7 ways AI innovations make life easier for fashion companies and fashionistas alike

Geeky has never been so glamorous.

To help shoppers find fashions that fit their bodies, budgets and styles, more retailers are turning to artificial intelligence (AI). As one of today’s hottest technology trends, AI translates vast volumes of retail big data into rich, actionable insights, so executives can make smart decisions faster.

AI has evolved into a strategic asset in retail, as the sector faces overwhelming upheaval. Amid the blistering pace of innovation, and empowered consumers who expect omnichannel excellence, affordable fashions and personalized marketing, AI guides retailers by delivering fact-based intelligence to delight shoppers.

AI and retail: A perfect fit

Today’s retailers and brands are drowning in data and struggling to make sense of the abundance of information on their own. AI solves this problem by offering “a cheaper, faster way of doing many tasks that companies currently employ humans to do,” like simplifying complex data analytics.

Companies also embrace AI to improve agility, top-line performance and customer service – all while lowering costs. For instance, retailers gain greater accuracy in predicting a consumer’s style at the exact moment an individual is shopping, leading to more sales, fewer returns and improved stocking ability.
These business benefits explain why 45% of retailers expect to use AI within 3 years.

Elevating CX to the next level

AI can also help consumers, who simply want “to buy stuff as quickly and easily as possible.”
Fashion retailers and brands can make the customer experience pleasant by applying AI to gain data insights down to the individual shopper level, which makes the experience more consumer-centric, relevant and personalized than ever before.

Whether a shopper’s look is casual chic, vintage inspired or haute couture, AI allows lets them perform easy product searches, so consumers discover fashions they will actually buy, saving them time and effort.

For instance, AI can help individual shoppers find the perfect pair of jeans by tracking their sales history, online browsing and Instagram likes for a seamless shopping experience.

7 ways AI is revolutionizing retail

Here are 7 ways AI innovations make life easier for fashion companies and fashionistas alike.

  1. The fastest way to find your look:AI-driven visual search helps shoppers to promptly find their desired fashions. Pinterest recently launched a visual search tool called Lens, which uses machine vision to detect items on the web or in the Pinterest library and suggest related items – like a Shazam for products. Neiman Marcus launched the AI-driven Snap. Find. Shop. mobile app; customers use their smartphone cameras to take pictures of an item they like and the app displays similar items from the store’s inventory. Retail brands using visual search for a superior online shopping experience include Asos, John Lewis, Shoes.com, Nordstrom, Hook (an aggregator of all brands) and Urban Outfitters.
  2. Hyper-personal product recommendations:For individualized service, IBM Watson partnered with The North Face to ask shoppers questions about their gender, time of year and technical product details, to tailor product recommendations. Blending AI and a human touch, style service Thread asks customers to complete a questionnaire and upload images of themselves. A stylist reviews the information to understand each client’s needs and uses the company’s AI algorithm to sort through thousands of products for personalized style suggestions.
  3. Unprecedented omnichannel service:Farfetch, the world’s top luxury online marketplace, uses AI to improve supply chain visibility. AI helps Farfetch’s partners, including 1,500 boutiques and over 200 brands, link their online inventory with inventory in their physical stores, and deliver services like click-and-collect and in-store returns. Also, AI-driven bots help retail companies engage consumers across channels, like Facebook, Slack or a retailer website. Burberry launched a Facebook Messenger bot during London Fashion Week to offer exclusive glimpses of the new collection before the runway debut, plus live customer service so users could buy the clothes immediately.
  4. A savvy social sales assistant:Pinterest also launched Shop the Look, a machine learning tool that identifies pinned items shoppers can buy, including fashions from major retailers. The insightful data analytics from Shop the Look can also tell retailers whether a sponsored post on the social media platform results in a sale.
  5. Shop like a stylist:get hook fashion banner
    To give tech-savvy, fashion-conscious millennials and Gen Zs a mobile personal stylist, the Hook team at Intelligence Node created the first AI-generated fashion feed for consumers. Hook learns, in real time, what a shopper likes just by analyzing which product images the individual has liked or added their online wishlisted, so shoppers find items they love, fast. The app sends shoppers real-time price drop alerts on their wishlisted or liked products. Hook helps consumers shop like a sophisticated stylist by gaining unprecedented search capability to find a specific item they want with a single view of the item across brands so they can compare prices and get the best deal.
  6. The ultimate trendspotter:To give retail companies certainty on up-to-the-minute fashion trends, AI can crawl e-commerce sites to pinpoint exactly which products are most visible. AI can also crawl social media sites to identify trends, helping brands be first to market with popular styles.Intelligence Node built the world’s most comprehensive retail crawling framework – like a ‘Google’ for fashion. The system crawls more than 1000 (?) websites and maps one billion products from 130,000 brands in 1400 categories in real time, using AI and machine learning for image recognition to rapidly process and make sense of the abundant retail market data.
  7. The best way to ensure the price is right:Big data tools like Incompetitor ™  help retailers maintain a competitive pricing strategy, combining AI and machine learning. Retailers integrate this API into a brand’s data, so there’s a rules-based engine that keeps prices optimized, depending on real-time external factors like inventory, competitors’ out-of-stock situations and discounts. This integration allows retailers to optimize their price and, for instance, ensure they always sell a specific SKU 10% cheaper than Amazon.

