Organizations all over the world use their experience to decide the roadmap for the future. The practice of gaining insight from past performance in order to drive business strategy and planning is known as Business Analytics (BA). The field of business analytics offers endless possibilities today and prescriptive analytics is the latest development in this field.
What Exactly Is Prescriptive Analytics?
Data, in unprocessed form, does not offer a lot of value to organization. Proper application of the correct tools can transform raw data into practical insights. But, is it possible to make the right decisions out of the various choices that you get through analytics?
Business analytics has undergone several phases, which has resulted in the development of the following models:
- Descriptive Analytics Helps users gain insight from past data through reporting, clustering, and scorecards.
- Predictive Analytics Uses statistical techniques that study historical and current data to make predictions.
- Prescriptive Analytics Helps users make the right decisions through simulation and optimization.
Descriptive analytics is the first phase of business analytics. Most of the business analytics used by organizations today is of this type. It analyzes historical data in order to understand what happened and why it happened. Thus, companies can identify the reasons for successful or failed events within the specified timeframe and then move forward with the decisions that worked better in the past.
Predictive analytics is the second phase of business analytics. It provides estimates about the probability of a particular outcome. To carry out this analysis, organizations need to gather huge amounts of data in real-time and merge it with their existing data. This type of analytics may predict what might happen if you take certain decisions, but it does not help with solving the problem of identifying the best option for your business.
Referred to as the final frontier of analytics capabilities, Prescriptive Analytics is the latest technique in business analytics. The goal of this type of analytics is to improve the eventual outcome. Thus, it attempts to quantify each of the potential decisions before any action is really taken and also selects the best decision that could be taken in a given situation.
A Detailed Look At Prescriptive Analytics
Prescriptive analytics is the final stage in business analytics as it goes beyond descriptive and predictive analytics, especially the latter.
- It not only anticipates possible outcomes (what will happen), but also the reason for their occurrence (why it will happen) and time of their occurrence (when will it happen)
- It suggests decision options for the possible outcomes. For example should the organization take advantage of a new opportunity or handle a potential risk instead.
- It shows the possible conclusions for each of the decision options.
Companies generate fresh data every minute. This data can be fed into the prescriptive analytics engine at regular intervals, resulting in a revision of predictions based on micro and macro events happening across the organization and the external world. Thus, organizations can get accurate predictions and better decision options.
Prescriptive Analytics In The Retail Sector
Prescriptive analytics is relatively new and may require several years to become part of the business mainstream. It is estimated that a mere 5-10 percent of companies use this technique. As more and more organizations understand its significance, momentum is building in favor of prescriptive analytics. Many businesses are getting increasingly intricate in the way they handle customer data and have started utilizing prescriptive analytics effectively as they move up in their analytics maturity.
For decades, retailers collected information about their customers that they used to create focused campaigns and increase sales. Target went a step further. Instead of targeting customer segments, the retailer targeted individual consumers by suggesting specific products for their specific needs. An article in the New York Times reported that a Target analyst established a customers pregnancy because of her previous purchases. The company then advertised specific products to the customer. Since the implementation of this strategy, the retailers sales and revenue skyrocketed. Barring a few misses that went on to become a PR challenge for the company (customers found the strategy as invasion of privacy), it was still a huge success as a promotion strategy.
Boots, a popular pharmacy retail chain, is utilizing prescriptive analytics to identify the best store layout. They can answer the questions related to inventory, marketing, promotions, and advertising for individual store and plan accordingly.
Retailers constantly need to provide attractive offers to catch the consumers eye. Often, they have to make a marketing-related decision which offer should they promote to a specific customer? Through prescriptive analytics, businesses can identify the most relevant and attractive offer for the customer. Analytics can consider various parameters such as the customers likelihood to respond, the customer segment and business-defined rules before suggesting to business the best offers they can push to each unique customer.
Applications Of Prescriptive Analytics Beyond The Retail Sector
Many industries are getting into utilizing various forms of prescriptive analytics. For instance, Google is using the prescriptive analytics technology to facilitate automated vehicular driving. Vehicles are able to make driving decisions automatically using algorithms and data points.
Package delivery services are increasingly utilizing prescriptive analytics to optimize delivery routes and in order to deliver maximum number of packages in a day. They are even taking real-time decisions by changing the routes in case of road blockages, slower traffic, etc.
Using prescriptive analytics, airline carriers can adjust the ticket price as per the demand-supply ratio to maximize profits. The analysis of itinerary data enables airlines to update their fares at regular intervals, which could even be hourly.
Getting Started With Prescriptive Analytics
Identify the decision-making areas that require improvement
If your organization is planning to adopt prescriptive analytics, you need to first identify the decision-making areas that need improvement. Thus, the champions of business analytics must sit with managers and subject matter experts across the organization Sales, Marketing, Merchandising, Suppliers, Finance, Operations and HR to name a few to identify the critical areas.
The following is a short list of decisions that can be improved through the application of prescriptive analytics:
- Decisions influenced by multiple competing objectives
- Decisions influenced by multiple factors and personnel
- Decisions not executed perfectly by personnel
- Decisions too expensive to be carried out by personnel
Get started with a combination of outsourcing and packaged applications
You also have to think about identifying the best way of acquiring prescriptive analytics capabilities build, buy, or outsource. While some organizations might find it attractive to build their own capabilities, it is often easier and less expensive to buy packaged applications or outsource the capabilities. Although, end-to-end prescriptive analytics solutions have not come up in the market yet, you can opt for advanced predictive analytics tools and then get product or service for prescriptive analytics.
Transitioning through the phases of business analytics
Organizations that have adopted prescriptive analytics have had significant experience with predictive analytics. While this seems to be the norm, it is possible to jump to prescriptive analytics from descriptive analytics. Descriptive and predictive analytics are inclusive components in a prescriptive strategy, and they are a part of a continuously calibrated decision lifecycle of making a prescription, measuring its impact, using the data to refine the predictive modeling and so on.
Conclusion
While prescriptive analytics provides tremendous capabilities, it is not flawless. The same problems that can trouble descriptive and predictive analytics data limitations and external events, for instance can affect prescriptive analytics as well. Many parameters like consumer behavior, purchase contexts, real time situations are changing constantly, so prescriptions have to stay relevant all the time. Moreover, data privacy is a big concern with consumers and being more insightful about your customers needs might make you look creepy.
However, a large amount of computing power is within reach with big data technologies. Large-scale big data can now be connected to real-time applications for instant decision-making capabilities. These technologies are paving the way for prescriptive analytics. As businesses achieve greater analytics and data maturity, they want to do more with the information they have at hand and prescriptive analytics can help them unlock that hidden potential.
