The ShiSh List

Data Lakes Stories of Relevance to Retail

S h i S h — Sat, 04 Nov 2017 15:05:48 GMT

The offline recommendations engine is used to batch-process user telemetry to build multiple machine learning models to be hosted by the online recommendations service. User interaction telemetry is stored in Azure Data Lake Store for long-term storage. Competing versions of the user and product vector models are generated, using the Apache Spark MLlib machine learning library in Azure HDInsight using Python LightFM and TensorFlow. These are then bulk-loaded by Azure Data Factory into Azure Cosmos DB.

Ignition AI: http://bit.ly/2zd3GWg

Turning marketing from an art to a precise science requires ingesting vast amounts of live information from numerous sources such as social media, browsing data and previous purchasing patterns and deploying highly specialised technology. Ignition AI uses Microsoft Azure Data Lakes to manage the data because it provides a cloud based platform to store, process and analyse any amount or type of data at incredible speeds.

Acxiom: http://bit.ly/2sdSgkM

Acxiom is using Azure Data Lake and Azure HDInsight to build a data exchange platform and marketplace where it can share its data with third parties.

Damco: http://bit.ly/2ze3c4Y

Damco’s disruptive Logistics application makes extensive use of Microsoft Azure cloud technologies to improve insight into potential supply chain problems, reduce the amount of manual effort involved when workarounds for disruptions are necessary, and proactively notify clients. The app uses Azure Event Hubs and Azure Stream Analytics to combine and analyze external data from news and weather feeds and internal data from the Damco supply chain management solution. All of this data is ingested, parsed, processed, and stored using Azure Data Factory, Azure Data Lake, and Azure SQL Data Warehouse, and it is visualized so it can be easily turned into actionable business insights.

Sustainalytics: http://bit.ly/2AjtqjR

Sustainalytics, a global responsible investment research firm, adopted a broad range of Microsoft Azure services for processing and integrating a large volume of structured and unstructured, high and low velocity data as part of its research activity. Sustainalytics built a Data Lake on top of Azure HDInsight in order to consolidate and streamline structured and unstructured data from 20 different sources.

Digital Transformation in the Retail Store

S h i S h — Fri, 17 Mar 2017 23:50:19 GMT

The fourth industrial revolution is here. The digital world is advancing at amazing speeds, and it is all connected to the cloud. When your customers and employees are connected, empowered, and engaged on a digital level, the power of your retail business as a disruptive force increases. You can think of Digital Transformation as the digitization of processes, products, and services. Over the last few months, I had the opportunity to meet with some interesting solution providers that deliver capabilities for enabling digital transformation in the Retail Store. Here is a look at some of them from around the world:

PaperVault: Digital Receipts

I believe that one of the biggest aspects of digital transformation for retail is in digitizing a lot of the manual and paper-based processes that are common in retail stores. Digitizing the exchange of paper records, whether it be receipts, manuals, warranties enables better data gathering, analytics and as a result optimization of the business as well as enhanced customer engagement. PaperVault, drives a lot of value in this space helping retailers quickly digitize their paper processes and gain actionable insights from the data that is aggregated in the process. The data empowers retailers to optimize their operations and deliver a higher level of value to customers. PaperVault uses Azure Machine Learning as well as Visual Studio and SQL Server, to improve the quality and usability of their product. PaperVault uses Azure to manage files and metadata in order to store, organize, and track all digital receipts.

TruRating: Seamless Customer Rating

If there is a way to digitally capture customer feedback and rating in a very easy seamless manner, the data can be used for gathering insights combined with store sales data as well as external signals like events, weather and so on. TruRating lets you rate your experience with a business at the moment you pay for it. It’s seamless, easy to use abd The ratings for each business will be shown on the TruRating website. TruRating uses Azure to host their solution since they felt it was more developer-friendly than its primary competitors, They also use Azure Machine Learning to deliver predictive capabilities to their customers.

