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.

 

 

 

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.

 

1474550735760

 

“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/

Startups Pitch Their Innovations at Walmart Technology Open Call

One of the teams pitching at Open Call is Fast Back, which earned its way to the event by winning a Walmart intern hackathon in July 2016.

When nearly 30 tech startups from across the globe convene in Bentonville, Arkansas, on Thursday, Oct. 6, they’ll have the chance to pitch their ideas to the world’s largest retailer. At the 2016 Walmart Technology Open Call, the startups will showcase their solutions to Walmart Technology associates in areas that include sustainability, fresh produce, augmented reality and artificial intelligence.

Open Call not only keeps Walmart on the cutting edge of technology, but also gives startups a better understanding of working with Walmart and other retailers.

As Tom Douglass, director of Walmart Lab 415-C, states to participants, “Our goal with this Open Call is to provide innovative companies like you the opportunity to present your ideas and inventions in front of decision makers at the Walmart home office here in Bentonville.”

Walmart gives startups from around the globe the chance to participate and pitch their ideas to the world’s largest retailer.

 

While solidifying investment in a company is not the primary goal of Open Call, these startups have the opportunity to partner with Walmart in many ways. For early stage companies that haven’t raised a funding round, Walmart could offer seed capital. For those still working to define an MVP, Walmart could offer its considerable engineering expertise, as well as access to facilities at Walmart Technology headquarters in Bentonville.

Each company will get three minutes in a “pitch battle” to present its innovative solution to Walmart Technology associates. Throughout the day, the startups will have face-to-face meetings with stakeholders and Walmart leaders from all over the world. The companies will also hear from panelists, hailing from IBM and Rockfish Interactive, among others, about tech disruption in retail, agile company development and more. In addition, an open expo area will allow for startups to demo their solutions.

The tech Open Call is a partnership between Walmart Technology and the Northwest Arkansas Tech Summit.

Take a look at the companies who will be attending this year’s tech open call:

  • Air Cross: Polymer technology that cleans the air and sanitizes surfaces.
  • Cimagine Media: Augmented reality tool allowing customers to see how products will look and fit in their home.
  • Criteek: Video review and data platform of authentic customer-generated video product reviews.
  • Elixer Marketing: Disseminates essential oils or fragrances by breaking down microscopic particles.
  • fNograph: Catalog of videos and audio transcripts in an easy-to-access online platform.
  • FastBack: Faster returns online. This is the winning hack team from the 2016 Walmart Intern Hackathon.
  • ForwardFunded: Gives shoppers the ability to budget quickly during checkout.
  • Freshspire, Inc.: App that connects grocers and customers to choose produce and reduce food waste.
  • GoSpotCheck: Collect information to leverage the data and insights that e-commerce is currently capitalizing on.
  • Grey Jean Technologies: AI-powered engine provides contextually relevant messages to customers.
  • GrowTech Industries: Produce growing year-round in a secure, local, controlled environment.
  • Ikonomo: Grocery price comparison app at item or basket level.
  • Info Scout: Incentivizes shoppers to snap receipt pictures after shopping.
  • InvenSense: Indoor positioning solution and analytics service.
  • Omniaretail BV: Machine learning algorithm that unifies pricing, marketing and promotional strategies.
  • PeriRX LLC: Patented, noninvasive salivary biomarker kit that can be used to detect oral cancer.
  • Rapport: Environmental health management tool for suppliers.
  • ShelfZone: Virtual reality shopping experience.
  • simMachines: Machine learning marketing analytic solution.
  • SPLAT: Provides customers the ability to see a product they are shopping for in their own space before purchase.
  • TabAssist: Mobile solution providing associates instant access to critical information right at their fingertips.
  • Tap Media Worx: Tap streaming media, static images and other advertising content to purchase the item.
  • Total Containment: Method to reduce human, ecological and environmental impacts when dispensing fuel.
  • Ubiquitous Energy: Transparent solar technology.
  • Wiserg: Captures and stabilizes nutrients in wasted food, repurposing them in a liquid fertilizer.

