27 Apr Report: 9 Ways AI Helps Retailers Recover from the COVID-19 Pandemic
COVID-19 has hit non-essential retailers harder than any other market segment — shuttering stores, locking up seasonal inventory, and massively shifting demand to channels that weren’t ready for a large influx (ecommerce, store pickups, etc.). Despite the difficulty and uncertainty surrounding the non-essential market right now, there are 9 specific things that every non-essential retailer can be doing to address immediate issues, shorten their recovery, and set themselves up for success in the long-term.
This report provides a framework for addressing some of the biggest problems retailers are facing today, using proven and tested artificial intelligence technology.
From this report, you will learn and understand how to:
- Build a short, medium, and long-term recovery plan
- Build demand forecasts based on “What-If“ Scenarios
- Optimize e-commerce order fulfillment.
Coronavirus Impact on Retailers
The Covid19 Pandemic that led to a shutdown and economic downturn has impacted all retailers. The sudden change in consumer behaviour has triggered a dynamic shift in demand that has a different effect on difference retail verticals.
Essential Retailers that remained open experienced out-of-stocks fueled by panic-shopping, as well as on-going increase in demand. While other retailers were forced to sharply pivot to a digital commerce model.
We saw product categories such as home office, personal care, and fitness gain an incredible boost in demand, while other categories like automotive, party supplies and travel sharply decline leaving some retailers overstocked.
Fortunately, as the data continued to pour in from around the world on the development of the Coronavirus as well as retail sales and product demand trends, we were able to start building multiple scenarios, and building simulations to predict demand.
As retailers begin to recover, restore operations and supply chains, many are asking similar questions, including:
- How do I build up store assortment and inventory levels without unnecessary overstock?
- How do I effectively switch allocation & replenishment processes back to stores?
- How will the outbreak permanently affect my e-commerce business?
- How do I use my stores as order fulfillment nodes?
- How much inventory should I be holding back in my DCs?
- If suppliers have limited inventory, how do I efficiently allocate inventory across my stores and DCs?
- How early do I need to make orders with suppliers to protect myself?
These are some of the many questions that our team has been helping out customers answer. Having an ability to leverage our Retail AI and Predictive Analytics engine enables us to help retailers in several ways.
A Guide on how Retail AI Helps Retailers Recover from Covid19 Pandemic
We’ve released a report highlighting 9 ways we are using our Retail AI engine to help retailers recover, learn, and prospect from the Covid19 pandemic. The guide is divided into short term actions, medium-term recovery, and long-term strategies.
NOTE: We also featured a webinar on this topic breaking down several of these strategies as we as a short Q&A with Retalon CEO, Mark Krupnik, and our VP of Sales Dianne McCoubrey. Available here.
Re-Opening Stores after nation-wide shutdown
Returning to regular operation will not be easy because the next several months will be dynamic depending on legal restrictions, transmission rates, operational capacities, and consumer behaviour. It will be important to be able to adjust to the reality on the ground as retailers begin to open their stores.
Retalon’s AI and predictive analytics engine will help retailers adjust financial and merchandise assortment plans, generate optimal purchase orders accounting for dynamic lead times and deal end-of-life products more efficiently.
Moving forward retailer will be able to dynamically balance assortment across all locations, and DCs to optimize order fulfillment. Using this approach retailers will be able to automate a great deal of decisions and look at data more gradually which will translate to better agility and a significant decrease in time and labor costs.
Cleaning Data, Post-Pandemic Trends, and Learned Lessons
Retailers will want to make sure their data is not corrupted by the impact of this event. Using sales data from 2020, or even past years will lead to inaccuracies and too many forecast exceptions. There are far too many outliers, and misleading trends that will make future decisions more difficult and past data unreliable.
There will also undoubtedly be long-lasting and even permanent changes to supply chains operations, e-commerce channels, and consumer shopping habits. Identifying these changes and adjusting to them will help retailers gain market share over those stuck in the dark.
Our analytics team has been able to use our technology to help retailers identify the true impact of the event and uncover real demand down to every store/SKU. This means that going forward planners, buys, and merchandisers will be able to work with meaning information.
Learn more about how Retail AI can help your retail business with this free guide.