How artificial intelligence fits into e-commerce

Three elements of artificial intelligence—data mining, natural language processing and machine learning—can help online retailers improve results.

 

According to the recent Forrester report, Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution, AI will grow 300% in 2017, and “will steal $1.2 trillion per annum from their less informed peers by 2020.” Numbers like these are behind the surge in retailers betting big on AI. Particularly for retailers looking to gain a leg up in e-commerce, AI is hard to ignore.

Still, while retailers have generally recognized the importance of AI, there is also plenty of confusion when it comes to relevant terminology and real-world applications because of the marketing noise around the technology.

 

So, what exactly is AI?

Market confusion on what AI is and what it’s capable of has in large part been driven by the simple misuse of the term AI.

AI is not a singular technology. It’s comprised of multiple components, such as machine learning. These component technologies that make up AI each have their own inherent value—but as with many things when it comes to marketing cutting-edge technology, the nuance is lost for the buzz-worthy.

This is one reason AI has become a catch-all word for multiple technologies. To move AI forward, we need to embrace the nuance of what it actually is.  Here are three important aspects of AI that e-commerce businesses need to understand:

  1. Data Mining – Often also referred to as Knowledge Discovery, this includes the technologies and methodologies for identifying useful and meaningful patterns in data. This is often based on extremely large data sets.
  2. Natural Language Processing (NLP) – This is the process used to assign meaning to human sentences. In the parlance of the industry, it provides machines with the ability to understand the languages that humans speak. Within the context of the e-commerce, it allows computers and software programs to understand the sentiment of a consumer’s written word—whether it’s email, online, social media, etc.
  3. Machine Learning – This is a specific process within AI, and refers to the science of self-learning algorithms. At its core, Machine Learning is about the use of statistics to solve problems using the data from the knowledge discovery process. The driving concept of machine learning is using technology to help humans think better.

Beyond these three components, AI also encompasses Neural Networks (computer systems modeled on the human brain and nervous system) and Robotics. However, neither of these fields are relevant for e-commerce at this time.

 

Depending on your goals, find the gaps in your data and fill them

 

AI Applications in E-commerce

By better understanding the technology underpinnings of AI, you can better view the world of possible applications and the specific impact it can have on retailers. A few examples include:

  • Dramatically Improved Search Capabilities – Although retailers have made significant gains over the years, today’s search algorithms still lack the ability to understand a given search query with the nuances of language, as a human would. It just takes a look at Siri to see we have a long way to go. Machine Learning combined with NLP capabilities, which fall under the AI umbrella, can improve search engines’ ability to learn from each new interaction, better understand what a customer is querying for, and deliver more relevant results—even if the wording isn’t exactly as programmed.
  • AI-Fueled Personalization and Predictive Recommendations Drawing on all three aspects—Data Mining, NLP and Machine Learning—retailers are now able to combine data gathered from transactions across all channels with actions taken by the consumer throughout the day, even those that aren’t necessarily related to shopping, to gain a deeper understanding of consumers’ wants and needs. For example, say a consumer tweets about it being cold outside. A brand may know from purchase history that she recently bought a winter hat, and that many hat buyers also like to purchase gloves. They could then respond to the consumer with some glove recommendations, or a discount offer. Beyond the “personalization” of the past, e-commerce companies can now understand what drove individual product purchases, and use that information to predict which products customers might be interested in, and even how those products could be customized to fit their personal preferences.
  • Improved Customer Interactions63% of consumers are highly annoyed by the way brands continue to rely on the old-fashioned strategy of blasting generic ad messages repeatedly (Marketo). Retailers can avoid this customer fatigue and drive e-commerce sales today by using Data Mining and Machine Learning to understand how each customer wants to be reached, and how often.

As an example, marketers already measure open rates on email, so they know if they’re getting customers’ attention. They also measure website clicks as a gauge of customer interest. If that click is on the “thank you” page on an ecommerce site, they know the customer has already purchased something, and likely even what that item is. Using this data, enriched with other data about the individual, purchase history and other factors through Data Mining and Machine Learning, allows marketers to communicate with consumers in real time with the right offer at the right place and time. They can understand when each customer will be most receptive to and offer, and what that offer should entail in order to capture their attention.

  • Concierge as a Service – Chatbots give marketers the ability to interact with the customer in real time and learn on-the-fly what the customer needs and deliver specific prescriptive guidance and results. Though the idea of bots has been around since the ‘50s and ‘60s when Alan Turing and Joseph Weizenbaum invented the first “chatterbot” program, Eliza, AI has given the technology new teeth—enabling conversational commerce.

As an example, a customer may be shopping for a particular brand of mascara on a website. Using Data Mining on past information about the customer and others like her, and Machine Learning to react to this new data about the customer, a bot agent could pop up and give her exclusive personalized offers that meet her needs and are relative to her color preferences, demographic, time of year, price triggers and sense of style. Given the amount of data to create a personalized interaction, a mere decision-tree logic would not work. Deep-learning customer models must be created in order to “react” to the consumer in real time, and NLP is needed to interact with her in a conversational way.

 

First Steps to Implementation

The list of applications for AI could go on, but the first steps for implementing the technology are similar regardless of how you choose to employ it.

First, get a comprehensive view of your existing data points. This could include CRM data, transactional data from online or mobile, demographic information, third-party sources—any and all context you can gather on your customers and their preferences can be relevant.

Second, determine your goals. Do you want to increase sales among your existing customers? Bring in new customers? Determine when you should have sales? Figure out which products you should be stocking? Having a specific goal will help you determine the best route to get there, and gauge the effectiveness of your efforts.

Next, depending on your goals, find the gaps in your data and fill them. The power of AI is that it can uncover correlations that aren’t readily apparent, and no company has access to 100% of the data they need on their own. For example, Starbucks may not know how heavy traffic is today or what the weather is in your current location, but there could be important correlations between those pieces of data and whether you are buying a coffee because you’re tired or because it’s cold outside.

Finally, it is important to understand that the Data Mining and Machine Learning process takes time. It is still a form of A/B testing, just done much more rapidly and on a massive scale. You can’t expect immediate results as the system needs to learn from successes and failures. For those that get a head start now, however, there is an incredible opportunity to grab a larger slice of that $1.2 trillion pie in the years to come.

Grey Jean Technologies provides personalization technology to retailers.

