Walmart Labs, the technology unit of retail firm Walmart, has acqui-hired its third Indian startup, Dataturks, a Bengaluru-based Machine Learning (ML) -based Data Annotation Platform that do image bounding, document annotation, NLP and text annotations. The acquisition will help Walmart to use Dataturk's machine learning capabilities for content enrichment. Post acquisition, the 5-member team of Dataturks will be part of Wamart's merchant technology team for catalogue enrichment. Founded in last year, by Gajendra Dadheech and Mohan Gupta, Dataturks makes ML data annotations super easy by allowing teams to upload data, invite other team member to upload and build training/evaluation dataset in hours and find labeled datasets. In December 2018, Walmart Labs had acqui-hired Int.AI, a Bangalore-based mobile analytics aka MicroApps startup and in September last year it acquired Appsfly, a Bangalore-based micro-app startup.

A top priority in any business is a constant need to increase revenue and profitability. Within the retail industry, the main source of revenue is based on the purchases of customers. For this reason, companies need to focus on customer retention. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. When a customer leaves the business without any form of explanation or notice, the company may find it hard to respond and take corrective action.

Levi's has hired its first artificial intelligence (AI) officer. Katia Walsh, who was most recently Vodafone's chief data and analytics officer, will begin her tenure at Levi's on April 29. According to a company statement, Walsh will focus on building "data, analytics and artificial intelligence enablers" that will strengthen the company's present and future business ventures. Levi's hasn't been more specific than that, but it's a safe bet Walsh will tap her expertise to build on the AI efforts specifically -- and the digital advances in general -- Levi's has made in recent years. Total Retail's Take: Quite the departure for the 146-year-old denim brand, but this is a sign of the times in the retail industry as technology plays an ever-increasing role.

This is a Guest Post and does not necessarily reflect the thoughts and opinions of Techzim. We have a strong filtering process of what makes it to our blog and are confident that you'll enjoy the article below. With the advancement of scientific technologies, we have come across a various scientific contribution in recent times. This scientific approach has surely influenced human nature and with recent studies show that the decisions we make are influenced by the technology around us. A strong example of this is the retail industry and the latest integration of Artificial Intelligence.

One of the elements of success in online shopping, is the possibility to analyse the client, to follow them along their journey and to derive useful information that may improve their experience within the retail environment. Customer Experience (CX) Management has become a fundamental component in every marketing strategy and its attempt to increase clients' satisfaction. With the benefit of new technologies and new analysis models, today it is now possible to provide consumer behaviour analysis for brick and mortar shops. Consumer behaviour is the study of individuals or groups and their processes to select and use products, services and experiences. The goal is to understand the needs and the impacts that these processes have on the consumer and society.

NOTE TO PROSPECTIVE BUYERS: Save your money and read the Intuition Machine blog on Medium. It's free, and most of the material from this book is just compiled from the author's blog posts. Pros: 1) Thought-provoking survey of ideas at the forefront of AI research Cons: 1) The book is full of glaring grammatical errors that an editor should have caught. This makes whole sections of the book incomprehensible on the first pass. I know this because there are chapters where the author fails to remove lines such as "in this post..." Bottom line: the book lacks polish and, as such, is way overpriced.

Demand forecasting, for all of its importance in business, has had a mixed run in retail. Even in fairly predictable categories in general merchandise, it's far too easy for retailers to start the current year's plan by loading in all the assumptions made from the year before, rather than starting clean with a new demand forecast. In fact, according to RSR Research's benchmark, even though 68% of better-performing retailers ("Retail Winners") and 53% of all other retailers believe that starting with a demand forecast as the basis for the next year's plan is very valuable, only 49% of Winners and 29% of their peers actually do so today. Part of the reason why is because forecast error in retail is high, as high as 32% according to some estimates. And, the more sporadic or non-repeatable the demand is, the more forecast error occurs – thus, grocery retailers operating a replenishment strategy have a far easier time using a forecast than a fashion retailer introducing a high-fashion item that responds to a new trend. Additionally, not all products face the same demand profiles.

As 2019 gets underway and your marketing plan unfolds, you've probably set some goals for the coming year: We're going to break down the data silos that keep us from understanding our customers. We're going to improve our messaging relevance. We're going to target customers more accurately on their preferred channels What if you could just find the time to make any one of these resolutions a reality? Although the promise of one-to-one marketing has been around for many years, brands still send customers too many marketing messages that are irrelevant, generic or only slightly personalized. The problem is that marketers today have too much data and not enough creative time to respond to soaring customer expectations for a personalized buying experience. Enter artificial intelligence (AI) and machine learning-based marketing tools that are changing the nature of how marketers make decisions and deploy campaigns.

ViSenze, an image recognition startup that delivers visual search tools designed for online retailers such as ASOS and Rakuten, recently revealed that it had secured $20 million in a Series C funding round. Both Sonae IM and Gobi Ventures co-led the financing round, which also included the participation of other investors, including returning backers WI Harper and Rakuten. Established back in 2012, ViSenze has so far raised a total of 34.5 million dollars (its previous funding round was a Series B held in September 2016). ViSenze customers include the likes of Uniqlo, Zalora, and Urban Outfitters, who bill the Singapore-based company's software portfolio as a "personal shopping concierge" that helps shoppers in finding or discovering products based on automatic photo tagging, visual search, and suggestions based on their browser history. The company's verticals include intellectual property, furniture, jewelry, and fashion.

Innovation is a top priority for almost every organization. But to achieve success through innovation, companies must put as much energy and investment into marketing new offerings as they do in generating them. The role of marketing in some companies seems to have diminished in recent years, with the growth of artificial intelligence-driven algorithms and predictive analytics that offer up information, goods, and services to customers. The popularity of private label goods and products from companies like Brandless and others that seem to eschew marketing also seems to make the argument for less marketing, rather than more. But marketing is and should not be executed merely through tactical functions of acquiring and retaining customers, as many companies practice it today. The search, content, and loyalty campaigns that most managers call marketing these days are common downstream tactics for generating or maintaining awareness or repeat purchase; the full, business-growing power of the marketing function comes way upstream -- from creating markets.