Driving ecommerce profitability: Four building blocks you can't ignore
Although several retailers have embarked on their ecommerce profitability journey, few have adopted a holistic approach to drive sustained returns
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For traditional retailers, this meteoric rise of digital is enough cause for concern. Often, their fastest growing channel is the least profitable, as same-day deliveries, long-tail product line, and higher returns chip away at increasingly thin margins.
Thus far, most retailers have considered online as a separate channel and P&L, or at best, simply like a large store. To survive and thrive in the next wave of disruption, retailers need to transform deep into the heart of their business.
The focus needs to be on ‘profitable growth’ rather than on “growth” alone. In that context, it pays to deep dive into the key drivers of e-commerce profitability:
Product returns in e-commerce are 2x to 3x higher compared to traditional retail, making profitable returns a necessity. Data makes it possible to gain insights into reasons for returns and identify improvements at every stage of the customer journey. Retailers can segment customers and products based on returns history and promote or suppress marketing efforts based on the customer profile and product attributes. At the point of order, retailers can predict the profitability of each basket and improve margins through a variety of interventions. Post-order, retailers can fast-track re-commerce to maximise margins.
The first step may be to leverage cost to serve analytics to refine the product catalogue based on the profitability of each item. Retailers should evaluate strategies for “CRaP” products (acronym for “can’t realise a profit”), like limiting the number of items per order, order value thresholds, default to bulk ordering and shipping terms, among others.
Pricing is another important consideration. Retailers could wisely plan and push digital offers and promotions considering e-commerce margins. For some products, it may not be prudent to offer the lowest price on the digital channel and incentives like free two-day shipping.
Recent research from Gartner suggests that 86 percent of customers are willing to pick up items purchased online in stores to avoid shipping fees. By culling the product catalogue and providing options at differentiated price based on delivery type, it may be possible to steer customers to more profitable channels for the retailer.
The ability to effectively market long-tail products is key to improving ecommerce profitability. As an example, a digital native home improvement player provides a feature in their mobile app for customers to upload pictures of their rooms. Applying Deep Learning on the image data sets gathered helps predict individual customer styles. This then makes it possible to not only provide product recommendations on standard parameters (customer search, most viewed/last viewed/last purchases, etc.), but also context-based recommendations aligning with customer styles, while customers may not be aware or actively looking for those products in the catalogue.
An obvious one is data analytics, artificial intelligence (AI) and machine learning (ML). With enough high-quality data, retailers can identify trends that can help anticipate buying patterns, optimise inventory levels at the right locations at the right time, optimise store and fulfillment staff, and set prices and promotions that drive profitable growth. These technologies draw on a vast array of data sources, including past transactions, behavioural data, social media activity and geographical locations, to create algorithms and models that give the organisation a more complete customer profile and greater awareness of the business.
Significant opportunities to drive efficiencies exist via warehouse automation—to reduce efforts spent at every stage (receiving, picking, packing, loading, shipping) using RPA and other technologies like scanners, conveyor belts, robots, automated forklifts, exoskeletons and drones.
The 'cloud' holds particular promise. While many retailers have embarked on their IT modernisation journeys focusing on the front-end experience of their ecommerce platforms (like Browse, Search), they need to accelerate modernisation across core front, mid and back office functions. Areas like cart and checkout, inventory, demand planning, sourcing, order management and fulfillment, product recommendations, POS, customer service, payments offer strong value potential.
Laying the right foundation via customer data platforms is important to assimilate a holistic view of customers across all interaction points, second and third-party data, which can then be mined to analyse buying behaviours, perform real-time customer segmentation, and improve marketing effectiveness.
Retailers may also want to consider the differences between new and existing customers, and shape retention strategies accordingly. Loyalty, as a concept, is universal. However, the motivators for each customer are unique. As retailers begin to use Big Data to create deeper levels of segmentation within the customer group, it is possible to identify new ways of reaching the customer via 1 to 1 personalised offers that are most relevant to them.
The writer is, GVP-North America Retail Growth & Strategy Lead, Publicis Sapient.