 

PYMNTS : AI + Consumer Behavior Data = Sales Growth

“With the popularity of Amazon, Pandora and Netflix, today’s consumer has the expectation that retailers will know them personally,” said Craig Alberino, president of Grey Jean Technologies. “When they walk into the store or browse online, they expect an experience tailored to their unique needs, desires and wants.”

Grey Jean Technologies marries artificial intelligence with customer and payment data to generate accurate predictions of consumer behavior that ultimately focus on driving sales figures. The New York City-headquartered company uses its proprietary AI-powered recommendation engine called Genie to quickly merge existing data sources with more than 500 consumer behavioral attributes to identify what patterns connect consumers with specific products. These consumer insights are used by marketers and executives to engender ways to enhance interaction with consumers and ultimately improve sales.

“The right recommendations serve as a valuable discovery mechanism that connects customers with the content and products they actually want,” said Alberino. “ECommerce personalization helps retailers meet their customer’s needs more effectively and efficiently, making interactions faster, easier and more satisfying — encouraging repeat purchases and creating loyal customers.”

Back in 2015, conversations about the business began to swirl between the two founders, Cosmas Wong and Alberino, as a way to apply their combined experiences of working with Big Data in the financial and retail sectors. Grey Jean launched in May 2016 at the Shoptalk Conference, where Genie was unveiled, along with the work the firm was doing with its first two clients: Hiro Sake, a handcrafted premium spirit company, and Namco Pool, one of the largest dealers of swimming pools.

“Predicting what shoppers will buy next has long been a dream for retail marketers, but very few have had the technology to pull off that vision,” said Alberino. “While many advancements have been made over the last 20-plus years, true one-to-one, real-time personalization is still a rarity in retail, due to its historical struggle with lack of data, followed by too much unorganized data.”

Alberino said that, through advances in artificial intelligence and machine learning, retailers have the ability to harness the power of their data to predict consumer purchase behavior and more effectively target those customers based on their unique preferences and behaviors. And, over time and through more data, Genie’s predictive algorithms inspire better recommendations, which, in turn, is an increased benefit to retailer clients that are looking for a slew of outcomes.

“Genie has demonstrated a 72 percent accuracy rate in predicting a next likely purchase at the category level. This accuracy enables our customers to improve personalization and micro-targeting but also take actions that will be the most effective with each consumer,” said Alberino. “This results in more conversions, higher redemption of coupons and promotions; increased visit frequency, foot traffic, time in store and basket size; and greater brand affinity.”

Alberino gave the example of one of its new clients that has already achieved some quick success. Pure Green, a New York City-based juice bar, had Grey Jean provide insights into its customer base as it sought to expand the number of its retail locations. First, there were three locations, but the hope is for 30 by the end of 2018.

“Leveraging Genie has allowed Pure Green to have a greater understanding of their consumers’ demographics, preferences and behaviors, helping them identify who their target audience really is and, subsequently, where they would have the most success with a new storefront,” said Alberino.

Ultimately, more time and more data only helps this type of technology. The company said that, each passing year, Genie’s algorithms get stronger and more refined. And it helps that more people are shopping digitally, where AI can easily live.

“We plan to stay ahead of the curve by continually integrating new attributes and data points that keep Genie the most accurate recommendation engine for predicting consumer purchase behavior,” said Alberino. “We also want to help shape retailers’ understanding of AI, which is currently used in many ways within the market.”

While some retailers currently use AI technologies — such as natural language processing — for sentiment analysis, Alberino said that this application alone will not give the full picture of what retailers are looking for in terms of consumer shopping behavior. While it may be helpful to know how a consumer feels about an ad or product, retailers also need to understand what drives purchasing.

“Focus on using artificial intelligence to drive actions, rather than just delivering insights,” said Alberino. “We actually help retailers take an action with their customer, rather than just presenting their data in new ways and leaving them wondering what to do with it.”

The Changing State Of Retail In 8 Charts

Even as investors are pulling back on e-commerce, retail corporates are making big acquisitions.

From increased investment to in-store tech startups, to the development of next-gen distribution methods, to trends among retail corporates, we look at the most important developments shaping retail.

1. Investor excitement surrounding in-store technologies

Funding to startups offering technologies for use in-store is rising to an all-time high this year. These technologies, ranging from store management platforms, to wearables for store staff, to beacons for in-store analytics and proximity marketing, are on track to see over 170 deals worth nearly $800M in total this year.

In Store Tech Funding Slide

 

2. Smart money VCs lead the in-store investment charge

In an even more positive signal for in-store technology, smart money VCs are also ramping up their investment activity. Notable deals with smart money participation this year include a $19M Series B to Index, a software platform for offline retailers, and a $30M Series B to Zenreach, a platform that provides guest Wi-Fi systems and traffic analytics to retailers and cafes.

In Store Tech Smart Money

3. 133 startups transforming in-store retail

The market map below highlights the growth in diversity of in-store technologies. Categories including point-of-sale financing options, connected beacons and sensors, interactive in-store displays, robots and chatbots for in-store use, and even music management systems for retailers have become crowded with startups.

In Store Tech Market Map Slide

 

4. New distribution methods bring the transaction to the consumer

While technology can help turn stores into valuable data collection centers, other technologies are enabling a greater dispersion of the point of sale than ever before. Some of these separate the consumer from the purchasing decision. Personal styling services and subscription services, for example, curate selections of items for the customer, eliminating the need for a customer to choose a brand. Others aim to make purchasing as instantaneous as possible. For example, Kwik and Hiku produce connected at-home devices, similar to Amazon Dash buttons, that let customers quickly re-order items by voice or by pressing a button. Cargo, another next-gen retail distributor, helps Uber drivers sell packaged goods to riders from the backseat of their cars.

Distribution Market Map Slide

 

5. E-commerce sees investment slump

While in-store tech is seeing a rise in investor interest, the much larger e-commerce category is in a slump. Investment into the space has declined dramatically this year. Deals to e-commerce startups are on track to fall below 2013 levels in 2016.

Ecommerce Funding Slide

 

6. Smart money VCs have pulled back even more on e-commerce

In contrast to the increased excitement we saw among smart money investors toward in-store tech, smart money VCs are pulling back on e-commerce even more dramatically than investors in general. They began turning away from e-commerce earlier than other investors, as well, with deals falling in 2015 even as total deals among all investors reach an all-time high.

