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.




Partnership on AI: Protection from Robots, or Protecting Privacy?

The Partnership on AI – a group founded by Amazon, Apple, Facebook, Google/DeepMind, IBM, and Microsoft – held its first meeting recently, and should be sharing more details on its plans soon. For now, here’s what we know:

Established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

While we’re growing accustomed to tech powerhouses uniting behind larger causes, the idea of partnering on AI has caused a bit of a stir in the media. According to one article from Business Insider, for example:

“…[The] Partnership on AI is working to ensure that AI is developed safely and ethically, thereby avoiding the nightmare robot uprising scenarios that have been described by the likes of renowned scientist Stephen Hawking and Tesla billionaire Elon Musk.”

Partnership on Artificial Intelligence hopes to invite ‘academics, non-profits and specialists in policy and ethics’ to join. Photograph: Alamy

For all the talk of a post-apocalyptic world run by robots, AI itself is not scary. It is a tool being used to serve customers and businesses. It’s important to be clear about what AI is (a lot of math!), and what it’s not (a machine coming to get you).

The one thing we do need to be concerned about is privacy. AI projects today are powered by the big data stores that companies have been amassing over the past decade-plus. In many cases this includes loads of individual customer data. In order to use AI safely and ethically, businesses need to be transparent with the types of data they’re collecting, and how they’re using it. Currently, those best practices and industry standards don’t exist.

When it comes down to it, using AI technology can actually help protect consumers’ privacy. By anonymizing data in a way that only machines can read, we can keep personally identifiable information out of the hands of humans while still providing the personalization benefits to businesses and their customers. We at Grey Jean Technologies have taken this approach from day one, and will continue to do so moving forward.

In short, our hope for the Partnership on AI is that it will help businesses be better proprietors of data. Most current AI methodologies serve business needs rather than prioritizing customers. Our goal at Grey Jean is to help turn that tide, and create a dynamic that is beneficial to everyone, from organizations to individuals. If that vision is aligned with the Partnership, you can expect to see us joining forces.

5 Key Marketing Predictions from HubSpot 

Without a doubt, marketing has evolved dramatically over the past several years. Big data and advanced analytics have transformed daily tasks across myriad industries and Artificial Intelligence (AI) has started changing the way marketers target and engage with consumers.

Given how quickly marketing has evolved, there’s been a lot of speculation about its future. At Inbound 2016, HubSpot co-founders Brian Halligan and Dharmesh Shah shared their take on the future of marketing, and I found their insights particularly relevant. Below are five predictions Halligan and Shah made that marketers should recognize:

Photo credit: Precision Marketing Group
  1. Human-machine conversations will replace human-computer conversations.

Marketers have long worked to effectively communicate with customers and prospects on behalf of their brand, and historically, those conversations have occurred via a computer and keyboard. In future, though, those conversations will be more natural and fluid, leveraging technology like voice input and visual outputs to make communicating more efficient. As Shah said at Inbound, “Conversational UI is going to be an even bigger leap in software than we had with the shift to web-based software… We will have voice input because it’s much more efficient [than typing] and visual output because it’s more efficient than listening.”


  1. Customer engagement data will drive all content.

In the past, inbound links and search boxes alone determined which content and/or products were displayed. Think about it: Google has indexed and mapped connections between every page on the internet and displays websites based on their popularity (rather than quality). Facebook has linked 1.79 billion people and according to Shah, its search box is being used 2 billion times a day. The future of marketing, however, will focus more on engagement. Rather than determining what consumers see based solely on popularity, the quality of content and/or products will be considered, and that quality will be determined by the number of consumers engaging with it.


  1. AI will automate major components of sales and marketing.

Machine learning and AI are already improving sales and marketing software by providing the ability to take action without input from a human. As Shah said, “In the next few years, we’re going to have autonomous, self-driving marketing automation” and as a result, complex yet crucial tasks such as predictive lead scoring, content recommendations and email acquisition will become a lot easier. Additionally, as Shah described at Inbound, “ for leads” will emerge, in which leads will automatically be routed to the most appropriate salesperson based on lead analysis and salesperson data.


