Retail in the Age of Disruption (Part Four)
This is the fourth entry in the CityStream Solutions blog series discussing Four ways that Big Data, Data Science, and Predictive Analytics must evolve and innovate. Navigate all Age of Disruption series entries: (1) | (2) | (3) | (4) | (5)
From the “portfolio rationalization” store model to the “omnichannel hybrid” store model
Retail was already in the midst of transformation when it suddenly found itself among the first and most affected sectors in the current shutdown. Retail was quickly bifurcated into essential and non-essential. Weaknesses in both brick-and-mortar models and online-only models became immediately apparent. Even the invincible Amazon had to give up on free 1- and 2-day shipping and prioritize essential items over others.
Retailers (both in-store and online) and distributors will have to transform their business models in order to survive. This was obvious before the pandemic, but it has sped up the timeline dramatically. Developing a unique experience that can’t be replicated at home is important to getting people into stores, but not of much help during shutdowns. While brand experts and architects and designers are on the case, what is the role for data analysts?
We need to move away from thinking of stores as singularly in-person selling points and toward thinking of them as multi-use, adaptable nodes in the network. Retailers shouldn’t jump to conclusions about which stores to close and which stores to keep based on in-store sales performance alone. Analysts must help retailers understand the potential for current stores to transform into mini-nodes in their distribution and delivery network before retailers let go of their locations and have to go through the expense and difficulty of getting it back.
The design and architecture of stores should evolve so that the “back-of-house” areas are much larger and the “front-of-house” areas are much smaller. When the public is allowed out, retail spaces must function more like showrooms than stores, where customers can access in-person, touch-and-feel, try on experiences. But stacking and hanging inventory on the selling floor, with piles of sweaters in various colors and sizes or stacks of routers for people to grab, won’t be a viable use of store space anymore. It takes away from the ability of people to spread out and creates high-touch areas. Furthermore, the space should be able to adapt when stores have to be closed to the public, easily rearranged by employees for making to-go orders or assembling baskets for pick-up. We need to move from BOPIS (buy online pickup in store) to BOPOS (buy online pickup outside store), creating spaces in parking lots or sidewalks, or cutting new windows with counters/shelves at the bottom, so people don’t have to come all the way inside to make a pickup.
But the real potential in existing brick-and-mortar store portfolios is to leverage stores as distribution points. Analysts will have to make sophisticated models based on retailers’ e-commerce channel to show them how shipping from stores will be more efficient and create faster deliveries than shipping from mega-warehouses. This will also help retailers be more resilient during disruptions, as they will have more supply spread out over more geography, in case one section of the country gets cut off or stressed. In order to achieve this, analysts must help retailers integrate their online systems with their in-store systems.
Point-of-sale (POS) system providers will start partnering and merging with e-commerce support providers. Customers will have to surrender a small amount of privacy when visiting stores, interacting via an app or allowing their phone to identify a geo-fencing beacon so that retailers can match the in-store customers to their online browsing and purchases. With integrated (“omnichannel”) customer data, analysts can develop models of stores as hybrid warehouses and in-person experiences that bring shoppers to the stores. A data-driven retail and distribution model must incorporate all channels (in-person shopping, BOPOS, and delivery) to create real estate and location strategies that will compete in the Age of Disruption.
By adding pack-and-ship capabilities to the back-of-house sections of existing stores, retailers can leverage their inventories to offer 1- and 2-day delivery to compete with Amazon. Store employees will have to be adaptable to multiple roles, moving from in-person customer service and interaction to warehouse picker, packer, and shipper. However, by keeping employees trained on both sides, retailers can avoid laying off or furloughing workers during disruptions.
Instead of trying to keep inventory to a minimum (just-in-time may be financially efficient, but it’s a risky strategy in the Age of Disruption), retailers will begin to keep additional inventory for last-mile delivery. Stores have the potential to become hybrid showrooms, pick-up points, and mini-warehouses, developing a network of distribution and delivery that is resilient in the face of new, unexpected events (like a pandemic) and able to offer delivery times that compete with the big guys. In order to achieve this, retailers will have to unite their in-store sales systems with their e-commerce systems to create a data-driven platform that enables a seamless omnichannel customer experience.
Continue on to part 5 here.
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