The COVID-19 crisis has significantly impacted businesses across the globe. With government-imposed lockdowns and social distancing norms more people today are shopping online than before. While the crisis has driven the demand for online groceries and essentials, it has severely impacted the retail supply chain function. On one side, e-commerce companies are witnessing a tremendous surge in online orders. On the other side, they are now facing new challenges to manage the demand surge with limited resources and manpower.
In such trying and uncertain times, BigBasket – India’s largest online food and grocery store successfully managed a spike of 900% in demand with just 50% of its staff. BigBasket played a critical role in delivering essentials to citizens staying at home during the period. BigBasket achieved this operational efficiency and dexterity through AI-driven analytics. Like a white knight, the company has emerged stronger from what seemed like an impossible challenge during the nation-wide lockdown.
We have summarized the key takeaways and points discussed in this insightful webinar session here:
The impact of COVID-19
As the nation went under lockdown to contain the spread of COVID-19, BigBasket witnessed a surge of 5X-6X in online orders on its platform. With the pandemic crisis and the resulting restriction on movement, the team was left with just half of its staff to sustain day-to-day operations. The supply side for brands catering to BigBasket was also affected. This drastically brought down the Stock Keeping Units on the platform.
Role of AI-driven analytics in the emerging scenario
Since its inception, BigBasket has been a data-centric company that has heavily leveraged AI, and data analytics to drive operational and strategic decisions. The team’s analytical capabilities and data-driven culture played a crucial role in driving operational efficiency during the crisis.
Mani explains how artificial intelligence and machine learning algorithms can be applied to a variety of use cases in e-commerce, enabling teams to scale operations even during an unexpected situation like the COVID-19 crisis. In the wake of the crisis, the team rapidly initiated data-driven efforts to optimize resources, manage the supply chain, and efficiently handle the surge in orders. The team had to ramp up operations in a short period.
Anomaly detection for operational decision making
As the situation continued to change, new norms were set in terms of performance benchmarks and metrics. The goal was to deliver goods to as many customers as possible in a day.
With automated analytics and dashboards, the team was able to analyze and understand the impact on key metrics such as average basket value, delivery efficiency, customer complaints, etc. The team could track how anomalies in these metrics were affecting customers and revenue. By actively monitoring the datasets for customer interactions, customer delivery time, delivery lead time, operations, and merchandising, the team could identify the key problem areas such as unavailability of fast-moving products, etc.
The self-learning algorithms could detect incidents and patterns in real-time, allowing for faster issue resolution and better resource optimization. The team could actively spot trends and incidents to drive operational efficiency even with limited resources. Overall the team achieved a 26% increase in productivity during the period.
The team effectively prioritized incidents and took appropriate actions quickly. These included the reservation of delivery slots only to premium members and the launch of bulk delivery service to apartments.
With constraints in terms of manpower to collect items from warehouses, the merchandise assortment was reduced, and only essential items were made available. In addition to embracing new norms such as thermal scanning of delivery personnel and contactless delivery, BigBasket also made several strategic changes at an operational level including increasing the duration of delivery slots.
Data analytics strategies from BigBasket
In times of uncertainty, it is even more critical to quickly eliminate any unplanned revenue loss and capitalize on new opportunities for growth. Artificial intelligence and data analytics empower retailers to make timely and strategic decisions to drive revenue and growth. Here are some key recommendations to get the most out of your data:
1. Actively monitor ROI with diagnostic models
Businesses will have to prioritize customer metrics that matter the most and work with diagnostic models using data to identify the key business levers to sustain operations and growth. With the current crisis and cash crunch, there is a greater need to monitor the ROI of campaigns to channel funds to high performing campaigns. For instance, to understand the success of marketing campaigns, businesses will have to quantify metrics such as CPC or cost per conversion and then prioritize strategies accordingly.
2. Identify opportunity costs
With higher demand for online shopping, e-commerce companies will have to rigorously measure customer metrics to identify opportunities for cost optimization and realign business levers to drive a frictionless customer experience.
3. Fine-tune the target groups
Data analytics teams can also fine-tune the target groups for offers within their customer base by segmenting customers based on their historical responses to offers. With predictive analytics, businesses can also predict the market demand more accurately and optimize existing resources to cater to those needs without overshooting costs.
You can access the Webinar Presentation here.
Watch webinar recording: The BigBasket success story: Leveraging big data for big success
Rohit Maheshwari is the Head of Strategy and Products at CrunchMetrics. He is responsible for delivering business growth using innovation and product strategy. He leverages his expertise in artificial intelligence (AI), analytics and digital services to contribute to Subex’s solutions and enables its clients to build new offerings, drive business growth and deliver great customer experience.