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.

Artificial Intelligence Fuels Juice Bar Expansion

by Angela Diffly, SMB Retail Technology News

pure-green-menu-1Green Apples

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

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

Ripe Opportunity

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

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

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

The Right Pick

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

 

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

 

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

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

 

 

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

Wal-Mart to hear 26 startups’ ideas

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

By Robbie Neiswanger

Arkansas Democrat-Gazette, Inc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Business on 10/06/2016

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

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

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

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

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

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

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

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

 

Retail AI market map

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

final-retail-ai-market-map

Category breakdown

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

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

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

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

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

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

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

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

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

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

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

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

 

 

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

Insights Are Overrated

Image courtesy of McKinsey&Company

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

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

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

Getting Actionable

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

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

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

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

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

Technologies to Consider

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

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

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

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

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

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


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