Shelfie: Automated on-shelf stock management

Microsoft partner Lakeba created Shelfie, an in-store robot that scans shelves to enable real-time stock reporting, and the identification of sales trends and insights to help optimize merchandise layouts. Available as a robot or drone, Shelfie travels the store scanning shelves, enabling real-time stock reporting, identifying sales trends and providing intelligent insights to help optimize merchandise displays. With data analytics solutions running in Microsoft Azure, Shelfie empowers retailers to grow sales revenue and optimize profit margins — and frees up employees to focus on tasks that add bottom-line value.

Philips Lighting: Indoor Positioning System

Philips Lighting utilizes a combination of its Visible Light Communication technology, Bluetooth, and the smartphone’s inertial sensors to deliver an indoor positioning system that offers 30 cm accuracy plus orientation, in-pocket notifications, and analytics. Visible Light Communication works by individual light points transmitting their location through modulation of light that can be detected by a smart phone camera but not by the human eye. The data stream is one-way and no personal data is collected by the lighting system. Philips Lighting has rounded up IT companies including Microsoft to develop compelling data-linked uses to enable managing, storing and analyzing data to drive customer engagement.

Wanzl Smart Trolley: Indoor Positioning & Customer Engagement with Smart Trolleys

Wanzl’s Smart Trolley, fitted with an RFID Tag, can provide retailers with indoor position, traffic information, usage patterns, average shopping times, length of time spent in front of particular shelves. As well as generating user data, it can also prevent theft of goods or of the trolley itself. The Smart Trolley enables connection to SmartPhones and additional use case for shopping lists, personalized offers and self checkout & pay. Wanzl makes use of the Microsoft Azure Cloud for capturing, storing and analyzing data.

Kroger R&D’s EDGE Digital Shelving: Smart Shelves for Dynamic Pricing and Inventory Management

EDGE Digital Shelving gives retailers the ability to deliver relevant, personalized information to consumers right at the shelf where purchasing decisions are being made. It also provides operational efficiencies, enhanced marketing capabilities, and improved customer service by reaching the right audiences with the right information instantaneously. Each shelf has an integrated bluetooth radio, wifi radio, and zigbee radio. This allows tags to be updated in real-time, so they’re always accurate and up to date.

Shelf Edge 2016 - DSE Apex video from Kevin Fessenden on Vimeo.

Mindtree Floorsense: Enhance Customer Engagement using Cameras

This is an interesting solution from Mindtree that can use the existing security cameras in Retail Stores to analyze the videos using Machine Learning algorithms. They analyze the patterns to determine if a customer is in need of assistance and sends a notification to a store associate. The cloud-based platform which runs on Microsoft Azure, offers intelligent recommendations that connect the right store associate with high potential shoppers needing assistance.

SML RFID’s Clarity Suite: Inventory Management using RFID

The Clarity Application Suite uses radio frequency identification (RFID) technology to enable customers to see their entire supply chain, end to end, with greater precision and granularity than ever before, driving better insights, analytics, and decision making. Running on Microsoft Azure, the solution scales to meet retail and supply chain customers’ needs. Customers can simplify and expedite inventory management to reduce costs, provide more accurate insights, and make better business decisions.

mPort 3D Scanning: 3D Body Mapping for Apparel Retailers

Found in several major shopping centres across Australia, the mPods from mPort also allow people to order custom-made clothes to their exact measurements. When using the mPod customers step into the box and just over a minute later they are presented with a 3D model of themselves with information about their height, weight, hip, calf and bicep measurements. They can then access these measurements via a smartphone app. Customers are able to scan themselves as often as they like and for a monthly fee will be able to track the changes in their body shape over time. The measurements can then be used for ordering custom-fitting clothing from apparel retailers either online or in store.

Join us at Euroshop 2017

S h i S h — Sat, 04 Mar 2017 08:49:48 GMT

With over 127,000 square metres of exhibition space, 18 halls, 2350 exhibitors from 61 countries, 110K visitors from all over the world, EuroShop 2017 will be the biggest in its 50-year history. Microsoft will have a big presence at Euroshop 2017 and we will be in Hall 6 Booth 24. If you are at the event, we welcome you to meet us and our partners at the Microsoft booth.

The Shelfie Robot had a big interest from retailers at the NRF Big Show in New York, and we will have them in our booth at EuroShop as well.

