Mobile location analytics (MLA) is a type ofcustomer intelligence and refers to technology forretailers, including developing aggregate reports used to reduce waiting times at checkouts, improving store layouts, and understanding consumershopping patterns. The reports are generated by recognizing theWi-Fi orBluetooth addresses of cell phones as they interact with store networks.[1]
By seeing the movement of devices, retailers can gather data that will help them optimize such things as floor plan layouts, advertisement placement and checkout lane staffing. MLA products work by capturing a device'sMAC address, the unique 12-digit number that is assigned to a specific hardware device. This number can be detected by Wi-Fi or Bluetooth sensors. There are separate MAC addresses for Wi-Fi and Bluetooth.Beacons are also used for MLA purposes and they work with Bluetooth. Through this technology, they are also able to sendpush notifications. Recently companies started using the combination of Wi-Fi and Bluetooth to improve accuracy and reliability of the MLA devices. The technology works as people walk through stores; the tracking companies find their wireless signal and assign the device a random number. They monitor that number as it moves across the screen and analyze patterns in the data.
Because of the use of the automatically transmitted MAC address, the customers need not belogged into the shops' Wi-Fi or website. This feature is highlighted in the alternative term "offline tracking" (as opposed toonline tracking[2]) that is sometimes used for "MLA" e.g. in Germany.[3]
A number of industries can benefit from MLA services including retail,real estate,energy,insurance, manufacturing,healthcare, government, planning, andpublic safety. For example, retail businesses can comparesales revenue and evaluatemarketing campaign effectiveness. Businesses can determine where to open stores and distribute their products. MLA is also beneficial for emergency cases. Hospitals can determine demand for new vaccines or make sense of suddendisease outbreaks. This is possible because every information system, desktop solution, ormobile app can take advantage of the location.[4]
The physical stores have tools to collect data on their shoppers by monitoring their movement and their pauses.Video monitoring can provide up to 10,000 data points per store visitor. This allows stores to developheat maps so they can put the items they want to sell in high traffic areas. If amobile device is stolen by theft, this can be found by police bytracing the mobile number.
According to a study,brick-and-mortars accounts for 93% of sales.[5] Therefore, the retail store remains a critical focus.
In-store analytics has become more likeonline storeanalytics. Using MLA, stores can see where shoppers go and where they linger, detect whether they are shopping alone or with friends or children, and match shopping to weather. One company with small stores located in malls found that the space just inside the entry was a dead zone, so they moved the popular items further inside the store. Another store couldn’t tell which display sold more effectively because they had duplicate inventory. They were about to remove the wall displays when they decided to check traffic with a Mobile Location Analytics company. After creating a heat map, they made the floor displays smaller and easier for customers to walk through to reach the wall displays. The stores want analytics to see if the displays erected at the end of aisles eat away the sales of the same item stacked halfway down the aisle or if they contribute to additional sales.[6]
MLA-based counts can help ensure stores are staffed appropriately for the traffic at all times of the day. It helps businesses make correlations between transaction data and traffic.
The privacy agreement comes at a time when brick-and-mortar retailers are eager to have access to the kind of information about consumer behavior that can matchWeb retailers like Amazon. The move also reflects how industry is responding to public concern over thecollection ofpersonal data. As companies increasingly use data in more robust ways such as targetingonline ads ortracking physical location, they are realizing the need to give users more control over how data is used.[7]
Although products dealing with mobile location analytics do not recordpersonally identifiable information about specific customers, they have generated concerns aboutcustomer data integration andconsumer privacy.[8] Several MLA companies have worked with United States Senator, Charles Schemer and theFuture of Privacy Forum to develop asmart phone trackingcode of conduct. Under thevoluntary code of conduct, MLA vendors and retailers will inform customers when they are being tracked and allow individual customers toopt out.
Nowadays most Wi-Fi router orwireless access point vendors provide an API for listening the device's MAC address of the signals toidentify smartphones, this is the base of solution vendors of Wi-Fi tracking andanalytics systems, but almost all new smartphones emit more than one MAC address when they are not connected to the Wi-Fi. AniPhone can produce a lot of different and/or false MAC addresses when visit a venue during 30–40 minutes, because every time you touch the screen and awake fromsleep mode the MAC address changes.
Wi-Fi tracking solution vendors are dealing with data based on these false MAC addresses if they try to detect not associated devices, and only if the smartphone connects to the Wi-Fi access point to have free internet access (then it is associated) they can detected the true MAC address of the iPhone. But few customers use thefree internet service - in general less than 10-20%.
... show how to track the hits that may happen while the user is offline (due to a connectivity gap). .. showcase some cool and relatively little-knownGoogle Analytics JavaScript API functionalities.