Guest Post by Jessie Brandrick 

Digitalist Magazine reports that it wasn’t until 2017 that a significant number of entrepreneurial companies decided to adopt business analytics into their operations. Since then organizations have collected data to help with marketing strategies and improve customer service. Data analytics has become such a vital tool that businesses that don’t use it can quickly fall behind their competitors. It is for this reason that the analytics industry is growing at a record pace. Maryville University’s industry evaluation for data analytics graduates reveals that the market will exceed $95 billion by 2020. As the numbers show, where once data analytics was considered a luxury, now it is considered a necessity by most businesses, including the retail industry. 

There are four main kinds of analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive focuses on what happened, diagnostic answers why it happened, predictive helps businesses “see” the future, and prescriptive use data results to recommend the next best course of action. In the retail industry, descriptive and predictive analytics are the most effective.

Information Age points out that descriptive analytics allows organizations to see what is happening in real time and collect that data in one hub. The site notes that: “this data helps businesses to spot potential issues with current care procedures, key trends in demands and to improve the overall efficiency of the customer hub.” Retail trends move very quickly and descriptive analytics allows managers to keep on point with consumer habits.

Nikki Hallgrimsdottir’s feature on how retailers use data to create new products looked at how Coca-Coca was able to create a new flavor using prescriptive analytics. They set up a freestyle fountain dispenser that allowed customers to customize their flavors. The mixtures were collected and analyzed, with Cherry Sprite coming out a clear winner and subsequently, they made it available to the consumer market. Using prescriptive data analytics, retail companies can remove a lot of the guesswork that comes with developing new products. This leads to a much higher chance of a successful product launch. 

Lithium Technologies chief scientist Dr. Michael Wu told Information Week that predictive analytics doesn’t predict one possible future, but rather “multiple futures” based on the decision maker’s actions. Predictive analytics is especially useful in e-commerce, especially for brick and mortar retailers. Forbes notes that malls are currently hard at work to come up with a way to create an experience that cannot be replicated online.  New and emerging digital goggles can give executives new angles on how to analyze the data. Through predictive analytics, malls can create a one-of-a-kind personal experience for shoppers. Data that includes shopping history and product preferences allows retailers to make personalized recommendations in store with the aid of employees. 

On the flip side, it can also help prevent PR disasters. For instance, Costco has been collecting customer data since 2014 and was able to warn every purchaser who bought contaminated plums within a 24-hour time frame. This same data also helped identify a brand of salami that had caused widespread food poisoning. This shows how retailers can use data to not only improve the level of service they provide to their customers but also stop any damaging incidents that may hurt the company. 

As the above examples show data analytics is now a vital part of any successful retailer’s business strategy. And although all four types of analytics make a difference in the retail industry, descriptive and predictive analytics are the most valuable. As the world becomes more connected, expect the reliance on analytics to increase as a direct result.

Amazon’s Alexa platform may be helping users get accustomed to voice-driven interactions, but its underlying purpose isn’t just to help get weather reports or control smart-home devices. As a storefront, first and foremost, Amazon’s original intention with Alexa places a virtual assistant at your fingertips or in your home to help you shop.

Through AI tools like natural language processing, Alexa has led the retail industry in its rise towards conversational commerce. As if a customer was interacting with a clerk in a retail store, conversational commerce makes it possible for users to engage with software to research, purchase, or get customer assistance with products and services across a wide range of industries.

With Alexa, for example, users can ask any Alexa-enabled device to add an item to an Amazon shopping cart, set a purchasing reminder when a product is running low, or carry out a complete purchase without having to access a shopping cart. The result is a seamless conversational experience that enables consumers to carry out transactions as quickly as it takes to speak a sentence.

On other chat-based platforms, conversational commerce makes it easy to engage with brands without the need for human intervention. AI-based chatbot technology gives users a recognizable chat-based interface to ask product questions or purchase items. Chatbots also don’t always require their own separate app download, offering interactions over popular platforms like Apple’s iMessage, Facebook Messenger, and Google Home.

For many common user requests, it’s possible for chatbots to offer better user experiences with more efficiency than a phone call or email to customer support. A product return, for example, could be automatically coordinated through a chatbot instead of having to go through the lengthy process of speaking to a representative for return authorization.

One of the clearest models of the future of conversational commerce is Jetblack, the product of Walmart’s Store №8 tech incubator for the future of retail operations. Jetblack is an entirely chat-driven store currently serving Manhattan, where users can make shopping requests, get customized recommendations, and process returns.

