#Image recognition in CPG market

[stayinfront123]

Why CPG Leaders are Using Image Recognition for Perfect Store Execution

Execution Image recognition in the CPG market is revolutionizing the way Consumer Packaged Goods (CPG) companies ensure their products are displayed and promoted according to plan on retail shelves.

https://www.thenewsbrick.com/why-cpg-leaders-are-using-image-recognition-for-perfect-store-execution

[consumer-goods-software]

In the Consumer Goods (CPG) industry, manufacturers have dedicated merchandisers for executing the brand’s promotional strategies within retail stores. Traditionally, this was performed manually at the store level, but the Image Recognition technology has ushered in automation to support faster execution and improved productivity.

Projections indicate a robust growth trajectory for retail execution, anticipated to expand at a Compound Annual Growth Rate (CAGR) of 9% from 2023 to 2033. The most recent Market Study Group (MSG) report suggests that the market’s potential will ascend to 875 million USD by 2033. In response, the CPG industry has witnessed an upsurge in the number of solution providers eager to become technology partners.

Selecting the right retail execution software for your Consumer Goods enterprise is pivotal, as it holds the key to elevating sales performance and operational efficiency. This comprehensive guide outlines a systematic approach to evaluating and choosing the most suitable retail execution software tailored to your unique business requirements.

[globalestimates123]

As per another statistical surveying report distributed by Global Market Estimates, the Image Recognition in CPG Market is foreshadowed to observe the magnified development of CAGR 20.5% during the viewpoint term (2020-2026).

Browse 175 Market Data Tables and 93Figures spread through 163Pages on "Image Recognition in CPG market - Forecasts to 2026" https://www.globalmarketestimates.com/market-report/image-recognition-in-cpg-market-2312

[grocergirldetroit-blog]

Conscious Capitalism in 2018, aka #BrandsTakingStands

One of the very first brand sponsorships I remember was the Red Bull air race back in the 90s. Red Bull was relatively new - energy drinks were just starting to take hold as a category - and I had no idea what an air race was, so the dissonance was particularly jarring. “What even? Is this?!”

Photo credit: GeneralAviationNews.com

The brilliance in this sponsorship was that extreme sports had little to no marketing presence at all before Red Bull started to put their name all over those events. They cornered the market by creating the market, in a sense. As a result, the image they were trying to project as a brand - you can do anything if you drink Red Bull! - was encapsulated in this specific type of advertising and customer outreach. Success, defined purely as brand recognition, was achieved through a new type of sponsorship.

Fast forward, and we see brands making alliances everywhere - from the ubiquitous Coca-Cola and Olympics partnership to Rap Snacks - but marketing success isn’t so narrowly defined anymore. Basic sponsorship is table stakes, so companies are going a step beyond to ensure that their customers know what their brand stands for. Refreshingly, this now means doing more than just throwing advertising dollars at an event. In response to consumer demand, some companies are making real change to their products, practices and partnerships: the new bar is brand associations with concepts, political movements, or social change, aka #BrandsTakingStands. This isn’t always simple or easy - my favorite recent news story about stellar grocery chain Wegmans and Trump wine may give you a chuckle - but most companies are tackling it in one way or another.

Photo credit: Getty Images via NYTimes

Pepsi’s purchase of SodaStream made headlines this past week, an example of consumer packaged goods (CPG) companies amping up acquisition, an age-old business practice that is often exercised to increase that company’s market share. We know that companies aren’t just purchasing smaller brands out of the goodness of their hearts; there’s business behind that business, but what exactly is the primary motivation? I believe (as many folks do) that older CPG companies are seeing the power and relevance of their core brands fade as millennials come into their buying power. In response, some have begun snapping up the smaller brands to fortify their sales, market share and brand image. These smaller companies often lead with a social mission (like Ben & Jerry’s, acquired by Unilever), make a point of minimizing environmental impact (Sweet Earth, acquired by Nestle), have a focus on health (Rx Bar, acquired by Kellogg’s) or otherwise tout more than a single bottom line. This all goes to show that today’s consumer is very much aware of the impact of their purchases: the millennial shopper is voting with their wallet, and finally the big brands are taking notice.