Ecommerce Smart Money Funding Slide

 

7. Corporates opening their wallets for top e-commerce players

On the other hand, corporates look like they’re becoming more active. Three of the six largest e-commerce acquisitions in history took place in 2016: Walmart’s acquisition of Jet.com, Alibaba’s acquisition of southeast-Asia-based competitor Lazada, and Unilever’s acquisition of Dollar Shave Club. Alibaba’s bid for Lazada may have been more indicative of a strategy of geographic expansion, while Walmart and Unilever used these acquisitions to expand their e-commerce footprint.

Ecommerce Top Exits Slide

 

8. Retailers’ private market activity more focused on product than on tech

Walmart has been particularly innovative in acquisitions, with four acquisitions of digital startups since 2012. Most retailers have been less active. Furthermore, based on our analysis of all investments and M&A deals by top US retailers since 2010, most deals have been focused on taking on new product lines rather than investing in technologies that could more fundamentally impact business processes. Traditional retailers have yet to seriously leverage startups as “outsourced R&D,” but going forward, there is certainly plenty of innovation coming from startups, which could continue to transform retail in the future.

Product Process Slide

 

Artificial Intelligence Fuels Juice Bar Expansion

by Angela Diffly, SMB Retail Technology News

pure-green-menu-1Green Apples

It’s easy for a small business to get lost in the center of the universe. In the big apple, small business is big business – but the competition is as fierce as the fashion. According to New York’s Small Business Development Center, small businesses make up 99 percent of all New York businesses. Getting neighbors to like you, and come back often, is the lifeblood of small retailers everywhere, but New Yorkers are an especially tough crowd. We found one NYC retailer looking to grow exponentially over the next two years – turning to technology to literally lead the way.

Juice and smoothie bar Pure Green is enjoying start-up success in three hot locations, the financial district, in the heart of NYU’s campus and close to The Empire State Building. But the company is on a growth trajectory, planning to open seven more stores by the end of this year, and a target of 30 locations in 2017. Founder and CEO Ross Franklin has a unique background building brand equity for high-end, highly competitive NYC health clubs, spas and wellness brands. So it’s no surprise he’s looking to do the same for his own brand using predictive technologies. “We’re really serious about expanding our brick-and-mortar business, but we’re also very much a tech company,” Franklin told us. “We’re always interested in the most cutting-edge technology. We’re in the process of switching over our point-of-sale systems to Square, because we love the integrated dashboard and the ability to really analyze the metrics over the cloud.”

Ripe Opportunity

Franklin understands the value of marketing the right products to the right people at the right time, especially in Manhattan where every enclave has a unique and distinguishable demographic. He’s hoping to achieve the ideal balance with an artificial intelligence (AI) personalization platform called Genie from Grey Jean Technologies. “We are gaining new insights about our current customers, so as we expand, we can identify those areas with the highest concentration of our type of consumer,” Franklin told us. “We’re very optimistic that this platform will help us locate the right brick-and-mortar locations as we grow.”

CEO of Grey Jean Technologies Craig Alberino has a background that traverses big technology and big brands, and is hyper-focused on consumer behavior and loyalty. His fascination with marketing, psychology and systems led him to what’s now the Genie platform. The company’s initial goal was to help brick-and-mortar stores compete with the big online pure-players, like Amazon.com. Alberino wondered, “What if, in real time throughout your daily life, you could interact with retailers and products you love, welcoming messages, getting value from messages, reducing the noise and creating clarity and fidelity from the marketers that want to reach you?”

Alberino told us Genie optimizes the relationship between your product and service and those buying it. The AI-powered engine predicts consumer purchase behavior based on over 500 different data attributes, including transaction history, demographics, location, time, social media activity, preferences and behavior. By learning each customer’s “digital fingerprint”, the company claims Genie can predict their next purchase with 72 percent accuracy at the category level, and the next likely purchase down to the actual product SKU nearly half the time. “With new product purchases, we’re predicting with 25 percent accuracy, which is unheard of,” said Alberino. “If I can get my hands on that kind of information, it adds tremendous value,” added Pure Green’s Franklin.

The Right Pick

Since Pure Green has a full retail model and an abbreviated kiosk model, it’s important to gather insights as they grow to understand which model works best where. “If I know what customers are most likely to purchase next, and I know which location they’re in, I can predict how new locations may perform and which model may be more successful in that area,” Franklin explained. “We see a difference in what’s popular among business areas versus residential areas, so the product mix needs to reflect that. Genie can help us nail down which type of customer is more likely to purchase based on demographics. The more we understand our consumers, the more we can predict which products will be most popular in new areas,” he said. The platform also zooms into Pure Green’s social media followers, to ascertain where the fans are concentrated and what products are resonating with them. The more data the platform receives, the more accurate it becomes.

 

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“Ross (Franklin at Pure Green) is a rock star – he understands his business inside and out, but AI can help him scale better than he can on his own,” commented Alberino. “There’s a precision that comes with it.” Grey Jean is helping Franklin look across his physical properties, along with his distribution networks, to really understand where and how his customers are interacting with the brand in places other than his stores. “For example, what else is in the basket? What does purchase intent look like? What does purchase cadence look like? How do we build loyalty and engagement for him so his consumers become brand passionate and loop others into loyalty? We’re helping him go deeper into those relationships,” Alberino explained.

 

Grey Jean was recently invited to Walmart to showcase what Genie can do, but the platform was built to super-charge SMB retail businesses. “I was pleasantly surprised; Walmart’s mantra was the customer is number one. Every retailer – from the largest to the smallest – is trying to engage and appreciate customers on a more personal level.”

If personalization is the name of the game, artificial intelligence is the rulebook by which to play it. The Grey Jean name hails from Jean Grey, the superhero X-Men character born with telepathic and telekinetic powers (fitting for an AI platform). “She reads minds and tells the future, and she can influence people. Besides being data geeks, we’re comic book geeks,” admitted Alberino. I wonder if Genie can predict what Grey Jean will do next?

 

 

Originally published on SMB Technology News  http://www.smbretail.com/artificial-intelligence-fuels-juice-bar-expansion/

Wal-Mart to hear 26 startups’ ideas

ERIK S. LESSER/ EUROPEAN PRESSPHOTO AGENCY
ERIK S. LESSER/ EUROPEAN PRESSPHOTO AGENCY

By Robbie Neiswanger

Arkansas Democrat-Gazette, Inc.