 

(let’s think how to replace Incompetitor ™ with Hook analytics giving retailers real-time intelligence on the consumer’s voice and preferences).

 

AI-driven tools like Hook analytics helps retailers gain a deep understanding of each shopper as a unique individual. They help retail companies understand a shopper’s size, and preferred styles, colors and price points. AI takes this combination of data and translates it into real-time intelligence that reflect product recommendations at prices that make the shopper more likely to add them to their cart. This deep consumer knowledge matched with relevant products and pricing help the shopper feel understood – and more willing to buy.

To avoid getting left behind during this period of digital transformation, fashion companies should be injecting artificial intelligence into their business strategy. AI simplifies retail big data, which helps companies improve their processes and bottom line – while keeping shoppers happy, loyal and chic.

We’ll be up looking at the data while you’re chasing bigger targets. Optimize your fashion business and always stay ready for the New Age Shoppers. Request a Demo !

How Artificial Intelligence(AI) Is Helping Retailers Predict Prices

It safe to say that artificial intelligence (AI) is changing every aspect of modern living. From our phones to cars to healthcare and every other industry, AI is slowly becoming a common part of today’s environment, deeply embedded in everything we do. Retail is no exception, entering a new era of predictive commerce. Thanks to AI, retailers are able to cater to their customers to the tiniest detail possible, while also leveraging the technology to improve their business operations.

For this post, we’ll focus on how AI helps with one retail aspect that often troubles retailers the most – pricing. The tech behind AI, how it uses it to predict prices and what are the results – it’s all there in the following lines.

Inside AI

At the heart of AI is machine learning (ML), a process that has the ability to learn on its own without being explicitly programmed. Machine learning uses data to detect patterns in data and adjust actions accordingly so that, when it’s exposed to new data, it develops programs that adapt to that information. ML algorithms are closely related to a number of computational methods, such as computational statistics and mathematical optimization.

While this may sound rather boring, especially for those that never really liked math that much or were garbage at it, the reason why we are mentioning all the math babble is because ML is a standard method used to create complex algorithms that possess predictive powers. You might know this as predictive analytics, a number of analytical models that uncover insights through learning from trends and historical information in the data set.

Predicting prices

Machine learning has many approaches that constitute it – different types of learning if you will. It’s in your Facebook’s News Feed. It’s making Tom Cruise’s life a living hell in Minority Report. It can even predict when you are going to buy soup. Thus, we’ll spare you the nitty-gritty of it and focus on how the technology helps with price prediction.