In addition, there will be showcases from Robotron, Avanade, Philips and many more. I would also encourage you to join my session at Euroshop where I will delve into use case & case studies in Retail for Machine Learning, Artificial Intelligence and Cognitive Services. I will also (along with Greg Gomez of Neal Analytics) talk about the Machine Learning/IoT implementation at Mars Drinks: http://bit.ly/2ktzkcO

Follow me on twitter for live updates from EuroShop 2017 @5h15h

Cortana Intelligence Suite Retail Industry Solution How-to Guides

S h i S h — Tue, 28 Feb 2017 03:37:15 GMT

The Cortana Intelligence Suite is a fully managed big data and advanced analytics suite to transform your data into intelligent action.

These retail industry solution how-to guides contain all the necessary materials and instructions needed to put together an end-to-end pipeline for each use case using the tools available in the Cortana Intelligence Suite:

Demand Forecasting and Price Optimization

While pricing is recognized as a pivotal determinant of success in the retail industry, it is also one of the most challenging merchandising tasks. Retailers face many challenges when choosing pricing strategies to maximize profit, including accurately forecasting financial impact of potential pricing tactics, taking reasonable consideration of core business constraints, and fairly validating the executed pricing decisions. Expanding product offerings add further computational requirements to make in-time pricing decisions, compounding the difficulty of this already overwhelming task. LINK TO SOLUTION HOW-TO GUIDE

Personalized Offers in Online Retail

The Cortana Analytics Personalized Offers for Online Retail Solution Package contains the resources you’ll need to deploy a functional, end-to-end pipeline for generating personalized offers on a retail website. Using this guide, users can understand the technical steps required to develop their own solution. This package utilizes a simulated data generator to imitate an online retail website with customer traffic. LINK TO SOLUTION HOW-TO GUIDE

Retail Customer Churn Prediction

The objective of this guide is to demonstrate predictive data pipelines for retailers to predict customer churn. Retailers can use these predictions to prevent customer churn by using their domain knowledge and proper marketing strategies to address at-risk customers. The guide also shows how customer churn models can be retrained to leverage additional data as it becomes available. LINK TO HOW-TO GUIDE

Call Center Automation

Call centers are essential to businesses of any size. In fact, 61% of consumers prefer to communicate over the phone, and most of them prefer self-service [1]. Given that customer satisfaction is a priority for all businesses, it is clear that self-service is no longer optional. This solution package contains materials to help both technical and business audiences understand our call center solution built on the Cortana Intelligence Suite and Microsoft Cognitive Services. LINK TO HOW-TO GUIDE

Webinar: Top 5 Strategies in Retail Data Analytics

S h i S h — Fri, 17 Feb 2017 00:28:42 GMT

Top 5 Strategies in Retail Data Analytics	February 22nd, 2017 at 1:00 PM Eastern / 10:00 AM Pacific
With Eric Thorsen, Hortonworks and ShiSh Shridhar, Microsoft	CLICK HERE TO ATTEND

It’s an exciting time for retailers as technology is driving a major disruption in the market. Whether you are just beginning to build a retail data analytics program or you have been gaining advanced insights from your data for quite some time, join Eric and Shish as we explore the trends, drivers and hurdles in retail data analytics.

January, as always, is a big month for us in the Retail Industry with the NRF Big Show and all of the learnings and insights to be gained from it. Eric Thorsen and I ran a recap of our individual learnings from the NRF Show last year and thought it would be great to do it again this year. Thank you Eric and Hortonworks for organizing this.

Hortonworks and Microsoft enable seamless implementation of Apache Hadoop to help you gain Actionable Intelligence faster with full interoperability, portability and the ﬂexibility to build and deploy Hadoop in a hybrid cloud architecture.

Webinar: Top 5 Strategies in Retail Data Analytics

S h i S h — Fri, 17 Feb 2017 00:11:44 GMT

Top 5 Strategies in Retail Data Analytics	February 22nd, 2017 at 1:00 PM Eastern / 10:00 AM Pacific
With Eric Thorsen, Hortonworks and ShiSh Shridhar, Microsoft	CLICK HERE TO ATTEND

January, as always, is a big month for us in the Retail Industry with the NRF Big Show and all of the learnings and insights to be gained from it. Eric Thorsen and I ran a recap of our individual learnings from the NRF Show last year and though it would be great to do it again this year. Thank you Eric and Hortonworks for organizing this.