Convenience Store Decisions: Boosting Your Data IQ

From analyzing customers to assessing their inventory, more convenience stores are using operational data to their advantage.

By Howard Riell, Associate Editor, Convenience Store Decisions

Today, collecting data is clearly essential for convenience store retailers. But capturing and making use of the most pertinent information—from customer analytics to competitive intelligence—can spell the difference between operating in the black or not.

Data solutions aren’t always black and white, however. Often, it requires operators to assess category gross margins and related variables—especially in the form of inconsistencies or problems—and figure out how to address them.

Within a c-store today, being able to track customer tendencies, the workforce, fuel and product sales and inventory is vastly important to a retailer’s competitiveness.

“Typically, they are not properly accounting for variations in inventory,” said Steve Montgomery, president of b2b Solutions LLC , a Lake Forest, Ill.-based consulting firm, referring to typical scenarios. “The second reason is that something is amiss at the sites. This is true if the margin is higher or lower than is normally achieved.  In either case it should be investigated. Are the changes following the same pattern as their other locations as far as sales, margins, etc.? If not, why not?”

For many retailers, knowing how to gather and assess information from fuel inventory and customer sales is an important aspect of any store operation that sells fuel.

“Variations can be reported in both spread sheet and graphical format. Specific items can vary by chain. However, everyone should be monitoring sales (dollars, units and gallons), margins, customer counts, transaction size and expenses,” said Montgomery.

C-stores must also track fuel pricing processes and procedures.

“Obviously being in a c-store, fuel pricing is a huge deal and so you want to make sure that you are keeping up with your competition on that. It’s an easy question but a hard answer,” said Karla Grimes, director of operations for the Kent Cos., which runs 40 Kent Kwik Convenience Stores.

Being in the fueling industry, Grimes continued, many operators should ensure that they are staying up with that crucial parts of their business, as well as the competition.

“Of course, as far as consumer analysis goes, you’ve got to get into your competition and look at their stores and see what they are doing; how they take care of their customers,” Grimes said. “That’s a big part of it.”

“We do a lot of our data in-house,” added Alex Garoutte, marketing director of Kent Cos., based in Midland, Texas. “There are a lot of (data analysis) companies out there that are valuable. You just have to determine if it’s worth spending the money on.”

Inside sales, of course, deserve just as much focus.

“What are you doing, and what differentiates you from your competition?” Grimes said again.

STRATEGIC INITIATIVES
A good strategy for a chain of stores, according to Montgomery, is to look at the same type of data gathered from all locations at one point in time, and the same locations over time to determine patterns of activity. “The first allows the retailer to compare the results of his stores. The second will show whether changes are following the same pattern as their other locations,” Montgomery said.

Deciphering the data and then putting the results to use are the next steps. Disseminating the necessary operational information to key store personnel such as managers is vital and too often overlooked, Grimes added.

“You’ve got to decide what data you want to use and then figure out how to push that out. It could be a group of store managers, it could be employee leaders,” Grimes said. However you are going to do it, I think you have to go about getting that pushed out to every level of the company. And what you need is to have key players who are going to do that for you.”

SALES INFORMATION
First, a retailer must determine what data to share.

“Our experience tells us that the most important data that convenience store operators use is their own sales data,” said Brian Nelson, co-founder and chief operating officer of NewsBreak Media Networks Inc., a Knoxville, Tenn.-based programmatic merchandising platform for the convenience store industry that converts fuel-only customers to multi-product purchasers.

“The average c-store completes around 1,100 transactions (or more) per day. That is 1,100 unique customer surveys on what products customers want, and what products they will likely purchase together,” Nelson said. “A c-store’s proprietary sales data is a constantly updating data set that can be used to map and predict market trends, purchase patterns and inventory expectations. Ask any researchers what they would give for the ability to consistently collect 1,100 surveys daily.”

Data analysis and predictive analytics are areas that are experiencing rapid growth and innovation. If operators don’t seek help from experts, Nelson said, their category managers, marketing directors and executives are going to need to become data experts, which takes those individuals away from their proper roles and responsibilities.