FreshSpire Inc. was founded when five high school friends decided they wanted to do their part to help solve the issue of food waste and hunger.

Just two years later, two members of the North Carolina-based startup will get to show their solution to the world’s largest retailer. Their idea is an application that can notify consumers of grocery store discounts on food items nearing the end of their shelf lives.

“We feel like the way that FreshSpire can really make the most impact is to partner with a global-scale store, and Wal-Mart is the largest retailer in the world,” said co-founder Shraddha Rathod, who is now a student at North Carolina State. “So it’s kind of unbelievable that we’re getting this opportunity.”

FreshSpire is one of 26 startups invited to present their ideas to Wal-Mart as part of today’s Technology Open Call in Bentonville. The event is being held in conjunction with Friday’s Northwest Arkansas Tech Summit in Rogers.

Wal-Mart declined to reveal the number of startups that applied, but Wal-Mart Lab 415-C Director Tom Douglass said in an emailed statement that participants were selected on the basis of relevance to the retailer’s corporate strategy, the innovative nature of the technology and how it relates to customers.

The startups’ officials will spend the day with Wal-Mart representatives demonstrating how their ideas could provide solutions for the retailer in areas like food waste, sustainability, security, augmented reality and artificial intelligence.

Wal-Mart said in a post on its technology website that investing in a company is not the primary purpose of the open call. The open call offers partnership opportunities through seed capital, engineering expertise or access to Wal-Mart Technology’s headquarters.

Technology has been a priority for Wal-Mart under Chief Executive Officer Doug McMillon. The retailer recently purchased Jet.com for $3.3 billion, and has expanded tech-centered services like grocery pickup and Wal-Mart Pay.

“As part of Walmart Technology, Lab 415-C actively seeks to engage emerging technology in order to better understand how to serve our customers,” Douglass said in a statement last spring. “Our goal in the event is not only to offer these companies a once-in-a-lifetime opportunity, but to keep Walmart on the cutting edge of technology.”

So startups like FreshSpire and New York-based big data company Grey Jean Technologies recognize the possibilities of a potential partnership with Wal-Mart.

“There’s only one Wal-Mart-sized opportunity, and that’s Wal-Mart,” said Craig Alberino, chief executive officer of Grey Jean Technologies. “So it’s huge for any company, let alone a company that’s less than 20 people and less than two years old.”

Alberino said his company began with the idea of improving the accuracy and reliability of marketing messages to consumers. The result is an artificial intelligence engine — a tool called Genie — to provide what he said are more accurate predictions of consumer purchasing behavior.

Grey Jean Technologies has worked with big clients and Fortune 500 firms, but the open call will be its first meeting with Wal-Mart officials. While the event is important for his company, Alberino said, it’s also a chance for Wal-Mart to turn to smaller companies for innovative retail ideas.

“In an ideal world, we’re partnering with that organization to transform one of the world’s biggest and finest retailers that there is,” Alberino said. “Quite honestly, I think some outsiders can actually do a lot of good there, and I think they believe that too.”

Participating startups are based in U.S states like New York, Colorado, California, Florida, Pennsylvania, Maine and South Carolina.

GrowTech Industries, based in Rome, N.Y., will show Wal-Mart its verticle farms, which are fabricated from recycled shipping containers. New York-based Criteek will demonstrate technology that enables customer-generated video product reviews for product pages.

Other countries will be represented as well, with startups from places like Italy, Israel and Denmark selected to participate. Israel-based Cimagine Media will pitch its augmented reality tool, which allows customers to see how products look and fit in their homes before purchasing them.

Joe Recchia, who is the company’s vice president of sales and business development, said the open call is not the first time he has met with Wal-Mart representatives. Two previous opportunities have not led to a partnership, but Recchia is hopeful the open call produces different results.

“I’ve been close so many times,” said Recchia, who is based in New York. “This is just one more opportunity there.”

Meanwhile, the FreshSpire team is hopeful its first Wal-Mart meeting will be a success.

The founding members of the company, who all attended the North Carolina School of Science and Mathematics, began discussing ideas late in their senior year of high school after recognizing the food waste problem. They entered their technology-based solution in a venture competition, which helped the idea gain momentum.

The five women now attend four different colleges, and the distance led to some complications as they developed the technology, but Rathod said each wants to “really see this through.”

Rathod and Mona Amin, who attends East Carolina University, will pitch the technology to Wal-Mart today. Both said it’s the biggest opportunity for the young company so far.

“When we started we never really thought FreshSpire could go that far because we were students and we were young,” Rathod said. “This is just something we tried out. When we got momentum and people believing in us, we realized that our idea was actually very valid, and it was something that could make a difference. … Now we’re going in front of Wal-Mart, and I think we’re ready.”

Business on 10/06/2016

Print Headline: Wal-Mart to hear 26 startups’ ideas

Startups Pitch Their Innovations at Walmart Technology Open Call

One of the teams pitching at Open Call is Fast Back, which earned its way to the event by winning a Walmart intern hackathon in July 2016.

When nearly 30 tech startups from across the globe convene in Bentonville, Arkansas, on Thursday, Oct. 6, they’ll have the chance to pitch their ideas to the world’s largest retailer. At the 2016 Walmart Technology Open Call, the startups will showcase their solutions to Walmart Technology associates in areas that include sustainability, fresh produce, augmented reality and artificial intelligence.

Open Call not only keeps Walmart on the cutting edge of technology, but also gives startups a better understanding of working with Walmart and other retailers.

As Tom Douglass, director of Walmart Lab 415-C, states to participants, “Our goal with this Open Call is to provide innovative companies like you the opportunity to present your ideas and inventions in front of decision makers at the Walmart home office here in Bentonville.”

Walmart gives startups from around the globe the chance to participate and pitch their ideas to the world’s largest retailer.

 

While solidifying investment in a company is not the primary goal of Open Call, these startups have the opportunity to partner with Walmart in many ways. For early stage companies that haven’t raised a funding round, Walmart could offer seed capital. For those still working to define an MVP, Walmart could offer its considerable engineering expertise, as well as access to facilities at Walmart Technology headquarters in Bentonville.

