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 Changing State Of Retail In 8 Charts

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

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

1. Investor excitement surrounding in-store technologies

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

In Store Tech Funding Slide

 

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

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

In Store Tech Smart Money

3. 133 startups transforming in-store retail

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

In Store Tech Market Map Slide

 

4. New distribution methods bring the transaction to the consumer

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

Distribution Market Map Slide

 

5. E-commerce sees investment slump

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

Ecommerce Funding Slide

 

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

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

Ecommerce Smart Money Funding Slide

 

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

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

Ecommerce Top Exits Slide

 

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

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

Product Process Slide

 

Artificial Intelligence Fuels Juice Bar Expansion

by Angela Diffly, SMB Retail Technology News

pure-green-menu-1Green Apples

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

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

Ripe Opportunity

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

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

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

The Right Pick

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

 

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

 

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

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

 

 

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

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 Jet.com, 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.

Wal-Mart to hear 26 startups’ ideas

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

By Robbie Neiswanger

Arkansas Democrat-Gazette, Inc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Business on 10/06/2016

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

Startups 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.

 

Adaptability

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.

Insights Are Overrated

Image courtesy of McKinsey&Company

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

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

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

Getting Actionable

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

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

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

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

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

Technologies to Consider

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

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

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

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

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

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


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