  1. Marketers will evolve beyond rote work.

Some marketers worry that AI-powered technologies will take over their daily responsibilities and render them obsolete, however that’s not where the industry is headed. Instead, AI will enable bots to work in the background (like virtual assistants for busy marketers), taking on responsibilities too tedious and time-consuming for humans. As a result, future marketers will be able to focus their time and intellect on more creative tasks, like “understanding the customer, figuring out what the overall positioning is, and having actual conversations with other humans,” according to Shah.


  1. Algorithms will become a commodity.

Algorithms used to require years of experience, extensive knowledge and significant time to build, but now they’re available for purchase in just a matter of clicks. As Shah said, “Mere mortals like me don’t have to learn about machine learning per se. More companies will start doing things that we thought required 100 PhDs.” The new hurdle, however, will be the collecting, storing and leveraging of data to feed those machine learning algorithms. Marketers capable of doing so will be the ones succeeding long-term.


No one can definitively say what marketing will look like in the future, however what appears certain is that machine learning and AI will play a large role. Rather than ignore or fear this reality, embrace it. Leverage AI to better understand, target and engage with consumers and take advantage of its ability to automate mundane tasks. In doing so, you’ll be able to innovate and succeed along with the ever-changing marketing landscape, rather than get left behind.

The Rise of Predictive Mobile Commerce in 2017

If the 2016 holiday season has taught us anything, it’s that mobile commerce is skyrocketing. After years of hype and speculation, we’re finally witnessing mobile commerce emerge as a major pillar of global retail. According to recent findings from Adobe Digital Insights:


  • Smartphones made up the vast majority of mobile transactions between November 1 – December 20 with 68% (or $16.63 billion) of mobile sales coming from smartphones, compared to 32% ($7.92) from tablets


  • On Thanksgiving Day, a record $449 million in U.S. revenue was reported in mobile commerce


  • Black Friday was the first $1 billion mobile-shopping day in U.S. history!


  • On Cyber Monday, mobile commerce generated $1.07 billion in sales, representing a 34% increase from last year


These figures are impressive, however mobile commerce growth stands to gain even more momentum in 2017. To capitalize on this trend, retailers need to optimize their mobile experiences in order to better convert consumer traffic. They also need to find new ways to capture mobile consumers’ attention and engage with smartphone shoppers. Also worth noting, these types of mobile content and engagement tactics need to be relevant to each shopper and delivered at the most optimal time of the customer journey.


The move to predictive mobile commerce

To more effectively and efficiently engage with mobile shoppers, many retailers are investing in predictive mobile commerce initiatives. In doing so, they’re reevaluating their mobile commerce experiences and moving away from relying on simple buy buttons. Instead, retailers are beginning to take each user’s preferences, moods and tastes – and how they evolve over time – into account, and during every interaction.


Powered by machine learning and Artificial Intelligence (AI) technology, predictive mobile commerce solutions have the ability to leverage retailers’ point of sale, CRM and loyalty systems to instantly and continually produce valuable customer transaction data. The resulting benefits include less noise for consumers (i.e. fewer undirected mass email blasts) and higher promotion response rates for brands, which in turn leads to improved user experiences, engagement and brand affinity.


How to best support mobile shoppers

Perhaps most importantly, predictive mobile commerce technology offers retailers the power to help consumers find what they want in the precise moment of need (and, in an ideal scenario, even before consumers recognize they have a need). To join the predictive commerce movement and remain more relevant to today’s shoppers, regardless of channel, retailers should consider the following best practices:


  • Use Existing Data: Retailers are sitting on massive amounts of data, but many don’t yet understand how to make it actionable. Partner with a company that can help you leverage your existing data and combine it with their own to create the most spot-on predictions that can be turned into actionable marketing messages. For instance, retailers can leverage consumers’ social media sentiments for even better insights and highly targeted messaging capabilities.


  • Use Existing Infrastructure: Most retailers have already invested millions in their ecommerce and marketing infrastructure and, as a result, they’re not interested in a complete rip and replace. Look for someone who can make your investments more intelligent by offering predictive consumer insights that are added to the back-end functionality of your existing systems, thereby doing away with the need for separate applications.


At Grey Jean Technologies, we deal with predictive mobile commerce intelligence every day. Our platform, Genie, combines retailers’ massive data sets (purchase history, brand preferences, price sensitivities, etc.) with third-party data to learn patterns for each customer.