Retail Sector Datasets and Competitions on Kaggle

S h i S h — Tue, 07 Feb 2017 18:08:12 GMT

Description	Details	Dataset
House Prices: Advanced Regression Techniques	Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. The potential for creative feature engineering provides a rich opportunity for fun and learning. This dataset lends itself to advanced regression techniques like random forests and gradient boosting with the popular XGBoost library. We encourage Kagglers to create benchmark code and tutorials on Kernels for community learning. Top kernels will be awarded swag prizes at the competition close.	Data
Grupo Bimbo Inventory Demand: Maximize sales and minimize returns of bakery goods	Planning a celebration is a balancing act of preparing just enough food to go around without being stuck eating the same leftovers for the next week. The key is anticipating how many guests will come. Grupo Bimbo must weigh similar considerations as it strives to meet daily consumer demand for fresh bakery products on the shelves of over 1 million stores along its 45,000 routes across Mexico. Currently, daily inventory calculations are performed by direct delivery sales employees who must single-handedly predict the forces of supply, demand, and hunger based on their personal experiences with each store. With some breads carrying a one week shelf life, the acceptable margin for error is small. In this competition, Grupo Bimbo invites Kagglers to develop a model to accurately forecast inventory demand based on historical sales data. Doing so will make sure consumers of its over 100 bakery products aren’t staring at empty shelves, while also reducing the amount spent on refunds to store owners with surplus product unfit for sale.	Data
Outbrain Click Prediction: Can you predict which recommended content each user will click?	Currently, Outbrain pairs relevant content with curious readers in about 250 billion personalized recommendations every month across many thousands of sites. In this competition, Kagglers are challenged to predict which pieces of content its global base of users are likely to click on. Improving Outbrain’s recommendation algorithm will mean more users uncover stories that satisfy their individual tastes.	Data
Expedia Hotel Recommendations: Which hotel type will an Expedia customer book?	Planning your dream vacation, or even a weekend escape, can be an overwhelming affair. With hundreds, even thousands, of hotels to choose from at every destination, it's difficult to know which will suit your personal preferences. Should you go with an old standby with those pillow mints you like, or risk a new hotel with a trendy pool bar? Expedia wants to take the proverbial rabbit hole out of hotel search by providing personalized hotel recommendations to their users. This is no small task for a site with hundreds of millions of visitors every month! Currently, Expedia uses search parameters to adjust their hotel recommendations, but there aren't enough customer specific data to personalize them for each user. In this competition, Expedia is challenging Kagglers to contextualize customer data and predict the likelihood a user will stay at 100 different hotel groups.	Data
Home Depot Product Search Relevance: Predict the relevance of search results on homedepot.com	Shoppers rely on Home Depot’s product authority to find and buy the latest products and to get timely solutions to their home improvement needs. From installing a new ceiling fan to remodeling an entire kitchen, with the click of a mouse or tap of the screen, customers expect the correct results to their queries – quickly. Speed, accuracy and delivering a frictionless customer experience are essential. In this competition, Home Depot is asking Kagglers to help them improve their customers' shopping experience by developing a model that can accurately predict the relevance of search results. Search relevancy is an implicit measure Home Depot uses to gauge how quickly they can get customers to the right products. Currently, human raters evaluate the impact of potential changes to their search algorithms, which is a slow and subjective process. By removing or minimizing human input in search relevance evaluation, Home Depot hopes to increase the number of iterations their team can perform on the current search algorithms.	Data
Yelp Restaurant Photo Classification: Predict attribute labels for restaurants using user-submitted photos	Does your favorite Ethiopian restaurant take reservations? Will a first date at that authentic looking bistro break your wallet? Is the diner down the street a good call for breakfast? Restaurant labels help Yelp users quickly answer questions like these, narrowing down their results to only restaurants that fit their nuanced needs. In this competition, Yelp is challenging Kagglers to build a model that automatically tags restaurants with multiple labels using a dataset of user-submitted photos. Currently, restaurant labels are manually selected by Yelp users when they submit a review. Selecting the labels is optional, leaving some restaurants un- or only partially-categorized.	Data
Airbnb New User Bookings: Where will a new guest book their first travel experience?	Instead of waking to overlooked "Do not disturb" signs, Airbnb travelers find themselves rising with the birds in a whimsical treehouse, having their morning coffee on the deck of a houseboat, or cooking a shared regional breakfast with their hosts. New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand. In this recruiting competition, Airbnb challenges you to predict in which country a new user will make his or her first booking. Kagglers who impress with their answer (and an explanation of how they got there) will be considered for an interview for the opportunity to join Airbnb's Data Science and Analytics team.	Data