An interesting aspect of this capitalistic evolution is the recognition by powerful CPGs that the future of market dominance doesn’t look like a single, megastar brand anymore. Instead, companies like PepsiCo are focusing on how to develop smaller brands that reach more niche audiences. We see the same development in grocery, with small markets reaching customers in a more personally engaging fashion, a move that giant chains can’t emulate. In fact, according to a study by GlobalData, 42% of consumers trust small grocery brands more than the big guys.

Source: GlobalData Research

Grocer Girl’s Take: This self-conscious movement gives me some hope for the future of capitalism as we know it. Conscious capitalist brands *can* help communities economically, socially, and environmentally - instead of being extractive, we can be additive. This particular approach to brand leadership is really only possible if you truly know your customer and community, otherwise the new branding is just another version of sponsorship. At Neighborhood Grocery, we’re starting a conversation with our neighbors, leading from the customer and her needs - and developing our brand from there. That’s our #BrandsTakingStands approach at Neighborhood Grocery.

[tri-report]

CPG Software Market COVID-19 Impact Analysis 2020 and Precise Outlook : Fishbowl, SAP, Microsoft, Adobe, NetSuite

The Global CPG Software Market Research Report 2020-2025 is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand, and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data that enhances the understanding, scope, and application of this report.

MarketsandMarkets forecasts the global image recognition in CPG Software market size is expected to grow USD 3.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.7% during the forecasting period.

Top Companies in the Global CPG Software Market: Fishbowl, SAP, Microsoft, Adobe, NetSuite, Deskera, Systum, Salesforce, Odoo, Zangerine, Agiliron, Epicor, Infoplus, Sage, Acumatica And Other

Click the link to get a Sample Copy of the Report: (Special Offer: Available Flat 30% Discount for a limited time only):

https://www.theresearchconsultant.com/reports/4793/cpg-software-market-status-and-trend-analysis-2017-2026-covid-19-version-/request

This report segments the CPG Software Market on the basis of by Type are:

On-Premise Cloud-Based Others

On the basis of By Application, the CPG Software Market is segmented into:

Mac Win Others

Regional Analysis for CPG Software Market:

For a comprehensive understanding of market dynamics, the CPG Software Market is analyzed across key geographies namely: United States, China, Europe, Japan, South-east Asia, India, and others. Each of these regions is analyzed on basis of market findings across major countries in these regions for a macro-level understanding of the market.

Explore Full Report With Detailed TOC Here:

https://www.theresearchconsultant.com/reports/4793/cpg-software-market-status-and-trend-analysis-2017-2026-covid-19-version-    

Points Covered in The Report:

The points that are talked over within the report are the major CPG Software Market players that influence the market such as raw material suppliers, manufacturers, equipment suppliers, end users, traders, distributors etc.

The all-inclusive profile of the companies is specified. The production, price, capacity, revenue, cost, gross, gross margin, sales volume, sales revenue, consumption, growth rate, import, export, future strategies, supply, and the technological developments that they are creating are also incorporated within the report. Besides the historical data from 2014 to 2019 and forecast data from 2019 to 2025.

The growth factors of the CPG Software Market are deeply discussed while the different end users of the market are underlined.

Data and information by manufacturer, by region, by type, by application and etc., and custom research can be added in line with the specific requirements.

TheCPG Software Market report also considers the SWOT analysis of the market. Finally, the report concludes with the opinions of the industry experts.

What are the market factors that are explained in the report

Further in the CPG Software  Market research reports, following points are included along with in-depth study of each point:-

Production Analysis – Production of the CPG Software Market is analyzed with respect to different regions, types and applications. Here, price analysis of various CPG Software Market key players are also covered.

Sales and Revenue Analysis – Both, sales and revenue are studied for the different regions of the CPG Software Market. Another major aspect, price, which plays important part in the revenue generation, is also assessed in this section for the various regions.

Supply and Consumption – In continuation with sales, this section studies supply and consumption for the CPG Software Market. This part also sheds light on the gap between supple and consumption. Import and export figures are also given in this part.

Competitors – In this section, various CPG Software Market leading players are studied with respect to their company profile, product portfolio, capacity, price, cost and revenue.

Other analyses – Apart from the aforementioned information, trade and distribution analysis for the CPG Software Market, contact information of major manufacturers, suppliers and key consumers is also given. Also, SWOT analysis for new projects and feasibility analysis for new investment are included.

Customization of the Report: This report can be customized as per your needs for additional data up to 3 companies or countries or 40 analyst hours.