Each company will get three minutes in a “pitch battle” to present its innovative solution to Walmart Technology associates. Throughout the day, the startups will have face-to-face meetings with stakeholders and Walmart leaders from all over the world. The companies will also hear from panelists, hailing from IBM and Rockfish Interactive, among others, about tech disruption in retail, agile company development and more. In addition, an open expo area will allow for startups to demo their solutions.

The tech Open Call is a partnership between Walmart Technology and the Northwest Arkansas Tech Summit.

Take a look at the companies who will be attending this year’s tech open call:

  • Air Cross: Polymer technology that cleans the air and sanitizes surfaces.
  • Cimagine Media: Augmented reality tool allowing customers to see how products will look and fit in their home.
  • Criteek: Video review and data platform of authentic customer-generated video product reviews.
  • Elixer Marketing: Disseminates essential oils or fragrances by breaking down microscopic particles.
  • fNograph: Catalog of videos and audio transcripts in an easy-to-access online platform.
  • FastBack: Faster returns online. This is the winning hack team from the 2016 Walmart Intern Hackathon.
  • ForwardFunded: Gives shoppers the ability to budget quickly during checkout.
  • Freshspire, Inc.: App that connects grocers and customers to choose produce and reduce food waste.
  • GoSpotCheck: Collect information to leverage the data and insights that e-commerce is currently capitalizing on.
  • Grey Jean Technologies: AI-powered engine provides contextually relevant messages to customers.
  • GrowTech Industries: Produce growing year-round in a secure, local, controlled environment.
  • Ikonomo: Grocery price comparison app at item or basket level.
  • Info Scout: Incentivizes shoppers to snap receipt pictures after shopping.
  • InvenSense: Indoor positioning solution and analytics service.
  • Omniaretail BV: Machine learning algorithm that unifies pricing, marketing and promotional strategies.
  • PeriRX LLC: Patented, noninvasive salivary biomarker kit that can be used to detect oral cancer.
  • Rapport: Environmental health management tool for suppliers.
  • ShelfZone: Virtual reality shopping experience.
  • simMachines: Machine learning marketing analytic solution.
  • SPLAT: Provides customers the ability to see a product they are shopping for in their own space before purchase.
  • TabAssist: Mobile solution providing associates instant access to critical information right at their fingertips.
  • Tap Media Worx: Tap streaming media, static images and other advertising content to purchase the item.
  • Total Containment: Method to reduce human, ecological and environmental impacts when dispensing fuel.
  • Ubiquitous Energy: Transparent solar technology.
  • Wiserg: Captures and stabilizes nutrients in wasted food, repurposing them in a liquid fertilizer.

Convenience Store Decisions: Boosting Your Data IQ

From analyzing customers to assessing their inventory, more convenience stores are using operational data to their advantage.

By Howard Riell, Associate Editor, Convenience Store Decisions

Today, collecting data is clearly essential for convenience store retailers. But capturing and making use of the most pertinent information—from customer analytics to competitive intelligence—can spell the difference between operating in the black or not.

Data solutions aren’t always black and white, however. Often, it requires operators to assess category gross margins and related variables—especially in the form of inconsistencies or problems—and figure out how to address them.

Within a c-store today, being able to track customer tendencies, the workforce, fuel and product sales and inventory is vastly important to a retailer’s competitiveness.

“Typically, they are not properly accounting for variations in inventory,” said Steve Montgomery, president of b2b Solutions LLC , a Lake Forest, Ill.-based consulting firm, referring to typical scenarios. “The second reason is that something is amiss at the sites. This is true if the margin is higher or lower than is normally achieved.  In either case it should be investigated. Are the changes following the same pattern as their other locations as far as sales, margins, etc.? If not, why not?”

For many retailers, knowing how to gather and assess information from fuel inventory and customer sales is an important aspect of any store operation that sells fuel.

“Variations can be reported in both spread sheet and graphical format. Specific items can vary by chain. However, everyone should be monitoring sales (dollars, units and gallons), margins, customer counts, transaction size and expenses,” said Montgomery.

C-stores must also track fuel pricing processes and procedures.

“Obviously being in a c-store, fuel pricing is a huge deal and so you want to make sure that you are keeping up with your competition on that. It’s an easy question but a hard answer,” said Karla Grimes, director of operations for the Kent Cos., which runs 40 Kent Kwik Convenience Stores.

Being in the fueling industry, Grimes continued, many operators should ensure that they are staying up with that crucial parts of their business, as well as the competition.

“Of course, as far as consumer analysis goes, you’ve got to get into your competition and look at their stores and see what they are doing; how they take care of their customers,” Grimes said. “That’s a big part of it.”

“We do a lot of our data in-house,” added Alex Garoutte, marketing director of Kent Cos., based in Midland, Texas. “There are a lot of (data analysis) companies out there that are valuable. You just have to determine if it’s worth spending the money on.”

Inside sales, of course, deserve just as much focus.

“What are you doing, and what differentiates you from your competition?” Grimes said again.

STRATEGIC INITIATIVES
A good strategy for a chain of stores, according to Montgomery, is to look at the same type of data gathered from all locations at one point in time, and the same locations over time to determine patterns of activity. “The first allows the retailer to compare the results of his stores. The second will show whether changes are following the same pattern as their other locations,” Montgomery said.

Deciphering the data and then putting the results to use are the next steps. Disseminating the necessary operational information to key store personnel such as managers is vital and too often overlooked, Grimes added.

“You’ve got to decide what data you want to use and then figure out how to push that out. It could be a group of store managers, it could be employee leaders,” Grimes said. However you are going to do it, I think you have to go about getting that pushed out to every level of the company. And what you need is to have key players who are going to do that for you.”

SALES INFORMATION
First, a retailer must determine what data to share.

“Our experience tells us that the most important data that convenience store operators use is their own sales data,” said Brian Nelson, co-founder and chief operating officer of NewsBreak Media Networks Inc., a Knoxville, Tenn.-based programmatic merchandising platform for the convenience store industry that converts fuel-only customers to multi-product purchasers.

“The average c-store completes around 1,100 transactions (or more) per day. That is 1,100 unique customer surveys on what products customers want, and what products they will likely purchase together,” Nelson said. “A c-store’s proprietary sales data is a constantly updating data set that can be used to map and predict market trends, purchase patterns and inventory expectations. Ask any researchers what they would give for the ability to consistently collect 1,100 surveys daily.”

