral Health Procedures Partnered With Their Physical Medical Procedures
One third of the population suffering from physical ailments also suffer from an accompanying mental health condition exacerbating the physical illness, reducing quality of life, and increasing medical costs. Some companies like Quartet are finding that if they help improve the mental health along with the physical health of their customers, it helps improve their overall health and reduce costs for the patients. Quartet is working on a collaborative health ecosystem by curating effective care teams and combining their expertise with data-driven insights.
We have also worked with insurance providers on similar projects where we helped them calculate the overall ROI of their new behavioral health plan that they had implemented to help deal with a specific physical pathology. It not only opened their eyes to the effects of their program, it also found 300k of savings. We are glad to see that larger companies like Quartet are taking this problem on as well!
Data science is a tool that allows companies to better serve their customer and their bottom line. However, it all starts with making sure your company is asking the right questions. If a company doesn’t start with the right use cases and questions, it can cost thousands to millions of dollars. Most of this comes down to communication breakdowns. It can be very difficult to translate abstract business directives into concrete models and reports that provide the impact and influence on decision making that was required.
Our team wants to help equip your data scientists with the tools to increase their personal growth and your departments performance. If you want to start seeing growth in your team and your bottom line, then please feel free to contact us here!
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One of the popular use cases for predictive analytics is analyzing customers' buying behavior in retail industries. Companies use advanced analytics to identify the buying behavior via customers' purchase history. Ecommerce retailers incorporate predictive analytics in PoS to predict customer purchase patterns.What is a use case example for predictive analytics? ›
One of the popular use cases for predictive analytics is analyzing customers' buying behavior in retail industries. Companies use advanced analytics to identify the buying behavior via customers' purchase history. Ecommerce retailers incorporate predictive analytics in PoS to predict customer purchase patterns.What is the use of data science in predictive analytics? ›
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.What are 4 example of applications of predictive analytics? ›
Many industries use predictive analytics to improve their results and anticipate future events to act accordingly. You can find successful applications in retail, banking, insurance, telecommunications, energy, etc.What is an example of a use case scenario? ›
A use case scenario might look something like this: A user goes to the website and browses through the product catalog. The user attempts to add a product to their shopping cart, but discovers the product is out of stock. The user contacts customer service to inquire about the product.What is a use case sample example? ›
- A customer browsing flight schedules and prices.
- A customer selecting a flight date and time.
- A customer adding on lounge access and free checked bags.
- A customer paying with a personal credit card.
- A customer paying with UpCloud loyalty miles.
Predictive analytics is used in a variety of industries including finance, healthcare, marketing, and retail. Different methods are used in predictive analytics such as regression analysis, decision trees, or neural networks.What are the three most used predictive modeling techniques? ›
Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks.Is predictive analytics and data science same? ›
Predictive analytics, as a quantitative discipline, is a branch of data science. Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data.What is use case in big data analytics? ›
Big data provides valuable insights to help companies design new products and features. An improved understanding of customer behavior enables companies to tailor services to different customer segments for future offerings. This use case requires analyzing high-volume product-log data in different formats.
Also called “Spark,” this is an all-powerful analytics engine and has the distinction of being the most used data science tool. It is known for offering lightning-fast cluster computing. Spark accesses varied data sources such as Cassandra, HDFS, HBase, and S3. It can also easily handle large datasets.
There are two types of predictive analytics: classification models and regression models.Which of the following is the best example of a predictive analytic? ›
Weather – to forecast the temperature, rainfall, and cyclones. Finance – to predict fraudulent transactions, risk assessments in giving loans.How does Amazon use predictive analytics? ›
Predictive analytics is an Amazon feature that analyzes a specific buyer's browsing and buying history to help predict their behavior. Through this, Amazon helps make educated guesses about the buyer's future behavior.What are the best case scenarios? ›
Best Case Scenario Meaning
It can be used to describe outcomes in short-term or long-term events. If something is described as the best-case scenario, this means that it is what people want to happen and are working to achieve. It can be compared to the worst-case scenario, which would be the worst possible outcome.
