Retailers have used the most basic form of business intelligence for as long as they have been buying and selling goods to their customers. Consider all the factors that went into successfully running a typical colonial-era general store. Owners had to anticipate factors such as increased demand for farming instruments around harvest time, or the desire of customers to purchase fresh fruit as soon as it was in season.
But decisions these retailers made were often based on insufficient information. Today, the problem is almost the complete opposite. Many retailers find they have too much information to turn into actionable insights without hiring very specialized — and expensive — expert help.
“Business intelligence has been around for a very long time, but we are still at the early stages of being able to take something like Big Data and operationalize it for retail,” said Brad Shimmin, service director for business technology and software at Current Analysis. “The first steps to take advantage of that happened in the 1980s and 90s, where we saw firms putting sales and customer data into big repositories where they could query it to try to make valid connections.”
But implemention of the first customer relationship management (CRM) systems was far from perfect, says Mary Shacklett, president of Transworld Data, a technology and market research firm. “Oftentimes, the data inputted by call centers for those early CRMs was incomplete and inaccurate,” she says. “Many salespeople didn’t trust the data, and that perception persists today with a lot of folks.”
In fact, even mining the imperfect data from repositories is difficult. Many retailers must pay data scientists to create queries and get results, which adds a layer of complexity to Big Data analytics and can obscure its insights.
“It’s a matter of knowing the right questions to ask if you want to get the best results,” Shimmin said. “The data scientists who are working the repositories don’t know retail or the subtleties of the business that are required to tweak the questions to get the best results, but the salespeople who could make the most use of the data don’t know how to mine it. The exciting thing in retail BI now is trying to democratize that data so that anyone can use it.”
Retailers that make effective use of this data can see their profits rise on the strength of increased sales and improved efficiency. Business intelligence can yield a better understanding of customers’ needs, allowing retailers to target specific goods and services directly to specific customers. It also helps them to manage inventory and merchandise more strategically, reducing unnecessary inventory and bolstering efforts at marketing and e-commerce.
Asking the Right Questions
Achieving the insights necessary to see these benefits requires more than teaching business owners or members of a sales staff how to send in queries. Retailers must also make it simpler to craft those questions so that valid comparisons can be made. “What everyone is trying to do in this field is decipher what is data and what is actual intelligence,” says Carlos Soto, senior vice president of technology operations for the Tech Writers Bureau. “As an example, a retailer doesn’t care that a customer is 40 years old. It’s just data. But knowing that the customer is 40, a male, makes $80,000 a year, has a specific job and visits certain websites to look for products or services on a regular basis becomes intelligence. Armed with that, you can sell something to anyone.”
Efforts to analyze retail data come at a critical time, because data stores themselves are growing rapidly. This growth increases the complexity of analysis but also presents opportunities for retailers who use business intelligence to find important insights. The so-called Internet of Things (IoT) is a trend that puts sensors inside a wide variety of endpoints and devices — everything from coffee makers to smartphones — and then connects those billions of devices to the Internet. Retailers can analyze data captured by these devices using business intelligence software. They can glean insights from information such as how many times various doors in a store open or where customers with smartphones wander within a store. “For retail, the Internet of Things and the data it collects can give stores a true 360-degree view of their customers, possibly for the first time,” Shimmin says.
Protecting the Data
While it offers numerous benefits for retailers, the IoT also raises serious concerns about security. As more devices collect and transmit customer data, the potential exposure of that data to unauthorized users — whether unintentionally or nefariously — increases.
Many firms are looking to the cloud to meet their storage needs, but this could increase the risk of data falling into the wrong hands. “Many industries like banking and finance are doing cost-benefit analyses and finding too much risk,” Soto says. “For retail, it might eventually work, but special precautions must be taken to protect the data from both competitors and hackers.”
Projected Number of Objects Connected Through the Internet of Things:
2015: 18.2 billion
2016: 22.9 billion
2017: 28.4 billion
2018: 34.8 billion
2019: 42.1 billion
2020: 50.1 billion
SOURCE: Cisco, “Connections Counter: The Internet of Everything in Motion,” 2013
To learn more about how CDW can help retailers use technology to get an edge on competitors, visit CDW.com/retail.