In 2004, when Hurricane Frances hit Florida’s Atlantic coast, people were ready…with six packs of beer and, wait for it, pop-tarts! Days before the hurricane hit, Walmart had stocked up on the stuff in stores that were in the hurricane’s path, thanks to predictive and prescriptive analytics. Learning customer preferences with business analytics paid off for the retailer — they registered huge sales at the time.
Descriptive, predictive and prescriptive analytics is what business analytics is all about.
This article breaks down their definitions and how they work together to provide priceless business insight.
An Overview
- Descriptive analytics tells what happened in your business in the past week, month or year, presenting it as numbers and visuals in reports and dashboards.
- Diagnostic analytics gives the reason why something happened.
- Predictive analytics determines the potential outcomes of present and past actions and trends.
- Prescriptive analytics offers decision support for the best course of action to get desired results.
These are part of business analytics, which extract value from your business data. They support operation streamlining, enhanced customer experiences and revenue-generating initiatives.
How do they fit into business analytics? It’s a multi-dimensional field and incorporates the below three elements.
- Business Context
- Technology
- Data Science
- Business Context: All business analytics projects start with the business context, which involves assessing what your business needs to perform better. Maybe there’s a demographic you haven’t captured yet. Target’s pregnancy prediction uses descriptive analytics to identify an unlikely segment for marketing and promotions — expectant mothers.
- Technology: It includes data and information technology, for instance, capturing point-of-sale data for previous buyer purchases. Technology includes software like R, Python, SPSS, SAS, TensorFlow, Tableau, and more, which helps manage the complete data lifecycle, including unstructured information.
Automated tech supports deploying business solutions like sending personalized offers to customers or email nurturing workflows. Think HubSpot, Marketo, Eloqua and Pardot, among others. - Data Science: It includes statistical techniques, machine and deep learning algorithms. How does data science contribute to prescriptive vs. predictive analytics? It helps identify the most suitable technique, algorithm or statistical model to use based on a measure of accuracy.
Diagnostic, descriptive, predictive and prescriptive analytics cover all the above aspects and are intertwined.
A typical example of descriptive, predictive and prescriptive analytics is analyzing seasonal purchasing patterns to determine the best time to launch a new product. As consumers are creatures of habit, looking at historical data predicts their responses.
Under the umbrella category of business intelligence, business analytics focuses on predictive and prescriptive analytics, while big data analytics tackles massive data sets. Embedded analytics provide insight within your applications, and enterprise reporting slims down the analytics suite to offer a lean module of reporting tools.
What Are Descriptive Analytics?
It is the practice of assessing the business performance through existing data using descriptive statistics, reports, dashboards and visualizations. It helps generate planning insight by identifying existing trends through data summaries.
Look at a few examples of descriptive analytics.
- Most shoppers turn to the right when they enter a retail store. Retailers use this knowledge to place their high-priced items on the right side, where they’re more visible.
- RadioShack and BestBuy identified from descriptive insight that stores with more female employees registered a higher number of sales.
- David McCandless, a writer and designer, identified a spike in break-ups during spring break and in December before Christmas by studying relationship statuses reported on Facebook.
A visualization showing peak break-up times during the year per Facebook updates. Source: David McCandless and Lee Bryon
Consumer product companies can draw valuable insight from such information.
- Traffic to online dating sites is likely to increase during December/January.
- Relationship counselors and lawyers will be in demand.
- Housing prices and rentals might increase with the rise in demand.
- More people are likely to change the beer brand they drink, wanting to forget the past.
Read our Business Analytics vs Data Analytics comparison to learn more.
What Are Diagnostic Analytics?
Diagnostic analytics is the practice of evaluating existing data to determine the reasons for business performance – the “why.” Diagnostic analysis is the default next step to gaining descriptive insight. It helps answer questions you can’t answer at first look by digging deeper into trends and patterns.
- Why did a marketing campaign fail?
- What’s the reason for a sudden sales spurt without any change in marketing strategy for a particular region?
- Why did employee performance fall during the last quarter?
Diagnostic analytics takes you to the root cause of the issue, so you have the answers that directly impact your business approach.
What Are Predictive Analytics?
Predictive analytics is forecasting the probability of a future event, such as predicting product demand, customer churn, employee turnover, loan defaults, stock market fluctuations and fraud. Having this information ahead of time helps organizations prepare better.
Predictive analytics follows descriptive analytics in the analytics capability maturity model (ACMM). Statistical functions in BI and analytics software allow you to forecast business performance by building data models using historical and external data.
Previously, people relied on experience or hunches to forecast business patterns. Predictive analytics is the most in-demand module when searching for software solutions. It’s the proverbial crystal ball.
FATPOS Global forecasts the predictive analytics market to surpass $23.4 billion by 2030, at a CAGR of 24.9%. Tech innovations like artificial intelligence, the promise of risk mitigation and improved customer experience are the driving factors behind this growth.