“Seeking support from experts can help operators get to actionable information faster,” Nelson said. “Experts often have the infrastructure in place to parse, analyze and model historical and dynamic data quickly, instead of building it from scratch.”

Each time a customer completes a transaction, Nelson explained, he or she is giving the c-store operator direct feedback.

“Taking that feedback across multiple customers and multiple locations can give operators great insight into how they promote their products and even what products they carry,” Nelson said.

Market-basket data can be analyzed to help operators decide what products to promote together and what time of day they should be promoting those combination deals. For example, by promoting a discounted salad with a high-end water product, a c-store could increase unit sales for the promotional product while simultaneously driving up the total market basket.

C-stores can utilize data analytics to drive overall marketing initiatives, promotions and even inventory management and product selection. Analysis can include integrating key consumer buying variable data and demographic data into a store’s existing sales data in order to build location specific, programmatic merchandising campaigns.

Nelson encouraged operators to innovate and try new product offerings and marketing strategies, but warned that caution is called for. “Use data analytics as a tool, but don’t get stuck in the data.”

While a c-store operator will likely never get sales data from his competitor, operators can get a sense of how they stack up against the market, state or region from their vendor representatives. The companies that represent the brands that are sold through the c-store industry, Nelson said, can be a great resource to operators and category managers for target demographic information and customer analytics.

IDENTIFYING BEHAVIOR
Craig Alberino, the CEO of Grey Jean Technologies in New York City, said his firm generally works with behavior and identity data. “That’s things that you buy, places you go, even the weather. It could be various influencers.”

Knowing why consumers buy what they buy helps drive engagements and forge deeper relationships, resulting in more predictable revenue.

“It’s our view that all retailers, convenience stores included, could be serving customers better by knowing them more intimately,” he added.

Taking transaction and loyalty information inventory numbers, promotional calendars and more, and enriching it with elements like census statistics, weather and social media can provide a sharp focus on buying patterns for small geographic area around the store.

“Even if there is somebody in my dad’s age category, which is 70-plus, he’s still carrying an iPhone,” Alberino said. “He’s still looking for deals, and he is hypersensitive to gas prices. He knows if there is two cents less per gallon down the road, and he’s going to go to that other pump. Even though he might be spending more, it’s the trigger and it’s kind of a win, that he beat the system. And so, you are almost ‘game-ifying’ it a little.”

COUNTING CORRELATIONS
Retailers naturally want to drive up customer visits and average sales per transaction. More convenienc stores are evaluating customer sales over time to gauge future buying tendencies.

“This data is far more informative that just total sales,” said Montgomery. “It can tell why sales are up or down. Is it because fewer customer are coming to the store, or because they are buying less each time they come or both?”

Another tool is the correlation between gallons sold and store sales changed, he added. “Are they still buying your fuel but shopping more or less frequently inside you store? The data will not solve your problems, but should tell you where they are occurring.”

One of the easier analytics that can be checked is the correlation between fuel sales and in-store sales, Montgomery said. “Does an increase in gallons result in increased store sales? If so, does it impact certain categories more than others? These types of insights can help retailers determine what role they want a category to play in their overall marketing strategy.”

Montgomery suggested retailers benchmark their data with sources such as the National Association of Convenience Stores (NACS) because it might show opportunities to better sales and margins or control expenses.

“However, every store (or) market is different. The most important benchmarking retailers can do is against their own data,” Montgomery said. “That the industry averages ‘X’ is not as meaningful as whether you are making steady improvements in sales, margins and expense control.”

MORE DATA
In retail, data and data gathering comes in many forms and the benefits are just as varied.

“In any retail setting, convenience stores included, we generally work with behavior and identity data,” said Alberino. “Identity data would be things that you buy, places you go, even the weather. It could be various influencers. So generally when we are working with a retailer we will take transaction information and any loyalty information they might have, inventory, promotional calendars, and we will enrich it with census, weather, social media, etc. What is happening in the vicinity of your physical presence, within a mile of that convenience store?”

Among the data he suggests is scheduling of local events.