To learn how Genie can add precision to your mobile commerce strategy in 2017, visit: Invaluable, customer insights await and better yet, machine learning and AI enable Genie to improve and get more sophisticated over time, ensuring your brand the most accurate and productive intelligence for years to come.







Big Data: In Search of Benefits

Big Data means big business for grocery chains.

Kroger leverages data to drive basket size, shopping visits and retention over time via highly targeted promotions. Raley’s, in Northern California, creates a world-class customer experience by analyzing its transactional and shopper card data, connecting this with customer comments, and by listening to shoppers on social media.

“In order to use Big Data to the fullest, grocers need transaction history with data like demographics, social media activity, geolocation, and personal preferences and behavior, to predict their consumers’ next product purchase and deliver coupons, offers and messaging that they’re actually going to respond to and use to make a purchase,” explains Craig Alberino, CEO of Grey Jean Technologies, a New York-based AI-powered personalization company.

But not every grocer has the resources of a Kroger or a Raley’s. Experts advise food retailers of all sizes to make leveraging Big Data more of a priority, and perhaps partnering with a consultancy to guide them within the limits of their budgets.

Why? Because the insights from Big Data may prevent them from losing customers, and will enable independent and midsized retailers to compete more effectively with larger chains.

“It’s clear that grocery chains are taking learnings from their data and using it to target customers in smarter ways,” affirms John Kyriakides, assurance office managing partner with BDO USA, a Chicago-based professional services firm. “We’re seeing this in strategic shelving as well as how they send targeted coupons to customers through email, text and sometimes in-app.

“As for the smaller and more niche grocery stores, they know that they must be conscientious of their regular customers,” he continues. “It’s impossible to say, in general terms, whether all grocery chains and all small grocers are using data to the fullest, but it’s obvious that many are clearly using it to their advantage in smart and meaningful ways.”

Here are some of the specific benefits that grocers can gain by leveraging Big Data:


“One of the biggest benefits grocers can acquire from Big Data is a better understanding of their customer base, which in turn drives revenue,” says Eileen Kolev, marketing program manager for Tysons Corner, Va.-based MicroStrategy, provider of an enterprise analytics platform. “This understanding is especially critical in the grocery industry, where margins are razor-thin and food waste is a crucial issue. By effectively leveraging shopper data, grocers can customize marketing activities, pricing, product assortments and customer service in order to build consumer loyalty and increase revenue.”

She adds that one available source of Big Data — mature loyalty programs — provides grocers with a wealth of customer insight that can be used to identify product segments, silo shoppers and define product affinities. By combining these data with other sources of information — nutritional trends, preferred method of receiving promotions, weather-related events and customer traffic patterns — grocers can focus on improving the overall shopping experience and drive revenue, according to Kolev.


“With Big Data, grocers can understand which items to sell at which prices to which shopper segments that will drive loyalty of trips and stimulate incremental demand,” notes Brian Elliott, CEO of Periscope by McKinsey, a global consultancy. “This insight into consumer behavior impacts pricing, promotions, assortments, personalization and even vendor negotiations.”

Elliott gives an example regarding assortments: Big Data enables grocers to optimize which products shoppers see in the store, how many facings are needed, and the total linear feet by category, given the shopper segments in the store.

“With Big Data and advanced analytics,” he explains, “we better understand which store clusters need to be sharper on price and which can save money by not investing in price quite as deeply. With a better understanding of willingness to pay by customer segment, by key value item and by store cluster, retailers are better able to make investments in loyalty that pay off.”


“Making correlations between verified price-to-consumer information and a retailer’s own POS data allows individual stores to optimize pricing by location,” points out Guy Amisano, CEO and founder of Salient Management Co., a Horse-heads, N.Y.-based software provider. Retailers can do this in three ways:

  • Finding the best price point for a specific product or an entire brand
  • Effectively offering promotion
  • Tracking product flows and understanding profitability

“Combining the mass amounts of data already at a retailer’s fingertips — from invoice info to scan records to vendor rebates — allows them to gain a clear picture of profitability by day, as well as digging deeper into performance of each vendor, department or individual store as a whole,” he says.


