Features, Technology

Startups with Big Retail Impacts

The Robin Report has written extensively about the many retail business models that are rooted in the 80s, mostly oblivious to the technological advances that could streamline inefficiencies, better allocate capital while also increasing customer satisfaction. Today, most retail C-suite executives are inundated with a steady stream of new technology vendors that promise the moon and the stars. These retailers know technology will support omnichannel customer-centric initiatives; the question is, how to choose?

Three retail tech startups are worth a read, and more. They solve big problems, are easily implemented, and can have a positive impact on sales and margins. They are early-stage companies with “genius” founders that have attracted smart investors with a deep understanding of retail’s pain points and how these solutions could move the needle.

Solving for Speed at PredictSpring

Social and mobile commerce apps have rapidly become a part of the daily life of most consumers who have expectations for instant answers, experiences and products. Call it insatiable instant gratification. Consumers demand a millisecond response time. According to Google, it takes 22 seconds for the average mobile landing page to fully load, Research indicates 53 percent of people will abandon a mobile site if the load takes longer than three seconds, clearly leading to low mobile conversion rates. In fact, Amazon reported that one second in latency costs them one percent in sales — or approximately $13 billion dollars annually.

PredictSpring solves this requisite need for speed with its mobile commerce platform and technology that powers native mobile and in-store apps with Instant Search, Dynamic CMS, and One-Touch Checkout. Its competitive advantage is speed, with the ability for mobile web and mobile apps to load more than 30 times faster than the leading existing technology, or in 2/10s of a second. PredictSpring easily integrates directly to the existing e-commerce platform, providing a quick, seamless omnichannel experience for consumers to shop anywhere, anytime, with a single touch of a button. Consumer engagements and conversions improve.

In addition to consumer-facing mobile solutions that make a mobile app launch easy from web, email, social media and paid channels, PredictSpring developed two in-store technologies: a store associate clienteling app that supports personalization strategies; and an endless aisle for in-store kiosks. These solutions have attracted 30 retailers and brands to date, including Calvin Klein, Cole Haan, Tommy Hilfiger, New York & Company, Skechers, and Vineyard Vines. Typically, they have seen strong growth in user engagement and GMV (gross merchandise value), such as 100 to 300 percent increase in app conversions and 10-15 times higher engagement on mobile apps versus other mobile platforms.

Mobile is today’s nexus, connecting us to all things social and commerce. According to Deloitte, more than half of all time spent on retail sites occurs on a mobile device. M-commerce is the fastest growing retail channel, accounting for 35 percent of all retail e-commerce transactions. Brands that leverage mobile as a primary channel to engage with consumers, whether online or in-store, will outpace brick-and-mortar retail. Platforms that remove barriers to purchase and simplify browsing, checkout and payments will win too. PredictSpring is that platform. Avery Baker, chief brand officer at Tommy Hilfiger, summed up PredictSpring’s benefits, saying “Our mission was to democratize the runway and make every look immediately available to all consumers around the world. Our social channels amplify the dynamic, engaging story around our runway shows, and we wanted to take this to the next level with the integration of shopping functionality via a dedicated mobile app. The PredictSpring approach creates a seamless consumer experience that reflects how the current generations of digital natives are using social media and interacting with their favorite brands today.” Founded in 2013 by Nitin Mangtani, the visionary behind Google Shopping and other early mobile commerce pioneers VISA and Motorola, PredictSpring has attracted a number of highly regarding early-stage investors and VCs, led by Felicia Ventures and its founder Aydin Senkut and includes Novel TMT Ventures, founded by Silas Chou’s two sons, Luis and Bruno Chou; Ken Seiff’s Beanstalk Ventures; and the Benvolio Group, the investment arm of Lew Frankfort, chairman emeritus of Coach. The total raised to date is a $11.4 million Series A closing in June 2016.

Moving Off-Price from Excel into the 21st Century

Off-price has been one of the more robust retail channels since the Great Recession, capitalizing on consumers’ desire for fashion and branded goods at value prices. High-touch service isn’t an issue, just leave them to their treasure hunt. Even the 10-minute wait to checkout isn’t a deterrent. And so far, e-commerce isn’t a threat, with little demand from loyal off-price shoppers demanding omnichannel functionality. In fact, e-commerce represents one percent or less at TJ Maxx and Ross Stores, versus an average of 17 percent for specialty apparel brands and department stores.

Shopping in a huge box with few amenities and stock full of low-priced branded product is the physical retail format of the decade, and the outlook remains solid, driven in part by value-seeking millennials. Since the financial crisis of 2008, off-price retailers have gained share at the expense of traditional department stores and specialty apparel retailers. Off-price retailers will be a solution for many shopping center developers seeking to lease shuttered JC Penney, Macy’s, and Sears locations. Brands often prefer working with off-price retailers too, given the simplicity of the transaction, i.e., no allowances or markdown money as is common practice with department stores.

