Big data is not a new thing, but many business leaders are at a loss with how to manage their knowledge assets to their full advantage.
Long gone are the days where companies could simply look at consumer research and make an intuitive guess for the best move forward. Today, with AI, machine learning, and oceans of data just waiting to be analyzed, companies need to use the most advanced analytics to keep up with the competition.
Advanced analytics enable industry leaders to make better decisions and improve customer experiences. Given the wild pace of advancement, it is normal to feel overwhelmed by the pressure to adopt new data practices.
Instead of scrambling to try the next demo, take a moment to slow down and learn the basics of one of the most widely incorporated advances in business solutions—predictive analytics.
Read on or use the links below to navigate to each section:
- What are Predictive Analytics?
- Descriptive vs. Predictive vs. Prescriptive Analytics
- Predictive Analytics Applications
- Predictive Analytics Techniques
- Predictive Analytics Tools
Free Guide: Click here to download Wonderflow’s free whitepaper, “How to Turn Customer Feedback into Valuable Insights”
Predictive analytics is a hot topic today. An IDG poll found that IT leaders believe that their greatest increase in budget allocation will be in predictive analytics:
But what’s the big deal?
Predictive analytic solutions forecast future outcomes by reading and interpreting historical data. Through the use of statistics, advanced algorithms, and machine learning, quantitative and qualitative information is transformed into predictions.
Though predictive analytics has existed for several decades, we are now seeing software companies offering predictive services left and right. Tech companies throw this term around like candy but often leave clients in the dark about what types of outcomes the software can predict, how, and the accuracy of the prediction.
Don’t get us wrong. The power of predictive analytics is indeed growing. There is more data available to collect. Storage and software solutions are more affordable. And the economic market has been transformed by digital leaders, such as Amazon and Facebook, into an economy that demands customer-centricity more than ever.
But if leaders don’t fully understand how predictive analytics works, then they may procrastinate on investing in this necessary tool.
A TIBCO study found that 90% of participant businesses think that advanced and predictive analytics are important to some extent, yet only 23% actually use them:
Why is there such a large disconnect?
Perhaps, these businesses have barriers getting in the way of accessing what they need to get started—data. Not just any data, but plenty of good, quality data.
Bill Su, CEO of Humanlytics, and his team interviewed 60 small and medium-sized businesses (SMBs). They found that collecting high-quality data was one of the participants’ biggest challenges to join the predictive analytics revolution. Participants say that collecting data is pricey or nonexistent. SMBs struggle to capture qualitative data on customers, convert that into quantitative data, or make it standardized enough to enable analytics.
Here at Wonderflow, we get that. We pour our energy into figuring out how to capture high-quality qualitative customer data and turn that into actionable insights on future decisions:
Data must be of good quality to serve any purpose. This means that the data should be collected in a consistent, reliable, representative, and timely way.
We can’t neglect to mention that quantity matters too. Statistical predictions require large data sets. The more data a company has to work with, the stronger their foundation will be for an accurate prediction.
Some industries have been collecting data for years and years (i.e. weather patterns). Other industries are interested in more novel datasets, such as social content or customer feedback. It can be a challenge for companies to collect enough of this data if they do not have a plan in place and the right team to support them.
Forecasting the future is not a new concept. We all make predictions to guide our current decisions. For example, when we see dark clouds, we put a rain jacket on. These intuitive predictions are extremely valuable. And though there will (probably) always be room for human intuition in the workplace, statistical predictions are based on vast amounts of experiences and higher-quality data points than our human gut can fathom.
These predictions are changing the way business leaders across industries approach data and make decisions.
Most people are familiar with descriptive analytics. This type of data shows what happened in the past. Google Analytics shows businesses how many people visited their website, where visitors are from, how they arrived there, and many other valuable data sets. These numbers tell a story of what happened in the past; hence, they are descriptive.
Descriptive analytics and predictive analytics both use real data from the past. The difference is that predictive analytics typically uses regression analytics or other techniques to convert data from the past into predictions of the future.
Descriptive analysis can tell you where your customers are located.
Predictive analysis can tell you that if you add another language to your product, your revenue will go up by X%.