According to Elliott, the consultant, there are two ways that Big Data enables companies to motivate their customers. The first is localization, which allows companies to tailor which products are available in which stores and which promotions best appeal to the local shopper market.

“The second approach is personalization, which is a step beyond localization,” he explains. “With this, companies move from a segment of many to a segment of one. This can be reflected in simple targeted pricing promotions as well as ‘awareness’ promotions targeted to shopper interests without requiring a price promotion to get their attention.”

He gives the real-time example of a store sending a text to alert a shopper that an item they have purchased a lot previously is currently on clearance in a nearby store, or to share a recipe near dinnertime to spur an incremental trip for the ingredients.

Summing up the benefits, Alberino, of Grey Jean Technologies, stresses that the biggest piece of knowledge grocers can take away from the data they’ve accumulated is that perceived “volume” of data doesn’t matter.

“What you do with your data is much more important than how much data you have,” he says. “In order to provide the biggest value for your customers — and, consequently, grocers themselves — grocers need to make those data insights actionable. The key to successful Big Data use is the ability to identify exactly which customer data points will help them understand individual buyers, what motivates them and what drives them to purchase — and understand that this data can and will change from purchase to purchase.”


Shopping for AI at Walmart

cosmas-and-craig-walmartThe Grey Jean team recently returned from Bentonville, Arkansas, where we had the amazing opportunity to participate in Walmart’s Technology Innovation Open Call.

We were fortunate enough to be one of a small handful of technology companies that were invited to present to a captive audience of stakeholders about how our AI-powered recommendation engine Genie has the potential to change the way people shop at Walmart.

In looking back at our presentation and the reaction we got from Walmart leaders in attendance, I was struck by two primary thoughts:


1 – Walmart really gets it

It’s hard to look at the biggest retailer in the world as an innovator, but I was blown away at how smart and thoughtful the team was, specifically when it comes to how data and technology can transform the retailer/shopper experience. As seen through their recent acquisition of, Walmart is committed to providing a premier online/offline experience. They understand that doing this includes providing very contextual and relevant promotions to their customers when, where and how they want them. This is what leads me to the second “revelation:”


2 – The market opportunity to provide a better shopping experience keeps growing

It was heartening to see just how big of a market opportunity there is for AI technologies like Genie. Retailers of all sizes are sitting on mountains of data that can transform their marketing activities and usher in a new era of sales for both the retailer and the customer. The reception we received after demonstrating how Genie can predict shopper behavior with incredible accuracy made the trip worthwhile. Smart retailers like Walmart understand the future of shopping will be incredibly personal and predictive.

Startups vs. Monoliths: Who Does Big Data Better?

startupsvsmonolithsAs advanced analytics continues to become more sophisticated, businesses across industries are realizing how much business value there is in turning data points into actionable insights. With demand for big data ever increasing, a host of startups have emerged alongside the Microsofts and IBMs of the world to help organizations integrate, understand and leverage big data insights.

When it comes to big data, many people have the perception that bigger is better. They want to use services provided by the biggest companies, with the perception that the large the organization, the more access they must have to data. Big companies have so much first party data, they must know much more about consumers, right?

It may seem counterintuitive, but this is simply not true. With so many different data points out there, certain points will be more valuable to you in some instances than in others. In order to create a comprehensive data set, even the monoliths need to purchase additional points in order to fill the gaps in their own data.

Consider this in another way – a surgeon could have the choice between a scalpel and a machete. But just because the machete is bigger, doesn’t mean it is more useful to the surgeon. He needs the right tool that will allow him to make small, anatomical dissections. Having the right data is similarly important, allowing companies to target and personalize at a more precise level.

The real differences between startups and monoliths when it comes to big data has nothing to do with accessibility – and everything to do with service. Qualities such as adaptability, time to market for innovations, ability to be prescriptive and access to quality resources within the company will distinguish smaller companies’ big data services from the monoliths.



Startups have a big advantage over larger companies when it comes to adaptability. Shifting strategies with big companies is much more difficult for the simple fact that they have a lot more layers of management and much longer approval processes. Clients are often made to work within the monolith’s existing products and frameworks. Startups are leaner by nature, meaning their products are often more adaptable to your own business compared to those offered by larger companies.


