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Guidance and results for the data-rich, yet information-poor.

Prescriptive Analytics or Predictive Analytics

Your company is most likely already making use of Descriptive Analytics, which is often described as the simplest form of analytics as it is essentially your raw data in interpreted and summarized form. It is your sales counts, social engagement metrics, customer statistics and other data that lets you know what has happened in your organization.

The next two forms that will help transform your organization are predictive and prescriptive analytics. This will turn the data gained from descriptive analytics into optimal decisions and valuable insight. When used together, both analytic options can help your organization form the most robust and effective business strategy available.

In today’s increasingly fast-paced landscape, predictive analytics alone may not be enough to keep up with your competition. With prescriptive analytics, gain valuable recommendations for the best next steps for almost any business process in order to accelerate success or arrive at your desired outcome. While predictive analytics forecasts possible outcomes, prescriptive analytics can help you change it to your benefit.

Prescriptive analytics complements predictive by providing insight to key decision makers about varying available choices, and their forecasted impact on several KPIs (key performance indicators). A popular example is a traffic navigation app that allows you to pick a start point and end point, before presenting you with different available routes and the likeliest ETA for each.

Another example is how a nationwide clothing store might use both predictive and prescriptive analytics together to forecast that the sales of lighter clothing will increase as warmer weather slowly arrives. Based on this insight gained from predictive analytics alone, most companies might increase inventory for warm-weather clothing at every store. But the reality is that the spike in sales won’t happen at once all over the country—it will most likely begin in the southern parts of the country before moving upwards based on seasonal patterns. With prescriptive analytics, you can input sources like climate and weather data to implement a better action based on analytical recommendations.

Descriptive, Predictive and Prescriptive Analytics 101

Due to the endless flow of data made available to organizations, businesses look to analytics solutions to draw conclusions and to help their decision making process. As evidenced by the organizations that have lowered operating expenses, boosted customer service, increased revenue and improved their processes, a significant return on investment can be enjoyed by those who employ analytics.

With all of the available choices for analytics solutions, determining which ones can help your business can be a difficult task. While one is not necessarily better than another, these analytic options can definitely complement each other when used properly. They can be categorized into three specific types:

Predictive Analytics
Forecasts and predictive models are used here to provide insight on what could happen in the future.

This form of analytics is all about understanding the future and its ability to forecast what could happen. Companies are able to gain actionable insights based on collected data from various sources. While no algorithm can completely predict future outcomes, predictive analytics is able to provide estimates about their likelihood of occurring. Whenever you need to fill information that you lack or to know something about the future, predictive analytics is the key.

Prescriptive Analytics
Advice on how to optimize processes to handle possible outcomes is gained by using simulation algorithms and optimization techniques.

The newest of the three forms of analytics, prescriptive analytics provides different possible actions that a user can take, and guides them towards the best solution. This analytics solution aims to quantify the effect of a decision and advising on possible outcomes before the choice is actually made. This form of analytics is ideal for times when you need to provide users with advice on what choice is best.

Descriptive Analytics
This type of analytics uses both data mining and data aggregation techniques to provide insight into what has happened.

With descriptive analytics, raw data is summarized and “described” or made into data that can be interpreted by humans. Whether the data comes from a second ago or several years ago, this analytic option can describe the past and allow us to glean valuable information from past behaviors, and find out how these behaviors can influence possible future outcomes.

Boost Mobile App Marketing with Predictive Analytics

Smartphone users spend the majority of their time on apps, so it’s the perfect time for your organization to build one. A study has shown that 90% of a customer’s time is spent on a mobile app than on a browser, so it’s important to branch out and refrain from sticking to mobile optimized websites.

Marketers need to collaborate closely with app developers to create and implement strategies that will help acquire and retain the mobile majority. While getting users can be challenging, keeping them is even more difficult. Several articles have talked about how to retain mobile app users, and the most convenient way is to prioritize mobile app marketing with the help of Predictive Analytics.

Mobile app teams spend thousands on acquiring users, in the hopes of monetizing them. But getting users is only half the battle. Retaining users and maximizing their value is essential to staying competitive. If the cost of getting users is higher than their lifetime value, you won’t see any profits soon.

