nology. Recently, major US department store retailer, Nordstrom, embarked on a program that tracks customer behaviour via their mobile phone when they enter the store. Unbeknownst to customers, Nordstrom accessed any smartphone with wi-fi turned on scanning for networks. As you can only imagine, the hit rate was quite high, given that most people have this turned on as an automatic setting. The sensors would then make note of the device’s MAC address (an address that’s unique to a mobile phone) and use it to identify and follow the device as it moved about the store. Utilising this information Nordstrom was able to analyse how frequently the device visited the store, which departments it stopped at, and how long it spent there. This intelligence provided some interesting insight not only into customer traffic, but supported initiatives around better service platforms. While a mobile phone is not a person, the retailer was unable to do specific analytics around age and gender, but if you were to include this data with surveillance video and transactional information, certain patterns would begin to arise. Ultimately this was the gold Nordstrom was looking for, and as a major operator in the US in a challenging economy, customer intelligence is where it saw a point of differentiation. With the rise of big data and large amounts of unstructured data, there has been an increase in market insight not seen previously. Big data has been linked to predictive analysis. Take for example discussions regarding your business or the market captured via Twitter or Facebook that can pick up on what customers are saying to predict the latest trends in your product or services. This is extremely valuable information, but because of the nature of big data it requires infrastructure not normally found in traditional IT environments. This technology can also come with a lofty price tag and might not match the budgets or ROI expected of many mid-sized retailers. Then there are those key pieces of intel that are not as easily captured via technology. This is where virtually any retailer can enhance their business intelligence. An example of this is the weather or average localised temperature, which can provide valuable insights into buying patterns and supports benchmarking between multiple locations. We might all agree that the weather is getting warmer, but how does this translate to sales during those periods, and how do we reference this against hard transactional data in the future? A snapshot of the number of car spaces available throughout the day, events such as sporting matches, or local theatre may cause spikes or dips in spending that are often difficult to explain or reference when reporting on sales weeks later. Whether it’s through manual entry or a reporting tool set with an option for adding these bits of detail via the POS, it can prove to be vital when used in conjunction with the transactional data. With reporting, it’s not always the most sophisticated capturing methods that pick up on the greatest pieces of information. Retailers might discover that sophistication comes in the form of simplicity. Whether it’s a complex solution or simple data entry the key is discovering those pieces of information that as retailers we might not be capturing. These might just enable us to accelerate our decision making and promote more efficient strategic planning. Stephen Duncan is product marketing manager, retail and SCM at Pronto Software. He can be reached at stephen.duncan@pronto.net or (02) 8875 3033. This article first appeared in Inside Retail Magazine’s October/November 2013 issue. To scubcribe, click here.