What Is Prescriptive Analytics?

When faced with any decision, most business leaders look to data to inform their course of action. Sometimes, that data may be too vast for the human brain to process — which is when prescriptive analytics can be incredibly powerful. Prescriptive analytics uses large data sets to understand and model what’s happening in the business, and then make recommendations for the future. In other words, it answers a question and then provides a path forward given that new context.

Typically, analytics are divided into four main categories — each of which can stand alone or be used in tandem with the others. Types of analytics include descriptive, diagnostic, predictive, and prescriptive analytics. All four work slightly differently and answer different types of questions.

  • Descriptive analytics answers the question “What happened?” It uses historical data to understand changes that have happened.
  • Diagnostic analytics answers, “Why did it happen?” and helps connect causes to effects.
  • Predictive analytics answers, “What might happen in the future?” It uses past trends and patterns to forecast future outcomes.
  • Prescriptive analytics answers, “What should we do next?” It uses data to suggest an ideal action to lead to optimal outcomes. 

Prescriptive analytics is especially useful for helping organizations prepare for likely outcomes. For example, it might recommend addressing risky accounts to prevent fraud or proactively reaching out to an unhappy customer to improve retention.

How prescriptive analytics works

Key components of prescriptive analytics include defining the objective, collecting and preparing data, analyzing and modeling data, performing scenario analysis, generating recommendations, and implementing those recommendations.

By using prescriptive analysis, businesses can make informed decisions based on suggestions that incorporate thousands of data points. One human brain may not be able to compute that many points and recognize patterns, but computers can—and, thanks to machine learning (ML) capabilities, they can suggest appropriate courses of action for business leaders.

Prescriptive analytics is inherently an iterative process. While you might use it to determine a course of action for a specific business problem or question, it’s wise to continually gather and mine data for relevant insights and recommendations.

Predictive vs. prescriptive analytics

In a nutshell, predictive analytics provides the “prediction” that prescriptive analytics uses to make its recommendation. Without predictive analytics, prescriptive analytics couldn’t exist.

Predictive analytics often use techniques such as regression, classification, clustering, and time-series models to predict and visualize future outcomes. Prescriptive analytics then takes these models one step further by actually adding a recommendation of what business leaders should do based on these predictions. Prescriptive analytics also remove the risks of human error, since it’s the computer making the recommendations using ML rather than a human. 

Industry use cases

Prescriptive analytics can help your company make any forward-looking decision that relies on understanding past and/or current performance. These decisions vary widely from industry to industry and company to company, but many common use cases by industry include:

  • Financial Services: To analyze credit risk and recommend which customers are the best candidates for loans.
  • Healthcare and Life Sciences: To discover patterns in patient readmission and recommend ways to keep patients healthy and out of the hospital.
  • Public Sector: To recommend resource allocation to highly populated areas.
  • Retail and Consumer Goods: To understand a customer’s preferences and purchase behaviors to serve them dynamic, personalized pricing and marketing offers.
  • Communications and Media: To recommend prescriptive targeting and real-time performance insights.
  • Industrial Products: To forecast demand and recommend how to staff and prepare to meet it.

Here are two real-world case studies showcasing how these companies use prescriptive analytics to optimize their operations and drive strategic decisions.

 

Example: LinkedIn

Before employing prescriptive analytics software, LinkedIn relied on one analyst to manage daily service requests from more than 500 sales people — leading to a 6-month queue for reports. LinkedIn began using analytics software to help manage its massive amounts of data (sometimes nearly a petabyte or more) and empower its sales team to track customer churn, risk indicators, and sales performance in real-time. Prescriptive analysis forecasted potential churn to LinkedIn’s sales teams, recommending courses of action to proactively engage with at-risk accounts. This ultimately reduced churn and increased customer engagement.