Walmart Recruiting: Trip Type Classification; Use market basket analysis to classify shopping trips	Whether they're on a last minute run for new puppy supplies or leisurely making their way through a weekly grocery list, classifying trip types enables Walmart to create the best shopping experience for every customer.Currently, Walmart's trip types are created from a combination of existing customer insights ("art") and purchase history data ("science"). In their third recruiting competition, Walmart is challenging Kagglers to focus on the (data) science and classify customer trips using only a transactional dataset of the items they've purchased. Improving the science behind trip type classification will help Walmart refine their segmentation process.	Data
Rossmann Store Sales; Forecast sales using store, promotion, and competitor data	Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.In their first Kaggle competition, Rossmann is challenging you to predict 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams!	Data
Western Australia Rental Prices : Predict rental prices for properties across Western Australia	Property rental prices are a key economic indicator, often signaling significant changes in things like unemployment rate or income. Accurately predicting rental prices would help organizations offering public and commercial services with the ability to better plan for and price these services. Weekly rental values for properties vary due to a broad mix of factors. Some measures are objective, like proximity to hospitals, schools, transport, and coastline. Others are more subjective, like the aesthetic value of your backyard garden.	Data
Springleaf Marketing Response: Determine whether to send a direct mail piece to a customer	Direct offers provide huge value to customers who need them, and are a fundamental part of Springleaf's marketing strategy. In order to improve their targeted efforts, Springleaf must be sure they are focusing on the customers who are likely to respond and be good candidates for their services. Using a large set of anonymized features, Springleaf is asking you to predict which customers will respond to a direct mail offer. You are challenged to construct new meta-variables and employ feature-selection methods to approach this dauntingly wide dataset.	Data
Coupon Purchase Prediction: Predict which coupons a customer will buy	Recruit Ponpare is Japan's leading joint coupon site, offering huge discounts on everything from hot yoga, to gourmet sushi, to a summer concert bonanza. Ponpare's coupons open doors for customers they've only dreamed of stepping through. They can learn difficult to acquire skills, go on unheard of adventures, and dine like (and with) the stars. Investing in a new experience is not cheap. We fear wasting our time and money on a product or service that we may not enjoy or fully understand. Ponpare takes the high price out of this equation, making it easier for you to take the leap towards your first sky-dive or diamond engagement ring. Using past purchase and browsing behavior, this competition asks you to predict which coupons a customer will buy in a given period of time. The resulting models will be used to improve Ponpare's recommendation system, so they can make sure their customers don't miss out on their next favorite thing.	Data
Avito Context Ad Clicks: Predict if context ads will earn a user's click	In Russia, if you're looking to sell a tractor, a designer dress, a vintage lunchbox, or even a house, your first stop will likely be Avito.ru. As the largest general classified website in Russia, Avito connects buyers and sellers across the world's biggest country. Sellers are highly motivated to place ads on Avito, hoping to gain attention from the site's 70 million unique monthly visitors. There are three different types of ads available to sellers on Avito: regular, highlighted, and context. Context ads are seen as the best way to target users with goods and services. Currently, Avito uses general statistics on ad performance to drive the placement of context ads. Their existing model ignores individual user behavior, making it difficult to predict which ad will be the most relevant for (and earn the most clicks from) each potential buyer.	Data
Crowdflower Search Results Relevance: Predict the relevance of search results from eCommerce sites	So many of our favorite daily activities are mediated by proprietary search algorithms. Whether you're trying to find a stream of that reality TV show on cat herding or shopping an eCommerce site for a new set of Japanese sushi knives, the relevance of search results is often responsible for your (un)happiness. Currently, small online businesses have no good way of evaluating the performance of their search algorithms, making it difficult for them to provide an exceptional customer experience.The goal of this competition is to create an open-source model that can be used to measure the relevance of search results. In doing so, you'll be helping enable small business owners to match the experience provided by more resource rich competitors. It will also provide more established businesses a model to test against. Given the queries and resulting product descriptions from leading eCommerce sites, this competition asks you to evaluate the accuracy of their search algorithms.	Data
Walmart Recruiting II: Sales in Stormy Weather; Predict how sales of weather-sensitive products are affected by snow and rain	Walmart operates 11,450 stores in 27 countries, managing inventory across varying climates and cultures. Extreme weather events, like hurricanes, blizzards, and floods, can have a huge impact on sales at the store and product level. In their second Kaggle recruiting competition, Walmart challenges participants to accurately predict the sales of 111 potentially weather-sensitive products (like umbrellas, bread, and milk) around the time of major weather events at 45 of their retail locations. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it's difficult for replenishment managers to correctly predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. Walmart relies on a variety of vendor tools to predict sales around extreme weather events, but it's an ad-hoc and time-consuming process that lacks a systematic measure of effectiveness.	Data
