Can Retail Industry Bridge the Data Deficit Gap Created by COVID-19?

by June 23, 2020
Data Science

Image Credit: Unicommerce

With people locked in houses, due to COVID-19, the retail industry is struggling to keep up with the sudden customer behavior changes

Since COVID-19 first reared its horrid side, there have been casualties in the form of lives and livelihood. Several industries are teetering on the brink of a massive recession. Some of them have a chance of bouncing back as soon as the lock down is lifted and normalcy resumes. However, retail may still be caught in the hooks of irrecoverable losses. This is because like other customer-driven sectors, retail too thrives on customer behavior and engagement. Neither of which are available since lock down mandates came in force in March. 


Sales have plummeted for sure, along with that, retail is facing a data deficit. This data is the key to ensure enhanced customer experience and leads management. The crucial information based on sales that are sent to data repositories has fizzled in this period. And it was this data, which gave relevant insights to sustain customer loyalty schemes, AI-driven products, and services recommendations. It further, helped in planning strategies for marketing and business decisions. Whether it was independent or chain, brick-and-mortar or e-commerce, startup or established retail sector, all are affected by this deficiency. Thus, this situation points out how the buyer’s purchasing behavior impacts business in retail. Owing to the COVID-19 pandemic, businesses have to restart themselves to survive in a challenging market and reach changed consumers.


This brings us to certain risks that are likely to emerge now. With the fluctuating market and its unpredictability, leaders should refrain from making decisions based on pre-COVID-19 data. They need to realize that existing data models and dashboards are not of help now and devise plans that keep an eye on emerging behavior patterns. The newly calibrated analytical tools can be resourceful to stay engaged in the market and respond with creativity and innovation. Companies should also be conscientious when laying off their employees. Though cutting the staff can cancel out the profit losses, losing a skilled talent may seem unwise. Therefore, instead of blindly firing people, companies must understand which systems will suffer as a result of losing a specific role, then quantify and weigh the longer-term cost of any fallout. 


This sudden data deficit also has a silver lining. Since well-established companies who have been previously leaders in the customer-oriented market having leveraged data tools are to start from grass-root levels, firms, and companies who lagged can catch up to them now. In simpler words, the non-participants in the data trans-formative age having realized the value of data-backed services, and less-data-mature companies are blessed with a chance to emerge and lead a data populated industry. The takeaway is to identify the unique aspects of a service or product that makes consumers connect with the business brand. Retail majors should observe how their services perform in different customer segments, what attracts them, and measure how the company is fulfilling the demands and expectations.


Harvard Business Review illustrates how employing Data Science Tools can aid in this predicament. Using data science analytical tools, retailers can venture new streams of data collection. The founding context can be figuring out the products which are still in demand, preference for social distancing shopping, and study shift in purchasing patterns. Not all governments will be keen to quickly reopen stores when social distancing is a priority. So, conducting spatial analysis in conjunction with relevant Covid-19 health ordinances and regulations can help to know which areas need to focus attention upon reopening.


 Retailers too can go for a data infrastructure upgrade. This involves redesigning approaches to data collection and storage such that newly relevant data can be quickly mined for insights. Followed by re engineering predictive models using more-focused data sets; fixing glitches in website analytics and tagging practices that hinder the ability to draw accurate conclusions from website data. Lastly revisiting key performance indicators and scrutinizing each formula’s variables. Basically, it is about adapting to changes to succeed in post-COVID-19 times.