June 24, 2020 | Advanced Analytics

4 Ways Advanced Analytics Can Provide a Competitive Edge in the "Novel" Normal

By Ravi Narayanan

The pandemic has altered the where and how of global consumer spend.

Recent earnings reports1 from large retail companies show an increase in consumers’ essential spending for items like fresh/organic food, frozen food, and beverages, pickup/delivery of restaurant foods, cleaning products, preventive healthcare, vitamins while showing a decrease in discretionary spending as in travel, home furnishings, or luxury goods.

4 Ways Advanced Analytics Can Provide A Competitive Edge In The “Novel” Normal - NisumPicture Courtesy: Unsplash


Consumer spending has also widely shifted from stores to online, accelerating the need for traditional retailers and digital natives to increase their investments in digital and advanced analytics to compete effectively.

Conversely, as evidenced by some recent retail store closings and bankruptcy filings, not leveraging digital and advanced analytics effectively, could potentially place a retailer’s revenue and business model at risk.

Four Ways Advanced Analytics Can Help:

1. Build Automated and More Accurate Demand Forecasting Models

Given the variations and fluctuations in consumer demand in the post-pandemic world, it has become even more imperative for retailers to build more accurate and reliable forecasts for consumer demand and inventory. They must continually measure and improve the accuracy of their automated demand forecasting models to help boost revenue and margins.

2. Optimize Inventory, Store Layout, and Operations

In the wake of the pandemic, retailers must revisit their store operations, the safety of their customers and employees, and the transparency of their supply chains. Advanced and In-store analytics can track real-time sales, customer in-store heat-maps can help optimize store layouts, predict inventory needs in real-time, and provide more efficient store operations. 

3. Provide Real-time Notifications, Recommendations, and Personalized In-store Contactless Experience

In the post-pandemic era, retailers must move beyond the traditional tenets of loyalty and personalization, to provide an integrated and immersive customer experience across all channels - online, mobile, and in-store. They must incorporate relevant advanced AI and ML models to understand customer behavior and personalize customer journey across all channels, to orchestrate an integrated omnichannel experience.

4. Provide Optimal Media Mix and Maximize Sales

Leading retailers leverage advanced techniques like Bayesian Models2, Markov Chain Monte Carlo3 to understand more accurately the impact of media spending, the spending levels and profitability per medium, the environment (economy), and the competition, to maximize sales.

 

Advanced analytics could provide the necessary competitive edge and sustainable revenue growth, to thrive in the ‘novel’ normal.

If you want to accelerate your data transformation at scale and leverage advanced analytics to make smart real-time decisions based on fact-based intelligence, then contact us. Traditional retailers and digital natives alike have found that Nisum provides them a competitive edge through innovative approaches to advanced analytics initiatives. Clients leverage Nisum to make their diverse data sources ready for advanced analytics and to help them build an AI-ready organization.

 


About the Author: Ravi Narayanan is the Global Practice Head of Insights and Analytics at Nisum and a preferred advisor to leading Fortune 500 brands. He serves CxOs and their teams, helping them harness Advanced Analytics and BlockChain technologies to help transform customer experience, improve operations, and drive growth and innovation.


 

References:

1https://investor.costco.com/news-releases/news-release-details/costco-wholesale-corporation-reports-third-quarter-and-year-20

1https://ir.expediagroup.com/static-files/eec35497-7b71-4fef-a89f-e60b54a604f0

2https://blog.stata.com/2016/11/15/introduction-to-bayesian-statistics-part-2-mcmc-and-the-metropolis-hastings-algorithm/

3http://www.columbia.edu/~mh2078/MachineLearningORFE/MCMC_Bayes.pdf