The prescriptive analytics software market will reach $1.88 billion by 2022
The analytics world is fast changing! As the need to leverage data across functions becomes more and more imperative, organizations are fast deploying modern analytics tools that not only predict the future but also recommend the best course of action.
Given the 21st century’s incredible data explosion, historical data and shallow insights from traditional analytics like descriptive and diagnostic analytics won’t cut it anymore among decision-makers.
Today decision-makers need much more than just data – they need actionable insights which are crisp, meaningful and enable a forward-thinking approach. Wouldn’t it be nice if your marketing analytics tool harnesses your last quarter’s data and recommends how your marketing plan for the next quarter should look like?
This is just one of the many amazing things today’s advanced analytics tools can do. They’re prescriptive and predictive in nature.
Before delving further into prescriptive analytics, let’s differentiate between traditional and advanced analytics.
Traditional Analytics (Descriptive Analytics + Diagnostic Analytics)
Helps you understand: What happened?
Descriptive analytics is an initial stage of data processing. It uses data aggregation and data mining in order to understand “What has happened?”. It provides historical insights as to how the business has fared up until the present.
Helps you understand: why did it happen?
Diagnostic analytics go further than descriptive analytics and answer why something happened – why did my sales go up? why are my inventory costs reducing? etc.
Advanced Analytics (Predictive Analytics + Prescriptive Analytics)
Helps you understand: what will happen?
Predictive Analytics uses statistical models and forecasting methods to define “What might happen?”. It analyzes historical data to predict the likelihood of outcomes in the future. The goal of predictive analytics is to go beyond knowing what has happened to providing the best assessment of what will happen in the future. Predictive analytics uses a wide range of techniques such as data mining, statistics, modeling, and artificial intelligence to make the predictions.
A classic use case of predictive analysis is by credit bureaus which make use of information, including income, outstanding loan balances, credit history and so on, to develop a credit score for predicting whether the person is likely to be able to repay his or her present and future debts.
Helps you understand: What should I do?
Prescriptive analytics on the other hand, apart from predicting what will happen, also provides the best possible outcomes and then, prescribes a course of action to achieve it.
It uses a combination of optimization, computational modeling, and AI and machine learning algorithms to help you understand “what we must do.”It can harness data from both structured and unstructured data sources The relatively new field of prescriptive analytics essentially offers advice by predicting different possible actions and prescribing the right solution to the decision-maker.
Following are the two important aspects involved in prescriptive analysis:
- Optimization – Defines how to achieve the best outcome.
- Stochastic optimization – Defines how to achieve the best outcome while also accounting for uncertainty in the existing data.
Traditional Analytics vs Advanced Analytics
(Descriptive Analytics + Diagnostic Analytics)
(Predictive Analytics + Prescriptive Analytics)
Importance of Prescriptive Analytics
A truly modern analytics technology should improve the speed and accuracy in decision making.
Traditional and Predictive Analytics, despite being powerful technologies, come with some limitations. Traditional analytics only paint a picture of the historical and present conditions. They can neither predict the future nor prescribe an action. Further traditional analytics cannot harness unstructured data– another important limitation considering the rapid growth of digital and social data.
While predictive analytics go a step ahead and make a prediction into the future, it can’t come up with a recommendation or an important finding based on the data.
Prescriptive Analytics, on the other hand, go beyond descriptive and predictive analytics by recommending data-driven courses of action. It makes decision-making effortless by gleaning granular and actionable insights from data – users don’t have to go through and analyze the massive amounts of data. Equipped with AI and machine learning, prescriptive analytics effectively harnesses unstructured data and helps decision-makers build what-if scenarios.
For example, our trade promotion optimization software – Compass leverages prescriptive analytics and provides crisp recommendations to CPG revenue management and marketing teams on which type of trade promotion, at what time and which location.
A glimpse of marketing recommendations provided by prescriptive analytics
Compass also helps in building what-if scenarios to forecast sales for different promotion combinations. You can build different scenarios by changing different parameters like brand, price, promotion, location, duration, etc. and learn the estimated ROI and sales uplift before actually running the promotion.
Industrial Use Cases For Prescriptive Analytics
1.CPG and Retail
- Optimize the assortment of products in a retail store
- Optimally price items and services
- Find the best mix of marketing methods (online, print, radio, etc.)
- Measure trade promotion effectiveness and ROI and profitably optimize expenditure on promotions.
2.Transportation and shipping
- Improve driver retention to reduce training costs
- Eliminate unnecessary driving, flight, and sea transportation miles
- Increase driver productivity by improving routes and eliminating wait times to load/unload
- Increase speeds and reduce costs by optimizing distribution networks
- Eliminate nearly all warehouse packing errors
3.Oil and Gas
- Improve drilling completion rate by training machine learning models to recognize the most beneficial places to set up field operations
- Determine the best possible locations in a particular field to drill first
- Optimize equipment configuration to eliminate downtime due to breakage and maintenance
- Improve operational safety and eliminate potential environmental disasters
- Establish the best possible pricing by predicting the rise and fall of fuel markets
4.Financial Services and Banking
- Decrease transaction processing time
- Lower transaction costs
- Increase the total amount of possible transactions processed in a particular time period
- Create better portfolios for financial investment
- Optimize financial decisions like when to invest, how much to invest, etc.
- Reduce investment risk
Prescriptive analytics includes various other domains other than the aforementioned ones. Some other popular sectors that leverage it in their businesses are the renewable energy sector, healthcare, insurance, etc.
According to Reuters, the global predictive & prescriptive analytics market was valued at USD 5.52 billion in 2017, and is projected to reach a value of USD 16.84 billion by the end of 2023, at a CAGR (compound annual growth rate) of 20.43% over the forecast period, 2018-2023.
Transitioning from traditional analytical methods to adopt prescriptive analytics will provide organizations the much needed speed and accuracy in decision-making. If you need further insights into this topic or are planning to implement a prescriptive analytics solution, please feel free to get in touch with Acuvate’s data and analytics experts for a personalized consultation.