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"In today's retail environment, inventory optimization is more important than ever, but continues to be an incredibly challenging effort for retailers," he said. "With Celect's advancements in machine learning and high-scale optimization, retailers are now able to optimize their plans, product buys, allocations, and fulfillment strategies using the data they already have. Our customers are seeing higher margins and sell-through rates, which is a result of embracing analytics as part of their overall decision making throughout the merchandise planning process." View the Gartner Cool Vendors in Merchandising and Marketing, 2017 report. For more information and to request a demo, contact Celect. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Celect is a cloud-based, predictive analytics SaaS platform that helps retailers optimize their overall inventory portfolios in stores and across the supply chain, resulting in double-digit percentage revenue increases through optimized assortments and fulfillment. This groundbreaking advance in machine learning and optimization allows retailers to understand how an individual customer shopping in store or online chooses from an assortment of products, revealing true demand. The technology builds on a fundamental advance in customer choice modeling called by MIT's Computer Science and Artificial Intelligence Laboratory one of the 50 greatest innovations it has ever produced. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/celect-named-in-gartners-2017-cool-vendors-report-for-retail-merchandising-and-marketing-300462793.html


News Article | September 12, 2017
Site: www.prnewswire.com

In the report, Gartner analyst Robert Hetu states that, "Retailers are actively researching and implementing merchandise optimization applications. Cross-channel shopping behavior, combined with multichannel order management and in-store fulfillment capabilities, is causing an acceleration of interest in this technology." The report continues to reinforce the importance of data-driven decisions and optimization, "Traditional planning applications are good at managing and automating basic tasks. Algorithmic optimization takes this a step further by improving and automating the decision making required to support seven merchandising processes — assortment, space, allocation and replenishment, price, promotion, markdown, and size and pack — to adjust assortments by store, cluster or channel; determine buy quantities; optimize space allocation and planograms; and maximize product allocation and availability. Ultimately, these technologies will help stores achieve higher sales and margins in their local markets. They facilitate the complex management that customer-centricity requires, enabling the retailer to do more-detailed planning with fewer resources. This technology is required to support customer-centric merchandising." "Celect delivers an unprecedented forward-looking view of retail demand, with high-scale, interactive optimization. We have helped our customers optimize inventories and greatly simplify the complexity of merchandising decisions," said John Andrews, CEO of Celect. "Without the ability to understand future demand, retailers often understock higher need items or are forced to use markdowns to move stagnant inventory from over buying – in the end, sacrificing margin and profit." The Celect Inventory Optimization Suite enables retailers to leverage advanced analytics and machine learning to simplify the complexity of inventory and product assortments, while providing prescriptive decision support with data already on hand to become more effective during the merchandise planning process. With Celect, retailers benefit from true demand prediction, improving the performance of product assortments, merchandise buys, store allocations, and ship-from-store order fulfillment decisions. For more information and to request a demo, visit celect.com, or connect with us on Twitter or LinkedIn. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Celect is a cloud-based, predictive analytics SaaS platform that helps retailers optimize their overall inventory portfolios in stores and across the supply chain, resulting in double-digit percentage revenue increases through optimized assortments and fulfillment. This groundbreaking advance in machine learning and optimization allows retailers to understand how an individual customer shopping in store or online chooses from an assortment of products, revealing true demand. The technology builds on a fundamental advance in customer choice modeling called by MIT's Computer Science and Artificial Intelligence Laboratory one of the 50 greatest innovations it has ever produced.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2013