As mentioned, the three basic elements that make up a use case are actors, the system and the goal. Other additional elements to consider when writing a use case include: Stakeholders, or anybody with an interest or investment in how the system performs.What is a common use case? ›
A use case is a written description of how users will perform tasks on your website. It outlines, from a user's point of view, a system's behavior as it responds to a request. Each use case is represented as a sequence of simple steps, beginning with a user's goal and ending when that goal is fulfilled.What are the 4 main components of a use-case diagram? ›
How do you create a use case diagram? First, you need to organize your four key elements — system, actors, use cases and relationships. Then, arrange them visually in a way that makes sense and will allow you to see immediately the connections between them.How do you write a use case list? ›
Determine the target audience for the product. Select a user from that list. Determine what, exactly, the user wants to do with the product and create a separate use case for each action. Determine the typical flow of events for each use case when the user uses the product.What are use cases vs use scenarios? ›
Difference between scenario and use cases-
A scenario is a situation in which more than one actor is involved in performing a specific task. A use case is the description of the scenario which involves describing the functionality of the scenario.
A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it.What is the difference between predictive analytics and data analytics? ›
Data analytics is 'general' form of Analytics used in businesses to make decisions which are data driven. Predictive analytics is 'specialized' form of Analytics used by businesses to predict future based outcomes. Data Analytics consists of data collection and data analysis in general and could have one or more usage.What are the four types of data analytical method? ›
- Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
- Prescriptive data analytics. ...
- Diagnostic data analytics. ...
- Descriptive data analytics.
Advanced data analytics comprises three pillars namely speed, agility, and performance which are important to utilize the full potential from it. These pillars strengthen the analytics strategies themselves and improve your business multiple folds.What are the five stages of predictive analytics? ›
Five key phases in the predictive analytics process cycle require various types of expertise: Define the requirements, explore the data, develop the model, deploy the model and validate the results.How are companies using predictive analytics today? ›
Businesses have more data from various sources at their disposal than they may think. With predictive analytics, you can use past information to project future outcomes for your business. Analytics help you identify future opportunities, serve customers better and make more informed business decisions over time.What made predictive analytics so popular in the modern world? ›
Predictive analytics makes looking into the future more accurate and reliable than previous tools. As such it can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules, and configure store layouts to maximize sales.How to do predictive analysis data science? ›
- Identify the business objective. ...
- Determine the datasets. ...
- Create processes for sharing and using insights. ...
- Choose the right software solutions.
As per Glassdoor, the average salary of a data analyst in India is 6 Lac rupees per annum. In India, the average salary of a Data Scientist is 9 Lac rupees per annum.What are the big three in data science? ›
In this article, I will present the three building blocks of data science — statistics, computer science, and domain expertise — and discuss how each one is important to the field, as well as explore what can go wrong if one or more is neglected.
Python. Python is the most widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around since the year 1991. This general-purpose and dynamic language is inherently object-oriented.How is data science used in daily life? ›
A massive amount of data is captured from them, and then that data is utilized to monitor the environmental and weather conditions. Different agencies use data science technologies in different ways including weather forecasting, in comprehending the patterns of natural disasters, to study global warming and many more.What is the difference between data science and data analytics? ›
Data scientists use algorithms and machine learning to improve the ways that data supports business goals. Data analysts collect, store, and maintain data and analyze results.What 3 main functions do data science tools have? ›
The tools for data science are for analyzing data, creating aesthetic and interactive visualizations and creating powerful predictive models using machine learning algorithms.What are the use cases for predictive analytics in banking? ›
- Fraud Detection. One of the largest concerns for the banking and financial industry is fraud. ...
- Engagement/Churn Prevention. ...
- Customer Targeting and Lifetime Value. ...
- Liquidity Strategy. ...
- Managing Collections and Risk Management.
Predictive analytics enables the retailer to consider data like weather forecasting, real-time sales data, inventory levels, purchase history, product movement, and much more to arrive at an ideal price. Retailers can consider historic, most recent, and real-time data to predict potential future revenue.What is the most appropriate use of predictive analytics? ›
Examples of predictive analytics
Predictive models are used for forecasting inventory, managing resources, setting ticket prices, managing equipment maintenance, developing credit risk models, and much more. They help companies reduce risks, optimize operations, and increase revenue.
Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks. Regression (linear and logistic) is one of the most popular method in statistics. Regression analysis estimates relationships among variables.