- Predictive and prescriptive analytics are two important parts of a data strategy.
- Predictive analytics helps find potential outcomes, while prescriptive analytics looks at those outcomes and finds even more paths of options to consider.
- Both types of analytics can help any small business get ahead of the curve.
- This article is for small business owners who are considering implementing predictive and prescriptive analytics practices but don’t yet understand the concepts in a meaningful way.
Big data gets a lot of buzz in the business world. It’s true that data analytics can give you deep, useful insights about your business and its customers, but to benefit from those insights, you have to know how to interpret the data and apply it to your business strategy.
There are three main components of business analytics: descriptive, predictive and prescriptive. Descriptive analytics, the “simplest class of analytics,” is the raw data in summarized form, Michael Wu, chief scientist at Khoros/Lithium Technologies, wrote in a blog post. It includes social engagement counts, sales numbers, customer statistics and other metrics that show you what’s happening in your business in an easy-to-understand way. The other two types of analytics, predictive and prescriptive, take that data and turn it into actionable information.
Predictive vs. prescriptive analytics
Both predictive and prescriptive analytics inform your business strategies based on collected data. But the major difference between predictive analytics and prescriptive analytics is that the former forecasts potential future outcomes, while the latter helps you draw up specific recommendations.
Predictive and prescriptive analytics help you turn descriptive metrics into insights and decisions. But you shouldn’t rely on just one or the other; when used in conjunction, both types of analytics can help you create the strongest, most effective business strategy possible.
“Predictive by itself is not enough to keep up with the increasingly competitive landscape,” said Mick Hollison, chief marketing officer of sales-acceleration software company InsideSales.com. “Prescriptive analytics provide intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.”
What is predictive analytics?
Considered one of today’s more advanced styles of analytics, predictive analytics helps companies make sense of potential outcomes or the future repercussions of a decision. By leveraging mined data, historical figures and statistics, predictive analytics uses raw and up-to-date data to peer into a future scenario.
What is prescriptive analytics?
Prescriptive analytics also looks at future scenarios, but it employs a more technological approach. By utilizing complicated mathematical algorithms, artificial intelligence and machine learning, prescriptive analytics takes a deeper look into the “what” and “why” of a potential future outcome.
In addition to providing a more in-depth look into the future, prescriptive analytics can help a company see multiple potential options in its future and their respective potential outcomes. As more data comes in, prescriptive analytics can alter its predictions and suggestions.
“Prescriptive analytics can help companies alter the future,” said Immanuel Lee, a web analytics engineer at MetroStar Systems, a provider of IT services and solutions. Predictive and prescriptive analytics are “both necessary to improve decision making and business outcomes,” Lee added.
Key takeaway: Predictive analytics uses collected data to come up with future outcomes, while prescriptive analytics takes that data and goes even deeper into the potential results of certain actions.
Examples of predictive and prescriptive analytics in action
Both types of analytics are used in our everyday lives. We asked some experts to give us some concrete examples of predictive and prescriptive analytics working together to provide a more detailed look at potential outcomes.
Motorists everywhere rely on GPS-enabled navigation apps to get from point A to point B if the trip isn’t familiar. This is equally important for small businesses that rely on delivery services, both third-party and in-house, to deliver goods in a timely manner.
In this instance, predictive analytics can take existing travel data and map out a potentially faster route. Thomas Mathew, chief product officer at influencer engagement platform Zoomph, said that’s just where the effort starts.
“Prescriptive analytics builds on [predictive analytics] by informing decision makers about different decision choices with their anticipated impact on specific key performance indicators,” he said. Think of traffic navigation app Waze, for example, and pick an origin and a destination. A multitude of factors get mashed together, and the app advises you on different route choices, each with a predicted ETA. “This is everyday prescriptive analytics at work,” Mathew said.
As a small retailer, it’s common to want to know how much stock you need in order to fill your shelves. Though it’s always been possible to rely on educated guesses, analytics can help you plan a more precise stocking strategy.