“There could be a rock concert happening, for instance, and in fact one of the convenience chains we’ve spoken to was in charge of sponsorship for a very large concert venue,” Alberino said. “We were talking to them about how they could increase traffic. Because many people were making long journeys across the West from California to the venue, how do you get them to stop, not only at the pump along the way, but also to stop in and get other things?”

 

Originally published on Convenience Store Decisions

Boosting Your Data IQ

Taking Amazon-esque Personalization to the Brick-and-Mortar Store

amazon-brick-mortar-trans-300x300Amazon is now arguably today’s most popular and most shopped online retailer in the world, and is often looked upon as a personalization leader. This is in large part due to Amazon’s ability early on to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors.

 

Today’s shopper now has the expectation that all retailers will know them personally as a consumer and serve up personalized experiences the same way Amazon does. When a retailer communicates with them – whether online or in a physical store – they expect to receive messages, deals and offers that are relevant to them, at the exact time when the offer is most relevant, and in a manner they expect to be communicated with based on their personal preferences. In short, they want one-to-one personalization.

 

While physical store associates can provide great customer service, online retail has an advantage when it comes to personalization because the retailer can tailor its homepage and customer recommendations based on easily trackable clickstream paths and browsing data on recently viewed or purchased items. So how can retailers apply this level of Amazon-esque personalization in-store?

 

Enhancing Beacon Technology

Perhaps the easiest way to leverage personalization in-store is through beacon technology, which has grown in popularity over the last few years. Beacons can detect when a customer approaches or leaves a specific location, enabling retailers to push timely messages to shoppers that promote certain products or offer other useful information. While many retailers use beacons to entice customers to make a purchase with store-wide deals, this may not always result in a sale because the offer isn’t always relevant to all shoppers.

 

However, when beacon technology is coupled with data about consumers’ past behaviors, preferences or other valuable data about that specific customer and layered with predictive analytics, retailers can identify the messages that will resonate with each shopper and provide them with a truly personalized deal or offer.

 

Let’s say a customer is walking by a department store and receives a mobile notification on a 20 percent off deal on sneakers. The customer enters and roams the store, but ultimately decides not to buy anything. Perhaps they had just bought a new pair of sneakers, or the sneakers that were on sale didn’t include their favorite brand.

 

This is where predictive intelligence steps in. Now imagine the department store has all of this data on this same customer – data that reveals things such as:

  • Transaction History: The customer always buys a new pair of shoes during the first week of each month, but never on days in-between;
  • Likes: She particularly likes comfort over style, often buying sneakers, flats or low-heel boots over stilettos;
  • Lifestyle: She is a working nurse, and spends 12-hour shifts 4 days a week at the hospital running around from room to room checking in on patients;
  • Cause: She often contributes to charitable organizations that provide resources or aid to impoverished countries.

 

Knowing these data points – which provide context for when and why a consumer makes a purchase, and work in conjunction with the customer’s location – the retailer can now send a mobile offer that is truly tailored to the customer and is much more effective in enticing her to make a purchase.

 

Case in point: Perhaps the customer is in the store on Labor Day weekend. You now know she prefers to buy shoes at the beginning of the month, prefers comfort over style, is on her feet all day most days and has a soft spot for philanthropic endeavors. You know then that offering her a deal for 20 percent off a pair of TOMS would more likely result in a purchase than if you were to offer her 20 percent a pair of Sam Edelman heels. To top it off, the deal was sent to her at the optimal time she would buy.

 

Using this type of technology in conjunction with predictive intelligence allows retailers to personalize in-store offers to each individual shopper by speaking to their individual tastes – offering more compelling deals that have a greater chance of resulting in a purchase.

 

Delivering Deals Anywhere, Anytime, in Any Weather

Perhaps the greatest thing about predictive intelligence is that it’s not restricted to beacons or any one location. Predictive intelligence enables retailers to know the data points that are relevant in any purchase decision and send targeted offers based on this data, no matter where their customers are located. This not only encourages them to make a purchase, but can actually encourage them to come into the store if the deal is in-sync with when, where and why a customer prefers to make a purchase.

