Case studies from two large retailers
The computer giant Dell began using big data technology as far back as 2011 when a large retail customer requested help implementing recommendation engine solutions. Amazon was one of the first companies to roll out this feature that recommends certain products to customers based on their past buying behaviour. After working with big data specialists, Dell’s customer was able to build a cluster that grew from eight to 300 nodes in three years. The customer went on to develop logistics planning, price setting, supply chain analysis, and several other functions using analysis obtained from big data.
Staples, the office supply superstore, turned to Dell to help improve the data it collected from social media. It wanted to wade out useless background information and focus on the likes and dislikes of its customers. The solution was to create and implement a cloud-based social listening device that also provided immediate analysis. The technology enabled Staples to gauge the effectiveness of social media campaigns in real time to determine the return on investment. In addition to understanding customer product preferences, Staples also had a better understanding of which store policies were unpopular with customers. Factors to consider when implementing a retail analytics platform A big data analytic system in a retail setting
requires space dedicated to a data centre as well investing in the right software to obtain the desired results. Data comes from a variety of sources, including social media, accounting files, mobile users, and internal sales. Before making the leap to big data analytics, retailers should consider the following:
The retail business in the Electronic Age
- Agility: Does the retailer know its customers well enough to enter a new market quickly? Can it adapt to an ever-changing electronic landscape? An effective cloud-based system reduces complexity and allows employees to have access anywhere and anytime to collaborate on projects that will improve the customer experience.
- Availability: The increasing popularity of cloud services makes it possible for retailers to choose a provider that can meet its business and technical needs while sticking to a budget. Retailers should request information about how the cloud service provider has responded to needs of other customers in its niche.
- Cost: While cloud services were once cost-prohibitive for many retailers, its price continues to drop. This makes using the turnkey solution offered by cloud providers more attractive that building and maintaining a data center to store huge volumes of information on products, prices, and customers.
A number of technological advances have made it possible for retailers to collect and analyse big data
in truly useful ways. Some examples include:
- Guest Wi-Fi: Retailers can count the number of mobile devices in their store at any given time by providing free Wi-Fi access to customers. This provides data on the average time customers stay in a store and whether they choose to return.
- Projected Media: When a customer picks up a product off a display rack, a sensor activates an overhead projector that provides additional audio, images, text, and video about that product.
- Video Cameras: Once used only for security purposes, video cameras help retailers capture the age, race, and gender of their typical customer as well as how long the lines are at service counters and cash registers.
As these technologies and case studies demonstrate, retailers who embrace big data analytics can expect increased customer loyalty and profits for their efforts.