Guy Yehiav, CEO of business intelligence company Profitect, said that as the retail landscape changes, businesses can use prescriptive analytics to clarify predictive data and improve sales. To clarify how both types of analytics can be used together, Yehiav gave the example of a retailer that offers free expedited shipping to loyal customers. Based on past customer behavior, a predictive model would assume that customers will keep the majority of what they purchase with this promotion. However, one customer purchases eight items of clothing but decides to keep only one.
“The retailer paid for expedited shipping with the assumption that there’s this great consumer out there who bought eight items, so they’re willing to invest and lose a little margin” on shipping, Yehiav said. “The algorithm didn’t take [return] behavior into account.”
For this retailer, reducing its losses on “outlier” customers who don’t follow what predictive analytics forecasted means having policies in place to cover itself. Using prescriptive analytics, the retailer might come up with the options of giving an in-store-only coupon to customers who make returns (to encourage another purchase in which shipping isn’t a factor) or notifying customers that they must pay for return shipping, Yehiav said.
Predicting the weather can be a dicey proposition, but with the change of seasons comes the shift from indoor activities to fun in the sun. One small business sector that benefits from nicer weather and increased physical activity is sporting goods stores.
If the store’s forecasts indicate that sales of running shoes will increase as warmer weather approaches in the spring, it might seem logical to ramp up the inventory of running shoes at every store. However, in reality, the sales spike likely won’t happen at every store across the country all at once. Instead, it will creep gradually from south to north based on weather patterns.
Arijit Sengupta, former CEO of automated business analytics company BeyondCore and founder of Aible, said predictive and prescriptive analytics can help you plan for this type of scenario.
“To flip the switch on massive running-shoe distribution nationwide would be a huge mistake, even though the predictive analytics indicate sales will be up,” Sengupta said. “But with prescriptive analytics, you can pull in third-party sources, like weather and climate data, to get a better recommendation of the best course of action.”
Key takeaway: There are many uses for both predictive and prescriptive analytics, some of which involve navigation, forecasting and inventory. Your small business’s needs will determine how you can best utilize both types of analytics.
Putting analytics to work
Our expert sources offered a few tips to help you get the most out of your analytics programs.
Data analytics is a complex subject that can be overwhelming, and you don’t want your best insights to get lost. Lee advised thinking big with your overarching analytics strategy but starting small tactically.
“With the complexity of big data and the systems that manage and process data, we can easily overlook the fact that sometimes, there’s a solution in the simplest thing,” he said. “Small wins will help earn support for long-term analytics projects.”
Create rich data sets.
There are a lot of “what if” scenarios when you run and market a business, and predictive analytics doesn’t always account for alternate possibilities. Mathew said looking at your predictive analytics more closely to create richer information sets – for example, by accounting for demographics such as gender and age – will yield better results from your prescriptive recommendations.
“For example, social media marketers care about maximizing engagement and reach on their social posts,” he said. “Prescriptive analytics can make data-driven recommendations, such as use of a specific hashtag or emoji, to maximize social traction with a specific audience segment.”
Understand the reasons behind prescriptive recommendations.
Sengupta emphasized the importance of fully understanding the logic, nuances and circumstances behind the results of prescriptive analysis before taking action. You must be able to prove that your results are statistically sound.
“Pretty graphs can be very compelling, but this is only software, after all, and its analytical power is only as accurate as the human who designed it and data we feed it,” Sengupta said. “It’s critical that business users understand the ‘story’ behind the results and the prescriptive action suggested.”
Keep your systems up to date.
As your business grows and evolves, so should your algorithms. Hollison noted that both predictive and prescriptive analytics should be updated continuously with the latest data to improve predicted and prescribed actions based on real-time successes and failures.
“Predictive and prescriptive analytics depend on a solid data foundation,” Mathew added. “The analytics are only as good as the data that feed them.”
Key takeaway: Deep analytics capabilities don’t have to require a dedicated team. As long as you know your limitations from the start, you can create an analytics approach that fits your needs.
Andrew Martins contributed to the reporting and writing in this article. Some source interviews were conducted for a previous version of this article.