While the amount of big data is growing, organizations are looking for ways to use it to their advantage. And more importantly, the market competition is growing every year, and companies need to find a way to get ahead and drive a competitive edge. As a result, predictive analytics is booming now.
Following Zion Market Research, the size of the predictive analytics market was $3.49 billion in 2016, and this number will grow to $10.95 billion by 2022. In other words, the market size will grow by 21% in six years.
Why is it happening? Predictive analytics offers a new and effective way to look into the future, allowing entrepreneurs to optimize their marketing strategies and business processes, improve their relationships with clients, detect fraudulent activities, and reduce production risks.
In other words, the data you already have can become one of the most powerful instruments in your hands. Continue reading if you want to start using it now.
Predictive analytics is a type of data analytics that uses historical data, machine learning, and statistical modeling to predict the likelihood of a certain event in the future. The results of predictive analytics are highly precise – this is the strongest advantage for companies of any size and industry.
The ability to foresee the future of your business is made more accurate and reliable with predictive analytics than with any other tool previously used. What does it mean for your business? Here are six predictive analytics benefits that will help you cut spending, save time, and optimize resources:
- Building up marketing strategies. Predictive analytics helps predict the customer’s responses and purchases and find new cross-sale opportunities. In other words, it helps increase the number of loyal customers.
- Meet the buyers’ expectations. Retailers get a portrait of their current and potential customers and know exactly what they want. It provides a means to tailor individual offerings and increase customer loyalty.
- Controlling resources. Companies forecast how much inventory and products they will need in their daily work. For example, hotel managers assume how many guests they will have depending on the season or day of the week, and plan their resources accordingly.
- Reducing risks. For some industries, risk management is one of the key activities that helps a business stay afloat – banking and insurance industries are among them. Banks use credit scores to determine the state of a certain buyer. The ability to make secure loans quickly and conveniently for both the bank and the client is of high importance.
- Improve production processes. Manufacturers use predictive analytics to estimate future production errors and reduce the risk of production downtime. Also, it helps plan the amount of inventory more precisely and avoid any oversupply.
- Detecting fraud. Deploying several analytical methods at once can help you create a pattern of a person’s standard behavior. If this pattern somehow changes, it is a signal to check for fraud or criminal behavior.
Read also: Driving Digital Transformation through AI
With these points above, we realize that predictive analytics helps with both tactics and strategy. Now let’s take a closer look at the predictive analytics workflow.
There are three pillars in the predictive analytics structure and its flow:
- Data. This is the foundation of any predictive analytics strategy. Many organizations don’t have enough data to predict the future, so this is the first point you should check before moving any further.
- Statistics. Then, we have to analyze this data, and it usually implies various statistical methods and models such as a regression analysis. It helps identify connections between different variables.
- Assumptions. The past impacts the future – this is the main assumption of predictive analytics. The more precise data you provide, the better the prediction will be.
Now let’s see what models we’ve just mentioned. The top predictive analytics models include the following:
Classification model. As the name suggests, this model helps create data groups based on the provided historical data. The best way to use it is to ask “yes” or “no” questions such as, “Is this customer likely to make an additional purchase?” or “Is it a trustworthy loan applicant?”
As this model is straightforward and easy to understand, it can be used by numerous branches or industries.
Forecast model. This one is the most popular and requires solely numerical data to use. For instance, it can be any type of call center predicting how many calls it will receive within a day or week. Another example is online stores that must plan their inventory to meet the buyers’ demands.
Clustering model. It sorts out the data into several groups according to their similar attributes. For example, if you work with several target audiences, the clustering model will help you approach a certain group of individuals according to their unique traits and behavioral patterns. It can be applied to those branches with online buyers or bank clients – they have to deal with several target audiences.
Outliers model. If there is anything wrong or unusual with the numbers, this model will show you where to look for any wrongdoings or irregularities. It usually helps detect fraud in financial transactions or suspicious operations in retailing.
Time series model. Time is the key concept in this case because the model takes the historical data from a certain period in the past and predicts how it will evolve in the near future. The expected number of patients at a hospital, visitors at a restaurant, successful purchases – all these figures can be calculated with the time series model.
To obtain the most precise results, you often need to add more factors along with the numbers: the season of the year, weather forecast, location, purchase history, etc.
