Advancement in digital technology has led to the emergence of an all-new consumer who is more informed and connected. Not only does this new consumer have multiple paths to making a purchase, she or he also has higher expectations in terms of quality, service and end-to-end experience. This fundamental shift in consumer behavior has uprooted the old guard of retail as many incumbents are saddled by outdated value propositions and infrastructure. Moreover, millennials, one of the major driving forces behind this shift, will continue to challenge the status quo in retail as they enter what economists consider their prime spending years starting in 2020 (Please read Fairview’s insights on “Gen Z – The Digital Natives” which highlights the power of generational shifts). Faced with a decline in foot traffic and store closures, traditional retailers are being forced to rethink their digital operations and presence. As a result, new opportunities are emerging for consumer startups and venture capital investors.

“The pain of store traffic decline is inevitable. The degree of suffering from it remains optional.” - Forbes 2017

U.S. Store Closures by Year

Source: Credit Suisse
Note: Full year store closures. The 2018 figure is an estimate from Credit Suisse

Since the beginning of the e-commerce revolution, traditional retailers have responded to the change in the commerce landscape by developing their own respective online channels. These online channels, for most retailers, serve as their dominant digital strategy. However, many of them still struggle to adapt their brand’s offline value proposition into a cohesive and consistent online experience. Furthermore, in a digitally pervasive era, retailers are often not involved in a consumer’s shopping journey as they may have been in the past. Increasingly, consumers are seeking product information outside the realm of the retailer and savvy retailers are working hard to remain relevant. One outlet consumers now use to seek product information is social media and some companies have taken an early lead in leveraging internet platforms to connect more warmly with consumers. Take venture backed company Glossier as an example:

Glossier is a direct to consumer beauty company creating modern beauty products for women. Its founder, Emily Weiss, started the company because she was “…bothered by her experience with beauty brands, which she felt were talking at her. Beauty shopping, she felt, lacked the context of real women and real experiences.” In 2010, Emily started a blog called Into the Gloss, where she interviewed women celebrities on their daily beauty rituals. The blog quickly became a popular destination for those interested in beauty and fashion as it provides in-depth commentaries on the products. Today, Glossier’s 1.5 million monthly unique visitors generate a “two-way conversation’ with the company’s product team in developing Glossier’s beauty products.

“Not only did this digital connectivity usher in generational shifts in how these individuals live, work, shop and play, but it also altered consumer behavior along the way. Connected consumers now enjoy increased levels of convenience, simplicity and knowledge. As a result, these consumers are more impatient, demanding and informed than others. They expect brands to create experiences tailored to their individual stories. They expect companies to communicate in the way that works best for them, whether by phone, email, social networks or chat apps.”
- Forbes 2017

In the case of Glossier, what started as a beauty blog, focused on “celebrity beauty aspirations”, transformed into an enviable e-commerce company. Sites similar to Glossier, such as Stitch Fix, Goop and Hims, all venture backed companies in Fairview’s portfolio, have become fierce competitors for consumers’ mind shares. If traditional retailers take a backseat in consumer engagement, their role in the marketplace risks becoming that of just another inventory provider. Strategically, this position not only diminishes traditional retailers’ role in the marketplace but it also erodes profit margins as goods become increasingly commoditized.

However, it appears that the brick-and-mortar retail model is not dead after all, as consumers still clearly value the in-person shopping experience. Despite the growth in online-only retail outlets, in 2017 e-commerce sales only accounted for 9% of all retail sales in the U.S. But perhaps more interestingly, digitally influenced physical store sales are far larger than all online sales combined and consumers who use digital devices as part of their in-store shopping journeys transact at a 20% higher rate than those who do not utilize a digital device [1]. There has been a clear upward trend in overall digital usage while shopping, to the extent that in 2016, digital interactions influenced 56 cents of every dollar spent in brick and mortar stores – up from 36 cents just three years prior [2]. In fact, pure e-commerce companies are starting to open physical locations as some have reported a 5x to 8x increase in sales when they couple online offerings with physical stores. Nonetheless, questions still remain on how traditional retailers can effectively compete and build a seamless personalized shopping experience beyond e-commerce 1.0.

U.S. E-Commerce Share of Total Retail Sales

Source: Statista.com

Commerce 2.0
As offline, online, mobile, e-commerce, and brick-and-mortar continue to blend, retailers need to identify the points along the purchase journey that are critical to their consumers. One way for traditional retailers to compete and reach today’s digitally self-directed consumer is through the promise of deep machine learning and predictive artificial intelligence (“AI”). (For an in-depth discussion on advancement in machine learning technology, please see Fairview’s report on “Machine Learning: Predictive Software and the Next Wave of Information Technology Innovation”).

