How many decisions do you make every day? Thousands, right? What time will I get up, what will I wear, will I go running or not, should I buy this stock—decisions are the stuff of life.
Now, what if we asked you how often you track the outcomes of those decisions and use them to make better decisions in the future? You may have done this with a food diary or exercise app. Input data, track results.
Most of the decisions we make are relatively inconsequential in themselves (should I have salad or pizza)? But over time, a series of good or bad decisions can carry hefty consequences (I’ve lost/gained 20 pounds).
Simple concept right? Now let’s step into your office.
Why Big Data Is Driving Decision-Making At Work
Businesses make hundreds or even thousands of decisions every day, too. And just like personal choices, the important decisions aren’t just the ones where contracts are being signed. They’re the ones that affect both short-term and long-term outcomes whether we realize it in the moment or not.
Big data has become essential to business because we’re suddenly able to track reams and reams of information that we couldn’t before. We know more about our customers, employees, business processes, finances, facilities, and everything else than business owners could have dreamed of just thirty years ago.
The question is: What are we doing with all that data?
Big data has become an overused buzzword precisely because it holds so much potential. Everyone recognizes that we hold the answers to our most pressing business questions in the palms of our hands (or in a server or on a spreadsheet somewhere), and most businesses are tracking as much as they possibly can in hopes of finding the insights that will push their companies ahead of competitors.
But let’s ask another question. What if we could take all that data, and not only identify past behaviors (what our employees did in the past) but also what they will do in the future. And what if we could then use those insights to tell managers how to interact more effectively with employees and customers?
Now we’re in the realm of predictive analytics.
How Predictive Analytics Change Literally Everything
Data can tell us that something happened, but it can’t tell us why it happened. For example, we can see that 48 employees left the company last month, but we can’t know what triggered them to leave. But predictive analytics changes the equation by pulling in data from numerous sources, identifying trends, and making predictions about future behavior.
With predictive analytics, we can track the numbers and discover the why behind a given result. Once we know what patterns precede a particular outcome, we can use that information to make predictions about what will happen in the future.
It’s a fascinating science, and it’s changing the way we do business. Here are just a few applications we’re seeing in the marketplace:
By tracking customer behavior patterns and making predictions about purchasing based on that data, companies can identify the best ways to segment audiences, capture leads, create marketing messages, and target the right people.
With the right tools, you can score leads based on behavioral, psychological, demographic, and other data to predict which ones are ready for a call from sales and which ones need additional nurturing first.
Predictive analytics helps you anticipate how customers will react to different types of content and messaging. With that information, you can segment prospects more effectively to make decisions about content distribution, customer churn, and upsell/cross-sell opportunities.
Based on how customers and followers have responded to previous content, predictive analytics can help you determine which content channels and topics resonate best with your audience. You can use that information to develop your content and promotion strategy.
Lifetime Customer Value
Predict which customers will deliver the greatest ROI over time by comparing data from current customers against data from historical customer lifetime value.
The cost of a failed hire is too high for companies to make mistakes. But it’s not always easy to separate the wheat from the chaff. Predictive analytics can help you hone in on the best candidates early in the process by comparing their resumes, experiences, and interview practices with those of previous successful or failed candidates.
Data can predict which recruiting channels will yield the best results, helping you build a reliable talent pipeline and funnel more of the right candidates into that pipeline.
Predictive data informs your interview process and candidate assessments to show you which candidates will likely be the best fit for your culture, demonstrate the right blend of skills, and help you achieve your business goals.
Eliminate Recruiting Bias
Bias is notoriously difficult to remove from the hiring process. Everyone is prone to it, even if you don’t want to be. But predictive analytics can remove the subjective element from assessments and resume sifting, enabling you to identify the most promising candidates based on their credentials and attributes. It can also identify past examples of bias and help you make better hiring decisions moving forward.
Employee engagement is a huge predictor of turnover, productivity, and retention. Predictive analytics puts you ahead of the curve by identifying which employees are likely to leave the company, which new candidates will likely stay with your company longer than one year, how to increase performance, and how various engagement factors will affect other metrics like sales figures, efficiency, and ROI. Engagement is a tricky concept to measure, but as we become more proficient at aligning engagement with outcomes, we can expect to see increasingly reliable indicators of future performance.
Trends in customer behavior, seasonal changes, purchasing patterns, and customer demographics can help you create reliable predictions about where and how to improve sales over time. You probably collect and review all that data already; predictive analytics just takes you the next step by creating forecast models you can use to target your sales efforts based on demand and customer behavior.
Risk management requires close attention to detail to identify potential problems. It’s a function filled with subtle changes and potential waste if you focus on the wrong elements. But predictive analytics can handle much of that detail work for you, turning all the thousands of data points you collect from customers, vendors, and employees into actionable insights that help you reduce risks like customers defaulting on their payments, employee turnover, workers compensation claims, safety incidents, and much more. Predictive modeling can be used to address risk in every industry, from finance to transportation to manufacturing to healthcare, because it helps companies identify the causes of problematic outcomes rather than simple correlations.
Conclusion: Three Things You Need For Successful Predictive Modeling
Of course, there are plenty of other ways you can use predictive analytics to improve outcomes for your company, from business intelligence to fraud prevention. But for any of it to work, you need three things:
1. Clean, reliable data
2. Technology tools to store and retrieve the data in one place
3. Someone who can analyze the data effectively
The problem with big data is that it can’t tell you anything by itself. You can collect all the information in the world, but without the tools and the personnel to put that data to work, you’ll be left with a mountain of information you can’t decipher.
At Worthwhile, we specialize in creating the technology you need to collect, store, sort, manage, and analyze your data so you can gain the competitive insights you need.
We’ll leave hiring the data scientist up to you.