In my tribute to Steve Jobs in the days following his untimely passing, I described how he transformed simple geographical coordinates into popular applications such as tracking one’s running regime. Indeed, Apple’s iconic iPhone gave users a reason to use the device for data. Subsequently, app developers incorporated location elements in services such as Foursquare, TripAdvisor, and Facebook Places.
The usage behavior of mobile handsets is inevitably data-driven. An always-on (and invariably stressed) mobile network with increasingly sophisticated handsets priced for different customer segments is expected to create a “smart mobile user community” to experiment and accept innovative location-based services (LBS).
At this stage, you would notice I dropped the “advertising” part of the LBA acronym. Mobile users today are overwhelmed by a deluge of marketing messages that mute the “influence” of each message. More importantly, a seemingly uncontrolled sending of ad messages on SMS has blurred the supposed distinction between a SMS broadcast (which is not based on location) and a LBA message, which is predicated on the user’s physical location to the sender’s proximity. As a result, LBA’s future may be services driven, rather than ad driven.
One sector that benefits from LBS are retailers that own physical storefronts in shopping malls with high human traffic. A suitably scaled retailer (i.e. multiple stores in different geographical locations) can use LBS to implement a customer relationship management (CRM) solution to track, monitor, and analyze the location patterns of their customers.
Retailers should consider implementing a Wi-Fi wireless network with customized configuration designed for customers’ handsets. These customers are naturally pre-registered in a membership program (i.e. opt-in) that allows the retailer store to send a customized, customer specific messages to handsets such as Apple’s iPhone.
As a result, customers who are members of the retailer will be automatically signed on to the store’s Wi-Fi wireless network as they enter the store (i.e. proximity). Since the customer is now in an Internet-enabled environment, the advertiser should consider offering a mobile app that offers barcode scanning of products listed on the retailer’s shelves (i.e. detailed product information, product options such as colors, cutting, etc.) and feedback from the advertisers’ social networks such as Facebook and Twitter.
This shopping process is enabled on the user’s mobile handset, which is linked to the advertiser’s wireless network. Accordingly, the customer’s product browsing preference (from the barcode scanning) and how they interact with social media tools (e.g. Facebook) gives the advertiser deep analytics of the customer in a specific store location.
As a customer moves to stores in different locations (and connects with each retailer’s Wi-Fi network), advertisers will invariably collect location-specific data that can used to fine-tune promotion plans and communication messages to their members. The recording of the entire interaction between the advertiser’s store and the individual should trigger the following questions:
1. Was the individual interested in finding out more?
2. How many stages did the individual go through in the interaction before deciding on the next action? The stages are similar to what is described as the consumer purchase funnel.
3. How much time did the customer spend at a specific store? Is there a pattern that can be mapped into a two-dimension location and time matrix to identify the context that has the highest probability of sales conversion?
4. What is the next action of this individual? Accept the offer or decline?
5. If the offer is declined by the individual, what would be the reasons behind this decision?
6. When was the visit made – morning, afternoon, evening or at night?
For example, customers who keep appearing in a specific store at lunchtime may imply that they are frequently available in that geographical location. This insight, when applied to their demographic and occupation data, creates a specific market segment that will require targeted marketing strategies to influence their buying behavior.
This is one simple example of mixing location with wireless technologies to create customer centric services that seeks to “influence” the customers’ purchasing decision. Today’s mobile and wireless technologies are designed to increase the efficiency of managing information between users and advertisers. Indeed, the premise of augmenting location technology to CRM services is to capture, categorize, and assess customer information as the individual moves from one store to another. The implication from such customer movement is that the shopping behavior differs in stores at different locations.
Given that the benefits accruing from mixing location data with CRM technologies is multiplied by the number of stores in different geographical locations, advertisers today can now make better decisions on product development, segmentation, positioning and targeting following the analysis of such user information. As a result, service providers’ propositions are expected to be increasingly relevant to the needs of their users, thereby retaining their loyalty and reinforcing customer satisfaction. This is perhaps the next evolution of location in a mobile data-centric world.
Header bidding is a programmatic technique that allows publishers to offer their inventory through multiple ad exchanges before they serve up ads from their ad server.
All top Chinese retailers, banks and internet companies share mobile data in earning releases. None of the top 10 US retailers do, nor does Google. US banks and Facebook are better.
Whatever approach you take to your m-commerce project, one thing is certain: if you want it to deliver the results you’re expecting, context should be front and centre of your design.
American Apparel's chief digital officer discussed the future of retail, the importance of delivering value to the consumer, and strategies for an IoT and omnichannel world.