"Google Indoor Maps" has opened a whole new opportunity to retailers whereby they can enable their customers in getting easy and quick access to the products that they're looking for. In brief, "Google Indoor Maps" allows a person walking inside a store to navigate his travel within this indoor location just like what she(/he) would have done while driving a car using a GPS
Integrating these maps with Retailer's own mobile application (app) and tagging a store's aisles by "Product Categories" on these Google Indoor Maps for a large format store, going down to category of products available by Brands may be just the beginning of the thought as to how Retailers allow their patrons to directly reach out for the product that they are looking for rather than wandering through store departments or searching for a store associate which for some shoppers may be time consuming or even frustrating.
One of the best supporting feature on Google floor maps, is the ability to guide the user by individual floor's plans, whereby a large multi-level retail stores can also be covered easily and therefore we feel that some of the immediate exploits can be in the large scale Store or say Super Store
Let us try to understand how one such Retailer app may make the life a Retailer's clientele much simpler and the shopping experience much better. Assume the scenario that our loyal Customer "Louise" is entering such a Large Super Store for her weekly purchases which runs across several departments of the store like fresh produce, dry grocery, apparel, cleaning supplies, bath-ware, electronics, sports goods and the list goes on. Soon after she has parked at the store, Louise logs-in to the Retailer's own native Mobile app on her smart phone. The Retailer's app upon invocation on her smart phone, detects her geographical location and the store that she is visiting today via Location Based Services (LBS) wherein this specific store's latest tagged maps can be pulled and displayed on Louise's phone's screen
To make the whole shopping trip faster, Louise has keyed in the shopping-list beforehand in the app and a route map is prepared for her upon her check-in into the store via this app. This route map is based on the latest movement of shelves/racks in the store. Such a guided walk cuts down the Louise's walk through the aisles a short, easy and a confortable one
Louise was looking for a shirt for her son, but the size small does not appear on the shelf today... does the store have it? No problem... the check would be a quick one by Louise quickly getting to know this via her mobile app. Moreover, if it is not available in store right now, the app prompts her with an easy and quick site to store order which she can pick up during her trip next week
Another use for this app+google maps eco-system can be to integrate with the floor maps and publish current vacancies/next available time slots/expected wait times in Large Store's sub stores like ophthalmologist shops, saloons etc.
This Large Super Store's sub stores are very frequently publishing a status of a vacant customer spots/seat available or unavailability of the same on the floor map which when viewed by Louise, will give her an idea whether she needs to do the shopping first or go to the sub store for a quick visit to the hair salon
While these are just some of the initial thoughts, when pursued actively this specific technology can be utilized in umpteen ways to boost the store sales and to guarantee customer satisfaction. Overall, sky is the limit when one starts documenting the concept of such a solution/product. Customer purchase/return history and loyalty points can be utilized to highlight offers/deals when customer is approaching a specific aisle or when she has been looking for a specific product for some time
This article has been contributed by Ashutosh Kaushal - Senior Consultant (Sterling Commerce - Infosys Ltd). You can reach Ashutosh at Ashutosh_Kaushal@infosys.com.
Most retailers traditionally leverage sales data from POS terminals to analyze buying behavior. In some cases, loyalty card data is also used to determine appropriate assortment decisions. These data sources and their corresponding analysis have proven reasonably helpful, though they don't convey the whole picture. With the recent explosion in social and consumer related data on the web, there is a wealth of information that Retailers should be exploiting and incorporating into their merchandising decisions, primarily around assortment and space.
Social/Consumer genome related data provides rich insights into needs, wants and buying behavior of individuals. This data when combined with demographic and geographic data can provide a good map of consumer wants and needs for a given market for a set of product categories.
A major input into merchandising decisions / assortment plans is to determine consumer buying behavior to identify what products sell and what potential products could sell to increase sales. Based on this analysis, assortment decisions of inclusion/exclusion or allotment of space are provided. The means to identify this buying behavior was typically the use of POS data or data from Nielsen/IRI that provided good basis for WHAT was being purchased. When married with Demographic data, there was a good proxy for WHY the products were being purchased. Even when rigorous correlation and clustering analysis is carried out, the determination of buying behavior and the reasons for the same were proxies at best.
Now with the availability of consumer genome or social genome information, the analysis of WHY purchases are being made and what is being purchased with identification of latent and express needs becomes even more accurate as there is clear expression of wants and needs. This will significantly enhance the quality of assortment and merchandising decisions as the degree of error/approximation is reduced.
There are however some pitfalls to the use of this data. We cannot solely rely on this data as web usage and consumer/social genome information may not fully represent buying needs and wants for the entire market population. This data usage has to be married to traditional sales analysis to augment the decision making process.
There are no tools / application products in the market place that provide truly integrated capabilities. The holy grail for optimized merchandising would be to integrate social/consumer genome data effectively into the traditional clustering analysis and thereby into the assortment planning process. This might be a challenge for some of the retailers who struggle with traditional approaches. Asking them to adopt more advanced analytical approaches to incorporate social/consumer genome data would be a challenge.
The key would be to devise suitable technology/process platform augmented with robust analytics shared services that can leverage necessary data to enable optimized merchandising decisions.
This article has been contributed by Amitabh Mudaliar (Group Engagement Manager - RCL Infosys). You can reach Amitabh at Amitabh_M@infosys.com.