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.

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

How artificial intelligence is transforming retail personalization

ai-jpg__640x360_q85_crop_subsampling-2Retail personalization is certainly not a new concept, but there are many new advances in personalization helping retailers create deeper relationships with shoppers through meaningful, relevant and contextual experiences.

Personalization through the years

Ten years ago, Amazon and Netflix were the poster children for personalization. Amazon was commended for its ability to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors. Other retailers’ personalization efforts simply greeted returning customers by name or enabled them to save website preferences. Then there were those that took a one-to-many approach, such as versioning their site for entire segments of visitors.

These rudimentary approaches are now considered table stakes for today’s retail marketers. Consumers are far savvier than they were ten years ago. They not only want personalization, they expect it!

Today’s retailers are rising to the occasion. Retail personalization is having a major resurgence, thanks to the proliferation of big data, as well as the implementation of machine learning across distributed platforms.

 

Stitch Fix’s AI success

Some retail organizations have built entire businesses around AI. Take Stitch Fix, for example. The styling subscription service uses AI to tailor clothing and accessories to busy women’s personal tastes, budgets and lifestyles. Stylists work with a team of more than 60 data scientists to choose tailored items for each shipment. By applying machine learning to the process, the computers become smarter as they handle more and more data.

This strategy of marrying humans and machines has been wildly successful for Stitch Fix. Over 80 percent of clients return within 90 days for a second order, and a third of clients spend 50 percent of their clothing budget with the subscription service.

While we’ll continue to see businesses in and out of the retail space launch with AI at the core, this approach certainly won’t make sense for all retailers. Instead, established retailers can apply AI to different business units. Recently, Macy’s announced its customer service unit is testing a “mobile companion” tool using AI. The tool enables shoppers to get answers based on the store that they are physically shopping in rather than having to find a sales associate. While this is a great way to leverage AI to engage with current shoppers, what about using the technology to acquire new ones?

 

AI’s role in marketing

Applying AI to marketing not only helps retailers acquire new customers, but also encourages repeat business. As recommendations and offers become more tailored, shoppers’ loyalty will continue to deepen as they associate the brand with personalized and relevant experiences. For retail marketers that want to stand out from the hundreds of advertising and marketing messages shoppers see on any given day, AI-driven marketing is a must.

AI enables marketers to harness powerful algorithms to find patterns in internal and third-party data, and then look for repetitions in these patterns. One of the core underpinnings of AI that is transforming retail personalization is machine learning. Stated very simply, machine learning is about solving problems using probability and statistics. Used in the context of personalization, machine learning can continually adjust the data sets until the right marketing message for each individual shopper is presented at the moment, and through the channel that matters most.

 

This can all sound very daunting for marketers. For those just getting started in AI-powered personalization, keep the following in mind:

1. Don’t be overwhelmed by data. Many retail organizations have data everywhere and have no idea how to consolidate it, let alone make sense of it all. If you don’t have internal resources to organize disparate data sets, simply outsource this important task to a personalization technology partner.

2. Pull in as much third-party data as possible. From POS to loyalty data, retailers have a lot of information about customers. But to get a true 360-degree picture, it’s important to pull in as much third-party data as possible. I’m not talking simple demographics here (though that’s helpful as well). Your data combined with factors such as location, time, social media activity and price sensitivities can really make the difference in understanding how, why and when customers shop, as well as the right purchase triggers so retailers can present the most relevant marketing messages.

3. Determine the best output channel. Many retailers have seen the power of email personalization for driving traffic and sales to their stores, both online and off. But there are many other channels where personalization works well — in particular mobile. As we saw in the Macy’s examples, mobile can be used to augment in-store service, but it’s also a great vehicle for timely promotions and offers for shoppers on the go. In addition, the emergence of AI-powered chatbots is something to keep an eye on, as retailers look to follow their audience onto the appropriate channels, including social.

You don’t have to be a personalized subscription service like Stitch Fix to provide personalized service to prospects and customers. A huge impact can be made by applying AI to one area of your business at a time — from marketing to customer service to merchandising. A once over-hyped technology, personalization has arrived and is the future of retail thanks to the power of artificial intelligence.

 

Originally published on Retail Customer Experience http://www.retailcustomerexperience.com/blogs/how-artificial-intelligence-is-transforming-retail-personalization/

 

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/

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.

Alley Watch: This NYC Startup Just Raised $2M To Give You the Genie You Always Wished For

Grey_Jean_FITA_rev

With Big Data, humans have the greatest advantage in understanding consumers than ever before. But just because the data exists does no mean you know how to use it. That’s why we have Grey Jean Technologies, the company that uses AI to give you all the information you need to target customers at your fingertips. With its intense personalization functions, the company’s signature Genie runs all the numbers specifically for each person providing solutions for your marketing team and not just ‘best guesses’.

Today, we sit down with cofounder Cosmas Wong to discuss the company’s recent funding as well as the companies roots from Wall Street.

Who were your investors and how much did you raise?

Grey Jean’s funding came from angel investors and company management, and totaled $2 million. It was a seed round and was led by myself. I was also cofounder of my last company, ENSO Financial Management LLP (EFM), which was recently sold.