Because descriptive analysis relies on tangible and objective information, there is little room for error. Predictive analysis, on the other hand, cannot account for sweeping government changes, natural disasters, or unlikely circumstances. Predictive analytics cannot read the future precisely—but it can come close.
Another difference between the two is the approach that businesses take to decision making. Companies who solely rely on descriptive analytics often operate with a reactive mindset. For example, an executive might say “the numbers are down this quarter, so we will have to respond with a different strategy.”
Predictive analytics enables companies to shift to a proactive way of thinking. Leaders with a proactive mindset might say something like, “we ran the numbers and if we implement this new campaign this quarter, we will gain more conversions in this area,” avoiding the dip in conversions in the first place. This is obviously a simplistic example. There is a huge need for both descriptive and predictive analytics, but they are attached to different approaches of decision making.
Informed decision making is what analytics are all about, which brings us to prescriptive analytics.
Prescriptive analytics are “Predictive Analytics Plus.” Instead of merely offering a prediction, this branch of analytics also offers stakeholders actionable insights. After all, predictions are void of value if they do not result in changed products or company behavior.
Some people believe that the difference between predictive and prescriptive is too slim to give this practice a separate name, but prescriptive analytics makes room for automated decisions, which predictive does not. When paired with bigger and badder AI tools, prescriptive analytics will enable businesses to leave certain decisions and consequent actions left to machines.
Predictive analytics is used across industries in a number of ways. With oceans of data on individuals, data scientists can examine relationships between just about anything. Though the applications of predictive analysis are endless, there are some standout ways to use this type of research.
Marketing and Customer Experience
Predictive analytics allow you to get to know your customers better along their entire buyer journey. This use of data can be used to improve content creation and distribution, predictive lead scoring, predictive lifetime value of a customer, and to predict when customers are about to churn.
Just think of all of your personalized Amazon recommendations. Using your purchase history and views, the eCommerce platform predicts other products you would like to see. This makes your shopping process easier.
Predictive analytics can help businesses monitor and collect customer feedback on their products. There are many factors that reviewers highlight or criticize. It can be difficult to take all the feedback in without a solution to sort through it and put it into a succinct graph.
Predictive analytics solutions enable you to see the relationship between multiple variables in easy to read graphs, enabling you to call better shots with product development and customer relationship management.
There are several ways HR can implement predictive analytics. Reducing employee churn is a big one.
By studying patterns of employees who left in the past and then monitoring these factors among your current employees, you can reach out to restless employees before they leave you. This can reduce the cost of finding new employees, training them, and later missing out on your investment in talent.
Advanced analytics can also be used to make the right hires in the first place. Data from Linkedin is being used in the pharmaceutical and software companies to identify potential employees who are “high flight risk.” IBM and Mastercard are also using predictive analytics to identify employees most likely to remain loyal and to improve employee engagement.
WeSolv is a company that is using predictive analytics to reduce bias in the hiring process. This platform fits employers with employees that have their ideal skill set and experiences, decreasing gender and ethnicity preference blind spots.
Airplane delays are the worst. Sitting in an overcrowded airport and not able to escape, only dreaming of your destination on the other side. You feel helpless until they fix the plane’s mechanical issue.
But what if that could be avoided? Airlines are now using predictive analytics to monitor their aircrafts, flight histories, and routes to proactively perform maintenance on parts of the plane that need it.
Predictive maintenance is not limited to airplanes. The oil and gas industry also uses this data analysis to take care of their industrial equipment and to enhance safety and risk management.
IT can know about systematic failures before they happen and identify root causes. Predictive analytics takes all the data that humans have a hard time processing manually and making it visible—enabling teams to identify correlations between risk factors and investigate inefficiencies.
There are so many other innovative uses of predictive analytics. The Uber you took to get to your business meeting, public health professionals who identify people with high suicide risk, insurance companies creating appropriate quotes—these all use predictive analytics.
There are a number of techniques a data scientist can use to transform data into a prediction. Choosing a method typically depends on the type of data and what type of question analysts are trying to answer. These four techniques are well-established ways to extract predictions out of past occurrences:
Regression modeling is the simplest way to perform predictive analytics. Linear regression takes a look at two correlated variables—independent and dependent—and graphs them on a map. Based on the past relationship, an equation can be calculated and then used to predict what future outputs will be, assuming that the future is just like the past.