Data analysis and predictive analytics are areas that are experiencing rapid growth and innovation. If operators don’t seek help from experts, Nelson said, their category managers, marketing directors and executives are going to need to become data experts, which takes those individuals away from their proper roles and responsibilities.

“Seeking support from experts can help operators get to actionable information faster,” Nelson said. “Experts often have the infrastructure in place to parse, analyze and model historical and dynamic data quickly, instead of building it from scratch.”

Each time a customer completes a transaction, Nelson explained, he or she is giving the c-store operator direct feedback.

“Taking that feedback across multiple customers and multiple locations can give operators great insight into how they promote their products and even what products they carry,” Nelson said.

Market-basket data can be analyzed to help operators decide what products to promote together and what time of day they should be promoting those combination deals. For example, by promoting a discounted salad with a high-end water product, a c-store could increase unit sales for the promotional product while simultaneously driving up the total market basket.

C-stores can utilize data analytics to drive overall marketing initiatives, promotions and even inventory management and product selection. Analysis can include integrating key consumer buying variable data and demographic data into a store’s existing sales data in order to build location specific, programmatic merchandising campaigns.

Nelson encouraged operators to innovate and try new product offerings and marketing strategies, but warned that caution is called for. “Use data analytics as a tool, but don’t get stuck in the data.”

While a c-store operator will likely never get sales data from his competitor, operators can get a sense of how they stack up against the market, state or region from their vendor representatives. The companies that represent the brands that are sold through the c-store industry, Nelson said, can be a great resource to operators and category managers for target demographic information and customer analytics.

IDENTIFYING BEHAVIOR
Craig Alberino, the CEO of Grey Jean Technologies in New York City, said his firm generally works with behavior and identity data. “That’s things that you buy, places you go, even the weather. It could be various influencers.”

Knowing why consumers buy what they buy helps drive engagements and forge deeper relationships, resulting in more predictable revenue.

“It’s our view that all retailers, convenience stores included, could be serving customers better by knowing them more intimately,” he added.

Taking transaction and loyalty information inventory numbers, promotional calendars and more, and enriching it with elements like census statistics, weather and social media can provide a sharp focus on buying patterns for small geographic area around the store.

“Even if there is somebody in my dad’s age category, which is 70-plus, he’s still carrying an iPhone,” Alberino said. “He’s still looking for deals, and he is hypersensitive to gas prices. He knows if there is two cents less per gallon down the road, and he’s going to go to that other pump. Even though he might be spending more, it’s the trigger and it’s kind of a win, that he beat the system. And so, you are almost ‘game-ifying’ it a little.”

COUNTING CORRELATIONS
Retailers naturally want to drive up customer visits and average sales per transaction. More convenienc stores are evaluating customer sales over time to gauge future buying tendencies.

“This data is far more informative that just total sales,” said Montgomery. “It can tell why sales are up or down. Is it because fewer customer are coming to the store, or because they are buying less each time they come or both?”

Another tool is the correlation between gallons sold and store sales changed, he added. “Are they still buying your fuel but shopping more or less frequently inside you store? The data will not solve your problems, but should tell you where they are occurring.”

One of the easier analytics that can be checked is the correlation between fuel sales and in-store sales, Montgomery said. “Does an increase in gallons result in increased store sales? If so, does it impact certain categories more than others? These types of insights can help retailers determine what role they want a category to play in their overall marketing strategy.”

Montgomery suggested retailers benchmark their data with sources such as the National Association of Convenience Stores (NACS) because it might show opportunities to better sales and margins or control expenses.

“However, every store (or) market is different. The most important benchmarking retailers can do is against their own data,” Montgomery said. “That the industry averages ‘X’ is not as meaningful as whether you are making steady improvements in sales, margins and expense control.”

MORE DATA
In retail, data and data gathering comes in many forms and the benefits are just as varied.

“In any retail setting, convenience stores included, we generally work with behavior and identity data,” said Alberino. “Identity data would be things that you buy, places you go, even the weather. It could be various influencers. So generally when we are working with a retailer we will take transaction information and any loyalty information they might have, inventory, promotional calendars, and we will enrich it with census, weather, social media, etc. What is happening in the vicinity of your physical presence, within a mile of that convenience store?”

Among the data he suggests is scheduling of local events.

“There could be a rock concert happening, for instance, and in fact one of the convenience chains we’ve spoken to was in charge of sponsorship for a very large concert venue,” Alberino said. “We were talking to them about how they could increase traffic. Because many people were making long journeys across the West from California to the venue, how do you get them to stop, not only at the pump along the way, but also to stop in and get other things?”

 

Originally published on Convenience Store Decisions

Boosting Your Data IQ

CB Insights: 45 Artificial Intelligence Startups Targeting Retail In One Infographic

AI isn’t all self-driving cars and chess-playing computers. There’s an emerging market for AI use in e-commerce.

Investors poured a record high $1.05B into artificial intelligence startups in Q2’16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology.

Recently there’s been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai (formerly GoButler), another AI-powered searching tool. And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images.

Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

The area is emerging, and most companies focused on retail AI remain in the very early stages. However, we have seen several larger deals in recent months. ViSenze, which lets users search e-commerce sites by image or find visually similar items, raised a $10.5M Series B in September, while Trax Image Recognition, which visually tracks the performance of goods on grocery shelves, raised a $40M Series C in June. Several startups are backed by top investors from our smart money list, such as search optimization tool Zettata, backed by Accel Partners, and predictive customer targeting platform AgilOne, backed by Sequoia Capital.

While there are numerous other AI startups focused broadly on personalized marketing and ad targeting, we limited this market map to startups whose core focus is retail and e-commerce. The startups in this graphic have raised roughly $650M in total disclosed equity financing. Scroll down to view the graphic, category explanations, and a full company list with select investors.

 

Retail AI market map

See the market map below. This market map is not meant to be exhaustive of startups in the space. Graphic includes private, independent companies only.

final-retail-ai-market-map

Category breakdown

We divided our market map into the 12 categories listed below:

Real-time product targeting – Machine learning to present online shoppers with personalized product recommendations. These companies typically update e-commerce websites in real time to present product selections best suited to the individual shopper.

Real-time pricing & incentives – Machine learning to adjust pricing, sale options, rewards, and coupons in real time to try to push hesitant shoppers toward conversion.