 

For example, a local coffee shop learns the following about one of its customers:

  • Location: The customer works at an office two blocks away;
  • Time: He usually makes a coffee run at 2 p.m., when he hits his afternoon work slump;
  • Weather: He opts for a hot latte every time rain is in the forecast.

 

The next time it rains, the coffee shop can deliver a deal for this particular customer that is specifically tailored to his preferences and behavior – perhaps offering one dollar off a medium latte sent to his mobile just before two o’clock. They know they can get his attention due to the time, his proximity to the shop and his preference for hot drinks on rainy days, and they’ll be more effective in enticing him into their store because they were the only nearby coffee shop to offer a deal for what he wanted at a time when he wanted it.

 

Retailers can deliver this level of personalization anywhere their customers are located, whether they’re in an office, at home or even lounging on a beach. No matter where a customer is, what time of day it is or what the weather is like, predictive intelligence learns how to utilize the right combination of data points to drive a purchase. The possibilities are endless and the variables limitless; retailers just need to be armed with the right intelligence about their consumers’ unique preferences and behaviors.

 

Originally published on Multichannel Merchant http://multichannelmerchant.com/ecommerce/taking-amazon-esque-personalization-brick-mortar-store-14092016/

 

Tim Dileo Joins Grey Jean Technologies as SVP of Customer Success

Dileo Brings 20 Years of Retail and CPG Client Management to AI-Powered Personalization Company

New York City – September 14, 2016Grey Jean Technologies, the AI-powered personalization company that provides the most accurate predictions of consumer behavior, today announced the appointment of Tim Dileo to Senior Vice President of Customer Success. In this role, Dileo will be responsible for advancing the adoption of Grey Jean’s sophisticated recommendation engine, Genie, by building, maintaining and growing relationships with its customers

Throughout his career, Dileo has directed, managed and performed key functions including sales, marketing, strategic planning and customer relationship management. Most recently, at Vodafone, he led a team of 15 employees to manage Fortune 500 clients’ telecom management solutions, improve customer relationships, and ultimately preserve $25 million in annual revenues. While at HCPlexus, he built strategic partnerships with major clients including WebMD and Boehringer Ingelheim. Earlier in his career, Dileo held client management roles at Affinion Group, a direct marketer of reward programs, where he worked with banks, retailers and airlines, and Synapse (a subsidiary of Time Warner), where he managed major retail accounts including Bloomingdales, JC Penney, Ticketmaster and Guthy-Renker.

“Organizations of all sizes need to not only understand who their customers are at the individual level and what motivates their purchase decisions, but also how to act on that data,” said Dileo. “That is what attracted me to Grey Jean. Its Genie platform gives marketers a 360-degree view of each customer and also takes action with tailored messages and offers that can be delivered to any channel at the exact moment a consumer will be most receptive to the message. I am excited to help marketers realize the levels of success they can achieve with Genie.”

“Tim is a proven client success leader who has spent his career building collaborative working partnerships with clients and internal stakeholders. His experience will enable us to accelerate the adoption of Genie to help marketers more effectively reach their target audiences with relevant messages and offers,” said Craig Alberino, CEO of Grey Jean. “We are incredibly fortunate and excited that Tim has joined the Grey Jean team.”

Grey Jean’s flagship product, Genie provides the most accurate purchase behavior predictions, so marketers can target consumers with relevant messages that drive desired actions. For more about how Genie uses personalization and AI to improve customer acquisition and sales, please visit www.gjny.com.

 

###

 

About Grey Jean Technologies

Grey Jean Technologies is the personalization company that improves customer acquisition and sales across all retail channels. Genie, the company’s AI-powered recommendation engine, provides the most accurate predictions of consumer behavior, enabling retail marketers to target customers and prospects with contextually relevant messages that drive desired actions. The company is headquartered in New York. For more information, please visit http://gjny.com/.

 

Contact:

Keri Bertolino

fama PR for Grey Jean Technologies

617-986-5007

greyjean@famapr.com

Marketing in an ‘All-Channels World’

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

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

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

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

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

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

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

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

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/