Analytics is one of the most important tools a business can use today to gain customer insights. This is why, according to Valuates Reports’ latest estimations, the global big data and business analytics market is set to reach USD 512.04 billion by 2026. With a CAGR growth of 14,80 percent between 2019 and 2026, it follows that behemoths like Microsoft,
Amazon and Google, are investing heavily in their ability to enable data for enterprises.
As artificial intelligence (AI) and machine learning (ML) continue to evolve, the way businesses use data and analytics is also developing. In the past, businesses focused primarily on collecting descriptive and diagnostic data. This data told them a little bit about how their customers and products had behaved. However, businesses today are more focused on extracting predictive and prescriptive learnings from their data. They want to know what to prepare for.
First, what is descriptive, diagnostic, predictive and prescriptive analytics?
Descriptive analytics: This information tells what has happened in the organization, and is the most basic form of analytics. For example, a monthly sales report, web traffic numbers, etc.
Diagnostic analytics: This data explains why something has happened and is slightly more complex than descriptive analytics. The use of business intelligence (BI) tools, queries and alerts facilitates this form of analytics.
Predictive analytics: This is where data is used to give information about what may happen in the business. Predictive analytics uses complex ML and AI processes and algorithms to help forecast and predict key business outcomes. For example, how well a new product will perform, which customer segment will engage best with it, which marketing tactic will produce the best result.
Prescriptive analytics: This data not only gives businesses information on what may happen, but recommends actions that could improve a process, a campaign, or a service towards an optimal outcome.
Predictive analytics explained
Predictive analysis is an advanced analytical technique that uses data, AI, algorithms, and ML to forecast trends and make business projections. Essentially, predictive analytics uses historical data to project future probabilities.
The real question is not “What will happen?” but rather, “What is most likely to happen?”.
Unlike futurology, predictive analytics is an accurate calculation of possible outcomes in any scenario. This is done through powerful computer software and algorithms that process vast amounts of data. Patterns are identified in the data, and this is used to evaluate future possibilities.
It is important to highlight that this technology does not “predict the future.” Rather, it points to probabilities based on what has already taken place. In short, because predictive analytics finds patterns in data, it can effectively determine how likely it is for those patterns to emerge again.
This gives businesses and investors the opportunity to allocate their resources accordingly to take advantage of, and prepare for, potential future events.
How Predictive Analytics Works
Predictive analytics makes use of an array of statistical techniques to predict possible future outcomes. These include machine learning, predictive modeling, and data mining.
Machine Learning and predictive analytics
Machine learning (ML) plays a vital role in predictive analysis. ML is where a computer adjusts its behavior without external, or human input, based on patterns found in data sets. This is why ML is often adapted towards predictive analytics.
There are different ways that ML facilitates predictive analytics:
Data mining sees large tranches of data analyzed to detect patterns.
Image analytics scans images and can determine facial and emotional recognition.
Video analytics tracks and identifies single and multiple objects in the video content.
Natural language can be processed to produce logical answers from input questions.
Optical Character Recognition (OCR) converts handwritten material into digital text.
What are predictive models?
We now understand that predictive analysis takes data from the past and present and processes it through statistical functions to detect patterns and predict future behavior. A predictive model is typically used in this process.
A predictive model is created to answer specific questions and predict unknown values using relevant data and statistical methods. There are several types of predictive models, each used to answer a particular question or type of data set.
Some of the types of predictive models are:
Predictive models can be used in any industry where a certain answer is required. For example, a pharmaceutical company could use a predictive model on their order history to determine whether to increase the production of a particular product in the coming season. It would consider the weather estimates for the period (colder, drier, etc.), historical customer purchasing behavior, and medical data in its analysis.
All models use different methodological and mathematical calculations, but pursue the same objective: to predict future or unknown results.
About predictive analytics, big data, and data mining.
Predictive analytics would not be possible if it wasn’t for the rise of big data. It is estimated that we generate 2.5 quintillion bytes of data every day. Through the use of data mining, machine learning, artificial intelligence and statistical analysis, predictive analytics sees the collection and processing of this ocean of data in order to identify patterns. This is translated into meaningful insights and predictions about the future.
Big data forms the building blocks of predictive models. Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. This involves selecting the records, data, and statistics that will provide the best strategic information for the predictive model to be built upon.
This is why big data and predictive analytics go hand in hand. Predictive analytics applications need access to a vast amount of data if they hope to provide useful information that support the business’s goals of continuous improvement.
The 7 Steps of Predictive Analytics
To understand how predictive analytics works in the real world, let’s follow the main steps of the process.
Understanding the overarching business objectives
The best place to start is with a well-defined business objective. It is imperative that the analyst or predictive analysis partner understands the purpose of the analysis. Is it to:
Understand customer and employee behavior?
Predict sales trends?
Identify the most profitable products?
Lower churn rate or turnover?
Reach a new target audience?