Otto Group Product Classification Challenge: Classify products into the correct category	The Otto Group is one of the world’s biggest e-commerce companies, with subsidiaries in more than 20 countries, including Crate & Barrel (USA), Otto.de (Germany) and 3 Suisses (France). We are selling millions of products worldwide every day, with several thousand products being added to our product line. A consistent analysis of the performance of our products is crucial. However, due to our diverse global infrastructure, many identical products get classified differently. Therefore, the quality of our product analysis depends heavily on the ability to accurately cluster similar products. The better the classification, the more insights we can generate about our product range.	Data
Acquire Valued Shoppers Challenge: Predict which shoppers will become repeat buyers	The Acquire Valued Shoppers Challenge asks participants to predict which shoppers are most likely to repeat purchase. To aid with algorithmic development, we have provided complete, basket-level, pre-offer shopping history for a large set of shoppers who were targeted for an acquisition campaign. The incentive offered to that shopper and their post-incentive behavior is also provided.	Data
Restaurant Revenue Prediction: Predict annual restaurant sales based on objective measurements	With over 1,200 quick service restaurants across the globe, TFI is the company behind some of the world's most well-known brands: Burger King, Sbarro, Popeyes, Usta Donerci, and Arby’s. They employ over 20,000 people in Europe and Asia and make significant daily investments in developing new restaurant sites. Right now, deciding when and where to open new restaurants is largely a subjective process based on the personal judgement and experience of development teams. This subjective data is difficult to accurately extrapolate across geographies and cultures. New restaurant sites take large investments of time and capital to get up and running. When the wrong location for a restaurant brand is chosen, the site closes within 18 months and operating losses are incurred. Finding a mathematical model to increase the effectiveness of investments in new restaurant sites would allow TFI to invest more in other important business areas, like sustainability, innovation, and training for new employees. Using demographic, real estate, and commercial data, this competition challenges you to predict the annual restaurant sales of 100,000 regional locations.	Data
Sentiment Analysis on Movie Reviews: Classify the sentiment of sentences from the Rotten Tomatoes dataset	The Rotten Tomatoes movie review dataset is a corpus of movie reviews used for sentiment analysis, originally collected by Pang and Lee [1]. In their work on sentiment treebanks, Socher et al. [2] used Amazon's Mechanical Turk to create fine-grained labels for all parsed phrases in the corpus. This competition presents a chance to benchmark your sentiment-analysis ideas on the Rotten Tomatoes dataset. You are asked to label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others make this task very challenging.	Data
Click-Through Rate Prediction: Predict whether a mobile ad will be clicked	In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance. As a result, click prediction systems are essential and widely used for sponsored search and real-time bidding. For this competition, we have provided 11 days worth of Avazu data to build and test prediction models. Can you find a strategy that beats standard classification algorithms?	Data
Display Advertising Challenge: Predict click-through rates on display ads	Display advertising is a billion dollar effort and one of the central uses of machine learning on the Internet. However, its data and methods are usually kept under lock and key. In this research competition, CriteoLabs is sharing a week’s worth of data for you to develop models predicting ad click-through rate (CTR). Given a user and the page he is visiting, what is the probability that he will click on a given ad? The goal of this challenge is to benchmark the most accurate ML algorithms for CTR estimation. All winning models will be released under an open source license. As a participant, you are given a chance to access the traffic logs from Criteo that include various undisclosed features along with the click labels.	Data
Walmart Recruiting - Store Sales Forecasting: Use historical markdown data to predict store sales	In this recruiting competition, job-seekers are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and participants must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.	Data
Personalize Expedia Hotel Searches: Learning to rank hotels to maximize purchases	For this contest, Expedia has provided a dataset that includes shopping and purchase data as well as information on price competitiveness. The data are organized around a set of “search result impressions”, or the ordered list of hotels that the user sees after they search for a hotel on the Expedia website. In addition to impressions from the existing algorithm, the data contain impressions where the hotels were randomly sorted, to avoid the position bias of the existing algorithm. The user response is provided as a click on a hotel or/and a purchase of a hotel room.	Data
Online Product Sales: Predict the online sales of a consumer product based on a data set of product features.	The objective of the competition is to help us build as good a model as possible to predict monthly online sales of a product. Imagine the products are online self-help programs following an initial advertising campaign.	Data
dunnhumby's Shopper Challenge	dunnhumby is looking to build a model to better predict when supermarket shoppers will next visit the store and how much they will spend. The modelling data set consists of details of every visit made by 100,000 customers over a year from April 2010 to March 31st 2011	Data

