Future of Online Markets: Increasing Mobile Channel Differs From PC Channel

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The mobile channel is poised to be the future of online markets, according to ITOM Professor Tom Tan of SMU Cox. And, retail operations managers should not blindly treat the mobile channel like the conventional PC channel. In a working paper, Tan and co-author Nitish Jain make important contributions to innovative retail practices and operational decisions.

Shopping on a mobile device, or m-commerce, is a rapidly accelerating trend. Not only is online shopping overall growing at 25% per year —and predicted to reach $ 480 billion in 2019—but the m-commerce share of that total is increasing. In 2014, m-commerce accounted for 11.6% of the US’s e-commerce and is estimated to reach 45% by 2020. M-commerce is forecast to be 76.4% by 2020 in Asia Pacific countries. In 2018 worldwide revenue for apparel products, the subject of the study, was $418 billion and is expected to increase to $713 billion by 2022, according to another recent study.

Tan notes, “There is some debate about managing in a time of the omni-channel, encompassing online, offline, PC, and mobile channels. One would argue that all channels should be treated together as one single retailer and consider overall demand, ie., demand is demand.” Other schools of thought say different channels should be considered separately, given their uniqueness. “Our findings support this school of thought that managers should treat channels differently,” Tan observes. “Importantly, these nuances should be taken into consideration for inventory decisions.”

In terms of search costs, the mobile channel is actually similar to brick and mortar stores. “At stores, people do not search extensively,” says Tan. “Most people are attracted to popular products, or the hits, and this is observed in the m-channel in the research.”

The mobile channel offers search features distinct from conventional personal-computer-based (PC) channels. While its easy accessibility extends time available for customers to search, it imposes constraints from small screen size and single-tab browsing, which may inflate search costs. “Higher search costs would predict higher sales concentration,” says Tan. “Although mobile channel also happens online, it still has more demand for ‘hits’ or popular products than the PC channel. This finding is in opposition to the long tail effect, which predicted more niche products would be in demand from online shopping.” (A high sales concentration indicates that only a few products contribute to a large share of sales; lower sales concentration may warrant higher inventory levels, having to stock a wider variety of products.)

Search costs an issue
In the study, the authors collaborated with a large online apparel retailer in India to access customer-level transaction data from January 2015 through September 2015. They found a clean research setting from the online company because it stopped its PC channel for a period of time, typically operating both a mobile and PC channel.

The authors find that sales in the mobile channel are more concentrated than in the PC channel. In particular, it increases the share of popular products in all purchases by approximately 5%. The finding suggests that the high search costs on the mobile channel dominate the effect of more available search time. This search cost explanation is evidenced by the fact that the share of top-displayed products is significantly higher in mobile channel purchases than in PC channel purchases. The mobile channel may materially affect customers’ demand for different products due to its distinct search features.

Manager implications
The research highlights that the mobile and PC channels have different sales concentrations because of different search features. “It is imperative for managers to revise their status quo strategies on inventory procurement, assortment planning, and product display, when integrating m- commerce with e-commerce,” the authors write. Tan offers specifically, “In operations, if low sales concentration exists, ie., more demand for niche [products], then the retailers should invest more in inventory because the demand is uncertain. They can hit the bat anywhere—needing to purchase more inventory that covers popular plus niche products.” That’s the conventional prescription of operation management theories.

In the m-commerce era, given these new findings, the m-channel increases sales concentration. Old practices would dictate over-investment in inventory. “If we treat m-commerce differently from the PC channel and manage the inventory based on the sales concentration, companies can save significant amounts of inventory costs, estimated at 6.1 -17%,” advises Tan.

Tan further suggests that companies offer more selective offerings or more targeted, curated products offerings. They should also think about improving their recommendation systems, so that people can find products on mobile channels. Some company’s websites have rotating product assortments, especially fashion retailers, and often offer flash sales. “This is in the spirit of treating different channels differently,” he says. “The mobile channel does not have to have the same expansive variety or assortment as the PC channel.”
Demand on the mobile channel will be more concentrated for popular products than theory would suggest. The bottom line: the combination of a limited search budget and high search costs can dissuade customers from searching an entire product offering, thus increasing the sales concentration of popular products. Managers take note!

The paper “M-Commerce, Sales Concentration, and Inventory Management” by Tom Fangyun Tan of Southern Methodist University’s Cox School of Business and Nitish Jain of London Business School is forthcoming in Manufacturing & Service Operations Management.

Summarized by Jennifer Warren.

Tom Fangyun Tan is an associate professor at the Information Technology and Operations Management Department (ITOM) at the Southern Methodist University (SMU) Cox School of Business. His research interests include empirical operations management in service sectors. In particular, he researches on the impact of technology on service operations, and labor productivity in service operations.