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The global health crisis accelerated the adoption of omnichannel shopping and fulfillment.  Consumers spent $861.12 billion online with US merchants in 2020, up an incredible 44% compared to the previous year, which marks the highest annual growth in U.S. e-commerce in at least two decades. To keep up pace with this shift and more effectively sell, businesses have substantially moved investments to online infrastructures, such as e-commerce platforms, inventory management, product recommendations and chatbots and delivery.

The four customer challenges driving e-commerce profitability.
Infographic exploring the four customer challenges driving e-commerce profitability.

On one hand, setting up e-commerce sites and/or optimizing online stores means increased sales and market penetration; on the other, these benefits are potentially outweighed by the increased costs as retailers essentially shift a part of their business to logistics and fulfillment.  As businesses make the transition to online retailers, they will have to focus on these four key customer areas to ensure profitability: fraud, delivery theft, returns and customer service. Strategically approaching these focus areas with data and artificial intelligence (AI) brings visibility, accuracy and automation, which helps brands better serve customers,  provide a competitive advantage and drive loyalty -- key success drivers for e-commerce businesses.

The customer challenges retailers face have a significant impact on their bottom line. Here’s how data and AI can address them.

Fraud: Fraud has become too commonplace and on average costs companies 3.36x in chargeback, replacement and operational costs. Losses associated with fraud soared to $56 billion in 2020 and accompanied a huge dip in customer confidence in the brand. Data and AI can help retailers get ahead of fraud and avoid financial and reputational damages, especially when it comes to proactive approaches.

At Databricks, we have a suite of Solution Accelerators that use rules, machine learning and geospatial data to detect and prevent fraud. Learn how to quickly get started with the solutions here.  Additionally, the Databricks Lakehouse Platform, which delivers the data management and performance typically found in data warehouses with the low-cost, flexible object stores offered by data lakes. effectively enables anomaly detection at a massive scale to protect losses caused by fraud in real time.

Delivery package theft: Exacerbated by the rise of online shopping from the pandemic, package theft is a huge operational burden on retailers. Some estimates report that, in 2020, 1.7 million packages were stolen or lost daily. The shipping industry is also using AI to enhance security measures, both within and outside of business grounds. Shipping carriers use drones to patrol the grounds around their warehouses to collect real-time information and data. Big data analytics can help logistics providers identify common sites of traffic accidents or package thefts and design their services around those. Locker services for apartment lobbies, alternative pickup spots, specific time deliveries are some examples. The Databricks Lakehouse Platform enables analytics and AI use cases to identify such hotspots and frame apt responses.

Returns & reverse logistics: Companies have also incorporated predictive analytics using data and AI in their returns and reverse-logistics operations, leading to an improvement in service levels with fewer queries and reported issues. They’ve also achieved freight savings between 5-10% by reducing last-minute load requests. The Databricks Lakehouse Platform allows companies to tap into vast amounts of data and unlock actionable insights to determine high-return items or customer behaviors to help retailers prepare strategies to minimize returns

Customer service and cost to serve: Customer satisfaction, customer retention and cost to serve are three factors that can define the long-term profitability for retailers. The drivers of these KPIs are strongly interlinked. Although customer issues can be wide-ranging, many issues will be common amongst customers. Natural Language Processing (NLP) tools can analyze call notes to identify the straightforward and most common issues. These can be tackled by blending digital and call center channels and driving self-service usage for common queries. According to IBM, businesses can reduce customer service costs by up to 30% by implementing such solutions. Additionally, a database of customer context insights can be connected to care flow through IVR to intercept complex calls and route them to the agents who are equipped to resolve the identified issues. This avoids long wait times, multiple transfers and repeat calls -- all of which can impact customer satisfaction, retention and cost to serve. Finally, businesses can arm their care agents with an at-a-glance view of customer health, context, history and next-best-step suggestions while the customer is on the call. This is especially useful while dealing with an at-risk customer identified by ML models who has been routed to the retention specialist.

Check out our new e-commerce infographic for a look at the quantifiable impact of these challenges affecting every retail business. The issues outlined in this infographic have a severe impact on customer experience and can prevent them from coming back again. Learn how Databricks can help take on these challenges with our ebook The Retail Lakehouse: Build Resiliency and Agility in the Age of Disruption.

Open your world with Data & AI to uncover the customer journey and solve for these challenges. Data and AI can help you achieve customer-centricity, win win loyal customers and generate sustainable revenue over time.