Digital Entertainment: The Next Step in the Grey Jean Journey

Earlier this month, we had the honor of being named the winner of the 2017 Digital Entertainment World (DEW) Startup Competition, which recognizes innovation in digital media. As one of twelve competing startups, we introduced a room full of leading investors, venture capitalists and digital media executives to our recommendation engine Genie, and explained why artificial intelligence and machine learning have an important role to play in the multimedia industry.

The thing is, although we at Grey Jean are often talking about using AI and machine learning for retail, the goals and application can be mirrored across industries. Publishers fundamentally want the same thing as retailers – but instead of targeting current and prospective customers with relevant products, they want to target current and prospective readers with relevant content.

In today’s crowded media world, relevance has become more important for publishers than ever before. The staggering volume of content saturating the internet gives readers easier access to more choices, meaning individual publications’ readership and subscriber levels are dropping to all-time lows. To combat dwindling subscribers, publishers must capture readers’ attention and connect with them through targeted content that is delivered at the right time and in the most appropriate channel.

Today’s consumers not only expect this, they prefer it. It’s no secret why Facebook is the most engaging site on the internet – every piece of content is tailored for each individual user.

AI can help publishers to identify and deliver content tailored to individual readers across all of their digital and print properties, creating more meaningful interactions. Genie, specifically, helps publishers better understand reader behavior across all publications and channels by creating a unique “fingerprint” for each reader. This fingerprint gives publishers new insights into each reader’s behaviors and preferences, enabling them to create more targeted editorial content. With Genie’s AI-powered insights, publishers can:

  • Deliver messages from a publisher that are relevant to individual audiences;
  • Create better digital marketing experiences that drive engagement and brand affinity;
  • Personalize call-to-actions across digital channels; and
  • Provide publishers with actionable data analytics and reporting.

Insights on a reader’s preferences and behaviors also provides value to a publisher’s brand partners. With AI, publishers can design new advertising packages to help brands more effectively engage with target audiences in a personalized and meaningful way. This in turn delivers maximum return on investment on a brand’s ad spend.

At Grey Jean Technologies, we believe data-based insights have a place in every industry. Everyone from retailers to digital publishers can benefit from better serving their customers through more personalized marketing. DEW was the next step in our journey to improve the customer experience across a wide range of industries, and we’re excited to see how it unfolds.

 

 

 

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.”

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/

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/