Note: All the reports that we list have been tracking the impact of COVID-19 on the market. Both upstream and downstream of the entire supply chain has been accounted for while doing this. Also, where possible, we will provide an additional COVID-19 update supplement/report to the report in Q3, please check for with the sales team.

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[letsdreamilyhappycollectorm-blog]

5 SECRETS: HOW TO USE ARTIFICIAL INTELLIGENCE TO CREATE A SUCCESSFUL BUSINESS

What does Artificial Intelligence (AI) mean?

Artificial intelligence (AI) is an arena of software engineering, where, the creation of intelligent machines that work and react like humans takes place.

The research involved in artificial intelligence is extremely technical and specialized. The main problems of artificial intelligence involve programming computers for certain attributes such as:

Learning

Knowledge

Planning

Perception

Problem-solving

Thinking

Ability to     manipulate and move objects

Knowledge engineering is a soul of AI exploration. Knowledge engineering is a field of artificial intelligence (AI) that tries to imitate the opinion and behavior of a human expert that has expert knowledge and experience in a particular field. If the machines have plentiful information with respect to the world, they can often act and react like humans. Artificial intelligence should have access to objects, categories, properties, and relations between all of them to enforce expert systems. Introducing common sense, thinking, and problem-solving power in machines is a tough and tiresome task.

Moreover, robotics is also a major field related to AI. Robots need intelligence to deal with tasks such as object handling and navigating, along with sub-issues of positioning, motion planning, and mapping.

“AI offers the opportunity for businesses to automate routine tasks so that humans can better use their time, providing higher value contributions. —Ellie Mirman, CMO, Crayon

Over recent years, Artificial Intelligence has acquired strength in every industry. Agriculture, healthcare, manufacturing, transportation, logistics, retail, and more have started to use AI-based applications to enhance efficiency and performance.

The most ideal approach to assess any arising technology is to discover and understand its practical use in your business. Some organizations have already generated huge profits from their AI initiatives. Global information services and publishing company utilized machine learning to drastically improve its clients' capacity to acquire insights from thousands of legal documents they processed. A leading communication service provider has directed an AI program to refine its network uptime by opening a cognitive intelligent platform that combined machine learning, analytics, and robotic automation to predict and prevent network failures.

But, despite such AI and automation success stories, most organizations we know are not pretty sure where to direct their AI investments. That's why, they move forward with caution, whereas a distinct minority runs forward. Market research companies are taking advantage of AI’s image recognition capabilities by using it to gather market data. Data providers use AI to evaluate legal documents and detect potential problems. Telecommunications companies implement AI to diagnose network problems. CPG companies use AI to anticipate and avoid fraudulent invoices. And, retailers use AI-enabled chatbots to help customers on the internet.

A leading airline gives another example. In this industry, on-time functioning is a principal customer experience indicator and flight delays are a major concern. As an airline cannot control the climate, reduction of the impact of delays and proactive communication with customers can make a difference. For instance, quick insights into forthcoming delays can enable an airline to implement more accurate schedules for airport gates, ground crews, flight crews, and other crews.

Another one major airline utilized advanced machine learning techniques to establish two predictive models—one for flight takeoff delays and the other one for arrival delays of flights—to predict whether a flight departing later in the day would take longer than scheduled. The resultant analysis led to a more distinct understanding of the reasons for flight delays and the effect of delays on the flight network. The airline was able to enhance operational effectiveness by adjusting staffing and other resources for predicted schedule changes, thus strengthening brand impression.

Organizations, that have empowered AI at the business level are growing operational proficiency, making faster decisions and innovating new products and services. However, challenges remain for those who aren’t expanding AI in this potential. Because of the lack of clear AI strategy, support from the c-suite and a specific set of metrics, businesses struggle to make progress.

Advantages of AI in Business

Artificial Intelligence in business will keep on offering numerous advantages.

AI can now process and pass on data a lot quicker than any human can. This extreme ability of AI can have great impacts in business if we figure out how to maximize it.

Client Understanding

Organizations can utilize AI to better understand clients and connect with them. In today’s digital world, there are such huge numbers of approaches to associate with clients that there is an astonishing size of information available with respect to customer patterns, trends, and preferences. AI helps you to sort through data to identify the consistently changing image of your clients while measuring how your brand is recognized on social media and other important channels.

