Benefits of predictive analytics
From fraud detection to improved business operations, there are numerous benefits of using predictive analytics in the workplace, including:
Fraud is typically detected by looking for patterns of activity over a period of time. That is, someone engaging in fraud has figured out the best time or window of opportunity to engage in fraudulent activities.
It's difficult to cover up the evidence of fraud as electronic tracking leaves traces behind that are uncoverable by using search queries in databases and machine learning. These tools find unusual patterns and report them to an end user, who then analyzes the data for evidence of fraud.
Operations, whether it's the way in which a cashier rings up a customer or how a quality control monitor looks for imperfections, consist of a set of processes that can jam up over time.
Predictive analytics takes the data from these processes to show different outcomes based on different parameters. It can be applied to improving the efficiency of an operation in all divisions and levels.
Some examples of how predictive analytics tools can be used include determining the cost of fuel prices in the future to improve a customer's checkout experience.
The ability to predict future events via predictive analytics means it's easier to anticipate potential problems and avoid them as much as possible. This is also known as risk reduction because the models can uncover and define an event you may not have foreseen.
The data doesn't define when and where an adverse event will happen so much as it shows the odds and possibilities of the occurrence of a defined event, enabling you to plan and react appropriately.
Optimizes marketing campaigns
One of the predictive analytics capabilities is the ability to make a marketing campaign more effective. A predictive model can be set up to use data points such as demographics and apply them to key aspects of the marketing campaign to determine how likely a certain type of shopper will respond.
Improves customer satisfaction
Predictive analytics can be used to gain actionable insights into customer behavior, their pain points when buying something, and what makes them feel good about their purchases.
This data can be used to remove the obstacles a customer experiences during their shopping efforts, resulting in less stress for the customer while improving their level of satisfaction.
Increases revenue growth
Making it easier for a customer to buy a product, making sure there's enough of a product available, and removing obstacles for the delivery of a product results in fewer work hours spent dealing with adverse situations.
It also means customers get what they want when they want it, and in sufficient quantities. Smooth delivery of goods and services increases revenue because less money is diverted to solving problems.
Challenges of predictive analytics
The field of data science is only as good as those who use predictive analytics software. It's not unusual for the resulting data to be skewed by bias, generate poor quality data because the source data is poor, and for the model to turn out bad data due to a lack of updates.
Just as there as benefits of predictive analytics, there are also some drawbacks, such as:
Using poor quality data is always going to deliver poor quality results due to the fact that most predictive analytics models are literal in terms of how they process data. Ultimately, the quality of the data delivered from predictive analytics depends on the quality of the data source.
People are prone to putting their inherent biases into their queries no matter how hard they try to keep them out. These biases then become part of the modeling process and result in data that isn't as accurate as it could be.
For example, the person setting up the model may prefer the color blue, but the person requesting the report wants data from a variety of colors. The inherent bias towards the color blue results in a report that leans towards a solid color instead of a rainbow.
Privacy and security
Privacy and security are two major issues businesses face when using predictive analytics. The data analytics process sometimes involves going through user accounts for data, something the user agrees to when they sign up.
However, the business is responsible for preserving account security and must act responsibly with the resulting data. Personal information is easily abused and used for fraudulent activities, and the business can be held liable for their failure to maintain privacy and security.
The effectiveness of the data that's delivered by predictive analytics is dependent on the person who's reading the data.
The data derived from the model may be excellent, but if the person reading the results doesn't have a good idea of what they're looking for, the data is essentially useless.
Whoever prepares the data needs to make it accessible for readers who can understand the results, but may not be able to parse the syntax, so the data makes sense.
Predictive models need regular updating and tuning to return high-quality results.
The results worsen when the person responsible for maintaining the models doesn't do their job or if someone in charge doesn't invest in the models. The parameters used for the modeling get out of date, creating faulty reports that don't do a good job of determining future outcomes.
The tools used for predictive analytics enable the user to refine their queries to guide the analysis.
For example, someone who wants to use machine learning to create multiple neural networks that anticipate human behavior can use a machine learning predictive analytics model to achieve their goal. Machine learning isn't typically used in predictive analytics, but it can be used in conjunction to help refine the resulting data and save time.
Other examples of predictive analytics tools include statistical tools to determine the average in a data set, machine learning tools for developing neural networks, and visualization tools that produce graphs, charts, and other types of imagery that show potential outcomes for a specific query.
The wide variety of predictive analytics tools means that just about every business can find an answer to their questions about potential problems in the future.