Time to Market

In the same vein, innovations can go to market much more quickly with a startup. With high volume, high variety data that comes in at a high velocity through real-time data streams, it is important for companies to be able to rapidly experiment with all sorts of combinations of data, and adjust the tools they’re using quickly to successfully predict trends, identify customers and push out promotions at the optimal time. Without the lengthy cycles of a large corporation, startups are much more flexible and capable of working with customers to get them what they need, when they need it. Meaning if your data conflicts with how you’re currently running a campaign, you can change tactics right then and there. If a new technology hits the market, or you need an integration that doesn’t already exist within the platform, a smaller company is nimbler, and likely able to turn it around for you quickly.


Prescriptive Ability

In addition to longer decision and go-to-market times, large companies can become entrenched in the traditional way of doing things, which affects their ability to administer prescriptive and innovative advice. With more invested in the status quo, they run the risk of becoming reluctant to invest in new technologies and methodologies that might require an overhaul of their current businesses. Startups are not hindered by deep roots within a particular product, meaning they’re willing to try innovative solutions – focused on figuring out what will deliver the best outcomes, rather than protecting their own heritage.


Access to Quality Resources

Who you decide to do business with determines what – and who – you get access to. Larger businesses often have procedures that prevents their executive management from engaging in close contact with their customers, delegating that responsibility to scores of account representatives instead. While there’s no harm in this, there’s no real benefit either. When working with a startup, customers have access to management (and expertise) much higher up in the food chain – sometimes all the way up to the CEO. Because of this, startups often develop much closer relationships with their customers and take a vested interest in making their data programs successful.


When it comes to big data, perceived “volume” of data doesn’t matter. What you DO with your data, and how a company works with you to accomplish it, is much more important than the size of the business you’re working with. The next time you consider leveraging big data, ask yourself: what data do I actually need, and how will I use it to benefit my business? That’s the key to mastering big data.

Marketing in an ‘All-Channels World’

omnichannelThe growing need for omni-channel strategies in which store, online and mobile experiences complement, rather than compete with, one another has left many retailers feeling overwhelmed about how to market to shoppers across channels in a personal, relevant and meaningful way. This pressure is heightened by today’s shopper, who now has the expectation that – given the amount of data available about them – retailers should know them personally.

When a retailer communicates with a shopper today – whether in a physical store, online or via their mobile device – consumers expect to receive messages, deals and offers that are not only relevant to them, but are delivered at the right time and in the right channel. Some shoppers may prefer to be contacted with coupons, offers or sales notifications through their email or their phone as soon as that coupon, deal or sale is active. Some consumers may prefer to be contacted only when they are shopping and are near the store. It’s all about understanding individual consumers’ unique preferences and behaviors. However, this is a challenge for retailers if they don’t have access to the right data from all channels.

To quote Hudson’s Bay CEO Jerry Storch, ‘It’s not an omni-channel world. It’s an all-channels world.’ Today’s consumers have the power to decide when they will be communicated with, how they will be communicated with, and what content they will respond to. Understanding these preferences for the individual shopper is the key to getting ‘all channels’ to complement one another.

To solve these challenges, retailers must become more agile. An agile retailer knows how to service their customers in the way they want to be serviced.

The first thing a retailer must do to become more agile is to eliminate data silos, and make it easier for key players in the organization – specifically chief marketing officers – to access the organization’s technical back-end systems where customer data is stored. A retailer’s marketing team are the ones who are face-to-face with the customer. It’s crucial for them to have access to the customer data that will tell them when, where and how to communicate with customers, and what they should say.

The simple fact that people think of the problem as brick-and-mortar vs. digital is part of the opportunity. Smart retailers are now thinking and designing in terms of customer experience using customer journey mapping.

An inside out design is a thing of the past. Retailers need technologies to go from the customer “into” the business and leverage all the data assets as well as the investments they have made: CRM, ERP, Inventory control, CMS, etc.

And this all has to be done with good data governance, security and protection of the consumer’s privacy. Not all retailers have that expertise in-house. They need partners and technologies that can remove the friction between the consumer, the retailer and the brands they carry. Let the machines do the machine work and focus the humans on creative, customer interaction, service, store design. It’s time for a new approach, new measures — that is how retailers can protect themselves from profit erosions from retailers like Amazon.