What is Contextual Data?

Contextual data is the background information that provides a broader understanding of an event, person, or item. This data is used for framing what you know in a larger picture. These relevant facts can be utilized to analyze your customers’ behavior patterns, thereby improving their experience.

Many industries use contextual data to get an edge and find unique ways to understand the information they’ve collected. Data isn’t produced in a vacuum. Contextual information can help companies better understand the details they’ve gathered about clients.

Getting Strategic Value from Data After Initial Attempts Fail

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For example, companies can include information about traffic or weather conditions when analyzing their sales data to see if either variable affects their bottom line.

One major point of concern with big data is that many times, information without a context lacks real value. Adding contextual information helps unlock insights and can lead to more informed and accurate decisions on an organizational level.

What Can I Use Contextual Data For?

The combination of contextual data and analytics produces a powerful tool for companies to gain insights on customer behavior and sales patterns. Your company’s data is probably growing exponentially, and is stored in the cloud or in data warehouses and lakes. Contextual analytics helps narrow down this wide scope to reveal insights that can be relevant to both customers and employees and accessed from anywhere.

As Sisense’s Charles Holive says, “These massive stores of data, plus any contextual data you can buy or partner with other companies to get access to, are the key to building successful algorithms to power your analytic apps, create value for customers, and differentiate yourself in the market.”

Digging Beneath the Surface: Contextual Data Mining

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To give a retail sales example, a drop in sales at a specific store location may be hard to pinpoint when the raw data is examined on its own. Adding contextual information about traffic patterns or similar stores in the area can help reveal data you can use to better understand customer behavior. Peak hours, for example, might see higher foot traffic, while times when there are few cars around could mean sales lulls.

Integrating contextual analytics into an organization’s marketing dashboard via a BI solution like Sisense can also yield new approaches to strategy that may not have been readily apparent. An unsuccessful strategy may have been targeting the wrong audience, or perhaps the right audience at the wrong time.

Adding information about when people browse the web, or how long people spend on a specific website, can show the best times to upload social media posts. Similarly, understanding people’s habits can give marketers clearer insights on how to better present their products or services.

Getting Strategic Value from Data After Initial Attempts Fail

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