The AI stack that’s changing retail personalization

By Sowmiya Chocka Narayanan. Original post: HERE Consumer expectations are higher than ever as a new generation of shoppers look to shop for experiences rather than commodities. They expect instant and highly-tailored (pun intended?) customer service and recommendations across any retail channel. To be forward-looking, brands and retailers are turning to startups in image recognition and machine learning to know, at a very deep level, what each consumer’s current context and personal preferences are and how they evolve. But while brands and retailers are sitting on enormous amounts of data, only a handful are actually leveraging it to its full potential. To provide hyper-personalization in real time, a brand needs a deep understanding of its products and customer data. Imagine a case where a shopper is browsing the website for an edgy dress and the brand can recognize the shopper’s context and preference in other features like style, fit, occasion, color etc., then use this information implicitly while fetching similar dresses for the user. Another situation is where the shopper searches for clothes inspired by their favorite fashion bloggers or Instagram influencers using images in place of text search. This would shorten product discovery time and help the brand build a hyper-personalized experience which the customer then rewards with loyalty. With the sheer amount of products being sold online, shoppers primarily discover products through category or search-based navigation. However, inconsistencies in product metadata created by vendors or merchandisers lead to poor recall of products and broken search experiences. This is where image recognition and machine learning can deeply analyze enormous data sets and a vast assortment of visual features that exist in a product to automatically extract labels from the product images and improve the accuracy of search results. Why is image recognition better than ever before? While computer vision has been around for decades, it has recently become more powerful, thanks to the rise of deep neural networks. Traditional vision techniques laid the foundation for learning edges, corners, colors and objects from input images but it required human engineering of the features to be looked at in the images. Also, the traditional algorithms found it difficult to cope up with the changes in illumination, viewpoint, scale, image quality, etc. Deep learning, on the other hand, takes in massive training data and more computation power and delivers the horsepower to extract features from unstructured data sets and learn without human intervention. Inspired by the biological structure of the human brain, deep learning uses neural networks to analyze patterns and find correlations in unstructured data such as images, audio, video and text. DNNs are at the heart of today’s AI resurgence as they allow more complex problems to be tackled and solved with higher accuracy and less cumbersome fine-tuning. How much training data do you need? Training a deep learning model from scratch does require a large amount of data, but thanks to a technique called transfer learning, deep learning models can be trained with little data as well. Transfer learning is a popular approach Read more…

Retail’s Next Act: The Connected Window Shopper

Consumers are individuals and want to be treated that way, but the brick-and-mortar experience was not always personalized. There was a time when consumers would visit retailers that offered one-size-fits-all options. Even with advances in technology, physical stores still lag behind in offering personalization, gamification and other kinds of data-driven experiences. Personalization is a key element of making the retail experience “more relevant and interesting,” Omer Golan, founder and CEO of Outernets, told PYMNTS in an interview. While personalization more often relates to eCommerce, digital-focused firms like Outernets aim to bring the concept to life in physical store locations. FULL PRESS CLICK HERE

Most of AI’s business uses will be in two areas

By Michael Chui, Nicolaus Henke, and Mehdi Miremadi . Original post: HERE An examination of more than 400 AI use cases revealed the two areas where AI can have the greatest impact, write the authors in Harvard Business Review. While overall adoption of artificial intelligence (AI) remains low among businesses (about 20 percent upon our last study), senior executives know that AI isn’t just hype. Organizations across sectors are looking closely at the technology to see what it can do for their business. As they should—we estimate that 40 percent of all the potential value that can be created by analytics today comes from the AI techniques that fall under the umbrella “deep learning” (which utilize multiple layers of artificial neural networks, so-called because their structure and function are loosely inspired by that of the human brain). In total, we estimate deep learning could account for between $3.5 trillion and $5.8 trillion in annual value. However, many business leaders are still not exactly sure where they should apply AI to reap the biggest rewards. After all, embedding AI across the business requires significant investment in talent and upgrades to the tech stack as well as sweeping change initiatives to ensure AI drives meaningful value, whether it be through powering better decision making or enhancing consumer-facing applications. Through an in-depth examination of more than 400 actual AI use cases across 19 industries and nine business functions, we’ve discovered an old adage proves most useful in answering the question of where to put AI to work: “Follow the money.” The business areas that traditionally provide the most value to companies tend to be the areas where AI can have the biggest impact. In retail organizations, for example, marketing and sales has often provided significant value. Our research shows that using AI on customer data to personalize promotions can lead to a 1 to 2 percent increase in incremental sales for brick-and-mortar retailers alone. In advanced manufacturing, by contrast, operations often drive the most value. Here, AI can enable forecasting based on underlying causal drivers of demand rather than prior outcomes, improving forecasting accuracy by 10 to 20 percent. This translates into a potential 5 percent reduction in inventory costs and revenue increases of 2 to 3 percent. While applications of AI cover a full range of functional areas, it is in fact in these two cross-cutting ones—supply-chain management/manufacturing and marketing and sales—where we believe AI can have the biggest impact, at least for now, in several industries (exhibit). Combined, we estimate that these use cases make up more than two-thirds of the entire AI opportunity. AI can create $1.4 trillion to $2.6 trillion of value in marketing and sales across the world’s businesses, and $1.2 trillion to $2 trillion in supply-chain management and manufacturing (some of the value accrues to companies, while some is captured by customers). In manufacturing, the greatest value from AI can be created by using it for predictive maintenance (about $0.5 trillion to $0.7 trillion across the world’s businesses). AI’s ability to process massive amounts of data, including audio and Read more…

