Defining the digital model for convenience retailing
The very definition of convenience retailing is an experience of access and ease for shoppers. There’s no ambiguity. The experience must be convenient, period. And the convenience store, or c-store, market is huge. In 2019, 3.1% of the US GDP resulted from $647.8 billion in combined in-store and fuel sales at c-stores. With nearly 153,000 c-stores across the country, just under 45% of the US population lives within a mile of a c-store. In some rural regions, c-stores are the primary destination for groceries, prepared foods, fuel, and other services.
With the “new normal” environment created by COVID-19, convenience retailers have been forced to quickly react to shifting customer behaviors, extensive safety and sanitation requirements, changing competition with grocers and restaurants, and an accelerating trend toward more in-store purchases—all against declining fuel sales, as stay-at-home mandates significantly reduced vehicle travel.
Determining whether a convenience retailer falls on the “leader or laggard” side of the shopper experience pendulum is based on how their digital innovation strategy is invested and directed toward these three key strategic priorities: the support of emerging customer experiences, the optimization of product and service offerings, and the efficiency of retail operations.
Supporting emerging multi-dimensional c-store customer journeys
The average US convenience store sees about 1,100 customer visits a day, which represents a broad combination of persona-driven shopper engagement needs. These deliverables involve engagement, influence, and fulfillment within the store, forecourt, and increasingly through a c-store chain’s digital platform. There are different product fulfillment expectations, such as a simple fuel fill-up, basic c-store snack and beverage purchase, buying prepared foods like pizza or sandwiches, or a combination of those scenarios. And, increasingly customers expect a personalized experience regardless of how or where they engage in the c-store. Here are a few examples of what I mean by emerging, multi-dimensional customer journeys:
While a customer pumps gas, he or she orders a slice of pizza at the gas pump via a voice-activated order system and uses the same system to pay for both the pizza and the gas.
A customer can place an order and pay for a made-to-order sandwich along with a list of in-store items, notify the store when they arrive in the parking lot, and the order is delivered to the customer in the designated parking space.
A shopper buys a fountain drink in a store and gets a personalized discount to buy fuel as an incentive to buy gas because the customer hasn’t purchased fuel at the store in the last month.
A customer grabs the item they want to buy and walks out of the stores without standing in line to pay at a traditional cash register.
The challenge in supporting this complex matrix of c-store customer experiences is that today’s convenience-focused customer expects zero guardrails impeding their use of whatever combination of touch point, channel, product, or fulfillment method they want to use to complete their purchase journeys.
Optimizing in-store product assortments, stock availabilities, and menu selections
While fuel remains the top selling c-store product category, in-store product sales and an extensive prepared food menu represent the largest overall sales growth categories, and on average, they are the biggest contributor to overall gross profit. The strategic focus must be on capturing the broadest spectrum of transaction details possible and applying model-driven analytics and machine learning (or managed services like Amazon Forecast) to generate hyper-accurate predictions of future demand. This transaction detail can help you optimize category plans, profitable private label assortments, high-selling menu offerings, and better in-store stock availability.
Safe and efficient store operations
The challenges and complexities of operating a c-store location (never mind an entire chain) have only become more complex in the world of COVID-19. Establishing protocols and processes for employee and customer safety and social distancing must be added to the list of core needs, like managing store labor, preventing loss, ensuring food and forecourt safety, and managing inventory. Aggregation of real-time store transaction data, device and video data, and forecasted demand data can be blended into valuable operations intelligence that allow simultaneous assessment of current store performance while recommending fine-tuning adjustments to financial levers, such as labor scheduling, product ordering, or kitchen production.
Digital model starting points
In future blogs posts, we will go deeper into the specifics of the convenience retailing digital model and provide a set of segment-specific tools helpful to any c-store brand’s cloud technology strategy. We also know that there are a few foundational IT priorities that underpin not only easier adoption of our emerging digital model, but also enable the agility and scale required to differentiate in this hyper-competitive segment of retail. These early priorities include:
- Building a cloud-based data lake: Immersing your convenience retail workflows in predictive insights and demand intelligence is made faster and easier with the implementation of an AWS Data Lake—a curated, secure, and scalable data repository that aggregates your organization’s data to allow a wide variety of data analytics.
- Developing a store and forecourt-wide IoT connectivity plan: Integrate all store-level equipment, including forecourt dispensers and controllers, sanitation management, video surveillance, beverage coolers, and food safety temperature sensors. AWS services such as AWS IoT Core and AWS IoT Greengrass for data management and Amazon Kinesis for processing real-time data streams help streamline both the connectivity and data aggregation that will be leveraged in future analytics. AWS has strong partner relationships with companies like Ayla Networks and TensorIoT that can add solution capabilities to orchestrate the complexities of c-store data management.
- Leveraging fully managed machine learning (ML) services: Two key elements of the AWS c-store digital model are advanced, hyper-accurate demand data and customer intelligence. AWS provides Amazon Forecast as an accurate time-series forecasting service that does not require deep ML expertise to adopt. Amazon Personalize provides a similar ML-oriented service capability to deliver highly effective, influential, and scalable customer experiences that are tailored to provide unique recommendations.
What comes next
Future blog posts will define our convenience retailing digital model with implementation best practices, reference architectures, customer use cases, and partner solutions that reflect our shared learnings and industry differentiation.
The AWS global retail industry team is Born from Retail, Built for Retailers. To learn how AWS can help you align retailer strategic solution priorities with the depth and breadth of the AWS services portfolio, feel free to contact us.