This Small Business Innovation Research (SBIR) Phase I project addresses the problem of learning predictive models of individual choice behavior using sparse information on the behavior of any single individual. The intellectual merit of the project is developing a novel parsimonious view of this problem by modeling choice behavior as a distribution over permutations of alternatives, and making this view implementable at scale. A unit of data in this paradigm is a single comparison between two alternatives. Data of this sort can be derived in a variety of contexts ranging from product reviews to transaction data. While being a parsimonious modeling viewpoint, exact computation, or even representing such models is intractable. The project will focus on developing approximate solutions that, in the spirit of recent advances in high-dimensional statistics, exploit the potential of sparse approximations to such models. Given the vast quantities of data available to build such models it will be important for the algorithms developed to be amenable to parallelization in a manner reminiscent of the Map/Reduce computational paradigm. The algorithms developed will fit this paradigm with key algorithmic steps decomposing across data collected for a single individual. In summary, this project will develop a massively parallelizable approach to modeling individual choice behavior using unstructured data from a variety of sources. The broader impact/commercial potential of this project rests in enabling the emerging, all pervasive transition from 'search' to 'discovery'. This transition can be witnessed in sectors ranging from e-commerce to offline retail to matching impressions to advertisers on demand side platforms. The key stumbling block in this transition is the seeming requirement to build attribute rich models for a given context as opposed to a black box approach. The approach taken in this project is of the latter variety. As a concrete example, the task of merchandising requires an offline retailer to decide on the right assortment of products to carry in segments ranging from tooth paste to clothing; the approach here will power such decision making in an entirely data driven fashion. In a different direction, serving ads based on models that capture a surfer's preferences across the various silos of products and topics on the web can be enabled at scale and incredible granularity using the approach here. The level of granularity made possible by the approach here cannot be achieved with 'parametric' attribute driven approaches. In summary, the tools developed in this project have the potential to do for `discovery' what the PageRank algorithm did for search.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 150.00K | Year: 2013

This Small Business Innovation Research (SBIR) Phase I project addresses the
problem of learning predictive models of individual choice behavior using sparse
information on the behavior of any single individual. The intellectual merit of the
project is developing a novel parsimonious view of this problem by modeling
choice behavior as a distribution over permutations of alternatives, and making
this view implementable at scale. A unit of data in this paradigm is a single
comparison between two alternatives. Data of this sort can be derived in a variety
of contexts ranging from product reviews to transaction data. While being a
parsimonious modeling viewpoint, exact computation, or even representing such
models is intractable. The project will focus on developing approximate solutions
that, in the spirit of recent advances in high-dimensional statistics, exploit the
potential of sparse approximations to such models. Given the vast quantities of
data available to build such models it will be important for the algorithms
developed to be amenable to parallelization in a manner reminiscent of the
Map/Reduce computational paradigm. The algorithms developed will fit this
paradigm with key algorithmic steps decomposing across data collected for a
single individual. In summary, this project will develop a massively parallelizable
approach to modeling individual choice behavior using unstructured data from a
variety of sources.

The broader impact/commercial potential of this project rests in enabling the
emerging, all pervasive transition from search to discovery. This transition can
be witnessed in sectors ranging from e-commerce to offline retail to matching
impressions to advertisers on demand side platforms. The key stumbling block in
this transition is the seeming requirement to build attribute rich models for a given
context as opposed to a black box approach. The approach taken in this project
is of the latter variety. As a concrete example, the task of merchandising requires
an offline retailer to decide on the right assortment of products to carry in
segments ranging from tooth paste to clothing; the approach here will power such
decision making in an entirely data driven fashion. In a different direction, serving
ads based on models that capture a surfers preferences across the various silos
of products and topics on the web can be enabled at scale and incredible
granularity using the approach here. The level of granularity made possible by
the approach here cannot be achieved with parametric attribute driven
approaches. In summary, the tools developed in this project have the potential to
do for `discovery what the PageRank algorithm did for search.


News Article | December 13, 2016
Site: www.prnewswire.com

BOSTON, Dec. 13, 2016 /PRNewswire/ -- Celect, a market leader in predictive analytics and inventory optimization for retailers, convened its first executive level event dedicated to the rise of predictive analytics in retail. Retail's Predictive Analytics Forum was held in New York City...


