Big Data gets a lot of buzz in the business world. It's true that data analytics can give you deep, useful insights into your business and its customers, but only if you use those insights to their full potential.
There are three main components to business analytics: descriptive, predictive and prescriptive. Descriptive analytics — the "simplest class of analytics," said Lithium Technologies' chief scientist Michael Wu — is your raw data in summarized form. It's your 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.
Predictive and prescriptive analytics are the next steps that 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. [Big Data: What Does Your Business Really Need?]
"Predictive by itself is not enough to keep up with the increasingly competitive landscape," said Mick Hollison, CMO 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."
"Predictive analytics forecasts what will happen in the future. Prescriptive analytics can help companies alter the future," added Immanuel Lee, Web analytics engineer at MetroStar Systems, a provider of IT services and solutions."They're both necessary to improve decision-making and business outcomes."
Analytics in action
Both types of analytics inform your business strategies based on collected data. But the major difference between predictive and prescriptive is that the former forecasts potential future outcomes, while the latter helps you draw up specific recommendations.
"Prescriptive analytics builds on [predictive] by informing decision makers about different decision choices with their anticipated impact on a specific key performance indicators," said Thomas Mathew, chief product officer at influencer engagement platform Zoomph. "Think of [traffic navigation app] Waze. Pick an origin and a destination — a multitude of factors get mashed together, and [it advises] you on different route choices, each with a predicted ETA. This is everyday prescriptive analytics at work."
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.
"Predictive analytics is great, but it's for the people who understand the report at the end," Yehiav told Business News Daily. "What it's missing is ... execution. Prescriptive give answers to the questions you don't know how to ask."
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.
Arijit Sengupta, CEO of automated business analytics company BeyondCore, offered another example of how a nationwide sporting goods store might use predictive and prescriptive analytics together. The store's forecasts indicate that sales of running shoes will increase as warmer weather approaches in the spring, and based on that insight, it might seem logical to ramp up 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.
"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."
Putting analytics to work
Our expert sources offered a few tips to help you make the most of your analytics programs.
Start small. There's a lot your business needs to think about in data analytics, 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. Running and marketing a business comes with a lot of "what-if" scenarios, and as demonstrated in the example above, predictive analytics doesn't always account for those alternate scenarios. Mathew said looking at your predictive analytics more closely to create richer information sets — accounting for demographics like gender and age, for instance —will yield better results from your prescriptive recommendations.
"For example, social media marketers care about maximizing engagement and reach on their social posts. 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," he said.
Understand the reasons behind prescriptive recommendations. Sengupta emphasized the importance of fully understanding the logic, nuances and circumstances behind a precription 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 continuously update 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."