Key Trends Reshaping the Retail Industry
With almost 50% of the population online, the world is witnessing a deluge of data like never seen before. In this wake, retailers are facing intensifying challenges. Among these challenges is the stagnation in retail brick-and-mortar sales. In fact, while overall retail sales have been growing slowly over the past couple of years, e-Commerce sales have been witnessing double digit growth (Exhibit 1).
In addition, customer behavior has shifted drastically over the past few years. First, consumers are making increasingly informed purchasing decisions, leveraging digital channels such as social media and search engines – a phenomenon labeled by Google as the “Zero Moment of Truth”. Second, millennials are becoming less focused on buying “stuff” and more interested in experiences. 
These trends, among others, are forcing retailers to find new ways to innovate in order to capitalize on the online boom and enhance the customer experience across all channels. Retailers are quickly realizing that data is one of their most valuable assets and are turning to machine learning (ML) to increase revenues and reduce costs (Exhibit 2).
Nordstrom Focusing on ML to Reap Maximum Value from Data
Nordstrom has been heavily investing in building its analytics capabilities to stay abreast of these trends. The retailer has been using ML for multiple use cases including in-store assortment optimization. Particularly, with exceptional customer service as its core value proposition, Nordstrom is leveraging ML to target its customers with customized and personalized marketing messages. In order to do so, the company relies on ML techniques that learn from historical data points (e.g. transactions, footfall, demographics) to predict future outcomes (Exhibit 3).
Nordstrom focused on leveraging data collected from its customers across multiple touch-points throughout their journey, including email campaigns, click streams and purchase data. After integrating data collected across various channels, Nordstrom leveraged ML to develop an “inferred scoring” system that allowed it to improve its marketing campaign efficiency and increase its email-to-dollar conversion by around 25%. In other terms, leveraging ML, the company was able to improve its predictions and offer customers more accurate suggestions of they want and/or need.
The company is also leveraging ML, particularly deep neural networks, to power its visual search engine. The visual search engine is focused on improving customer experience and boosting sales by allowing customers to upload an image of their product of interest to the search engine, which in turn offers to the customer direct or similar products. In order to deliver this functionality, Nordstrom partnered with visual search company Slyce.
Nordstrom is continuing to expand on its ML efforts through multiple initiatives such as the acquisition of two retail tech startups in 2018: BevyUp, a tool to empower salespeople to develop relationship with customers beyond the brick-and-mortar store, and MessageYes, a platform to enable Nordstrom to push personalized notifications to customers, requiring only a “Yes” response from customers to proceed with purchase.
Looking ahead, the company also established the Chief Innovation Officer role to focus on designing the“stores of the future” to allow Nordstrom to “better serve customers through further integration of digital and mobile”. In addition, the company is continuing to identify new ways to digitize multiple touch-points across the customer journey. A clear example is Nordstrom’s new location on West 57th Street in Manhattan deemed as a “sophisticated shopping-tech laboratory”. Digitizing touch-points will not only improve the customer experience but also allow Nordstrom to collect more data, especially in the brick-and-mortar environment.
Considerations for the Future
Nordstrom should continue exploring new ways to reap the full potential of ML and the digitization wave, not only to secure a competitive advantage but also to help address some of its key challenges. For example, one key challenge the company has been facing is a decrease in profits over the past years, some of which is attributed to its growing investment in its online platforms as well as the costs associated with managing increasing online sales (e.g. fulfillment).
Some initiatives that Nordstrom could explore include:
- Driving in-store purchases through its loyalty program: For example, similar to Macy’s, incentivize customers browsing online to complete the transaction in-store by providing additional loyalty points for in-store purchases.
- Leveraging ML to reduce in-store costs: For example, leverage ML for workforce management, including predicting store footfall data and scheduling employee shifts accordingly.
- Deploying artificial intelligence to continue providing exceptional customer service: For example, similar to North Face, deploy a virtual assistant to provide a personalized shopping experience for Nordstrom customers even when shopping online.
- Outsourcing some of its digital efforts to third-party providers, while keeping in mind privacy concerns.
Open Question: How to Survive the Amazon Era?
With around 60% of Nordstrom regulars having Amazon prime membership, how can the company find more ways to innovate to survive the Amazon era while remaining profitable?
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[Cover Picture] Kurt Schlosser, “In shifting retail battle, Nordstrom acquires two tech startups to further boost its digital credibility,” GeekWire, March 8, 2018, https://www.geekwire.com/2018/shifting-retail-battle-nordstrom-acquires-two-tech-startups-boost-digital-credibility/, accessed November 2018.