While an exclusively chat- or voice-based shopping experience for all scenarios may never completely replace the in-person experience, conversational commerce will continue to grow as an added method of convenient and efficient communication. As users continue to become more accustomed to engaging with chatbots and voice-driven interfaces, expect more innovations in the space as brands continue to develop their unique conversation-based solutions.

Article originally published on Medium and reposted with permission from Humans for AI.

The marriage of artificial intelligence (AI) and e-commerce is growing stronger than ever before — and it’s changing the way users find and purchase consumer goods across nearly every industry. Largely pioneered by Amazon’s Real-time Product Recommendations, sophisticated AI is used to measure and track a user’s purchasing habits to determine other products they might be interested in buying. These days, retail’s use of data is giving consumers more of what they want, when they want it.

What’s more, companies are increasingly finding ways to cut out the middleman in order to offer their own line of direct-to-consumer goods. Amazon itself already has already amassed more than 76 private-label brands, taking its AI-driven insights into the real world by guiding customers to purchase its own in-house product lines. For products purchased on a regular basis, such as cleaning supplies or toiletries, Amazon boosts its own customer relationship even further through its Subscribe & Save program, offering discounts to users who commit to purchasing on a recurring basis.

These initiatives all add up to a retail bet that’s placed firmly on personalization through direct-to-consumer subscription services. An early wave of this trend was seen with online-only retailers like mattress company Casper. These types of retailers eliminated physical stores altogether in favor of a digital product selection available for in-home delivery.

The next evolution brought forth brands like online beauty sampler Birchbox, which gives users the chance to subscribe to a rotating box of cosmetic products to match a user’s predetermined tastes. Even large, traditional retailers have decided to jump on the bandwagon: One example is babyGAP‘s decision to offer subscription options to its legions of parent shoppers.

But now, armed with insights gleaned from AI, companies are further refining their direct-to-consumer subscription models to offer products meant to appeal directly to a consumer’s interests. Meal kit subscription provider HelloFresh uses machine learning to determine which foods its subscribers prefer, creating a feedback loop that tailors its menu to better recommend meals its customers will enjoy.

And at Yoox, a private label online clothing retailer, the entire business model is predicated on AI. The company combines fashion trends from social media with on-site sales data to curate its very lineup of clothing.

What’s in store for tomorrow’s retailers? It seems the combination of AI, hyper-targeted advertising and the subscription model means that soon enough, retailers will know their customers better than they know themselves.

Article originally published on Medium and reposted with permission from Humans for AI.


Data & Society, a New York City–based research institute focused on social and cultural issues arising from big data and automated technologies, recently released a new report on the future effect artificial intelligence (AI) poses to the retail industry. “AI in Context: The Labor of Integrating New Technologies” analyzes how AI — despite fears about its potential to replace human labor — is setting the stage for workplaces that will continue to rely on human beings to carry out different tasks.

Amazon’s cashier-less retail store, Amazon Go, sparked several fears about a retail future bereft of human labor. Relying on a skeleton crew of human staffers to restock shelves or bake food, Amazon’s automated store seemingly figured out how to seamlessly create a physical retail experience that doesn’t depend on cashiers or other frontline workers.

But Amazon wasn’t the first company to begin the process of automating retail labor — it was the now-ubiquitous self-checkout machine used by grocers and convenience stores around the globe. After the self-checkout expanded in use throughout the 1990s, many speculated that it would render cashiers and related retail laborers obsolete (similar to the speculations about Amazon Go).

Ultimately, those fears proved to be misguided. The adoption and usage of self-checkouts has fluctuated over the past few decades, as users grew frustrated with unintuitive interfaces and cumbersome checkout procedures.

In turn, the skills and responsibilities of a cashier have adapted. Rather than simply ringing up a purchase, cashiers must now fill customer service roles by troubleshooting self-checkout issues caused by the very machines meant to make the checkout process more efficient. While self-checkout machines make it easier for shoppers to check out on their own, users who run into difficulty ultimately need a human cashier to resolve the issue.

Another promise of self-checkout systems was to reallocate cashier labor to other tasks throughout a store. Instead, those workers were often left to oversee the self-checkout operation or entice users to use self-checkout machines rather than the checkout lanes operated by human beings. Rather than replacing human jobs, self-checkout machines ultimately reconfigured the types of work that retail workers were responsible for carrying out.