Tell us about your product or service.

Grey Jean Technologies is a personalization company that improves customer acquisition and sales across all retail channels. Our recommendation engine, Genie, is powered by artificial intelligence (AI) and provides the most accurate predictions of consumer purchase behavior. This enables retail marketers to target shoppers with contextually relevant messages that drive desired actions, such as a store visit or redemption of a special offer. Genie uses big data and AI technology to organize retail’s unstructured data sets to connect the right deals to the right customers.

What inspired you to start the company?

Having spent years leading big data efforts in the financial sector for some of the world’s largest hedge funds, I saw firsthand the power of big data when used correctly – and also how challenging it can be to harness effectively. We had to understand how to preserve the integrity of sensitive data and use it to the benefit of all customers. My co-founder, Craig Alberino, was working on Madison Ave with big agencies in retail and CPG digital at the time, and encountered the same issue – retailers were struggling to convert their data into something real, such as increased sales or lead gen. Combining efforts and applying our combined experiences to retail marketing was a natural fit for us.

How is it different?

To date, personalization has largely been based on geo-location and basic demographic information. It’s essentially unsolicited targeting – more of a “best guess” rather than personalization. Genie uses the most comprehensive set of data points, including unconventional data points like political preferences and lifestyle, to create consumer profiles that are truly unique to each shopper. This is all continually updated through our eight proprietary algorithms to adjust individual customer profiles in real time.

Another way Genie is different is that it focuses on using artificial intelligence to drive actions, rather than just delivering insights. We actually help retailers take an action with their customer, rather than just presenting their data in new ways and leaving them wondering what to do with it.

Genie focuses on the person in personalization.

What market you are targeting and how big is it?

Grey Jean targets retail marketers. According to Juniper Research, global digital retail marketing spend will double by 2020, reaching over $360B.

What’s your business model?

Genie is a SaaS model. We bring data from disparate sources into a data management platform (DMP) using modern technologies for rapid data processing, like Spark, on the back-end. Our AI-powered recommendation engine, Genie, finds and weights underlying correlations, and converts that data into usable information, allowing retailers to make decisions and take action in real time. Ultimately, it enables retailers to do more than they could do otherwise, even if they had thousands of staff members.

Are there any concerns about privacy when using your solution?

If there are concerns, there shouldn’t be. We are housing anonymized versions of customer data. The analogy we often use is baking a cake – you add the butter, the flour – a bunch of individual ingredients which are identifiable and which you own, but ultimately once the cake is baked you can’t get the butter or the flour back out.  They’re not butter or flour anymore.  We’re creating fingerprints and personas for individuals in order to deliver the tailored and personally valuable deals for consumers, but we’re anonymizing the data in order to do that, and the end product ceases to be identifiable from its original form.

What was the funding process like?

The process for us was actually fairly unconventional. I believe most people are used to the typical “friends and family” Seed or A round of funding for a business. We funded a project to dissect a broken market. Out of that funded project came our business plan. From that solid foundation, it was pretty obvious what we had, and we were in the enviable position of having too many interested parties.

What are the biggest challenges that you faced while raising capital?

Our biggest challenge has been articulating how we’re different and better than other marketing tools out there. Genie sits at the intersections of a laundry list of buzz words – personalization, artificial intelligence, big data, machine learning – and each solution provider is using these terms in different ways. We had to explain how our product is unique, and being able to show our incredible results played a big part in that.

What factors about your business led your investors to write the check?

Our investors have worked with us before, but what its really came down to for us were the results, which speak for themselves. We can predict the next likely purchase at the category level (e.g. pool supplies) with 72% accuracy. In other words, the customer profiles that Genie creates are used to target consumers with individual deals that are relevant to them with such a high level of accuracy, generating more conversions, redemptions, visit frequency, foot traffic, time in store, basket size and brand affinity than other solutions.

What are the milestones you plan to achieve in the next six months?

Since the launch, we are in a period of rapid growth from both a corporate and customer standpoint. In the next six months, we’re expecting to expand our staff and close deals with some of the marquee clients we’ve been talking to. It’s a very exciting time.

What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?

There is a lot of uncertainty on the horizon for the VC and private funding market over the next year, but opportunities exist for businesses that can show value. My advice would be to focus on the fundamentals of building your business – the product/market fit, onboarding clients and hiring the best people to fill key roles, and then building out a revenue plan. Even in tough markets, if you have a product, a team and customers, there will be investors interested in your business.

Where do you see the company going now over the near term?

In the near term, we plan to keep growing our sales and marketing efforts, and continuing our conversations with leading retailers. We’re in an interesting time where retailers see the value of AI-powered personalization, but haven’t figured out how to achieve it yet.  Genie is an effective tool that can get them there – it truly delivers on the promise of personalization.

What’s your favorite rooftop bar in NYC to unwind?

It depends on who we’re entertaining. I like the Empire Hotel. The Press Lounge is also fun, but gets pretty busy. The Gansevoort at Park is great for lunch or early afternoon cocktails as it’s close to our NoMad office.

 

 

Interview with Cosmas Wong originally posted at: http://www.alleywatch.com/2016/07/nyc-startup-just-raised-2m-give-genie-always-wished/