Leveraging the Three Stages of Analytics

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Twenty years ago, companies based their customer relationship decisions largely on historical behavioral and buying data, as well as gut feelings. Today, analysis of consumer information has evolved to become a complex, multistage process that, so far, only a handful of companies have managed to master.

To many, Netflix is synonymous with video streaming. But this wasn’t always the case. The company, which was founded in 1997, started out in the DVD-by-mail business and did not introduce streaming media until 2007. In 2013, it added film and television production, as well as online distribution, to the mix. The company had 70 million subscribers in 2015 and increased that number to 93.8 million worldwide in 2016. This success is due in no small part to the use of analytics—Netflix stores all available subscriber information, such as the titles they watch, the order in which they watch them, and where they pause, and then uses this data to create customized experiences for each customer.

Amazon has used analytics in a similar way. The online retail giant crafts recommendations by taking into account products that consumers not only buy, but also view. Moreover, customers can improve their recommendations by assigning ratings of one to five stars to products, thus manipulating the influence of particular products on the recommendation engine. Furthermore, customers can elect to exclude individual items from the recommendation engine, or indicate that an item was a gift. Not only is Amazon able to craft a customized experience for each of its customers, it has empowered them to participate in the personalization process.

As companies look to tackle the complicated analytics environment in which they find themselves today, one popular framework for this process involves three distinct analytics stages: descriptive analytics, predictive analytics, and prescriptive analytics. Businesses need to comprehend each of these three stages individually—as well as how they function together—to better understand and serve their customers and, ultimately, meet their business objectives.


Although less involved than the other two analytics phases, descriptive analytics is not to be disregarded. The term refers to the “data that reflects what has actually happened,” according to Allison Snow, senior analyst serving B2B marketing professionals at Forrester Research. She adds that this historical data is “the foundation upon which predictive algorithms are developed, predictive analytics are identified, and, ultimately, from which prescriptive analytics are derived.”

Organizations need to understand two important aspects of descriptive analytics, Snow goes on to say. First, because organizations have collected vast amounts of structured and unstructured data from a variety of sources, including customer interactions, social media channels, and now any number of possible Internet-connected devices, having a data management tool is essential. Second, historical data needs to be groomed to ensure that it reflects current trends. “Inaccurate descriptive data will result in flawed models. It’s why we hear so much about data quality and data hygiene in the context of analytics,” Snow notes.

A similar definition of descriptive analytics comes from Jean Francois Puget, distinguished engineer of machine learning and optimization for IBM Analytics. “Descriptive analytics is about providing tools that can look at data, aggregate it, and be able to query and drill down,” he says.

Puget further illustrates this point using the example of a sales manager at a retail store. At one point, this person might want to view sales per region per month for each sales category; later, the manager might need to dive deeper into the sales of a single product geographically, or by product category. “Descriptive analytics helps understand what happened so far,” he explains.


Unlike descriptive analytics, which looks into the past, predictive analytics is, as one might expect, more future-focused.

“In simple terms, predictive analytics helps understand or identify what might happen next,” Kimberly Nevala, director of business strategies for SAS Best Practices and an advisory business solution manager at SAS, states.

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