Predictive Analytics To Increase Mobile User Retention

Analytics can provide valuable insight to what users are doing within a certain app and why they’re doing it. To better understand why some mobile users stay while others leave, predictive analysts compare the retention of groups of similar users who do different actions within your app. Your high-value users might be doing a specific action or using a certain feature that could be leading to a better retention compared to others.

Understanding how cohorts of retained users act in the long term can help you create the right product and marketing changes that will improve overall retention. The challenge lies in knowing which behaviors to look into and forming theories about which users are more likely to churn or to be retained. This is where Predictive Analytics is used best.

Predictive Analytics features are able to tell you which users are more likely to convert or to leave. They can utilize push notifications or targeted email campaigns to keep “high risk” users engaged. Some features can even tell you the correlation between a certain user behavior and customer retention, as well as the user behaviors that are highly correlated with overall retention. With tools like these, you will now have a sound start to creating hypotheses about how users use your product.

How Predictive Analytics Provides Value In Every Business Sector - Part 2

While deployment of Predictive Analytics is steadily becoming mainstream, its effect is only beginning to be felt by industries around the world. Expect drastic and welcome changes in the years to come brought about by the useful insights learned from data mining and analytics.


Capital markets now have the increased ability to analyze a large amount of data streams in real time from trading operations and other external sources, helping to expose illegal activity much more accurately compared to traditional methods. These same methods are also able to detect when set trading algorithms deviate for unknown reasons and allowing interventions that can prevent large losses.

Predictive analytics greatly helps consumer finance as well, helping banks detect credit card fraud as the transactions are happening and letting them take real-time action by blocking fraudulent transactions instead of waiting for them to be reported or experiencing a delay in becoming aware of the crime.

Transportation and Utilities

Government departments and train companies now use streaming and predictive analytics to make their track infrastructure as efficient as possible. This helps ensure that passengers are brought to the station platform that is best for them and minimizes delays in the process.

Utility companies benefit greatly from predictive analytics, letting them operate and manage their grids in its optimum form, especially in this time of renewable energy. As more residential and commercial properties generate electricity using solar means, it can be challenging to stay up to date with production and demand. With analytics and streaming data, it is now easy to reduce waste and match production with demand.


It has been evident that one of the most significant changes will occur in the field of medicine and healthcare. Patients with chronic health conditions can be tracked using a smart band that sends out a notification to remote caretakers whenever patients deviate from their usual patterns.

Those under acute care can be given sensors that monitor their vital signs, such as blood pressure and heart rate, and this data is fed into a predictive model that alerts staff if a negative event will soon happen. This model learns these insights by matching current data against the vast history of records covering past patient reactions.


How Predictive Analytics Provides Value In Every Business Sector - Part 1

Thanks to Predictive Analytics, data is now more than just a log of the past. We are now focusing on the future, analyzing past data to uncover important patterns. At this point in time, we are now capable, with the help of analytics, to combine the power of prediction with our current grasp of present events.


One common example occurs in the world of telecommunications, an industry that covers millions of subscribers. A certain telecom company makes use of a predictive model that handles data on how users are accessing their network—if they are using certain apps, making a call or accessing particular websites.

With this information, the company can then use prediction and event processing to push appropriate offers at the best possible time. The model they’re using will know if a customer tends to make international calls and might be better suited with an upgraded plan. Instead of sending an offer when the customer is likely to be busy, the telecom company knows to send an offer right after he or she ends an international call, making it much more timely and relevant.


Pushing an offer at the right place and the right time is a very powerful action. This is very evident in cases with retail establishments. For example, a customer who was browsing a retailer’s site earlier in the day or is logged into an account that has his or her mobile number stored walks past a branch of the said retail store. The customer then receives an immediate offer for a product they were looking at earlier, telling him or her that the store has it in stock and that a 10 percent discount is waiting if they decide to purchase now. The ability to interact in real-time with customers is a powerful weapon in retail.


Companies used to require long stretches of time just to overhaul generators because they would rarely know when maintenance is needed most. Now, with the help of data from sensors monitoring fuel use, temperature and cylinder pressure, it is easier to spot when parts are nearing their point of failure. With real-time analytics, the predictive model is able to notice a significant drop in pressure and, by comparing it with past records, is also able to predict that the current rate of use will lead to equipment failure within a set period of time.