From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.
1. Disrupt An Industry
Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments.
“There’s no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,” said Niven Narain, founder and CEO of BERG. “We use human tissue samples to learn about as many biological components as we can and we include that patient’s clinical and demographic data.”
Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug.
Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a “patient signature.” The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said.
2. Meet Customer Demand
Handmade photo product company PhotoBarn has increased its throughput 500% by creating warehouse software and lean manufacturing processes that are built around predictive analytics. Before its transformation in 2015, the company struggled to balance supply and demand.
About halfway through 2015, the company started using predictive analytics to forecast sales, inventory, and raw materials to anticipate what it would need before and during the holiday season. That and its new lean manufacturing process enabled the company to move five times more product using the same number of people.
“The spikes and volumes in the holiday period are hard to handle. In 2015, we reimagined our supply chain from suppliers to customers,” said PhotoBarn’s business analytics and marketing chief Ryan McClurkin. “We were able to handle the order volumes without hiccups [because] we’re anticipating versus reacting, and it pays huge dividends.”
3. Right-Size Resources
Predictive analytics has helped Alabama’s Birmingham Zoo more accurately forecast attendance. As a result of that, the company can make more informed staffing and marketing decisions.
“The number of people who attend the zoo affects staffing, marketing and events planning. You could look at historical averages, but we pulled historical data and correlated that with weather data, school calendars, national holidays, [and other] variables to predict how many people would show on a given day,” said Joshua Jones, managing partner at data analytics and data science consulting firm StrategyWise.
The information is displayed on a digital dashboard that provides a much more accurate forecast. Instead of guessing that 10,000 people will come to the park based on historical information alone, Birmingham Zoo can now see it is likely that 7,131 visitors (or whatever the number happens to be) will attend on a particular day.
4. Create The Perfect Game
Success in the lottery industry is all about finding the right payout levels. Two of the most important factors are the sizes of the prizes and the frequency of payouts, which is why prize values and odds vary significantly in a single game. However, some games are more popular than others.
“Lotteries want to maximize their revenues so they can [contribute to] education and whatever social programs the state wants to support,” said Mather Economics director Arvid Tchivzhel. “We’ve measured responses in tickets purchased due to changes in the payout structure. You can almost build the perfect game based on where you set the payout levels and the frequency.”
5. Sell More Effectively
Jewelry TV (JTV), like many luxury goods retailers, was hit hard by the recession. The company tried a number of tactics to improve sales that didn’t work as well as hoped, so it eventually embraced predictive analytics.
“A regression model helps you understand what’s impacting your revenue. When you start building a predictive analytics model, it can tell you why what you’ve been doing isn’t working — customers don’t care,” said Ryan McClurkin, former director of strategic analytics at Jewelry TV and currently chief of business analytics and marketing at PhotoBarn. “Predictive analytics can tell you your customers care about this [instead].” That’s the power of predictive analytics. It allows you to see the variables you can innovate around.
6. Improve Promotional Relevance
Some customers will respond to an offer and others won’t, but the big question is why? Consumer finance company Synchrony Financial uses predictive analytics to target offers that are more relevant to its customers.
“We use predictive modeling to determine which customers will likely respond to which offers, so that’s prior purchasing behavior data, who the customer is, what channels they’ve engaged with us before, the frequency of their purchasing, and how much they’re buying,” said Synchrony Financial CTO Greg Simpson. “If I’m doing a 10% off campaign, I understand which customers I should invest in communicating that offer to.”
Synchrony Financial also uses predictive analytics to create customer segments. The idea is to find similar customers who are distinct from other customers. As part of that, the segmentation includes customers’ preferred contact medium, so a carefully targeted offer can be communicated in a manner that’s most relevant to the customer.
7. Optimize Pricing And Profitability
Companies want to increase revenue. Customers demand value for their money. Balancing the two is a constant challenge that many businesses face. Add to that disruptive business models such as Amazon.com, and the calculus required becomes even more complex.
“Customer response to price, merchandising, and promotions are levers you have to pull to make the right recommendation. You have a forecast, you apply a market response model to it, and you have a holistic price-sensitive demand forecast that includes competition. Based on that, we can optimize what the price should be,” said Jon Higbie, chief science officer of pricing and revenue management consulting firm Revenue Analytics.
In the online world, dynamic pricing is relatively easy to manage. In the analog world, it’s much more difficult. However, using predictive analytics, Revenue Analytics was able to help a global hospitality group increase top-line revenue by 2.7% over a 13-week period throughout the Americas.
8. Minimize Product Returns
Returns are a harsh reality of selling. According to the National Retail Federation, almost $3.2 billion worth of merchandise was sold in the US in 2014, and 8.89% of that was returned — which amounts to $284 million in lost revenue. To date, returns have simply been a fact of life. Predictive analytics can help reduce the number of returns.
“When a customer orders three pairs of shoes, you know two of them will come back,” said Suresh Acharya, head of JDA Labs at JDA Software. “What you want to do is to predict at a distribution center level or at a store level how much of what was sold will get returned.”
Using predictive analytics, it’s possible to tell how much merchandise will likely come back and when. Because there’s a correlation between sales and returns, if forecasters and inventory planners can get that information, they can prepare for what’s coming.
9. Help Patients Avoid Hospitalization
Dialysis patients suffering from fluid overload may have to be hospitalized. DaVita HealthCare Partners is using predictive analytics to more accurately identify which patients need to be hospitalized and which don’t.
“The promise of personalized medicine translates to the right medicine for the right patient at the right time,” said Mahesh Krishnan, international chief medical officer and group VP for research and development at DaVita. “We’re able to leverage predictive analytics to identify patients at high risk.”
DaVita uses the data from its labs, dialysis machines, and other sources to enable accurate predictions. To date, it has operationalized the data across 175,000 patients in 2,300 clinics. Apparently, just getting that data to physicians has reduced the number of hospitalizations, Krishnan said.
10. Balance Appointments And Workflow
Some appointment-based businesses suffer from no-shows, as evidenced by the phone, text, and email reminders sent to clients, customers, and patients. While the outreach is prudent, it isn’t necessary in every case. However, if the reminders aren’t done, some percentage of the population will likely miss their appointments — 25% when it comes to Medicaid, according to StrategyWise managing partner Joshua Jones.
“People that receive healthcare paid for by Medicaid have no incentive to show up because there’s no cost if they miss the appointment,” said Jones. “We created predictive models of the likelihood of people showing up using factors such as the driving distance from the clinic, whether they have a work telephone number, their age, marital status, and past appointment-keeping behavior.”
All of that is factored into a diagnostic model for the call center staff so they can book appointments more efficiently and the healthcare providers can better balance workflow.