Shopping in retail environment, as we used to know, has undergone a massive change and more and more players are embracing the new trend, increasingly. Omni-channel shopping is the key objective brick-and-mortar retailers are trying to achieve, following the same line as that of their ecommerce counterparts. The recent trends are suggesting
a) Over 80% of retail shoppers prefer omni-channel shopping as they enjoy the scope of searching a particular product on one device (mostly mobile) and complete the process on another (generally a desktop or a laptop)
b) The idea behind omni-channel shopping became recognizably popular for the first time in last year (2013) only
In case of brick-and-mortar retail stores, the challenge against successful implementation of multi-channel purchasing facility is entirely infrastructural. Such improvements would not only require massive store automation but also development of business intelligence (BI) that would categorically deliver the right information about products to customers, in-store, in digitized format. Customers will receive real-time information, matching their shopping preference, in their smart devices. At the same time, they will enjoy convenience in checking out products and place order from their preferred device.
Omni-channel marketing has clearly turned the common notion of digital marketing upside down. Retailers and brands these days don’t have to rely anymore on email and phone calls to inform customers about their promotional campaigns or new additions to the store. The predictive intelligence BI gathers customer information, not only from his/her purchasing history but also from demographical data as well. The BI continues becoming more and more accurate in delivering the right information to the deserving customer.
Beyond delivering real-time promotional messages, the predictive intelligence system can also be used as a tool to establish stronger ties with customers and in turn, successfully boost brand loyalty building process. While developing comprehensive business intelligence is clearly a daunting task, to derive the desired results, retailers should include the following factors in their predictive intelligence framework, to make it truly intelligent:
- No silos: In this omni-channel domain, your business intelligence system cannot afford the luxury of rejecting any platform or component as a mere silo. While retailers should provide equal importance to data, gathered from both smart and traditional platforms, on the other hand, they also cannot reject any marketing channel, leading to address customers. The ultimate goal of this new, evolving system is using the right channel of communication and there is no knowing which outlet customers would prefer to respond back.
- Personalize Communication rather than Doing it in a Stereotypical Way: Brick-and-mortar marketing strategies need to get away from the trend of following a stereotype, a model when it comes to communicating with customers. Many customers love to stay perpetually connected with their favorite brands and stores. As loyal customers they expect personalized communication and their wish should be respected. Such realization should function as motivating factors when it comes to developing an intelligent communication system that triggers personalized, real-time messages and continue delivering relevant notifications throughout customer life-cycle.
- BI Development Should Have Comprehensive Attribution Measurement and Testing Facilities: The business intelligence for omni-channel platforms should encompass the facility to conduct new marketing experiments and measure their attribution rate. It is important to remember that with predictive intelligence, communication becomes real-time. Unlike the earlier methods, where strategies used to develop on the basis of weekly or monthly customer-store interaction reports, the new platform would require strategists to develop appealing marketing campaigs real-time. Furthermore, based on customer reaction the changes need to be made real-time as well. Without including testing and attribution measurement features, it will be impossible to receive the relevant data and decision making will largely rely on individual instinct, which an automated store, emphasizing on predictive intelligence, should never encourage.