The Four Pillars of E-commerce Profitability
How Retailers Can Optimize Digital Channels to Increase Organizational Efficiency, Improve Customer Centricity and Drive Growth
In less than a decade, e-commerce sales are expected to quadruple, accounting for 40 percent of total revenue1. For traditional retailers, this meteoric rise of digital is no small concern. In many cases, the fastest growing channel is the least profitable, as features like same-day delivery, long-tail product assortment and hassle-free returns made popular by marketplaces and digital natives chip away at increasingly thin margins.
And yet, an opportunity exists. The shift from one channel to another calls into question everything retailers thought they knew about market share, growth forecasts and customer loyalty. In this new landscape, as consumers re-evaluate their buying options, retailers have an opportunity to win new customers and increase their share of wallet with existing clients. What stands in the way of growth, however, is the profitability of their e-commerce channels.
To remain viable in the coming years, traditional retailers must consider how they can optimize digital channels by lining up operations, people and technology across the value chain to unlock new efficiencies and build value for both the customer and the business. To do so, organizations must assess each business function, identifying areas for cost savings and determining ways in which to improve the customer experience.
An efficient and cost-effective fulfillment process is the cornerstone of every retail organization. But as shopping continues to shift to digital channels, many retailers find that their fulfillment costs are rising. In fact, for the average retailer, fulfillment costs have increased 12 percent over the last 12 months.2 Even Amazon saw shipping costs jump 23 percent in Q418, reaching a record $9 billion.3
At the same time, research shows that shipping costs and delivery lead times are an important differentiator for shoppers. For example, 27 percent of shoppers abandoned a cart because same-day shipping was not available.4 Three in four shoppers (75 percent) expect shipping to be free, even on orders under $50.5 Thus, fast and free shipping is a driver of sales—even as it eats into profit margins.
How do retailers cope? Nearly all retailers can generate significant efficiency gains in the area of both fulfillment and returns by refining their supply chain. In many cases, making small changes can generate significant returns. For example, one way retailers can trim transactions costs is to play on human psychology by auto-populating the shipping option to one that is more cost effective for them (for example, 2 or 3 day shipping), while still allowing customers to change to a more preferential option if they prefer. For clothing retailers, limiting the number of items a customer can buy in different sizes to reduce the rate of returns and help avoid the need to discount unwanted items may be another effective strategy. For seasonal items, incentivizing customers to return unwanted items more quickly could also be effective.
Looking beyond these short-term implementations, retailers can address these issues at the operational level by investing in four key areas of supply chain optimization. These core changes will help enable them to be agile, predictive and responsive while maintaining a laser focus on costs:
- Develop a digital replica of real-world applications to plan, predict, sense and react to e-commerce operations. One client uses 440 billion points to model costs to serve across its network, thus driving a data centric decision to serve a shopper or store with a particular product.
- Optimize inventory costs through more precise demand and inventory predictions across ALL inventory. One client uses big data across more than two billion item combinations every three hours to drive forecast optimization.
- Optimize fulfillment to drive faster, more expansive, more cost-effective service through cognitive order optimization engines. Some of our clients use cognitive order management to determine optimal shipping rates and balance capacities across stores.
- Optimize the returns process through digital interventions and cost optimizers. Several of our clients are exploring ways to identify shoppers with a high rate of returns vs. those who do not. As such, it is possible to incentivize positive customer behavior or discourage customer practices that are costly to the business. In addition, having a better understanding of the return rate may help optimize the entire returns process by better assessing the cost of re-stocking against potential margins.
In the digital world, Amazon has many advantages, not the least of which being a long-tail product catalogue. As a marketplace, this organization can offer a wide range of products with minimal inventory and warehouse costs. Unfortunately for most retailers, the Amazon model, while effective, will be difficult to replicate.
So how do retailers compete? The first step may be to consider the profitability of each item in the catalog using granular cost to serve analytics. For example, bottled water, soft drinks and snacks are sometimes referred to as “CRaP” products, short for “can’t realize a profit.” Bought online, these items are heavy or bulky to ship, often resulting in high delivery costs, despite being a low-ticket item.