Simple linear regression modeling only involves two variables. This is good for identifying simple, linear relationships and future trends.
But very few things in this world only involve two variables. Other types of regression modeling allow for more advanced analysis. Multiple linear regression, logistic regression, ordinal regression, and more allow for more variables. But the more variables a data scientist adds, the less accurate and representative the model will be. This is where other techniques come in.
Decision trees are graphs that use branching to show possible outcomes of a decision:
This type of graph helps with classification and understanding the behavior of multiple variables. Though simple, business analysts find them effective.
Decision trees enable analysts to split different populations and identify the strongest variables that impact a certain outcome. The graphs start with a root node typically at the top that represents a population, sample, or main variable. Decision nodes (tested attributes), branches (potential results), and leaves/terminal nodes are all graphed to display what the potential outcomes are of a certain test.
For example, business analysts might want to determine the likelihood of a customer buying a select product.
They can build a tree with the root node being a customer score of likelihood to buy. The decision nodes can be broken down into different salary ranges with the branches leading to different nodes and variables. It can further break likelihood to buy down into gender or age categories. All these lead to the leaves or terminal nodes that list yes or no.
There are several advantages to this method:
- They are visually simple to understand for the most novice of analysts.
- Data trees can be used for data exploration and testing causal relationships between different variables.
- They also are not limited to numerical values, but can assess categorical variables.
Clustering works by taking a population or variable group and splitting them into different groups where the data points are more similar to each other:
Basically, the smaller groups—clusters—are organized by their shared traits.
One application of this is to create customer personas and to predict what type of marketing approach will be most effective with them.
Hard clustering assigns each data point to a group completely. Soft clustering assigns a probability that a data point will belong in a certain group.
The notion of separating data points into groups based purely upon similarity is vague. This can be approached many different ways. Over 100 clustering algorithms exist.
K-means clustering is one of the most popular methods. This method takes uncategorized data and separates them into an assigned number of groups, K. This method uncovers the centroids of the organically formed groups.
This method is used for behavioral segmentation, inventory categorization, and sorting sensor measurements (group photos, audio detection, health monitoring).
Predictive analytics tools combine these statistical techniques with machine learning to give you insight on future ambitions. You see the powerful potential, but how do you go about choosing the right tool in such a complex field?
There are a few factors you can consider first:
Who will be using this tool?
If your team is equipped with data analysts who are trained to use more technical platforms, a program language tool might be a more affordable and flexible option.
If this tool will primarily be used by business executives who need a more intuitive and user-friendly interface, then a GUI-based tool will be a better option. Reading customer reviews on trusted software sites can give you insight on which platforms are geared towards people similar to you.
What type of data will you be analyzing?
Predictive analytics tools can analyze numerical and written data, though some might place their focus on certain types of data. Some tools are catered towards sales data and others are catered towards social listening.
Wonderflow, for example, uses an advanced text analysis system to monitor customer satisfaction:
This can be used to detect which specific features of your product need to be improved to enhance your customer experience and feedback
Other tools are limited to statistical data and uses tightly rule-based algorithms to produce insights.
Choosing the right tool depends on what data you want to analyze and improve most. Many companies want to study everything. This approach can be overwhelming. It is better to prioritize what will impact your vision the most.
What price point are you looking for?
Some analytics solutions are open-source and available to anyone who can use the programming language. RStudio and Anaconda are prime examples. Other reputed solutions can cost around $5,000 per user. Lesser known, start-up solutions can be a more affordable option for smaller businesses.
Predictive Analytics Can’t Replace Your Gut – But They Can Sharpen It
Data drives the world today. It’s simply daunting to think about all the data being produced every second by multinational enterprises, media organizations, and consumers. It’s even more shocking to think about what to do with that data and how.
Predictive analytics helps you organize the data into useful insights that can help you achieve ambitious goals. But at the end of the day, a prediction is only a prediction. There are uncontrollable circumstances that can break all of your analysis into pieces. Human intuition is still very much needed in those moments.
Predictive analytics is simply a tool to strengthen, inform and then sharpen that instinct.