Example: Bayer

Similar to Linkedin, global life sciences company Bayer struggled to unify its data management across various sectors and departments. But by bringing together various data sources, conducting scenario analysis, and predicting the impact of policy changes and market behavior on business, Bayer was able to use prescriptive analytics to recommend more agile and targeted strategies to address its rapidly changing markets.

Using prescriptive analytics software, both of these companies were able to recognize patterns and generate recommendations based on massive amounts of data — amounts that likely would have been too much for one human brain. This is the power of ML.

Challenges and considerations in prescriptive analytics

Like any practice involving data and AI, prescriptive analytics is not without its challenges and considerations.

Data security and privacy

Companies that use prescriptive analytics still must be careful to keep customer data private, safe, and secure, all while adhering to the highest ethical standards.

AI bias

It’s also important to proactively recognize biases and ensure fairness in outcomes. Artificial intelligence can develop biases just as humans can, making it imperative that companies provide regulators with transparency into how their systems work. 

Implementing prescriptive analytics at your organization

Ready to start powering your future-looking business decisions with data? Use the steps below to turn data into insights using prescriptive analytics at your company.

1. Define your objective

Start by clearly defining the problem you’re trying to solve or goal you want to achieve. It might be minimizing costs, maximizing profits, or improving efficiency.

There’s no “right” goal to choose for your business or industry, and your objectives may vary widely from analysis to analysis. The most important thing is to choose something specific, measurable, and valuable to your company. If it’s more intuitive, you can also frame this as a question you want to answer, e.g., “What type of marketing content should I push out and on what channels to attract a younger audience?”

2. Collect data from a variety of sources

Next, gather the descriptive data you need to analyze. You’ll likely need to collect data from different sources — such as website traffic, social media platforms, customer interactions, and spreadsheets — to ensure that you’re painting a 360-degree view of what’s happening. You may also need to integrate prescriptive analytics into your existing business systems and processes.

Depending on the type of data you’re extracting, you may use tools such as SQL (structured query language) to query databases, APIs (application programming interfaces) to collect data from online platforms, or Web-scraping tools such as BeautifulSoup or Scrapy to pull data from websites.

3. Clean and prepare your data

Missing fields and inconsistent outputs can quickly ruin your analysis. Give your data a thorough scrub to ensure it’s clean and consistent. There should not be any missing values, outliers, or inconsistencies.

Once you have your data collected, combine it to create one unified dataset. At this stage, you may use ETL (extract, transform, load) tools such as Talend, Informatica, or Apache NiFi to integrate your data. 

4. Analyze the data

Now, it’s time for the main event: conducting your prescriptive analysis. Prescriptive analytics builds on the other three types of analytics: descriptive, diagnostic, and descriptive.

Analyze your data against these three categories first. Descriptive analytics will help you describe your data, while diagnostic analytics can help you determine why specific outcomes happened. Predictive analytics — which may also include augmented analytics will then help you forecast future trends — which prescriptive analytics will help you prepare for using data-driven recommendations.

5. Visualize your data. 

Data is only valuable if it can be easily understood. Visualizing your data helps quickly communicate insights using charts, dashboards, graphs, and other visual representations to quickly communicate to stakeholders important themes, findings, and recommendations.

6. Make informed decisions, monitor, and adjust as needed.

Now that you have your recommendations powered by analytics, you can make data-driven decisions that support the objective you laid out in step one. Whether that means changing your business strategy, pivoting your marketing campaigns, or shifting your product strategy, implement and monitor your outcomes using key performance indicators (KPIs). KPIs are a form of descriptive analytics, so you’ve already begun to repeat the process of implementing and monitoring.

When it comes to generating meaningful business insights and action plans, gathering data is only half the battle. True insights come from combining data with analytics. Prescriptive analytics incorporates predicted future outcomes with recommended actions for business leaders to reach their ideal outcomes, making it a powerful tool for any business leader’s arsenal.
 

Ready to harness the full potential of prescriptive analytics for your own company? Start your free trial of Tableau to experience first-hand how advanced analytics tools can help you turn insights into action.