Capturing value from your customer data

McKinsey Analytics March 2017 . Original post: HERE Companies can put their information to work by teasing out novel patterns, driving productivity, and creating new solutions. In an increasingly customer-centric world, the ability to capture and use customer insights to shape products, solutions, and the buying experience as a whole is critically important. Research tells us that organizations that leverage customer behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.1 Customer data must be seen as strategic. Yet most companies are using only a fraction of the data in their possession. Sprawling legacy systems, siloed databases, and sporadic automation are common obstacles. Models and dashboards may be forced to rely on stale data, and core processes may require considerable manual intervention. Often, too, organizations may not have a clear understanding of the specific outcomes they’re looking to achieve through data optimization. All that is leaving significant value on the table. How much, you ask? A McKinsey survey of more than 700 organizations worldwide found that spending on analytics to gain competitive intelligence on future market conditions, to target customers more successfully, and to optimize operations and supply chains generated operating-profit increases in the 6 percent range. Our client work suggests that these returns don’t have to be confined to a handful of top players. Rather, when it comes to generating measurable value from their data, most organizations have plenty of low-hanging fruit they have yet to harvest. Here are three of the most promising avenues available now to most organizations. Tease out critical patterns Information on what customers purchase, how many times they contact customer service, and how long they linger on a given website can create an insightful narrative about buying habits and preferences. Most organizations capture much of this information, but often in isolated packets. Too few marry it all together. A bank, for instance, can minimize churn, fraud, and default risk by pooling customer data and applying advanced analytics to understand the needs and possible next actions of key segments. Those patterns can be used across the business. Credit-risk teams will want to know if a customer whose bank balance falls into the red more than once a quarter could be at higher risk for defaulting on a mortgage. Marketing could use the data to pitch financial-planning and overdraft-protection services. Such customer data can also be packaged, sanitized, and sold to relevant third parties, such as credit bureaus and payments companies—allowing the initial investment of analytics time and modeling to yield multiple dividends. In addition, pattern data can be used to direct spending. An industrial-parts manufacturer, for instance, studied customer-buying histories, behavioral data, and surveys to understand the typical purchasing path for their highest-value segments. The data revealed that buyers were far more likely to rely on distributors for product recommendations and much less likely to be influenced by trade-show demonstrations and collateral. Marketers were able to reallocate budgets accordingly. Others, led especially by consumer companies, are taking things further Read more…

The Future of AI in OOH Advertising: Personalization

Personalization in AI is gaining traction in advertising and beyond. In Out-of-Home (OOH) advertising specifically, consumers are looking for personalization and interaction. Because personalized OOH ads can increase customer engagements, brand awareness, and revenue, the future of AI is a broader utilization of personalized ads writes Omar Golan, CEO, Outernets FULL PRESS CLICK HERE

The Shop Window Opens a Portal of Possibilities

Many of us are window shoppers, but online purchasers. That may be one of the factors in the widely reported decline of retail stores. To flip the situation around, it may be time to transform the shop window from static tableaux into AI-powered interactive displays that extract value from data. FULL PRESS CLICK HERE

Startup wants to pay landlords for digital storefront ads

A startup wants to pay landlords $10,000 per month or more to install intelligent, electronic window ads in retail storefronts. The company, Outernets, has already installed its displays at Dylan’s Candy Bar and McDonald’s locations. The screens collect data on passersby and change their advertisements depending on who is walking by. For example, a man may see an ad for ties, a woman an ad for high heels. FULL PRESS CLICK HERE

Retailers look to storefront digital ads to target passersby

Soon vacant storefronts may be filled with interactive digital ads that not only pitch products but also switch the offerings depending on who’s looking. The high-tech ads will then collect the raw data on the demographics of those who stop to gaze. Outernets, a company founded in New York by former Israeli multimedia artists Omer and Tal Golan, works with advertisers to provide digital ads that will enliven windows of shuttered stores by promoting products and availabilities — and even sell products right from the display. FULL PRESS CLICK HERE