When you have a clearer understanding of your client, it enables you to improve your strategies to use the best channels, create effectual marketing campaigns, and communicate with clients even more effectively. Using AI and machine learning can improve the way you connect with your clients, giving you a better advantage over a competitor that lacks updated or accurate information.

Data-Driven Marketing

In order to find new customers, marketing departments need to learn how to use engaging and instinctive strategies. With the help of big data to analyze marketplaces, businesses will be able to gather huge amounts of data with respect to buyer consumption and behavior. Without the help of AI, there would be no real way to effectively analyze market insights unless you’re willing to dedicate number of hours and your valuable assets onto it, which could be utilized to do other important tasks.

Using artificially intelligent machines helps you give exact forecasts about qualified leads and market trends, allowing you to build and segment your audience based completely around data-driven analytics. It also helps you better predict actions you will need to take in the future, enabling you to become more flexible in adapting to your client’s journey so you can suggest right product at the right time.

CRM & Sales

Imagine a world where technology so involved and it helped you save your valuable time by telling you which emails to concentrate on, and which to ignore for later? These systems such as Knowmail do exist and you would now be able to depend on AI to help you keep monitor your email, calendar, and to-do list more effectively than ever. Think of AI as a precious associate for sales that encourages you to concentrate on the tasks that matter most and get more out of your day. AI can likewise help automate CRM systems and produce targeted information compiled from market insights, enabling you to make more predictive forecasts.

Market Insights

The raw material of current digital economy is data. However, just like physical raw goods, it is worthless without special instrument that can retrieve and then process it, so as to convert it into useful goods. Think of AI as a tool that helps you in abstracting, analyzing, and decoding data so that you can use it almost immediately and can result to better decisions.

Automating Systems

The use of automation has expanded rapidly in the past few years. As more jobs can be replaced with AI, we can now assign the common and repetitive jobs to machines. AI technology can now automate regular tasks in the same way that the industrial era presented automated labor. From emailing to chatbots, booking systems, and more, AI is driving rapid growth.

Adapting to AI technology shouldn’t be viewed as any different from adapting to email or digital from paper-based systems. The best approach to success lies in building the right attitude and teaching your team to try to do the same; succeeded by an actionable step-by-step process. In the future, increasing number of businesses will be using AI and it’s up to you to beat the competition. The earlier you adopt, the better for your business.

                                                                                                                     - Softlabs Group

(Software Development Company)

[gyrlversion]

Amazons private-label business is on a tear and rival marketers are racing to stay a step ahead of its tactics

Amazon is aggressively growing its private label business, but at the same time, it’s trying to compete with Facebook and Google for advertising. Those two goals can conflict — causing headaches for some advertisers.

Over the past couple of years, Amazon has pushed into its own brands in areas like packaged goods, fashion and electronics and has used splashy ad formats on its own site to promote the products. That makes some advertisers uneasy and frustrated that Amazon gets preferential treatment in its own advertising network.

“Amazon is a huge threat to CPGs because any time Amazon decides to look at a category, they’re going to own it,” said Jon Reily, VP and global commerce strategy lead at Publicis Sapient. “It’s difficult to have a conversation about those products when the main avenue for selling things is also your main competitor.”

In fact, Amazon is under growing scrutiny from regulators for selling its own items in a marketplace that it manages. In March, Sen. Elizabeth Warren proposed splitting up Amazon for competitive reasons.

Amazon says about 1% of its sales come from private labels. Private label brands represent 25% or more of sales for other retailers, Amazon said in April.

Amazon’s savviest advertiser is Amazon

Amazon routinely uses prominent ad formats on Amazon.com to advertise its private label and exclusive brands.

One is sponsored brands, a large display ad that appears at the top of search results. Another is called hero quick promo that shows a product image and how many review stars it has next to it on product pages where consumers read reviews and add items to the shopping carts. Occasionally Amazon’s retail team will take over prominent placements and they’ll be unavailable to other advertisers, said Joshua Kreitzer, founder and CEO of Channel Bakers, an agency that helps marketers advertise on Amazon.

Earlier this year, Amazon tested a feature that promoted its own products as pop-up windows when consumers clicked on search listings for a competitor. Amazon said at the time that the test was not an advertisement but a way to show browsers cheaper product options.

“When they’re [advertising] on the actual detail page where the consumer is ready to buy, they’re closer to the bottom of the funnel and making purchase decisions,” Kreitzer said.

