In today’s competitive economic landscape, companies are turning to advanced analytics to predict consumer behavior, increase their competitive advantage and drive the bottom line. But organizations need data, and more importantly, they need to know how to look at the data in order to find the right insights to turn into action. Effectively, organizations need to be able to gauge the future, or risk a future without them in it. And when we can’t simply hop on a DeLorean and time travel to the next decade, we turn to the next best thing: predictive analytics.
What Is Predictive Analytics?
Predictive analytics is the use of historical data to identify the likelihood of future outcomes. It’s using data – what we know – to create a predictive model to forecast future events – what we don’t know. It uses a combination of data, analysis, machine learning techniques, and statistics to predict what will happen in the future, or to recommend actions for the best outcomes. Predictive analytics has gotten a lot of press time in recent years due to technological advancements – is it worth the hype? Let’s dive in and see!
Why Predictive Analytics Is Important
It’s simple to understand why predictive analytics is important. It allows businesses to differentiate themselves from competitors and stay relevant in a harsh business landscape. It also helps organizations discover new opportunities and solve tough problems.
Companies like Amazon and Netflix use advanced analytics to grow the top line and keep subscribers sticky. Amazon, for example, will offer product recommendations based on your search and order history. Netflix suggests what to watch next based on their predictive algorithms. Predictive analytics is important to organizations like these – and many others like them – by serving as key growth drivers.
How Can Predictive Analytics Be Used In Business?
Predictive analytics has the potential to provide immense value in business – from improving UI to informing decision making. So how can predictive analytics be used in business?
Predictive analytics are often utilized in the financial services industry to assess credit risk and predict and reduce fraud. They can also be used to forecast trends in the financial market. Retailers use predictive analytics to predict consumer behavior, both online and in brick-and-mortar locations. They use historical data to understand behavioral patterns along the customer journey, and then optimize their retail space – whether digital or not – to increase the likelihood of transactions.
Airlines use predictive analytics to set ticket prices. Hotels utilize advanced analytics to predict how many guests to expect each night. As more and more businesses realize the advantage that predictive analytics can provide their organizations, we expect to see a continued upward trend in its use.
What Are The Drawbacks Of Predictive Analytics?
There are drawbacks to the use of predictive analytics – what are they? For one, predictive analytics rely on substantial data sets to make predictions, and big data sets aren’t always easy to come by. Another drawback is that human behavior isn’t always predictable. It is always changing. A predictive model that was accurate in the early 2000’s may be irrelevant today in the face of changing consumer behavior. Or, let’s say, a global pandemic hits, all models go out the window. When implementing insights from predictive analytics, businesses must be aware of this.
One argument that detractors of advanced analytics and machine learning often make is that they can be used unethically. We agree – technology often reflects the ethics of the people that create and implement it. There is always opportunity for advanced tech, whether it’s machine learning, AI, or predictive analytics, to be abused. Do the pros outweigh the cons? We’ll let you decide.
How To Do Predictive Analytics In Excel
If you’re itching to get your spreadsheet game on, you may be thinking about how to do predictive analytics in Excel. Below are a few key guidelines for how to do predictive analytics in Excel:
- Identify what you want to predict – what is the goal/outcome?
- Prepare and clean up your data – delete outliers or unreliable data
- Classify the attributes you are trying to predict – split your data into clearly defined classes
- Identify the predictive model you want to use, then create a predictive analytics formula
- Evaluate the effectiveness of your model, then implement into your business
Despite several drawbacks, we see the use of predictive analytics only growing as businesses seek to gain a competitive advantage. As data sets get bigger, the use of advanced analytics to parse data for insights will only increase. The good news? Key insights are waiting to be discovered and turned into profit drivers!
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