Predictive data analytics uses data, algorithms, and artificial intelligence-driven machine learning to determine the probability of prospective outcomes. In business, predictive analytics is used to take historical data and provide an accurate assessment of what will happen in the future.
Interest in predictive data analytics has taken off in recent years. The global predictive analytics market is expected to grow 22% from 2019 to 2027, increasing from $7.3 billion to $35 billion.
Why the hype? For one thing, technological advancements have made predictive analytics more accessible and effective. Additionally, companies are seeking ways to stand out in an increasingly crowded market. Being able to make accurate predictions about consumer behavior based on statistical precedent is one way to gain a competitive edge.
This guide provides a primer on predictive data analytics and its many possible uses in business. Read on to gain a deeper understanding of the discipline and its potential use within your organization.
Predictive analytics: Forecasting the future
Predictive data analytics isn’t new. The practice has been around for decades. Say you own a flower shop. Every year, you see a spike in sales on certain occasions like Valentine’s Day or Mother’s Day. Based on this historical sales data, you can be sure that these holidays will be big sellers in the future and prepare accordingly by increasing your stock or hiring temporary staff to meet demand on these days.
As simple as it sounds, that’s all predictive analytics is. Today, modern technology allows for much more expansive and intricate predictive analytics. More businesses embrace predictive data analytics to improve conversions, boost sales, increase the bottom line, and gain a competitive advantage. It’s easier to harness predictive data analytics for a few reasons:
- Computers are faster, cheaper, and more accessible than in the past.
- Predictive analytics software is more user-friendly.
- Data collection processes have improved, spurring the “big data” revolution.
It’s easier than ever to gather, store, and organize mass amounts of data, thanks to modern technology. Businesses might collect data based on sales results, customer complaints, and marketing feedback (like how many clicks a Google Ad received). This adds up to a valuable trove of information, which businesses can mine to make smarter decisions, be it how they advertise or what products they provide.
Say you join a supermarket customer loyalty program to save money, for instance. Every time you shop, you give the supermarket data, like what days you shop and the products you buy. The store can then use targeted advertising to reach you, like email campaigns. If the store notices that you consistently buy a case of beer on Friday evenings, for example, they might send you a “buy two, get one free” coupon in hopes of increasing your spending. Revenues increase, while customer loyalty is simultaneously enhanced.
This is just one example of how businesses derive value when making predictions rooted in statistical probability. In competitive industries, particularly, the ability to make accurate predictions based on high-quality data can make or break a business.
Predictive analytics: New possibilities for your business’s data
Predictive data analytics provides valuable information that can guide business planning and development. Here are some ways your business can benefit.
Replicate sales success
Sales success marks a milestone achievement in any industry. Being able to replicate this achievement is what makes companies successful in the long run. Data science can identify sales success factors, whether it’s a question of timing or keyword targeting. If you can’t do this kind of work in your team, you can engage independent data scientists to leverage your data and even rely on independent talent to help you visualize that data.
Identify organizational bottlenecks and recurring challenges
Companies often use predictive models to discover bottlenecks and subsequently improve operations. For instance, a hotel might use predictive data analytics to forecast how many rooms will be booked on a given night and use this information to improve the odds of reaching maximum occupancy. If there’s a time of year affected by low occupancy, the hotel can boost its marketing efforts for that period.
This is just one example of how a company might use predictive analytics to pinpoint bottlenecks and respond to them accordingly. Historical data can help companies across various sectors, from the automotive to the fashion industries. A bottleneck is simply any hurdle that impedes operations and delays product or service delivery. Often, companies don’t recognize these trends until they crunch the numbers and visualize the data.
Plan for fiscal stability in the future
Cash flow is an issue for every business, regardless of the field. Companies must make sure that the amount of money they have coming in (profits) and the amount of money going out (expenses) is balanced in a way so that there’s always sufficient cash on hand to cover operating expenses. For example, if you run a restaurant, you need to pay your food suppliers to maintain service.
Predictive data analytics can help ensure sufficient cash flow and help businesses plan for fiscal stability. This can be done by tapping into historical data from previous years. Analysis can be broken down into quarterly or even month-by-month comparisons. By identifying trends in peaks and lows of cash flow, businesses can take care to always keep sufficient funds on hand.
Identify and encourage exceptional worker performance
Data is fast becoming “the new language of HR.” Human resources departments can use data about existing team members to figure out what technical backgrounds are and aren’t working in the company, and use this information to guide hiring. Data can also be used to motivate workers. Monitoring performance with measurable data allows companies to celebrate and reward wins.
For example, predictive data analytics can track success in sales teams by providing accurate data on who closes deals. What’s more, data can then be analyzed further for patterns of success, which can then be passed to others. For instance, it might turn out that the sales team member closing the most deals usually follows up their emails with not one but two phone calls. This can then be made a part of the general team protocol.
Detect risks of fraud and other threats
The digital age brings great opportunities, but it also brings added risks. Fraud is a common concern across industries. Any business that accepts online or credit card payments should protect sensitive customer data, for example. Predictive data analytics can be used to detect fraud for cybersecurity purposes. High-performance data analytics can be used to spot abnormal behaviors and respond to them quickly.
This logic inspires “geo control” measures that have long been in place in banking. Suppose you usually use your credit card in the United States because that’s where you live, and suddenly the card is being used in South America. In that case, algorithms can quickly detect this anomaly and shut down the card. This is just one example of how data analytics can identify potential fraud risks.
Improve customer retention through targeted adjustments
Business success requires winning new customers and, just as importantly, retaining existing customers. Predictive data analytics can be used to improve retention by providing information about repeat customer behaviors—and flagging instances in which customers drop off. Historical data can also be used to see if those “lost” customers can be won back. For example, if a client disappeared, does sending them an email with a special deal lure them back?
Analyze market position compared to competitors
Predictive data analytics doesn’t always have to be about what your company is doing. Sometimes, it can be about learning from your competitors. For example, you might analyze your market position compared to competitors and use this information to see what you can learn from those who are surpassing you.
If one company has a larger market share in a particular product area, what’s the reason? Are they doing more advertising in that area? Does their product have a feature that yours lacks? Have they managed to corner a certain niche audience that you’re currently missing out on? As you pinpoint the reasons for their leading market position, you can learn how to improve your own.
Implement predictive data analytics with independent experts
Predictive analytics requires a diverse range of applications and techniques. You need people who can design, install, maintain, and improve your organization’s data and analytics operations. If you’re starting to adopt this practice, independent professionals can help you swiftly implement predictive analytics tools for strategic benefit.