An Amazon spokesperson pushed back on the idea that it gets first dibs on advertising on its platform.

“Our ad policies work back from customers to provide them the best experiences in our store,” the spokesperson told Business Insider. “Both our selling partners and private brands can bid on sponsored ads. Our advertising algorithms optimize for the best possible customer experience, starting with relevance to the shopping query, and treat all bids equally when determining which ad to show.”

Read more: Amazon wants to take on OTT heavyweights like Roku for advertising dollars. Here’s the pitch deck it’s using to sell marketers video ads.

Amazon vets ad creative for competitors

Advertisers also have run into creative and messaging challenges on Amazon.

Most of Amazon’s ad revenue comes from ads that run on its own properties, but the company also programmatically places ads on publishers’ sites. The catch is that these programmatic ads can appear on Amazon’s own site, making them subject to vetting by Amazon’s policy team, said a manager who buys Amazon ads at a large programmatic agency.

The policy team also analyzes the copy and color schemes on ads to determine if an advertiser’s message looks too similar to Amazon’s own ads and products, and if it does, Amazon can stop campaigns from running, said the same agency executive.

Asked about the policy team’s role in vetting ads, the Amazon spokesperson said its ad policies are geared towards giving customers the best experience.

Amazon’s own advertising policy states that advertisers must include borders around ads with a white or off-white background to show the placement was paid for by an advertiser, for example. And if an ad campaign includes the names of both an advertiser and Amazon, “your brand name or logo must be the largest and most prominent,” the policy reads.

“Amazon wants any display creative that appears on Amazon.com to conform to its brand guidelines and not be a disruptive user experience,” said the agency exec.

When asked what a disruptive ad campaign looks like, the agency exec said that based on his understanding of Amazon’s policy, an ad with bright colors and a neon box on it because the colors could be considered distracting. Amazon also doesn’t allow advertisers to use rich media ads that use animated graphics to show a product or expand across the page when clicked on, according to its policy.

As Amazon expands into new business lines like delivery, it can be difficult to keep up with which advertisers Amazon deems competitors, the person said.

The same agency exec worked with a fast-food chain on a campaign that promoted a delivery service. While the fast-food chain itself was not a competitor to Amazon, the delivery service was a competitor to Amazon’s same-day delivery service, Prime Now. The agency had to tweak the creative to focus on the fast-food chain. The campaign ultimately ran, but the exec was unclear on what change ultimately led Amazon to run the campaign.

In another example, coupon sites — which could lead to non-Amazon websites — are not allowed to advertise on Amazon’s properties, according to the company’s policy.

“Because Amazon has so many business units, there’s not a hard and fast rule or a pre-existing list [of banned advertisers,]” the agency exec said. “It’s really difficult for us to know beforehand which advertisers will be competitive.”

Brands are creating their own keywords to get around Amazon’s brands

Amazon is known for buying up its own search ads targeted at generic keywords like “coffee” or “batteries.” Roughly 70% of Amazon searches are for these types of generic words.

One way advertisers are responding is by bidding on highly specific keywords that match people’s searches, like “dark-roast coffee” or “long-lasting batteries,” said multiple agencies.

These more niche keywords convert better, are cheaper, and less competitive, said John Ghiorso, CEO of Amazon-focused ad agency OrcaPacific. But brands need to bid against thousands of niche keywords to get the same scale as a generic keyword, he said.

Established brands may not have that much to fear from Amazon

Amazon’s move into private label and exclusive brands is similar to the practices of physical retailers like Walmart, Target and Kroger.

The difference with Amazon’s private labels is that competitors don’t have to fight for shelf space as they do with physical stores and have multiple digital tactics they can reach the exact customer they want, said OrcaPacific’s Ghiorso.

“If anything, there’s more ability to go after the customer segments that you want to capture,” he said.

It’s unclear how successful Amazon’s private labels have been outside of a few brands like AmazonBasics. Amazon also has a quick turnover strategy with private label and exclusive brands. If a product racks up negative reviews or doesn’t sell, Amazon swiftly pulls the product.

Eric Heller, EVP of marketplace services at Wunderman Thompson Commerce, said Amazon’s products don’t have the same name recognition and brand loyalty as established advertisers do.

“A lot of their weak spots are that they don’t know how to build what traditional brands already have: Brand love, loyalty, and trust,” he said. “Amazon’s generics are opening more people to be willing to buy and test product online; as long as you’re not selling a commodity, then there’s still room for brands to win plenty of business on the periphery.”

