Prescriptive analytics has been called “the future of data analytics,” and for good reason. This type of analysis goes beyond explanations and predictions to recommend the best course of action moving forward. It’s especially useful in driving data-informed decision-making.
There are four key types of data analytics:
- Descriptive, which answers the question, “What happened?”
- Diagnostic, which answers the question, “Why did this happen?”
- Predictive, which answers the question, “What might happen in the future?”
- Prescriptive, which answers the question, “What should we do next?”
When used in business, data analytics is often called business analytics. All four types can be used in tandem to create a full picture of the story data tells. You can start by describing trends you’re seeing, dig deeper to understand why those trends are occurring, and make informed predictions about whether the trends will recur. Prescriptive analytics takes things one step further and presents actions you can take to meet organizational goals.
Here’s a primer on prescriptive analytics and six examples of ways it’s being used across industries.
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Prescriptive analytics is the process of using data to determine an optimal course of action. By considering all relevant factors, this type of analysis yields recommendations for next steps. Because of this, prescriptive analytics is a valuable tool for data-driven decision-making.
Machine-learning algorithms are often used in prescriptive analytics to parse through large amounts of data faster—and often more efficiently—than humans can. Using “if” and “else” statements, algorithms comb through data and make recommendations based on a specific combination of requirements. For instance, if at least 50 percent of customers in a dataset selected that they were “very unsatisfied” with your customer service team, the algorithm may recommend additional training.
It’s important to note: While algorithms can provide data-informed recommendations, they can’t replace human discernment. Prescriptive analytics is a tool to inform decisions and strategies and should be treated as such. Your judgment is valuable and necessary to provide context and guard rails to algorithmic outputs.
At your company, you can use prescriptive analytics to conduct manual analyses, develop proprietary algorithms, or use third-party analytics tools with built-in algorithms.
6 Examples of Prescriptive Analytics in Action
1. Venture Capital: Investment Decisions
Investment decisions, while often based on gut feelings, can be strengthened by algorithms that weigh risks and recommend whether to invest.
One example in the venture capital space is an experiment—explained in the Harvard Business Review—that tested the effectiveness of an algorithm’s decisions about which startups to invest in as compared to angel investors' decisions.
The findings were nuanced. The algorithm outperformed angel investors who were less experienced at investing and less skilled at controlling their cognitive biases; however, angel investors outperformed the algorithm when they were experienced in investing and able to control their cognitive biases.
This experiment sheds light on the complementary role prescriptive analytics must play in making decisions and its potential to aid decision-making when experience isn’t present and cognitive biases need flagging. An algorithm is only as unbiased as the data it’s trained with, so human judgment is required whether using an algorithm or not.
2. Sales: Lead Scoring
Prescriptive analytics plays a prominent role in sales through lead scoring, also called lead ranking. Lead scoring is the process of assigning a point value to various actions along the sales funnel, enabling you, or an algorithm, to rank leads based on how likely they are to convert into customers.
Actions you can assign value to include:
- Page views
- Email interactions
- Site searches
- Content engagement, such as attending webinars, downloading e-books, or watching videos
When assigning each action a point value, assign the highest number of points to those that imply purchase intent (for instance, visiting a product page) and negative points to those that reveal non-purchase intent (for instance, viewing job postings on your site). This can help prioritize outreach to leads most likely to convert into customers, potentially saving your organization time and money.
Related: 5 Business Analytics Skills for Professionals
3. Content Curation: Algorithmic Recommendations
If you’ve ever scrolled through a social media platform or dating app, you’ve likely experienced prescriptive analytics firsthand through algorithmic content recommendations.
Businesses’ algorithms gather data based on your engagement history on their platforms (and potentially others, too). The combinations of your previous behaviors can act as triggers for an algorithm to release a specific recommendation. For instance, if you regularly watch shoe review videos on YouTube, the platform’s algorithm will likely analyze that data and recommend you watch more of the same type of video or similar content you may find interesting.
On social media, TikTok’s “For You” feed is one example of prescriptive analytics in action. The company’s website explains that a user’s interactions on the app, much like lead scoring in sales, are weighted based on indication of interest.
“For example,” TikTok’s website says, “if you finish a video, that’s a strong indicator that you’re interested. Videos are then ranked to determine how likely you’ll be interested in each video and delivered to each unique ‘For You’ feed.”
This prescriptive analytics use case can make for higher customer engagement rates, increased customer satisfaction, and the potential to retarget customers with ads based on their behavioral history.
4. Banking: Fraud Detection
Another algorithmic use of prescriptive analytics is the detection and flagging of bank fraud. With the sheer volume of data stored in a bank’s system, it would be nearly impossible for a person to manually detect any suspicious activity in a single account. An algorithm—trained using customers’ historical transaction data—analyzes and scans new transactional data for anomalies. For instance, perhaps you typically spend $3,000 per month, but this month, there’s a $30,000 charge on your credit card.
The algorithm analyzes patterns in your transactional data, alerts the bank, and provides a recommended course of action. In this example, the course of action may be to cancel the credit card, as it could have been stolen.
5. Product Management: Development and Improvement
Prescriptive analytics can also inform product development and improvements. Product managers can gather user data by surveying customers, running tests with a product’s beta versions, conducting market research with people who aren’t current product users, and collecting behavioral data as current users interact. All this data can be analyzed—either manually or algorithmically—to identify trends, discover the reasons for those trends, and predict whether the trends are predicted to recur.
Prescriptive analytics can help determine which features to include or leave out of a product and what needs to change to ensure an optimal user experience.
Related: Business Analytics: What It Is & Why It’s Important
6. Marketing: Email Automation
Email automation is a clear-cut example of prescriptive analytics at work. Marketers use email automation to sort leads into categories based on their motivations, mindsets, and intentions and deliver email content to them based on those categories. Any interactions leads have with emails can put them in another category, resulting in a different set of messages being triggered.
While this is pure algorithmic prescriptive analysis, a person should plan, create, and oversee automation flows. Email automation allows companies to provide personalized messaging at scale and increase the chance of converting a lead into a customer using content that applies to their motivations and needs.
Leveraging Prescriptive Analytics at Your Organization
If your organization is new to prescriptive analytics, there’s no better time to see how it impacts your decision-making processes. Start small with one question you need answered or one process you’d like to optimize. Gather data surrounding that question or process and move through each type of analytics to paint the full picture.
- Descriptive: What trends does the data show?
- Diagnostic: What factors contribute to those trends? Why are those trends occurring?
- Prescriptive: If applicable, determine whether a trend is one you can expect to continue or recur.
- Prescriptive: Finally, dive into prescriptive analytics. If you have a proprietary algorithm or third-party analytics tool, run it using your company’s data. Alternatively, conduct manual analysis of possible next steps based on what you’ve discovered about your question or process. How will each option impact the situation’s outcome and, thus, your goal?
Prescriptive analytics doesn’t need to be daunting; with the right foundation, it can be a powerful tool to help optimize processes, formulate strategies, and reach organizational goals.
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