Breaking Down Big Data for Better Queue Management
Big data. It’s the buzzword to describe the immense volume, velocity, and variety of information that continues to build worldwide at a minimum annual rate of 59%.
Big data clearly presents an opportunity for those with the ability to decode and understand it. In the retail space, a 2011 McKinsey Global Institute report estimated that retailers using big data have the potential to increase their operating margins by more than 60%. On the other hand, it also threatens to bog down and leave behind those businesses lacking the means to handle and envision its value.
A traditional view of big data is the “3 Vs,” put forth by Gartner’s Doug Laney in a 2001 research report, referring to the volume, velocity, and variety of data. But there’s a more current view of big data: the “3 Ws”– What, So What, and Now What—a view that highlights the need for decisions to be made and actions to be taken from big data.
The business challenge ahead is to break down big data into actionable “chunks” from which to make decisions and improve the customer experience.
Queue Management and Big Data
When it comes to queue management, big data is no stranger. Retailers and other service providers have unprecedented opportunities to capture data relevant to the waiting and checkout experience.
Current virtual queuing, electronic queuing, and video analytics systems enable businesses to track all aspects of the customer journey. From the moment of registration all the way through checkout, businesses are given valuable insight for optimizing customer throughput, increasing service efficiency, and enhancing direct customer communication, ultimately providing the customer with a better shopping experience. Today’s technology captures metrics such as average wait times, queue length, agent/cashier idle time, staffing allocations, customer arrivals, traffic patterns, etc.
The question is how can businesses transform all of this data to guide better decision making?
In our view, this challenge is met when data is collected and put to use in real time, rather than mere historical records.
Making data real-time useful.
The analytics provided by queuing technology is changing the game for managers by providing real-time alerts against a business’s unique performance indicators. Data is transformed into actionable intelligence to prevent problems before they happen. Here are a few examples of how real-time intelligence is being put to work in retail:
- There is a sudden spike in the number of customers registering for service via a store’s virtual queuing kiosk. The store manager receives a real time alert and immediately opens up additional service stations.
- The number of people waiting in line has reached a critical mass. Video analytics picks up on this and sends a real-time alert to the store manager to open up more register stations and to expand the length of the line.
- A VIP customer has just arrived and checked in at the virtual queuing kiosk for service and immediately placed forward in line. The appropriate service agent is also alerted via his PC dashboard and can address the customer by name.
- A store’s cashier is completing transactions at an unusually slow rate. The store manager is alerted and can explore the problem further.
As these examples illustrate, big data offers significant opportunities to improve performance and improve the customer experience.
How will your business put big data to use in real-time? Let our queue management experts show you the way.