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[retailtouchpoints]

Is Your Job Up For Grabs By Chatbots?

By Adam Blair, Executive Editor

Artificial intelligence (AI) is threatening/promising to remake the world’s workforce, and the retail industry will not be spared. (Nor will journalism, for that matter.) The futurists, analysts, brand and CPG executives who spoke at the ai.now workshop sponsored by Fractal Analytics, held July 18 in New York City, shared an over-arching warning: If your workload is data-driven and/or highly repetitive, a combination of AI and robotics could make your job obsolete.

One of the simplest examples is in health care: Nurses and primary care physicians who interact with patients are more likely to stay employed compared to radiologists, who examine X-rays for tumors and other health issues. Image recognition solutions powered by machine learning can be trained to review these X-rays, and they can examine many more than a human doctor ever could — and in some cases with greater accuracy.

The future isn’t exactly the bleak dystopia of the Terminator movies, however, even if the keynote by futurist Martin Ford was titled Rise of the Robots. Ford, author of a book of the same name, provided data that revealed how much the world of work has already changed due to changes in information technology. “New industries are not labor intensive,” said Ford, making the following comparison:

• In 1979, General Motors generated $11 billion in earnings (measured in 2012 dollars) and employed 840,000 workers;

• In 2012, Google generated $14 billion in earnings, 20% more than G.M., but employed only 38,000 workers — 4.5% of G.M.’s workforce at its peak.

“Since the 1960s, each succeeding decade has produced fewer new jobs (by percentage),” Ford noted. “There has been a hollowing out of the job market, and that’s almost certainly a result of technology.”

White-collar jobs are not exempt from these trends, Ford added: “In 2004, the median staffing of a corporate finance department was 120 full-time employees for each $1 billion in revenue,” he said. “By 2014 it was down to 71.26 employees. In journalism, the development of smart algorithms can tap into a stream of data, find the most interesting elements and create an article. One of these is published approximately every 30 seconds.”

People Skills Will Save People’s Jobs

Retail jobs that require complex human interactions — a sales associates helping a customer find the right product, for example — are less likely to be usurped by AI-powered technologies. “Human-to-human interaction is still a critical skill,” said Matthew Keylock, Head of Data and Analytics for Connected Solutions at Mars Petcare. He added that AI solutions rely on data, and lots of it, to function, and “there’s so little of context that can be captured in a data form, so understanding relationships and their context will still be important.”

People who can understand what’s happening inside the “black box” of an AI-powered solution also will remain employable. “With AI, governance is key,” said Keylock, noting that existing cognitive biases can affect how an AI/machine learning solution develops. “Companies need not only technical experts but also business translators, and those can be harder to find than the technical people. You don’t want an [AI solution] to run riot where you don’t know what’s happening with it minute to minute.”

How AI And Humans Can Work Together

Many AI-based solutions can serve as tools that improve a human’s ability to do his or her job. For example, “If you’re a typical customer service rep, you’re not set up well to succeed,” said Kjell Carlsson, a Senior Analyst at Forrester. “The economics of call centers means you aren’t paid very much, and you have no information about the person calling in or the types of things they are calling about.”

With an AI-powered “listening agent” capable of voice recognition and sentiment analysis monitoring the calls coming in, however, “the solution could present 10 likely options for what the call is about,” said Carlsson. “Then the human can quickly identify the one that is likeliest.”

Companies may be tempted to do a full rip-and-replace of their customer service apparatus and replace humans with chatbots, “but if you start with the perspective of augmenting someone that is already doing this job, it’s easier to make headway,” Carlsson noted. “If you can make a person just slightly more efficient, you can measure that. However, the sexiness of full automation and a soothing voice coming from the black box are the AI applications that get all the attention.”

One final note: This article was not written by a robot — but if it was, would you be certain that you could tell the difference?

[sparkleeveryday]

Exclusive: How AI Predicts the Biggest Trends of the Season

http://wwd.com/business-news/technology/ibm-watson-fashion-week-analysis-10842213/

The fashion industry is often gridlocked in a paradox that’s rooted in the tension between its creative side and the bottom line. And in a retail environment undergoing extreme upheaval due to the demands of consumer centricity, this tension is being further strained.

