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.

 

 

 

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/

 

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/