7 Use Cases For Data Science And Predictive Analytics
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!
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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