Automated technologies like self-checkouts may be highly efficient pieces of machinery, but human beings are still required to interact with the machines on a daily basis. Organizations developing the AI solutions of tomorrow may seem like they’re building applications meant to replace human labor. However, instead, that labor is being adapted into new skills and responsibilities that complement the work carried out by a machine. 

Big data helps retailers make better decisions. With the right data operation in place, any customer purchase or point-of-contact can become a useful piece of information that determines product strategies and identifies areas of improvement. Purchasing data, in particular, makes it easy to identify items or trends that are already popular with customers, guiding retailers towards offering customers products that they’re already interested in.

Here’s a look at a few retail companies that are using purchasing data to curate their own collections of best-selling products:

1. Coca-Cola

The Coca-Cola Company’s Freestyle soda fountain dispenses beverages in dozens of different flavors: from Diet Coke to sparkling Dasani water. Freestyle machines also let customers customize their flavor combinations, leading to thousands of different possibilities.

Armed with data gathered from the drinks customers were pouring for themselves, Coca-Cola determined new beverage flavors that were already a hit with customers to develop new retail products. One result was Cherry Sprite, a customer favorite that led to the release of its own ready-for-retail canned beverage.

2. Rent the Runway

The popular subscription fashion service Rent the Runway allows users to rent designer clothing on a one-time or recurring basis, giving subscribers access to high-end fashion at a fraction of the traditional retail price. To improve its product selection, Rent the Runway partnered with fashion designers Derek Lam, Prabal Gurung and Jason Wu to power its exclusive Designer Collective, a capsule of outfits created with the help of extensive customer feedback.

Using data gathered from customer surveys about each clothing rental’s fit, the occasion it was used for and the number of times it was worn, Rent the Runway helped the designers determine the types of rentals that were most popular with customers to help guide new designs.

3. Starbucks

Coffee giant Starbucks has embraced purchasing data to inform its entire retail operations. Using exclusive customer data gathered through its Starbucks Rewards loyalty program, the company gains insights into popular drink orders and determines how users are choosing to customize their beverages.

After data made it clear that customers don’t always add cream to iced coffee or add sugar to iced tea, Starbucks developed and released bottled unsweetened iced coffee and K-Cups of unsweetened tea to appeal to existing customer tastes. The frequency and popularity of customer purchases also help Starbucks determine where to build new locations, decide how to optimize its menu boards based on the weather or time of day and boost customer loyalty. 

For decades, consumers have relied on friends and family, product reviews and tastemakers when making purchasing decisions. A loved one could recommend a particular brand of tools that’s worked well over the years. A consumer watchdog publication could inform and educate on which car models offer the most reliability. A fashion magazine could highlight the latest trends that speak to any style. But while each of these different influencers remains relevant to today’s buyers, shoppers seeking out buying advice are increasingly being guided by artificial intelligence.

Through sophisticated AI, retailers are diving deeper into personalization by building solutions that suggest the best products for a user to purchase bolstered by data-driven insights.

Thanks to powerful AI-driven supply chain management, retailers can easily track what’s in store, what’s being shipped and what’s in the warehouse; ensuring customers can get what they want when they want it. But to create a more personalized shopping experience, retailers are also putting together better product collections, embracing trends like “showrooming” and crafting entirely new ways of shopping.

Here’s a look at how AI-driven personalization is transforming brick-and-mortar retailing:

1. b8ta

Retailers long maligned the trend of “showrooming” — that is, trying out a product in-store only to make an eventual purchase online. AI-driven supply chain management has allowed omnichannel retailing to alleviate some of these fears, but new retailers like b8ta have embraced this trend even further by building new stores around the showrooming concept. Offering retail-as-a-service, b8ta is an open-concept store that offers companies a flexible way of selling through brick-and-mortar locations. Companies can showcase products in b8ta stores from online brands that desire a physical presence.

For consumers who wish to purchase something online but also want to see it in person, b8ta changes the game. And for online retailers with a wide range of SKUs or a limited desire to expand into physical retail, b8ta offers the best of both worlds by showcasing products for limited amounts of time. Combined with AI-gathered data for personalized product targeting, a manufacturer could take advantage of b8ta by offering a small sample of their most popular products that customers wish they could try out in real life.

2. Amazon 4-Star

If you’re buying a kitchen gadget, how do you know it’s the best kitchen gadget? Well, if you’re Amazon, you know it’s the best because it’s got a wealth of customer purchasing data behind it. This includes star ratings, which lets users rank products after they’ve been purchased.