Natural language search – Algorithms that use natural language processing to improve search functionality in e-commerce websites.

Visual search – Image recognition platforms that help e-commerce websites let visitors search by image, instead of text, and match relevant products to specific images.

In-store visual monitoring – AI-powered software that analyzes photo and visual content of store shelves to help brands track how their products are stocked and promoted in real time.

Conversational commerce – Chat software and chatbots focused on helping shoppers make purchases in a conversational text format using natural language processing.

Predictive merchandising – Big data analysis to optimize purchasing, allocation, and product assortment across stores and e-commerce. The aim is to better predict demand in different geographies to avoid waste and prevent inventory from going out of stock.

Sizing & styling – AI-powered software to help retailers integrate improved product sizing and outfit-building tools into their websites.

Multichannel marketing – Startups using AI to create targeted marketing campaigns across desktop, mobile, email, and other digital channels. Inclusion limited to startups focused specifically on e-commerce.

Integrated online & in-store analytics – Startups that combine both digital and physical store analytics to help retailers better understand their customers.

Location-based marketing & analytics – Startups that combine digital and physical store analytics, while also integrating beacon technology to track shoppers’ locations.

 

 

Originally appearing on CB Insights “Don’t You Look Smart: 45 Artificial Intelligence Startups Targeting Retail In One Infographic”: https://www.cbinsights.com/blog/ai-retail-smart-shop-startups/

Insights Are Overrated

Image courtesy of McKinsey&Company

With the amount of digital data in the universe growing at an exponential rate — doubling every two years by most accounts — it’s easy to see how recent years could be dubbed as “The Big Data Era.” Retailers and marketers played a large role in this, focusing on gathering data from every source, and squirreling it away for a rainy day.

From there, we came into the “Era of Insights.” Retailers began to take their data stores and analyze the information hidden within them, hoping to glean insights that will help improve their interactions with customers. How can we summarize what has been happening in the market? What guesses can we make based on what happened in the past?

Most recently, we’ve come to realize that insights are overrated. By simply analyzing data, we’re creating more data — but then what? How can retailers achieve personalization based on their insights?

Getting Actionable

In order to take advantage of our masses of data, the next phase is moving from insights into real-world action. Of course this is easier said than done.

There are three key factors when it comes to enhancing personalization, generating leads and engaging consumers, and these can be the difference between effective campaigns and those that fall flat. Retailers’ actions must be:

  1. Timely: Any action must be taken in the right moment for each consumer — on the right channel, and while they are in the right location and receptive to the right message. While the definition of timeliness used to stretch over a day or two while the consumer thought through a purchase, today’s on-demand economy has changed our perception of timeliness to a matter of hours, minutes and even seconds.

  1. Forward-looking: In the Era of Insights, personalization was done based on your past activities and purchases. If you bought pants, you must like pants, so we’ll offer you more pants. Today, we’re taking a smarter approach. Looking at past information, we’re able to make better predictions about what consumers will purchase in the future, and what actions we can take to prompt them to make that purchase. So if, for example, you purchased pants during a pre-Fall sale at a 30% discount, we can predict that you might be interested in a shirt to go with it if we offer you 40% off.

  1. Strategic: Analyzing results is an important part of any campaign, but it is also often time- and labor-intensive. Simply quantifying success or failure is an underuse of your team’s time. Retailers should be using the analysis of their results — including weighting and scoring terabytes of data against actual purchase behavior — to inform future personalization efforts and improve the efficacy of their actions.

Technologies to Consider

Achieving timeliness for each individual, making forward-looking predictions and strategically scaling those efforts is all labor-intensive work. For retailers looking to convert their data insights into actions, technologies to help automate the process are an absolute necessity. Though the marketplace for these technologies is crowded, they generally fall into a few categories that work together to create a comprehensive solution:

  • Understanding Identity: There are many tools for gathering consumer data across touch points, including POS systems, social media, CRM platforms, mobile web, apps and more. The challenge to date has been finding a way to link these disparate data sources to create a clear, omnichannel view of the individual consumer. In order to do this well, martech (marketing technology) solutions you invest in need to play well with others.

  • Understanding Behavior: Rationalizing data and understanding the patterns within it can be done most effectively today through artificial intelligence (AI). AI technology can dive deep into data, and find links that could otherwise be overlooked. For example, you might guess that pool owners would need cleaning supplies when the weather started heating up, but perhaps they also purchase scrubbing tools every time they get a car wash. Using machine learning, a subcategory of AI, you can identify and use patterns like this to refine your targeting algorithms to better predict these purchase behaviors in the future. With companies like Google and IBM investing more deeply in AI technologies, the space is becoming noisier. The martech companies that will help retailers be the most successful with AI will be those that create the most accurate predictive algorithms — and the results will speak for themselves.

  • Understanding Location: Once you understand the person and their behaviors, a key component to targeting them at the right time with the right offer often comes down to location. The most popular technologies that can help with this include beacons, in-store devices that communicate with a shopper’s mobile device using Bluetooth connections, and geofencing, a software feature that uses GPS or radio frequency identification (RFID) to define geographical boundaries and identify mobile devices that enter those vicinities. While studies have shown that consumers like the hyperlocalized-based personalization you can achieve with these technologies, there is a fine line between helpful and invasive. Retailers should be careful about how they obtain and use location information, and ensure they are protecting consumers’ privacy.

  • Understanding How to Take Action: While personalization and targeting technologies have vastly improved, they still need human oversight in order to ensure your actions are mapping back to the overall marketing strategy. In addition, marketing teams need to have the skills to put these personalization technologies to use. An intuitive user interface or dashboard that pulls it all together and makes optimal actions easy to see and execute is increasingly vital for time- and resource-strapped marketers.

Big data and insights are still key components to successful personalization for retailers, but we also need to be able to take a step back and understand how turn those into actions that drive real, quantifiable results. There are still technological and social barriers to overcome, but by understanding the components that constitute a successful solution, and looking beyond just generating insights, we can begin to move forward and make the coming years into the “Era of Action.”


