Big Data's MoM (Metrics of Mobile)
By Roger Portela, Director, Product Management, GPShopper
The era of mobile and “Big Data” has changed the way consumers shop and how retailers engage with them. As our CEO, Alex Muller, pointed out in his blog post, The Rise of the Net New Minute, mobile users have added a new slice to the utilization pie which needs to be accounted for when analyzing usage reports. The relevance of location has become much more important in determining the best methods for engaging with an app’s and mobile website’s users. Understanding where users are is yet another growing piece in the puzzle to create valuable campaigns, to measure the effectiveness of those campaigns and to react to trends.
Our latest location-based reporting allows our clients to view in real-time the locations of the users who are actively using the applications and have opted in to location-based services within the application. This information is either based on precise latitude and longitude,
Reading the map is simple. An effortless hover over a visitor bubble will give insight into what actions are taking place. Viewing the usage in proximity to the brick and mortar store is also straightforward. But how do we gain understanding on what this tells us? Comparison of goals to usage is one way of measuring success. Is the goal to increase traffic to the physical stores, or simply to increase sales on mobile devices? With location as a measurable action item we can now quantify these goals in a way that tells us what effect the user on-the-go has on them. We are now able to measure the purchase patterns of users near stores and repeat visitors to locations near stores. And soon we will be able to add features to analyze locations within a configurable distance from stores. All these data points lend themselves to a greater understanding of the mobile user and how their “net new minutes” fit into the bigger picture.
To expand on this we can also integrate other features analysis into a mobile user’s location pattern. Location-based offers is one of the easiest metrics to obtain, answering the question of “how far away are users when the offer is viewed?” and “what affect did the offer have on driving purchases or in-store traffic?” Push notifications combined with location analysis can gauge what affect proximity to a store has on open rates. And the combination of offers, push notifications and store geo-fencing is the triumvirate of features working together to target, communicate with and retain customers.
Unifying the mobile experience with desktop and in-store shopping can also become less burdensome when fully integrated with a loyalty program that is interchangeable through these channels. With location features analytics, store operators get the insight that has been lacking until now – what are my loyalty members’ habits in comparison to their app utilization. Tying all these together with a strong knowledge of location and trends can be the driving factor in creating optimized and personalized campaigns that target the exact user type with the content they want to see.