The best thing about predictive analytics is that it is easily adapted to many different industries. Predictive analytics solutions are used in healthcare, finance, retail, manufacturing, and so on. Today, we are reviewing some of the brightest examples.
Retailers use predictive analytics for numerous purposes. It helps build solid relationships with clients: extracting customer insights and offering the right products or discounts to the right buyers. Also, business owners use predictive analytics to choose the marketing strategy, and stock the right products in the warehouses.
Walmart is among those stores that aim to optimize its stock. In 2017, the company started testing the predictive analytics approach that helped foresee which products will have the highest demand. It also helped pick the method of checkout for the store: a traditional one or self-checkout.
Predictive analytics in healthcare is used to anticipate and sometimes prevent the negative outcome of certain diseases or improve diagnoses. But there are more examples to add to the list.
AlayaCare, a software provider for home healthcare, is actively using predictive analytics in their business. One of the developed innovations concerns full-time nurses and their churn; losing even one nurse leads to lots of problems and uncomfortable situations for patients.
To reduce the risks, AlayaCare offers software that provides reports on the employees that are likely to leave soon. Using a list of factors, the tool indicates which workers have the most favorable and unfavorable working conditions.
There are the four most popular uses of predictive analytics in banking: detecting frauds, credit scoring, customer analytics, and optimizing financial processes.
Danske Bank worked in collaboration with Think Big Analytics to create an AI-based solution for fraud detection. The platform analyzes money transactions in real time to detect if there is any fraudulent activity.
The biggest advantage for the bank is saving lots of time analyzing every transaction and then investigating the issue. If the tool blocks a transaction, it provides an explanation for the customer, upholding a transparent status of the bank’s activities.
The breakdown of just one machine leads to a loss of time and profit, so manufacturers are looking for new ways to optimize their production processes. By using predictive analytics, companies can prevent equipment failures and improve their production line productivity and quality control.
Mercedes-AMG decided to use big data to improve their engine production process, and developed a quality-assurance platform for that. This solution tests the engines in real time and provides a thorough analysis of current production flaws.
Government & public sector
The government should be able to prevent problems – not only solve them on the go. Detecting frauds or anticipating epidemics – all of it has a huge impact at the local state, and national levels.
For example, inspectors can predict beforehand which cafes and restaurants to inspect for safety. The health department of Las Vegas analyzes thousands of tweets for specific keywords like “I feel nauseous” and identifies places and areas to check.
Read also: Building Smart Cities – Are We Future-Ready?
By using historical data, the government of Indonesia is able to better prepare for possible floods in the area. They use the software that analyzes data about previous floods and makes assumptions about future troubles.
Entertainment companies should know everything about their customers and their expectations. People turn the TV on to relax during the weekends or after long workdays. Expectations mean everything in this case.
For example, Netflix has millions of subscribers, and the company should be able to learn the most about every user, and predictive analytics is the perfect tool for that; the time and date when users watched a show, whether they paused it and then resumed it or not, what device was used, etc. – this is priceless information for analytics.
All these points allow for the creation of a list of unique personal recommendations and provide a means to grow and strengthen the audience of loyal customers.
Big data offers a lot of opportunities, but it requires moving step by step. That’s what you should do:
Step 1: Choose the exact data you want to work with. You can chase different goals using predictive analytics. If you want to improve relationships with customers, you will need specific information about buyers and the size of their purchases.
Step 2: Collect all the available data from different sources in one place. Considering that many companies don’t have an organized data storage, you might have to gather information from all possible sources to make the prediction accurate. But don’t forget about your initial goal – you don’t need all the data in the world, only the data required for the model.
Step 3: Divide the information into two sections: a training set and a test set. The first one is used to teach a predictive analytics model, and the second is used to check if the model works well before trying it out on real-life data.
Step 4: Pick a prediction model and values to use. This model, along with the test and training sets, define the framework of your predictive analytics approach. The modeling process is not an easy thing to do and usually requires some collective work and specific skills.
Everything that has happened to your business in the past can become a strong instrument for your future growth. Based on the historical data, predictive analytics gives you lots of opportunities to improve your marketing strategy, decision-making skills, and business processes.
The question is, do you know what model to choose and how to use it the right way?
At Eastern Peak, we build complex software that predicts your ups and downs and develops relationships with your customers that are built on trust. Contact our team to discuss your future goals that you want to achieve.