Craig Alberino is CEO of Grey Jean Technologies. An expert in consumer behavior and loyalty, Alberino has advised top agencies within the Omnicom, WPP and Publicis holding companies, where he defined the digital strategies for clients including FedEx, Kimberly Clark and Monster.com. He has been a speaker on the future of technology for iconic brands such as Chanel, Baccarat and the city of Beverly Hills. While at Accenture, he led the retail e-Commerce practice with clients including Chase, Citi, MasterCard, Visa, MCI, Digex, AT&T, and built the first e-Commerce site for Payless.

Alley Watch: This NYC Startup Just Raised $2M To Give You the Genie You Always Wished For

Grey_Jean_FITA_rev

With Big Data, humans have the greatest advantage in understanding consumers than ever before. But just because the data exists does no mean you know how to use it. That’s why we have Grey Jean Technologies, the company that uses AI to give you all the information you need to target customers at your fingertips. With its intense personalization functions, the company’s signature Genie runs all the numbers specifically for each person providing solutions for your marketing team and not just ‘best guesses’.

Today, we sit down with cofounder Cosmas Wong to discuss the company’s recent funding as well as the companies roots from Wall Street.

Who were your investors and how much did you raise?

Grey Jean’s funding came from angel investors and company management, and totaled $2 million. It was a seed round and was led by myself. I was also cofounder of my last company, ENSO Financial Management LLP (EFM), which was recently sold.

Tell us about your product or service.

Grey Jean Technologies is a personalization company that improves customer acquisition and sales across all retail channels. Our recommendation engine, Genie, is powered by artificial intelligence (AI) and provides the most accurate predictions of consumer purchase behavior. This enables retail marketers to target shoppers with contextually relevant messages that drive desired actions, such as a store visit or redemption of a special offer. Genie uses big data and AI technology to organize retail’s unstructured data sets to connect the right deals to the right customers.

What inspired you to start the company?

Having spent years leading big data efforts in the financial sector for some of the world’s largest hedge funds, I saw firsthand the power of big data when used correctly – and also how challenging it can be to harness effectively. We had to understand how to preserve the integrity of sensitive data and use it to the benefit of all customers. My co-founder, Craig Alberino, was working on Madison Ave with big agencies in retail and CPG digital at the time, and encountered the same issue – retailers were struggling to convert their data into something real, such as increased sales or lead gen. Combining efforts and applying our combined experiences to retail marketing was a natural fit for us.

How is it different?

To date, personalization has largely been based on geo-location and basic demographic information. It’s essentially unsolicited targeting – more of a “best guess” rather than personalization. Genie uses the most comprehensive set of data points, including unconventional data points like political preferences and lifestyle, to create consumer profiles that are truly unique to each shopper. This is all continually updated through our eight proprietary algorithms to adjust individual customer profiles in real time.

Another way Genie is different is that it focuses on using artificial intelligence to drive actions, rather than just delivering insights. We actually help retailers take an action with their customer, rather than just presenting their data in new ways and leaving them wondering what to do with it.

Genie focuses on the person in personalization.

What market you are targeting and how big is it?

Grey Jean targets retail marketers. According to Juniper Research, global digital retail marketing spend will double by 2020, reaching over $360B.

What’s your business model?

Genie is a SaaS model. We bring data from disparate sources into a data management platform (DMP) using modern technologies for rapid data processing, like Spark, on the back-end. Our AI-powered recommendation engine, Genie, finds and weights underlying correlations, and converts that data into usable information, allowing retailers to make decisions and take action in real time. Ultimately, it enables retailers to do more than they could do otherwise, even if they had thousands of staff members.

Are there any concerns about privacy when using your solution?

If there are concerns, there shouldn’t be. We are housing anonymized versions of customer data. The analogy we often use is baking a cake – you add the butter, the flour – a bunch of individual ingredients which are identifiable and which you own, but ultimately once the cake is baked you can’t get the butter or the flour back out.  They’re not butter or flour anymore.  We’re creating fingerprints and personas for individuals in order to deliver the tailored and personally valuable deals for consumers, but we’re anonymizing the data in order to do that, and the end product ceases to be identifiable from its original form.

What was the funding process like?

The process for us was actually fairly unconventional. I believe most people are used to the typical “friends and family” Seed or A round of funding for a business. We funded a project to dissect a broken market. Out of that funded project came our business plan. From that solid foundation, it was pretty obvious what we had, and we were in the enviable position of having too many interested parties.

What are the biggest challenges that you faced while raising capital?

Our biggest challenge has been articulating how we’re different and better than other marketing tools out there. Genie sits at the intersections of a laundry list of buzz words – personalization, artificial intelligence, big data, machine learning – and each solution provider is using these terms in different ways. We had to explain how our product is unique, and being able to show our incredible results played a big part in that.

What factors about your business led your investors to write the check?

Our investors have worked with us before, but what its really came down to for us were the results, which speak for themselves. We can predict the next likely purchase at the category level (e.g. pool supplies) with 72% accuracy. In other words, the customer profiles that Genie creates are used to target consumers with individual deals that are relevant to them with such a high level of accuracy, generating more conversions, redemptions, visit frequency, foot traffic, time in store, basket size and brand affinity than other solutions.

What are the milestones you plan to achieve in the next six months?

Since the launch, we are in a period of rapid growth from both a corporate and customer standpoint. In the next six months, we’re expecting to expand our staff and close deals with some of the marquee clients we’ve been talking to. It’s a very exciting time.

What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?

There is a lot of uncertainty on the horizon for the VC and private funding market over the next year, but opportunities exist for businesses that can show value. My advice would be to focus on the fundamentals of building your business – the product/market fit, onboarding clients and hiring the best people to fill key roles, and then building out a revenue plan. Even in tough markets, if you have a product, a team and customers, there will be investors interested in your business.

Where do you see the company going now over the near term?

In the near term, we plan to keep growing our sales and marketing efforts, and continuing our conversations with leading retailers. We’re in an interesting time where retailers see the value of AI-powered personalization, but haven’t figured out how to achieve it yet.  Genie is an effective tool that can get them there – it truly delivers on the promise of personalization.

What’s your favorite rooftop bar in NYC to unwind?

It depends on who we’re entertaining. I like the Empire Hotel. The Press Lounge is also fun, but gets pretty busy. The Gansevoort at Park is great for lunch or early afternoon cocktails as it’s close to our NoMad office.

 

 

Interview with Cosmas Wong originally posted at: http://www.alleywatch.com/2016/07/nyc-startup-just-raised-2m-give-genie-always-wished/