Collecting the data
The next step requires the most care. It involves identifying the data that will be used to answer these objectives. The quality of the data selected will determine the reliability of your predictive analysis, therefore choosing the best sources to collect the data (internal databases, social networks, research,
consultancy databases) is of the utmost importance. It is equally important to use an adequate collection and data analysis tool to ensure the data’s accuracy. Choose an application that is easy to use and that is preferably hosted in the cloud. This ensures you always get real-time results that you can access from anywhere, at any time
Preparing and cleaning the data
The data now needs to be prepared so that it is in the format that your analytics tool requires. Data cleaning removes unnecessary information, defines variables, sorts your data, and then structures it into specific sets. You can use these manual tools to do this: Excel and Power BI, for example. Or you can opt for easy-to-use, cloud-based tools like SEDGE.
Analyzing the data
Now that the data is properly structured, you can begin the analysis process. Your analytics tool will produce graphs and trend lines that will require your analysis and interpretation. If you need deeper analysis or interpretation it is best to make use of predictive analytics experts who can advise on best practice and facilitate even greater data analysis.
Predictive modeling
A predictive model can now be created by using the analyzed and tested data. This model is typically a standard of mathematical and statistical techniques used to process the data sets.
Modeling deployment
Once created, the model is deployed and predictive analytics will begin to return valuable insights into upcoming probabilities. The model should continue to be interpreted and recalibrated based on the predictions made until accuracy is optimized. Your Predictive Analytics tool should do this for you.
Ongoing monitoring
Now that the predictive model is created and deployed it needs to be monitored closely to ensure that results remain reliable. Ideally, this should be done on a monthly, quarterly, and semi-annual basis to ensure that the model’s performance is not affected by any possible changes in data.
The Importance of Predictive Analysis for Business
With an ever-evolving marketplace and always-connected consumer, companies are turning to technology for the competitive advantage.
When organizations make use of predictive analysis they can confidently and objectively make preemptive decisions on future marketplace opportunities and risks.
When organizations adopt predictive analysis they can:
Predict the next moves in the segment
Identify opportunities and stay ahead of the curve
Monitor and automate IT infrastructure and maintenance
Optimize marketing strategies and tactics
Quantify consumer and employee behavior and trends
Improve operations and increase efficiency
Reduce operational risks
Organizations can confidently and objectively make preemptive decisions on future marketplace opportunities and risks.
The Big 5 Benefits of Predictive Analytics
1. Predicting customer needs. The most basic, but perhaps the most effective, benefit of analytics is predicting customer needs. An example of this is when online retail brands use predictive analytics to up-sell and cross-sell products based on unique buyer trends. This ensures greater basket spend across the site.
2. Real-time product feedback. Predictive analytics is processed in almost real-time, which allows for tailored customer experience in a live environment. Consider platforms like Netflix and Spotify where a customer’s actions impacts on the next recommendations made.
3. Identifying flight risk factors. Analytics can identify the factors that lead to customer churn, and can pinpoint which customers are most at risk for leaving. This allows companies to reach out preemptively with messaging or products that would help retain these customers as clients.
4. Optimizing a more competitive pricing model. Many organizations base their pricing models on age, or gender, particularly in insurance. Now, with predictive analytics, they can personalize rates based on the individual’s likelihood of getting in an accident.
5. Staffing up or down. Predictive analytics can help companies staff according to anticipated peaks and troughs in demand. This could be based on website traffic, seasons, or purely on historical data.
Predictive analytics empowers decision-makers with the information they need to make faster, more accurate decisions to better prepare for what lies ahead.
Who Uses Predictive Analytics?
Any industry that has access to big data can use predictive analytics to lower risks, improve operations and increase revenue. Aside from retail, here are a few examples.
Banking and Financial Services
Thanks to access to huge amounts of data the financial industry has a long, successful history of using predictive analytics to detect and lower fraud, measure credit risk, maximize cross-sell/up-sell opportunities, set dynamic pricing, and retain valuable customers.
Maritime and Logistics
Predictive analytics is powerfully used in maritime and logistics to optimize cargo management, fleet planning and operations, vessel schedule reliability, predicting equipment imbalance, providing equipment repositioning strategies, predicting maintenance and machinery failure on board, predicting container maintenance and repairs, preventing accidents and incidents in the maritime industry, as well as overcoming volatility in freight rates.
Healthcare and Life Science
AI and ML technologies have long been embraced in the healthcare sector, with AI being used to efficiently diagnose and reduce error, to do screenings, diagnostics tests, blood work and protein sequences, and in Biopharmaceuticals and drug development. Deep learning is utilized for targeted treatment, and predictive analytics is used to improve patient experience and to streamline product manufacturing.
Any Project-Led Business
Complex product-development projects are often hindered by missed deadlines and cost overruns. The extra resources and costs associated with project delays, plus the indirect costs of lost sales and lost competitor advantage from stalled launches, can be devastating.
Empowered with predictive models and analytics a company can create higher-integrity plans for new products. It can also determine the risk of the current plan or create a more realistic staffing plan along with a healthy budget estimate and an achievable timeline.
Conclusion
Predictive analytics helps companies improve their customer experiences, improve operations, avoid risk, increase profit, and become future fit brands.
It is shown that technology and advances in computing continue to support people in their day to day tasks and activities. So too predictive analytics empowers decision-makers with the information they need to make faster, more accurate decisions to better prepare for what lies ahead.
Are you ready to know the future?
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