Forward thinking retailers have been using big data and machine learning algorithms to forecast demand and set prices. Big data help retailers curate assortments at the store level, anticipate changes in customer traffic and determine optimal distribution routes. Not only does this enhance the customer experience, it also improves unit economics. In a recent interview, on-line retailer Stitch Fix’s (please see below) CEO, Katrina Lake, touted the company’s data science team, which has developed dozens of algorithms to help create new solutions in every aspect of its fashion business [3]. In fulfillment and inventory control, she explained that data is helpful in keeping capital costs low, inventory moving and deliveries efficient. The company’s product development is also starting to leverage machine learning to design apparel. Perhaps the most insightful use of data is around the company’s collection of measurement data – i.e. sizes and fit. Through more than 2 million active clients, Stitch Fix now knows, for example, the optimal ratio of chest size to shirt width on men’s shirt.

Stitch Fix is an online subscription and personal shopping service founded by 2011. The company generated $730mm of revenue in 2016 and $977mm in 2017.

The company utilizes data science in every aspect of its business and employs 80+ data scientists to build algorithms around Stitch Fix’s customers and their needs.

Stitch Fix received venture funding from Benchmark, Lightspeed and Baseline Ventures totaling $42.5mm. The company went public in November 2017 and was valued at approximately $3.5 billion in August 2018.

Other areas of AI usage include but are not limited to [4]:

  • Communication – online personalization, conversational robots, chatbots and voice shopping
  • Optimization of shopping – demand forecasting, market and competitor analysis and promotional strategies
  • Rationalization of inventory – automated shelf and inventory audits, demand forecasting, automated replenishment and excess stock reduction
  • Experiential retail – delivery of new ways to discover, buy and pay

Moving forward into an era of predictive commerce, retailers need to be present and proactive in helping consumers find products in their precise moment of need. Amit Sharma [5], CEO of Narvar, a Fairview portfolio company, defines predictive retail as inspiring and engaging consumers in different contexts – before, during and after a purchase. The next generation of retail involves learning from user habits and picking up subtle behavioral patterns from a wide range of data sets including: consumer preferences, purchase histories, competitors’ pricing, inventory, current / forecasted product demand and more. This type of analytical power is made possible today as infrastructure, speed and scale allow for bolder algorithms to tackle complicated and ambitious problems [6].

Bottomline, data is the key to true customer personalization. In an illustrative purchase transaction, it may help to visualize the data needed to predict this personalized offer and the valuable data collected through the purchase process (for a larger version of the image below, please click here).

Many high touch retailers are already betting on AI technology. Earlier this year, Nordstrom acquired two retail technology platforms focused on personalization – BevyUp and MessageYes. BevyUp’s platform allows Nordstrom associates to engage with its clients through its mobile friendly tool Style Boards. Via Style Boards, store associates can curate personalized style pieces / recommendations for clients outside of the store setting.Here, we see the integration of physical and digital shopping experiences – a valuable benefit to brick-and-mortar retailers. Through MessageYes, Nordstrom will be able to leverage its AI technology and integrated payment systems to send highly personalized and automated product recommendations through text messages to customers. Both acquisitions demonstrate ways that a traditional retailer is taking its customer-centric roots into the digital age.

We believe more retailers, both old and new, will utilize data and technology to reinvigorate commerce. Some will develop these technologies organically while others may choose to acquire. As retailers re-design their consumer-centric ecosystems, the usage of AI will play an important role in bridging the physical and digital worlds. The new ecosystem will incorporate pieces of the retailers’ past, present and future into an “omni-state” where human connection, product storytelling, inventory fulfillment, price optimization and data all come together as one seamless experience.

Fairview’s Involvement in Consumer Technology
We continue to see top tier venture capital funds invest in AI and more specifically around its usage in the next wave of commerce. A new generation of consumer technology focused venture capital funds has emerged in recent years and many have made great strides in building their respective franchises. Firms such as Goodwater Capital, Foreunner Partners and Greenoaks Capital Partners, just to name a few, have been formed to capture the massive market opportunity the consumer space has to offer. Moreover, many tenured venture capital firms have established investment theses in the area of consumer technology and often employ partners who focus on the space.

Fairview’s venture capital portfolios have developed meaningful exposure to many of the most innovative and high potential consumer technology companies. Fairview’s approach to investing in companies developing consumer related solutions has been through participating in partnerships sponsored by top venture capital firms best positioned to identify, access, grow and successfully exit companies in the space. We expect to see the underlying trends supporting the rise of AI related consumer technology to endure and believe that the most transformative companies will have the potential to create tremendous value as independent companies as well as for traditional retailers.


Sources:
[1] Deloitte
[2] Deloitte: "The New Digital Divide"
[3] Harvard Business Review: "Stitch Fix's CEO on Selling Personal Style to the Mass Market"
[4] Coresight Research
[5] Battery.com: "How Predictive AI Will Change Shopping"
[6] Wired: "The AI Resurgence: Why Now?" by Babak Hodjat


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