But new technologies may be able to help — and some have knowledge greater than our own. Take Watson, IBM’s new artificial intelligence solution with the ability to crunch more data faster than anything else.

So, WWD wondered, what would Watson say about the latest collections shown during New York Fashion Week? Would it detect things merely human buyers and fashion editors missed? Would it at last be able to answer the question that the industry has pondered for decades: Is there a universal fashion truth?

That may be even beyond Watson. Nonetheless, WWD asked IBM to discern key colors, trends and even comparisons among New York Fashion Week fall collections that resulted in a comprehensive report on its analysis.

Watson made a splash for its introduction to the market in 2011 — appearing on TV show “Jeopardy!” and defeating former champions. IBM scientists developed the software on a Deep QA platform that runs on 2,880 processors activated by Power 750 computers. The technology is able to hold information equal to that found in one million books.

The development of this technology marked a new phase for machine-learning possibilities. Not only is Watson capable of applying a human-sense of uncovering facts, it does so with precision and speed. In the case of its “Jeopardy!” upset, Watson used more than 100 algorithms to source the most accurate answer. Since then, IBM researchers and scientists have only continued to evolve the technology. “At IBM Research, we are building a cognitive agent that can analyze fashion trends from multiple sources such as catalogues, articles, blogs, images and social media and forecast future fashion trends,” said Vikas Raykar, a researcher at IBM.

The Fashion Test

Access to fashion shows and thus the ability to gain exclusive understanding of emerging trends was formerly reserved for a minute group of merchandisers, editors, fashion forecasters and other insider roles — Watson democratizes fashion analysis. In an effort to comprehend the broad expanse of lines shown during New York Fashion Week, WWD selected 12 designers, both emerging and established, to review. They were Coach, Jonathan Simkhai, Delpozo, Marc Jacobs, Alexander Wang, Brandon Maxwell, 3.1 Phillip Lim, Public School, Ralph Lauren, Prabal Gurung, Jason Wu and Dion Lee. Watson analyzed 467 runway images to determine the results.

IBM employed two main methods in the study. Its Research Experimental Service coined the phrase “cognitive fashion” to describe the suite of application programming interfaces, assets and use-cases tuned for the fashion industry. “Before we provide the aggregate trend analysis, we apply a series of what we call fashion annotators such as face, apparel and pose detection in addition to color attributes that takes one or two seconds per image to run these annotators,” said Raykar.

Watson also utilized its visual recognition tool to break down aspects of each look to tag the image, find human faces and find similar images in a collection. Within this, Watson utilized body and apparel detectors to discern both body and apparel details.

Additionally, IBM’s Research Cognitive Fashion asset — an app developed for the purpose of this report — determined the main colors in each runway image. “Dominant colors for a collection of images were obtained using color trends asset from IBM Research’s Cognitive Fashion [app],” the report said. What’s more, it also catalogued the similarity between images.

The experiment included the deployment of a visual browsing app capable of discerning similarities between designers and repeating trends. “This tool is useful to interactively explore all the images and also see how fashion designers get influenced by each other,” said the report. “The notion of similarity can cover various aspects like color, pattern, cut and silhouettes.”

The Results

Dominant colors: For each runway image, Watson extracted the dominant colors of the styles. This bank of colors was analyzed to mine the top shades for fall. Watson determined that the most popular palette included Pantone colors Raisin Black, Pastel Brown, Pale Silver, Cedar Chest and Deep Koamaru. These share commonalities of moody neutrals and rugged earth tones found in Pantone’s key 2017 color palettes, Grand Canyon and Ethereal Materials.

Repeating patterns: Watson also discovered similarities in prints and patterns. It found comparable motifs to those seen at Prabal Gurung and Jonathan Simkhai. This is profound as fast-fashion retailers, home interiors and CPG companies have often had to rely on trend analysis companies to divulge which trends are the most worthy of download and manipulation. Now with the use of this technology, the need for outsourcing these services is lessened.

The web: After analyzing the collections, Watson drew conclusive correlations and comparisons. “We wanted to analyze the similarities between various designers and how they are influenced by each other,” said the report. “We computed similarity scores for each pair of designers by aggregating the similarity between their image collections.”