That’s the core concept behind Amazon’s newest retail store in New York City: Amazon 4-star. Carrying a curated selection of products that have all received large amounts of four-star ratings, Amazon uses its sophisticated product recommendation engines to bring its bestselling, most popular items into physical stores.

By offering a hand-picked selection of products that are beloved, trending or hidden gems, the service allows customers to shop from a collection of highly personalized recommendations in a brick-and-mortar setting.

Considering 35 percent of Amazon’s revenue comes from its AI-enhanced product recommendations, it’s a profitable shortcut to give customers what they already want.

Selling only the top-rated products might also be the right approach for adjusting an existing retail strategy. In early 2018, home furnishing retailer Crate & Barrel shuttered all physical locations of its children’s furniture chain The Land of Nod and began offering a smaller, curated collection of the same products under its in-store label, Crate & Kids. For Crate & Barrel, it became clear that offering a more personalized selection of products to its customers was more valuable than propping up an underperforming retailer that featured wider selections.

3. AlgoFace

Buying new makeup can be a long and messy process. Dropping by Sephora or the makeup counter at Macy’s means waiting for an associate to help you apply lipstick or eyeshadow to find the perfect color — from a tube that’s already been used by somebody else.

AlgoFace is making this process simpler (and far more sanitary) through its virtual-makeup SDK, which is available for makeup retailers to build into their apps. Shoppers can virtually apply an endless array of makeup shades to a live video of their face. Their AI-driven augmented reality interface makes it look like users are actually, physically wearing the makeup they’re thinking about buying.

The result isn’t just a highly personalized experience that lets users try out makeup combinations with no mess: It’s an incredible way to cut down on costs by saving on makeup samples. As for customer experience, this means being able to try out different looks in a mobile app or at a physical location.

 

Article originally published on Medium and reposted with permission from Humans for AI.

As the use of AI enabled platforms continue to grow within all industries and markets, we will also see a greater level of AI platforms being adopted by retail companies. There are four factors that will influence the adoption of AI in Retail:

Think Big, Start Small

Retailers who adopted AI early are already benefitting from this innovation. Retailers that are new to using AI in their day-to-day operations will benefit from starting with the “basics.” It is important for retailers to remember it is not about solving all their problems at once, but to focus on fixing one problem at a time. People often get caught up in the task at hand or distracted with too many problems. It is very important to remember the strategy of “test and learn.” Make one adjustment towards personalization for the consumer and test it before you move on to the next.

AI Boosts Conversions, Revenue, and Customer Satisfaction

IDC Retail insights predicts by 2019 40% of retailers will have developed a CX architecture supported by AI. IDC forecasts customer satisfaction scores to rise by 20%, employee productivity to rise by 15%, and inventory turnover to rise by 25%. This is all going to be possible due to AI paired with AR and IoT data which will give retail companies the ability to hyper-personalize each customer’s experience.

Mobile Devices Will Help AI Flourish

The vast majority of the population has access to mobile devices and conducts most their activities on these devices. This allows for a huge adoption in AI on this platform. The data collected from all these mobile devices will allow companies to improve their customer’s experience. One company that is already successfully implementing an AI platform is Starbucks. One thing their AI platform does is recommend specific orders for customers based on their prior purchase history. AI will play a big role influencing AI adoption in retail.

The Lack of Knowledge and Cultural Biases Will Hold Back the Adoption of AI

Two problems many companies face is the lack of knowledge and their cultural readiness for innovation within the company. These become a problem when people within the company are afraid to innovate new technology they don’t understand. Another hurdle retailers have to jump over is the cost of implementing an AI platform into their existing system.

Download the full report HERE

September 14, Talk Business and Politics

Machine learning falls under the realm of artificial intelligence (AI), and though the technology has been around for a long time, it’s becoming more relevant when married with big data, according to Amjad Hussain, CEO of Detroit-based Algomus, who provides AI assistance to retail and suppliers.

During a recent workshop in Bentonville, Hussain demonstrated how AI is used by some retail suppliers such as Sony. He said AI, when used as a business assistant, can enhance productivity in an office. Hussain said machine learning combined with human creativity creates collaborative intelligence. Mathematically, he said it’s something like 1 + 1 = 11.

“John Daly, senior vice president of worldwide production services at Sony Pictures, said the Algomus business assistant — aka Algo — makes it easy to target his underperforming stores and devise a plan to raise them.”

Read the Full Article Here