Location-Based Analytics Could Boost Retail Sales

By David Pring-Mill, Direct Marketing News

When Toys “R” Us filed for bankruptcy last year, it contributed to a public impression that timeless household names in retail may be anything but timeless. The company’s CEO noted that the Chapter 11 filing and resulting financial flexibility would help the toy store chain to grapple with “an increasingly challenging and rapidly changing retail marketplace worldwide.” This prompted a series of articles about brick-and-mortar businesses struggling to survive against competitive pressures in the digital age. It might not be so bleak. By leveraging customer data and location-based analytics, retailers could boost their sales, drawing newfound vitality from the digital era that threatens them.

Multiple tech startups are offering AI-powered solutions for retailers looking to get the most out of customer data. That data can be sourced through the retailers’ own apps or purchased from third parties. Tokyo-based company Tamecco offers a product that is currently deployed at 2,500 brick-and-mortar-stores across Japan. Their website states, “Know when your customers come in and where they go, maximizing customer satisfaction with loyalty rewards and store layout improvements.”

Grey Jean Technologies also utilizes location-based marketing analytics. Their product can potentially lead to increased store visits, foot traffic, and basket size by allowing retail marketers to target their customers and prospects with more relevant marketing. Their website explains, “Grey Jean’s AI-powered recommendation engine, Genie, provides the most accurate predictions of consumer purchase behavior based on transaction history, demographics, location, time, social media activity, preferences and behavior.”

Another company in this space, Teemo, describes itself as a “Drive-to-Store marketing platform that is revolutionizing retail advertising.” The platform has the ability to analyze consumer data, such as previously visited locations, and determine which ads resulted in store visits. Teemo’s website explains, “We target your prospects based on the places they attend in real life.” Teemo acquires this data through direct mobile app partnerships and, to date, it has collected over 1,170 billion geolocation data points. Here again, each customer’s location history is leveraged so that the retailer can identify the most valuable consumers, present a more personalized campaign, and increase conversions.

With all of these companies now extracting location-based insights on consumers, retailers have an opportunity to adapt. Is this the silver bullet against fierce eCommerce competition? Will consumers embrace a more personalized form of marketing, or will it prompt privacy concerns?

I spoke with Benoit Grouchko, CEO & Co-Founder at Teemo, to find out if location-based insights on consumers will enable brick-and-mortar to endure and potentially thrive. Grouchko noted that driving traffic in stores has become the number one strategic priority for many retailers. He co-founded Teemo because “there used to be a gap between the offline world of brick-and-mortar and the online world of digital marketing.” In the past, retail marketing was static whereas eCommerce businesses had access to better tools, allowing them “to personalize the digital marketing user experience, to target the right people, to measure the impact of the marketing program, and to optimize performance.” Teemo now offers these capabilities to brick-and-mortar. Grouchko also notes that “offline sales are still by far the biggest channel source of revenues for retailers.”

When asked what differentiates Teemo from its competitors, Grouchko replied that product performance and transparency are key factors. “It is very important for us to be very transparent with retailers in both the performance that we drive and how the product works,” he commented.

With a myriad of big data solutions on the market, Antoine Cormier, Marketing Director at Teemo, observed that it’s not easy for retailers to choose. He explained, “The work we’re doing is quite new, it’s quite innovative for the market. And the thing is, there’s no real standard in the market. So it’s quite complicated for retailers to benchmark all the solutions because every vendor will say different things.”

Ultimately, Antoine Cormier and Benoit Grouchko believe that the robustness and sophistication of Teemo’s technology will set them apart. Teemo was nominated as one of the “21 hot tech startups” at NRF 2018.

Craig Alberino, founder and CEO of ALTA AI Partners, previously a founder and CEO of Grey Jean Technologies, has long observed the inefficiencies that take place in digital ad spend when marketers treat consumers with a “one size fits all” approach.

“We bring together data assets from consumers and businesses and create a meaningful value exchange,” Alberino explained, when asked about his current venture. “Technology providers like my own company ALTA are there to help retailers push back on Amazon’s efforts, both in eCommerce as well as in brick-and-mortar. So, you know, the intent of these platforms is to level the playing field a little bit if you will.”

When asked how he would frame the benefits of these sophisticated, analytical approaches to a possible client who’s either a skeptic or a Luddite, Alberino responded, “I would say — you already have many of the parts that you need to be able to speak to consumers at a more individual level. The benefits of doing so are lowering your overall marketing cost, because rather than broadcasting the same thing to everybody, you’re narrowcasting very specific deals that are specific to individuals based upon their needs and their wants and their desires. You’re no longer sending them noise.”

He continued, “It’s individualized, which will get people to the bottom of the funnel sooner. So generally your marketing activities are more top of funnel. Digital spend gets you more towards middle of funnel. But you know if people are looking for attribution and measurement, the best attribution and measurement is going to be a sale. So, the ability for us to influence an actual sale is there, through the use of data and technologies like ALTA.”

According to Alberino, “There’s been a land grab for data. And I think a lot of people don’t know what to do with the data so they need a good partner.”

He explained, “We can help have a meaningful dialogue about what data assets you actually have and then implement specific signals and scores based upon our novel data, which is actually more implicit-based.”

The ease and convenience of at-home shopping will always be a lure away from brick-and-mortar. However, personalized ad campaigns based on customer data and location history could change the game, giving some iconic retailers a fighting chance.



Original article appears in Direct Marketing News here ->  http://www.dmnews.com/marketing-strategy/location-based-analytics-could-boost-retail-sales/article/738073/

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.


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, “Match.com 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: http://gjny.com/meet-genie. 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.







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.”

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.”


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.




“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/