As part of a holistic e-commerce profitability strategy, retailers can determine the best way to lower the cost of selling these particular items. Again, limiting the number of each item per order or only allowing purchase for orders exceeding a certain dollar amount may make sense. Another option may be to default to bulk ordering and shipping terms. In extreme cases, retailers may consider cutting the product altogether, though such a drastic step may not appeal to customers who crave the convenience and simplicity of one-stop digital shopping.
Another area for consideration is pricing. Many retailers continue to unnecessarily push digital offers and promotions, even though e-commerce profit margins are already razor-thin. In some cases, retailers even offer the lowest prices on digital channels. As customers continue to shift purchasing online, it may not be necessary to offer consumers incentives like free two-day shipping or discounts. Indeed, recent research from Gartner found that 86 percent of customers are willing to pick up items purchased online in stores to avoid shipping fees.6 Retailers should also consider robust automated price matching algorithms that can not only scrape data and analyze the best price from competitors but also identify the right source for comparison. For example, a third-party provider selling an obsolete product at a discount has the potential to trigger algorithmic price wars within the current catalog.
Part of retailers’ e-commerce strategy should also include determining the optimal price and product catalogue, not simply based on the existing numbers used in stores, but by the operating costs of digital channels.
Enabling e-commerce profitability through augmentation, robotics and cognitive process automation
The successful development and implementation of any e-commerce profitability strategy relies on two main areas: human capabilities and data. These are crucial enablers of any transformation effort, big or small. Because the digital world is largely uncharted territory, most businesses must assess both areas to determine gaps and also re-structure the organization in a way that enables profitability.
For human capabilities the most obvious concern is ensuring that the business has the necessary skills and experience. Beyond that, there may be a need to adjust the organizational structure in order to make the best use of each person’s skills. Flattening the organization and moving decision-making into the hands of team leads is one possible way to increase speed and efficiency. Further, augmentation of human capabilities through the use of robotics, such as exoskeletons in warehouse or process automation, is another way to augment the workforce.
No matter the approach, refining the organization’s e-commerce profitability efforts will demand a tremendous amount of flexibility not just for the organization, but everyone in it.
E-commerce profitability is tied closely to technology—and with good reason. Each of the examples discussed thus far within the supply chain and product catalogue rely on technology, in one form or another, to identify the opportunity and activate the solution.
Perhaps the most obvious example of the power of technology can be found in data analysis and, by extension, artificial intelligence (AI) and machine learning (ML). With enough high-quality data, retailers can identify trends that can help anticipate buying patterns, optimize inventory levels, right-size the workforce, and set prices and promotions that maximize revenue. These technologies draw on a vast array of data sources including past transactions, behavioral data, social media activity and geographical location, to create algorithms and models that give the organization both a more complete customer profile and greater awareness of the business. AI can also be used to power solutions – from 24/7 customer service chatbots to customized product recommendations – thus helping retailers increase the profitability in the burgeoning digital world.
An equally powerful opportunity lies in robotic processing automation (RPA). Many warehouses are woefully out of date, relying on humans to move throughout both the store floor and the backroom to collect items for digital orders. Through the use of scanners, conveyer belts, robots, automated forklifts, exoskeletons and drones, the time spent at every stage of the fulfillment process can be significantly reduced. Further, not having a centralized and automated shipping facility makes it nearly impossible for retailers to achieve the efficiency of Amazon or other modern e-tailers, all of which are leveraging the latest in robotics and automation technology to increase efficiency.
However, the deployment of data-enabled tools and robotics requires a level of speed and flexibility far beyond what is supported by many retailers’ current IT operations. While organizations appear eager to adopt advanced technologies such as AI and ML, their ability to effectively do so—and harness the applications’ full benefits—may necessitate infrastructure upgrades. The cloud holds particular promise as migrating key business services to a cloud-based system enables near real-time performance monitoring and inventory awareness. These insights can unlock new levels of efficiency across the value chain—from inventory optimization to product recommendations to customer service. Cloud-based technology also provides the speed and flexibility needed to scale, which is a crucial point as retailers attempt to expand successful pilot programs or test applications throughout the organization.
