Why Retailers Must Use Machine Learning During This Pandemic
The COVID-19 pandemic has dramatically disrupted and influenced the way people communicate, work, and shop. A PYMNTS study says, “Roughly 85 percent of U.S. consumers said they were concerned on some level about contracting the virus,” and a survey by eMarketer revealed that nearly 60% - 85% of internet users across China and South-East Asia have avoided crowded public places to mitigate the risk of contracting the virus.
Hence, social distancing and business closures have made consumers change their habits of what they’re buying, when, where, and how. This could also be termed as a paradigm shift in consumer shopping preferences.
According to an ACI Worldwide study, global eCommerce retail sales increased by 209% in April as consumers shifted from in-store shopping to online and mobile channels. Customers perceive this as a viable option, including those who have been reluctant to shop online previously, which negatively affects the footfall traffic of these stores. These recent changes have created uncertain times for many, but eCommerce has benefited retailers as shown by the increase in online sales.
Picture Credit: Unsplash
Now is the peak time for the sellers to think digital, and they should make every effort to stay afloat and relevant in the market by meeting the customer demand for essential and non-essential goods, quantity, and quality of product to provide better customer services and meet their preferences such as switching to online platforms, subscription services, curbside pickup, one-day delivery, self-checkouts, contact-less delivery, and many more as a response to the pandemic.
These efforts will prove to be fruitful when retailers embrace their digital capabilities and make informed decisions by analyzing customer data using artificial intelligence (AI) and machine learning (ML) to create recommendation-based algorithms. The pandemic has already proven how important it is for the sellers to know their consumers and serve them accordingly.
The retailers with eCommerce platforms in place are already seeing an increase in digital transactions compared to in-store. According to a report by digitalcommerce360, Target’s April digital sales increased by 275% as consumers shopped more online and avoided stores during the coronavirus pandemic.
Considering these changing trends, it seems like 2020 will make the adoption of eCommerce and mobile commerce or mCommerce platforms mainstream. That means there will be a new world order for operations, and sellers will have to adapt to the new norms of online shopping. Retailers who leverage their digital analytics capabilities by using machine learning will be able to sustain and recover from the ongoing crisis.
Some of the key areas where machine learning will be helpful to sellers are as follows:
1. Manage and Forecast the Business Demand Using Machine Learning
Given the current situation and the increase in demand for goods such as groceries, essentials, and healthcare products, and many more, there has been a significant rise in out of stock items so the ability to use machine learning to forecast patterns and manage demand using the product lifecycle to reduce capital becomes critical.
Demand forecasting helps a business streamline its production and procurement activities and helps them estimate budgets and financial planning. It is usually used to address supplier relationship management, marketing campaigns, order fulfillment and logistics, manufacturing flow management, and customer relationship management.
With machine learning predictive algorithms, businesses can take advantage of big data to automate and optimize business processes. Using the customer data in the process with better demand forecasting can be helpful in terms of improved gross margin, irrespective of seller size or segment.
2. Forecast the Financial and Product Reserves with Machine Learning
The financial stability of every business has been severely tested following the outbreak of the COVID-19. There is a need to reevaluate strategic ability, which can be accessed through:
- Gauging the current financial stability and understanding the customer lifetime value (CLTV, with an unlimited lifespan to understand cost per acquisition) and preparing budgets.
- Understanding the necessity for reserves (raw/finished goods), based on the bullwhip effect, to make informed decisions on inventory levels and reduction in out of stock situations.
- Predicting the effect of amendments to loans and financial agreements for the sake of preparation using AI, as quoted by KPMG: “COVID-19 has shattered all other forecasting models. No one is going to be able to create perfect forecasts right now. But companies that incorporate external signals into their modeling and harness the power of artificial intelligence will come out ahead.”
By using predictive machine learning algorithms, it is possible to predict any losses due to stockouts thereby enabling the business to cut costs using reduced shipment charges and improving order fulfillment.
3. Predict and Manage Supply Chain with Machine Learning
Inventory, order, and warehouse management systems seem to be becoming the crux for the management of disruptions in supply and the possible reduction in long term demands.
During these testing times, many sellers were unable to predict and meet customer demands due to the unavailability of better insights regarding their supplies. The focus now has moved towards improving the visibility of supply chain management.
This pandemic has brought forward new risks, and hence, cost evaluation becomes necessary. Not having the ability to understand and predict demand showcases how vulnerable the supply chain networks are, and with a better analysis of inventory data through predictive machine learning (ML) algorithms, sellers can have transparency of inventory and make informed decisions accordingly. Management of material sourcing, supplier risks, inventory under one roof (aka going digital) is critical, and using Digital Analytics is a step in the right direction.
4. Offer Better Customer Services by Using Machine Learning
Using a data-driven approach to resolve customer issues and reducing toil becomes the top priority for every seller in the longer run. Using ML, it is possible to understand customer actions as an individual data point in real-time, which helps in the prediction of their next course of action, and sellers should be able to have various up-selling and cross-selling opportunities. These extracted insights could be used to develop personalized services and marketing which in turn will boost sales.
An early prediction from Gartner says “By 2021, 15% of customer service interactions will be handled completely by AI.” Hence, chatbots (virtual assistants), content for customer self-help, predictive analytics (preferably customer-related analytics), are all the best ways to explore and implement.
One technique that could be useful for the businesses to measure is customer satisfaction (CSAT), which helps to tie-in the customer satisfaction surveys to key moments in a customer's experience provided as part of customer service. That way, sellers can tie their customer insights with business questions and measure the effectiveness of key moments such as user onboarding”
Hence, digital transformation is critical to maintaining business continuity, and using analytics in customer service will support you in gathering insights that can help optimize the service delivery.
Embrace Machine Learning Capabilities to Stay Afloat in the Market
Considering this shift, retailers must innovate to meet market demand and provide a better customer experience. This is possible if retailers use machine learning to enhance and master their digital analytics capabilities and have a platform with a unified approach to manage customer data. A customer relationship management (CRM) tool will allow retailers to adjust to consumer expectations in this evolving digital retail environment and make informed decisions.
Given the situation, not leveraging digital and advanced analytics effectively could potentially place a retailer’s revenue and business model at risk. Data analytics integration with an online store helps in converting the insights into actions and can be utilized to learn and analyze more about the most in-demand purchase options, preferences, managing the demand and supply, personalized experiences, and many more. With an integrated digital approach, sellers can be sure to meet and exceed customer expectations.
This crisis has impacted businesses worldwide but also, has created new opportunities and put a spotlight on the need to develop and grow digital capabilities. Retailers who will prioritize advanced analytics will likely face fewer hurdles and move toward the path of success when the world emerges from this pandemic. In our opinion as a business transformation firm, Digitization, Business Agility, and Advanced Analytics are the three pillars of focus for any business to enhance their digital capabilities.
About the author: Vyasraj Vaidya has been working with Nisum as a Senior Data Scientist. He has a Masters in IT from Technical University of Ilmenau, Germany, and has experience working in the media and entertainment sectors.