In a nutshell, you have analytics software whose machine learning component is using a technique based on a certain statistical model (Gaussian process regression, Bayesian linear regression, multi-task learning and a number of other models) to create algorithms that automatically identify patterns from the data and predict prices based on that information. Patterns from huge data sets range from competitors’ pricing and inventory, purchase histories, product preferences to product demand and anything closely related to pricing.

As you can imagine, these parameters are constantly in flux, which is where machine learning comes in and adds a bit of nuance to the whole process that goes beyond simple price history. Suddenly, you have an accurate prediction of customer behavior, a whole system built around the individual and its needs. All of this is followed by high levels of automation, where the execution of produced data-driven insights is instantly applied.

It’s actually a practice that has been around for a few years. Back in 2014, Amazon was granted a predictive stocking patent that allowed the online retailer to cut down on delivery time and cost by predicting what buyers are going to buy before they actually buy it. That’s just one of the ways the company is deeply integrated with AI. For instance, sophisticated sorting algorithms are in charge of its warehouses. When an order comes through, the system almost immediately works out where the item is in its inventory and then dispatches a human worker to go fetch it.

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With prices, it’s a bit more complicated than that as there are more factors to consider. However, the basic principle is the same due to the highly complex and sophisticated nature of the technology. You have a pricing engine that helps you monitor competitor prices real-time. It then sorts them out and compares them to similar products, depending on the wide range of attributes selected, and ultimately optimizes prices.

What’s in it for the retailers?

For once, they get price predictions and optimizations according to numerous market fluctuations that go in line with their pricing and volume goals, among others. Thus, the ever-elusive pricing is no longer a problem. There is no Indiana Jones-like adventure to find that pricing sweet spot that will attract and retain the customers whilst raising the level of profit margins. In a way, retailers get an effective guide for their retail life cycle decisions.

Also, they get precious time back. Automated solutions are the future of retailing, even if they are still on the margins. They handily reduce the enormous amounts of time need for manual labor regarding tracking the prices of your competition. By leaving everything in the hands of automated analytics software, retailers, both online and offline, have more time to focus on other important and time-demanding aspects of their business.

However, the benefits of technology don’t stop there. With the scope of modern technology, it would be foolish to think that there is only a single layer of this cake. Retailers also get to center their operations around customers and deliver them a personalized experience which matches their browsing history and wish lists with cross-sell and up-sell recommendations as it’s all part of the package. They get automated re-orders whenever certain product stocks fall below minimum required levels. Assortment optimization? Check. Various adjustments according to the occasion, competitor Intel, product, category, season and consumer behavior? Check.

In summary, they get real-time market intelligence with all bases covered – a detailed understanding of human behavior coupled with large-scale automation and data integration.

Conclusion

As witnessed, the technology has come so far that not even retail giants are immune to the raging AI evolution. On the contrary, they are actively developing machine learning algorithms to further improve their businesses and receive actionable insights that were formerly almost exclusive to human intuition. In that term, retailers should take the cue and start thinking and acting like tech companies, utilizing artificial intelligence to not just predict prices and recommend products, but to take care of their customers, as well as their business, in an all-encompassing way.

 

If you found this useful and you’d like to learn how to take your pricing strategy to the next level, we invite you to download our free 20 secrets to designing the best pricing strategy eBook. Click below to take advantage of this opportunity.

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AI- Seeing the Future!

We have heard this word many a times. Sci-fi movies are always quoting AI as if it’s a household word. Artificial Intelligence has interested scientists and enthusiasts for a long time now. Ever since mankind developed the computer, the ides of AI has taken root.

It would not be farfetched to assume that in a few decades artificial intelligence will be a large part of our lives. It already plays a significant role even today- just look at Siri or Cortana for example. These words are very familiar because we can ask them various questions and they probably answer them accurately.

Whenever we hear the words AI, our curiosity is piqued. So what’s all the fuss about? Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. . The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.

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