The most in sync? Brandon Maxwell and Alexander Wang, which was somewhat surprising. Though both bright young things in the industry, Maxwell and Wang’s aesthetics usually differ. The former’s pieces encapsulate refined, elegant eveningwear frequently seen on red carpets while the latter’s streetwear-inspired, elevated-grunge pieces are gobbled up internationally. “Alexander Wang and Brandon Maxwell are the closest in terms of color, cuts and knee-length,” said the report produced by IBM, detailing the results.

All designers were included in a web to depict how similar they were to one another. “We plot a graph where nodes are the designers and the edge length represent the similarity scores between two designers,” the report said. “That is, the shorter the edge the more similar are the designers.”

The Ramifications

Observed at NRF in January, artificial intelligence and machine-learning technology are seen as essential solutions to help retailers succeed in a challenging market. Upending the classic infrastructures of product developers, merchandisers and marketers, Watson is a disruptor in the purest sense. Shifting workflow from seasoned experts to computer scientists who needn’t speak fashion’s sometimes obtuse and deeply intellectual vocabulary poses both benefits and drawbacks.

Meanwhile, speed-to-market is nearing sprint levels of tempo. Today’s product life cycle process requires the ability to analyze trends and then develop designs relevant for a specific audience — and to do so at an accelerated pace. Keeping step with consumer demands is only possible with the use of the proper tools — product life-cycle management systems, AI and data collection and analysis. And these technologies require data scientists and engineers who are capable of culling the most information from it.

Perhaps Watson and other AI solutions can serve as a convergence technology between the data science side of the business and the creative side. But this convergence of fashion and technology will require discipline and balance. At this point, data scientists and designers will have to work in tandem. Designers will bring to the table context for the data to make sense, and to help inform product development — which also requires having insights into various audiences and consumer preferences.

From here though, retailers and brands can either train existing staff in technology such as Watson or educate computer scientists on what makes a fashion trend work for the masses — determining the answer to which is as much art as science.

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Image Recognition in CPG Market Size, Share and Global Market Forecast to 2025 | MarketsandMarkets

According to market research report on "Image Recognition in CPG Market by Component (Hardware, Solutions & Services), Application (Inventory Analysis, Product and Shelf Monitoring Analysis & Gauging Emotions), Deployment Mode, End User (Online & Offline), and Region - Global Forecast to 2025”, published by MarketsandMarkets™, MarketsandMarkets forecasts the Image Recognition in CPG Market size to grow from USD 1.4 billion in 2020 to USD 3.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.7 % from 2020 to 2025. Increased in the need for efficient and profitable retail execution processes with adherence to compliance standards and technological advancements to boost the adoption of Image Recognition in CPG Market across the globe during the forecast period.

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Based on component, the hardware segment to account for the largest market size during the forecast period

This segment includes image sensors integrated with cameras. Hardware is an integral part of image recognition technology in the CPG market. Many companies like IBM and Qualcomm provide their company made sensors which help detect products, shelf life and even trends. For instance, Qualcomm offers the Vision Intelligence 400 platform that is built with powerful image processing and ML for smart camera products; it performs face detection, face recognition, product classification, and license plate recognition.

Based on application, the product and shelf monitoring analysis segment to account for the largest market size during the forecast period

The pandemic situation has forced people to concentrate more on online e-commerce businesses. Many leading retailers and CPG companies have adopted AI and image recognition technology for shelf monitoring and product identification. Computer vision and image recognition are used for product discovery, product recommendations and trend analysis.

Speak To Analyst @ https://www.marketsandmarkets.com/speaktoanalystNew.asp?id=162002756 Among regions, North America to account for the highest market share during the forecast period

North America is a significant contributor to the global Image Recognition in CPG Market. The US and Canada are the major contributors to the North American market. Canada and the US are increasingly witnessing the merging of artificial intelligence with image recognition and augmented reality; this is expected to drive market growth in the region. The United States is home to major AI-based image recognition companies, leading to increasing adoption of the technology across the region. As a result, many companies are making their move towards the US market, intending to develop an assemblage of AI capabilities.

The major Image Recognition in CPG Market vendors include IBM (US), Google (US), Qualcomm (US), Microsoft (US), AWS (US), Trax (Singapore), Catchoom (Spain), Slyce (US), LTU Tech (France), Imagga (Bulgaria), Vispera(Turkey), Blippar(UK), Ricoh innovations (US), Clarifai(US), Deepomatic (France), Wikitude (Austria), Huawei (China), Honeywell (US), Toshiba (Japan), Oracle (US).

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