Analysis of the Cisco Meraki Analytics
The Meraki analytics is a Cisco product that enables the real-time display of location statistics. As a result, the customer engagement and loyalty across various sites improves. It is a way of using the intelligent access points to communicate the actual location data directly to the clients. It allows the inclusion of the third party applications that belong to the retailers and those that are custom-built. The Meraki Analytics has the capability of detecting the presence of users by inquiry requests, which are signals from various Wi-Fi devices such as tablets, laptops, and smart phones (Costello, Konary, Mahowald, &Mehra, 2015). Once data transfers to the Meraki cloud, a critical and comprehensive analysis is made to ascertain the quality of the information. Meraki will then offer a real diagnosis with the Wi-Fi devices present. Moreover, in the course of performing the analytics, there are automatic and customizable graphs that are usually present. These graphs enable the facilitation of important perceptions into styles such as the dwelling time of visitors, the new and usual visitors, as well as their movement within a particular time of the day. This form of visibility thus makes easy a deeper comprehension of the Wi-Fi hotspot’s visitors and recommends visions. The visions are such as the duration for a hotel stay in the lobby, capture frequency for a retail outlet or the organization’s branch, office or store.
Meraki analytics has various features that enable it to perform its functions. For instance, it has an APS that can detect any review requests made from the Wi-Fi enabled devices. In this way, it can capture several applications, including the planned and unplanned ones. Data directed to Meraki cloud is usually instant hence; providing the opportunity to combine and investigate as well as present results. Moreover, it also has statistics on the clients who are just passing by the site and visitors who are spending time looking at the information. The time taken by visitors within the hotspot is displayed as well as information on requests, which is the repeat and new visitors (Meraki, 2017). Meraki analytics also has a location-based understanding that drives revenue. The dashboard is an illustration of the performance indicators that are location-based. They comprise the rate of repeat visitors, the rate of capture, the median length of visit, and the total visits made for an individual site. The insights gathered from the location analytics is used to establish means of better engagement with the clients. For instance, it helps in identification of the users tastes and preferences. Additionally, the insights can also intensify the retail store traffic and further drive revenue (Sathiaseelan, Seddiki, Stoyanov, & Trossen, 2014).
The location API of the Meraki Analytics offers a wide range of flexibility. It utilizes actual objects such as HTTPS POSTs of JSON to deliver the raw form of data to clients who have the analytics applications. They also make the gathering and management of the location data to be quite flexible. Once the location API with the back-end CRM has been integrated with Meraki’s site, then the retailers have an opportunity to drive in-store the engagement of customers with notices to the staff or even the delivery of the customer offers that have been targeted (Meraki, 2017). Furthermore, the users of Meraki analytics can also use the API to come up with applications that enhance the existing records of clients. In this way, retailers will be in a position to determine their top customers and the number of times they visit a certain location. As for the framework of the enterprise, the location analytics data is used to update the policy decisions on the availability, savings on energy, and physical security of the Wi-Fi. Thus, the Cisco Meraki works in an efficient way with the best in class retail analytic sales persons. All this is done via the location analytics API that facilitates the plugging of raw data to be handled by the applications of the retail analytics (Costello, et.al, 2015).