You can use predictive algorithms to forecast business trends by capturing dataset correlations from your CRM, POS, HR, and ERP systems. Machine learning and natural language processing enhance your software’s analytics abilities.
- The most common commercial example is a credit score. Banks use historical information to predict whether or not a candidate is likely to keep up with payments.
- A primary concern of eCommerce retailers is converting user visits to transactions and order sizes. Viewing the impact of new initiatives ahead of time can make or break your decision to implement them. After launching HPDirect.com in 2005, Hewlett-Packard (HP) saw an increase in conversion rates and order sizes, thanks to predictive and prescriptive insight.
- Skywise is an Airbus initiative to harness aviation data across over 100 airlines and spare parts suppliers. The platform helps reduce maintenance issues, identify defect patterns, forecast machinery failures and optimize parts replacement. Additionally, it helps minimize the impact of flight schedule changes on passengers.
- Using descriptive vs. prescriptive analytics, Billy Beane, Oakland Athletics coach, steered the underdogs to victory in 20 consecutive matches in 2002, a first in American League baseball history. You might remember the movie Moneyball — that’s him. He upended sporting benchmarks by uncovering hidden strengths of previously underrated players through data analytics, building a winning team.
- The Predictive Index Behavioral Assessment is an industry gold standard for psychometric testing in the workplace. It owes its origin to Arnold S. Daniels, a U.S. Army flight navigator who logged more than 30 missions in the Second World War without losing a single man. What army psychologists didn’t know then was that it was descriptive, predictive and prescriptive analytics at work.
The most commonly used predictive analytics techniques are regression, logistic regression, classification, regression trees, forecasting, K-Nearest-Neighbor, random forest and neural networks.
Read our article on predictive analytics for more.
What Are Prescriptive Analytics?
Predictive prescriptive analytics is the application of mathematical and computational operators and functions to suggest decision options. It offers decision support through operational research (OR) techniques. Linear and non-linear programming, integer programming, goal programming with the analytic hierarchy process, combinatorial optimization and meta-heuristics are some.
Prescriptive analytics helps solve myriad problems like product mix, marketing mix, traveling salesman problem, vehicle routing, workforce planning, capital budgeting, transportation and capacity management. It explains why business analytics platforms that support prescriptive analytics are in demand.
Mordor Intelligence predicts the predictive and prescriptive analytics software market will grow to $29.97 million by 2026.
Prescriptive vs. predictive analytics is a common topic of discussion among industry execs. How are these two different, and which one is more business-critical? If predictive analytics answers, “What might happen?” then prescriptive analytics answers, “What do we have to do to make it happen?” or “How will this action change the outcome?”
Descriptive vs. prescriptive analytics are a world apart as the former is fact-based, while the latter deals more with trial and error and has a bit of a hypothesis-testing nature.
- Predictive analytics tells which customers will likely buy from you the next time. On the other hand, prescriptive analytics shows you which customers to target, and it’s likely to be a more extensive buyer segment than your regular customers.
- Amazon’s anticipatory shipping directly results from predictive modeling and prescriptive analytics and was patented in 2012. They stock products in advance in warehouses near areas where consumers are likely to order them, anticipating the demand by analyzing previous orders with other factors. The result — same-day delivery and satisfied customers.
- Prescriptive analytics plays a critical role in manufacturing and inventory management. Samsung capitalized on declining selling prices of RAM devices by reducing the manufacturing cycle time, saving an additional $1 billion in sales revenue.
Descriptive, predictive and prescriptive analytics are a potent force when applied together. Like predictive analytics, prescriptive analytics won’t be right 100% of the time since they work with estimates. However, they provide the best way to ” see into the future” and determine the viability of decisions before making them.
Software Selection
Selecting a BI and analytics solution is complicated, considering all the different options. Where do you start?
Identify Requirements
Creating a checklist by assessing your business requirements is a great way to start. Or, use our requirements template to sort out the must-have and the nice-to-have features. A template pre-filled with standard business analytics requirements will save you a ton of time.
Get Stakeholder Approval
Once you’ve updated the template with additional requirements, share it with stakeholders and address any concerns. Collaborative software suites like Google Drive make team collaboration straightforward.
Compare Solutions
Now you’re ready to compare software solutions, whether it’s predictive or prescriptive analytics software, based on how well they can deliver your requirements. Our software comparison report breaks down the industry leaders for individual features by score. We recommend selecting the top five to seven products that best match your needs.
Request Proposal
Submit an RFP to get an accurate price quote and demo of the product, maybe even a free trial. Follow the steps in this RFP guide to ensure you find the best fit for your business.
Or reach out to us to connect with sellers and walk you through the search and selection process.
To Wrap Up
Descriptive, predictive and prescriptive analytics help fine-tune business strategies to reap financial rewards. Advanced analytics tools with forecasting, data modeling and context-aware recommendations incorporate all of these analytics types. Knowing these terms can help you maximize your investment by using the capabilities to the optimum.
How did predictive and prescriptive analytics help your bottom line? What advice would you give to new analytics software users? Let us know in the comments below.