That said, pity today’s off-price buyer, armed with little more than an Excel spread sheet. The buying process of off-price retailing is archaic, reflecting 30-year-old business practices that are ripe for innovation. INTURN has the B2B software platform that can replace lengthy Excel sheets of codes and sale rate negotiations (order, volume, sizes, discount metrics) for each order with a more robust solution that pairs product information with images as well as all requests, offers/counteroffers, and offer calculations located in one place. This potentially reduces the back-and-forth buying process from weeks to as little as 20 minutes while reducing human error. In doing so, INTURN can correct for the bane of every wholesalers’ and retailers’ existence: aging and depreciating inventory, the single largest line item of most retailers’ and brands’ P&L. Quicker buys would bring fashion product to consumers sooner—in season, and potentially at higher prices for both wholesalers and retailers. Everybody wins, consumer, wholesaler, and the brand selling the goods and the off-price retailer buying the product.

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INTURN’s software is designed to improve pre-negotiation efficiency, improve vendor (seller) liquidity and, by tracking selling and buying data, create BI (business intelligence) that supports predictive analytics. An estimated $250 billion of off-price inventory is sold globally and current practices result in billions of lost revenues. To quote INTURN investor and managing partner of Beanstalk Ventures, Ken Seiff, “This is the last frontier untouched by retail technology. While nearly every aspect of the retail experience has been automated, the sale of off-price inventory remains a very manual and inefficient process.”

Founded in 2013 by Ronen Lazar, INTURN has customers in 35 countries, including some of the world’s most renowned brands and retailers. Investors and backers include Forerunner Ventures, Novel TMT (Andrew Fine, INTURN co-founder), Lerer Hippeau Ventures, Beanstalk Ventures, Benvolio Group, and other strategics. Total raised, $13.55 million in three rounds, most recently $9.7 million Series A January 2016.

(Addendum

INTURN recently announced that it has raised $22.5 million in a Series B financing round led by B Capital Group, the firm founded by Facebook co-founder Eduardo Saverin and Raj Ganguly.

Previous investors include New York City-based firms — Novel TMT Ventures, Lerer Hippeau Ventures and T5 Capital — as well as San Francisco-based Forerunner Ventures and Benvolio Group, the investment arm of Coach.)

Technology Driving Superior Localized Selection & Allocation

If retailers could get the right product in the right store at the right time, wouldn’t everyone—retailer, vendor and customer—be happy? Predictive analytics based on machine learning tools originally developed at MIT and considered to be “one of the 50 greatest innovations ever produced by MIT’s Computer Science and Artificial Intelligence Laboratory,” is helping retailers optimize their overall inventory in an omnichannel environment, resulting in sales and profit gains.

Celect, a cloud-based, predictive analytics SaaS platform, uses machine learning based on retailers’ existing data. This includes inventory, transaction data, online data such as browsing history, shopping carts, and purchases to reveal preferences, optimizing inventories and assortments across a retailer’s store fleet, and providing insights into high-potential departments and categories at specific stores. The platform reveals the best stores to source online purchases while incorporating new relevant data and trends.

Historically, most merchandising decisions have been made using simple spreadsheets and gut instinct. With Celect, retailers now have a more precise and granular way to understand how customers choose between products, and how products interact with each other. These insights reveal true product demand and surface new opportunities for optimization that might otherwise have been missed with traditional segmentation approaches.

Simply put, Celect’s software quickly processes millions of data points from multiple sources in order to predict future demand. This results in prescriptive recommendations that are actionable. In addition to standard data such as inventory and transaction logs, Celect encourages other types of data such as social feeds, product reviews, in-store analytics, and browsing history as they all hone in on customer preference and ultimately should result in better product assortments.

Bryan Eshelman, COO of ALDO Group, said it best, “We have now reached a tipping point with consumers expecting everything available everywhere all the time. Lip service is no longer an option—meeting this expectation while simultaneously reducing inventory is simply impossible without advanced analytics.” Celect’s technology boasts proven results, including a seven percent increase in in-store revenue with optimized product assortments and a 45 percent profit increase reflecting margin improvement and fewer markdowns. Celect experienced a 250 percent revenue gain in 2016 and has about a dozen retail clients.

Two MIT professors, Vivek Farias and Devavrat Shah, founded Celect in 2013 based on their research on modeling consumer purchasing choices. Total equity funding raised is $15.2 million; most recently $10 million Series B February 2017, led by Activant Capital with participation from Fung Capital and August Capital.

 

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