While virtually all organizations recognize the value of technology, many stop short of implementation, largely due to cost concerns. However, technology infrastructure investments are just that—an investment. Increasingly, these capabilities are not just an enabler of growth, but the key to viability. Fail to keep pace with digital applications, and the entire business will suffer.
There is ample evidence that cost-conscious retailers are being pennywise and pound foolish when it comes to IT modernization. Recent research from Publicis Sapient revealed that IT infrastructure investments can pay back in 2-3 years while continuing to create long term value7 through increased conversion, affinity and loyalty. Further, our research estimates an annual cost savings of 17.5 percent, as delivered through reduced operating costs, increased productivity and quality.8
Most major retailers know how much it costs to acquire a customer—and regain them if lost. But what does it cost to acquire a new digital customer? What is the most effective way to market to him or her? Perhaps more importantly, what is the cost of having a traditional customer switch to digital channels?
Just as pricing for a product may differ from channel to channel, so does customer marketing. Costs associated with drawing a customer into a store and onto a mobile site are markedly different.
Once again, data will help retailers find the answers. By analyzing sales figures against all marketing efforts, it is possible to understand what types of offers consumers are most responsive to. This provides the opportunity for optimization as the most expensive advertising options are not always the most effective. For example, some retailers may be able to focus efforts not just on the channels that generate the most return, but on the days and times that are most effective. In this way, retailers can shave marketing costs while still generating brand engagement.
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 with the products and services they most desire. With some advanced technologies, it is even possible to enable true personalization and treat each shopper as an individual, as opposed to part of a segment.
For example, many retailers’ eommerce strategy includes AI-enabled product recommendations. Typically, this effort is based on the customer segment and has a decent conversion uplift as compared to doing nothing. However, incorporating deep learning capabilities, as well as integrating multiple AI models into a single activation, can further enhance results. For example, offering truly personalized product recommendations can improve conversion by 2 percent. Incorporating additional models to determine what channel the shopper is most responsive to and what time of day they are most likely to engage can further increase conversion six-fold, from 2 to 12 percent.
Another way to offset some of the costs associated with digitization is to monetize proprietary data. CPG companies, in particular, are desperate to gain access to consumer data, especially as it relates to transactions and behavior. Selling this information to other organizations may provide retailers, especially in the grocery vertical, with a valuable revenue stream.
The changing retail footprint
As we discuss e-commerce profitability, we would be remiss not to mention how retailers’ physical footprint must also change as a result of shifting customer behaviors. Foot traffic to stores continues to fall, while online retail has grown 300 percent since 2008.9 Many retailers must adjust their physical footprint to accommodate these changes.
In today’s landscape, the strategy to open as many stores as possible is neither viable nor practical. Instead, retailers must focus on having effective stores. This may mean downsizing to smaller stores, moving to different, high-traffic, convenient locations, or reformatting to better accommodate in-store pickups.
While many retailers have already begun their e-commerce profitability journey, few are taking a holistic approach. This is not sufficient for long-term growth. To truly optimize, one must examine the process from end-to-end and combine process optimization with understanding of consumer needs and psychology. As a leader in this space, here we offer some thought-starters for organizations at any stage of their e-commerce profitability journey:
- How can we adjust shipping options to maximize profit margins without impacting the customer experience?
- How do we evolve our returns process to shorten the timeline and minimize loss?
- How can we use data and advanced technologies to predict demand, inform inventories and improve forecasting?
- How do we identify our highest and lowest profit items within the digital catalogue?
- How do we refine our digital pricing strategy to account for higher operating costs?
- How do we enable dynamic pricing to account for surges or lapses in demand?
- How can our organization leverage vast stores of proprietary and third-party data available?
- How do we introduce robotics and RPA within our organization?
- At what point will our organization see a return on IT investments?
- What is the cost of acquiring and retaining a new digital customer?
- What is the most effective way of reaching new customers or regaining old ones?
- How do we create a customer experience that discourages costly and inefficient consumer habits?
- How can we monetise consumer data to offset the cost of technology investments?
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