News Article | June 25, 2015
Site: thenextweb.com

Ecommerce companies are spoiled for choice when it comes to analytics services that can help them track and optimize their product sales. What about physical stores? Celect says its platform helps stores figure out how to stock their shelves for increased inventory turnover and revenue. It uses machine learning to determine the best assortments by taking into account what was available or viewed when a customer purchased an item. The technology is designed to give brick-and-mortar stores a chance to catch back up with ecommerce rivals. Founder Devavrat Shah, the Jamieson Professor of Electrical Engineering and Computer Science at MIT, has been deeply involved in machine learning and artificial intelligence research. In 2012, Shah teamed up with one of his students to create an algorithm that predicted Twitter trending topics up to five hours in advance with 95 percent accuracy. Celect also closed a Series A funding round of $5 million led by August Capital. The company offers its technology as a hosted service. Retailers can request a demo on Celect’s site. Read next: Ascribe is using Bitcoin’s blockchain to help artists claim ownership of their work


News Article | July 11, 2011
Site: www.cnet.com

How well does Netflix really know you? How far has Amazon crept under your pores in order to determine with arrant certainty that you would enjoy a little more Danielle Steel and a little less Tony Blair? A vast-brained MIT professor insists that these brands know you about as well as your subway train driver. Devavrat Shah, the school's Jamieson Career Development Associate Professor of Electrical Engineering and Computer Science, furrows his brow at the relative pointlessness of asking humble, subjective souls to rate books, movies, and even cars on an absolute scale, such as a five-star rating system. Your five rating might, after all, be my three--because I am simply an innately more difficult, cruel, and cantankerous human being. The Netflix algorithm, Shah says, doesn't account for my being an inherently nasty piece of work. Comparing two products against each other, though--at least according to Shah--neutralizes my unpleasantness. Moreover, Shah has a surprisingly simple way of explaining why, as human beings, we are always more likely to be accurate when merely comparing two things. "If my mood is bad today, I might give four stars, but tomorrow I'd give five stars. But if you ask me to compare two movies, most likely I will remain true to that for a while," he explained. You might imagine that Shah and his team are peculiarly attuned to improving the entertainment that humans get out of life. Indeed, they have built a Web site called Celect that intends to create a place where large groups of people (hullo, Congress) can make more harmonious and apposite decisions. Much of Shah's work has been with car buyers. These are people who claim, for example, that they hate white cars and don't want an Audi but end up buying a white Audi. The professor says his algorithm was able to foresee car buyers' true preferences with 20 percent more accuracy than previously existing algorithms. Those with mathematical innards will realize that the more comparisons Shah collects, the more permutations there are of ordering them. Not everyone is going to prefer, say, "La Cousine Bette" over "Uncle Vanya" or "Mommie Dearest." So, like all the finest rationalists, he makes assumptions. His press release uses the example of Robin Williams' "Patch Adams." This is a movie that unaccountably escaped my eyes until now, but is apparently the worst-rated movie on Netflix (of those that have a statistically significant number of ratings). Shah believes that "Patch Adams" is, as Malcolm Gladwell might call it, an outlier. It therefore has to be ignored in all orderings of preference, in order not to mess up the purity of the results. The assuming doesn't stop there. The next is to "choose the smallest group of orderings that match the available data." There's still one more step before an actual score is computed. This involves using "a movie's rank in each of the orderings, combined with the probability of that ordering." Have you got that? There will be a test at the end of this post. The lay mind can see what Shah is trying to do. Just. He wants to get numbers to more accurately reflect the true comparative scale of the way in which humans judge one work of art (or Pontiac) with respect to alternatives. Now, as he admits himself, he must put his algorithm into action in the real world, the world in which we get an e-mail from Netflix, try to remember what the movie was like and, usually, click "four stars" out of sympathy. I am sure that most of us crave the day when a computer can understand us--truly understand us. This nasty piece of work (who is hoping to be nicer) therefore gives the professor's efforts at life improvement a hearty three stars.

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