Collaborating Authors


How Private Equity Is Driving Change in Retail


Consumers have always demanded innovation from the retail industry. Shopping habits and product demands are constantly evolving, and retailers invest a significant amount of capital to monitor trends and cater to fluctuating behaviors. Recently, advancing technology has quickened the pace of change and made it even harder to win consumer attention in an increasingly crowded marketplace. More than ever, success requires financial and managerial flexibility and adaptiveness--areas where private equity can play a vital role. Below, read my thoughts on three key areas where our industry is partnering with retailers to help them keep ahead in the fast-changing sector.

How to invest in artificial intelligence


The first big investment wave in tech was the personal computer. Then came software, the internet, smartphones, social media and cloud computing. The next big thing is artificial intelligence, or AI, professional stock pickers say. AI is the science-fiction-like technology in which computers are programmed to think and perform the tasks ordinarily done by humans. The size of the global AI market is expected to grow to $202.6 billion by 2026, up from $20.7 billion in 2018, according to Fortune Business Insights.

Mining urban lifestyles: urban computing, human behavior and recommender systems Machine Learning

In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone and email communication to Twitter activity and open-source contributions on Wikipedia and OpenStreetMap. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns; call detail records (CDRs) present new opportunities to understand human mobility, analyze wealth, and model social network dynamics. In this chapter, we jointly model the lifestyles of individuals, a more challenging problem with higher variability when compared to the aggregated behavior of city regions. Using collective matrix factorization, we propose a unified dual view of lifestyles. Understanding these lifestyles will not only inform commercial opportunities, but also help policymakers and nonprofit organizations understand the characteristics and needs of the entire region, as well as of the individuals within that region. The applications of this range from targeted advertisements and promotions to the diffusion of digital financial services among low-income groups.

Why It's Hard to Escape Amazon's Long Reach


In 1994, soon after Jeff Bezos incorporated what would become Amazon, the entrepreneur briefly contemplated changing the company's name. The nascent firm had been dubbed "Cadabra," but Bezos wanted a less playful, more accurate alternative: "Relentless." Twenty-four years later, perhaps no adjective better describes Bezos' empire than the name he once wanted to give it. The company is known as the "everything store," but in its dogged pursuit of growth, Amazon has come to dominate more than just ecommerce. Amazon is a fashion designer, advertising business, television and movie producer, book publisher, and the owner of a sprawling platform for crowdsourced micro-labor tasks.

Remark Holdings' Improbable AI Claims


Remark Holdings (MARK) is what one would call a contested company. It has long supporters with strong conviction, but there has also been some kind of a short drive, and the present short count is almost 16% of the nearly 25M float. That short count is far from the highest we've seen, a company like Applied Optoelectronics (AAOI) still has 78% of the float shorted (at least according to the latest figures) but it seems to have done major damage already. Who to believe, the conviction longs or the shorts who put out a troubling report. Questions like these are very difficult to solve, especially if you're not a forensic accountant. Since we're no forensic accountants ourselves, we'll try to gather some stylized facts and see what these add up to, and whether there is some chance for the longs to recoup some of their losses. This company was one of two highest conviction longs for SA contributor Yale Bock, who is the President of Y H & C Investments (see here).



Hi Steemians, today I just want to share to you about "EKKBAZ", what is EKKBAZ? It is like being "Henry Sy" of your own. Base on their website it says "Powered by blockchain, artificial intelligence, social and collaborative technologies to significantly simplify direct to store interactions, reduce middlemen and empower root-level store owners in any corner of the earth. In short through ekkbaz we can now take advantage the B2B set up of business, overcoming some constraint in doing the traditional way of business. The goal of EKKBAZ will be developing BAZ Protocol, a decentralized and intelligent network with purpose-built components for FMCG businesses.

Large Scale Online Brand Networks to Study Brand Effects

AAAI Conferences

Mining consumer perceptions of brands has been a dominant research area in marketing. The marketing literature provides a well-developed rationale for proposing brands as intangible assets that significantly contribute to firm performance. Consumer-brand perceptions typically collected through surveys or focus groups, require recruitment and interaction with a large set of participants; leading to cost, feasibility and validity issues. The advent of web 2.0 opens the door to the application of a wide range of data-centric approaches which can automate and scale beyond the traditional methods used in marketing science. We address this knowledge area by exploiting social media based brand communities to generate a brand network, incorporating consumer perceptions across a broad ecosystem of brands. A brand network is one in which individual nodes represent brands, and a weighted link between two nodes represents the strength of consumer co-interest in these two brands. The implicit brand-brand network is used to examine two branding effects, in particular, positioning and performance. We use hard and soft clustering algorithms, Walktrap Clustering and Stochastic Block Modelling respectively, to identify subsets of closely related brands; and this provides the basis for examining brand positioning. We also examine how a focal brand’s location in the brand network relates to performance, measured in terms of relative market share. For this, a hierarchical regression analysis is conducted between brand network variables and brand performance. While the size of brand community on Twitter does relate to brand performance, the brand network variables like degree, eigenvector centrality and between-industry links help improve the model fit considerably.