Luxury Retail & IoT

S h i S h — Sat, 17 Dec 2016 05:00:24 GMT

A Big THANK YOU to Audrey Piquemal for the collaboration and opportunity to keynote the G9+ Conference (Luxe et Digital : Réinventer les standards de l’expérience client) in Paris last week. My session was on IoT and how it impacts the Luxury Retail Sector. The conference had an interesting panel discussion that included panelists from L’Oreal, Modelab, FIFG, Club Med and Institut G9+. The highlights of the conversation and presentations were captured via Livetweets:

Some of the topics that I referred to in my session included the IoT Project Gutenberg from Pernod Ricard. You can see the video at https://youtu.be/k9H-WCScXw8

I also referred to the interesting project where London Fashion designer, Martine Jarlgaard, partnered with the Fashion Innovation Agency and DoubleMe to bring her Spring/Summer 2017 collection to augmented reality. You can see the video at https://youtu.be/kmlWowKALjM

The CapsuleJIT project was another interesting conversation. Capsule Just in Time is a IoT project that manages the stock of coffee capsules through a mobile application that warns the user before the stock is empty. You can watch the video at http://www.capsulejit.com/

In addition, we also discussed how retailers are getting innovative with personalization, using Virtual Reality, Augmented Reality and Mixed Reality. We explored the Hololens project at Lowes to get an understanding of how Lowes tackles personalization using Mixed Reality. You can see the video here: https://youtu.be/mYdG8R7ldto

Fighting counterfeit goods is one of the most challenging problems facing the Luxury Retail World. We discussed how Blockchain and IoT has huge potential in battling counterfeiting.

Here are the slides from the session:

Digital Transformation through IoT from ShiSh Shridhar

Aurasens is delighted to attend @Microsoft 's"Big Data Ninja" ShiSh Shridhar's talk tonight! #luxe #digital #innovation #G9plus #loreal pic.twitter.com/sqbvZqV8gm
— AURASENS (@Aurasens) November 29, 2016

In addition to the presentation, I also participated in a video interview that can be viewed here: LINK

Hi @5h15h we mentioned you in our new article about the conference "Luxe & Digital" ! Sorry it's in French https://t.co/ISkoaFWhzm https://t.co/QdPKT8UBsF
— Ayako Dumont (@AyakoDumont) January 3, 2017

If you are heading over to the National Retail Federation’s Big Show 2017 in January, make sure to visit us at the Microsoft Booth (Booth #2803). In our booth this year, you can step into a day-in-the-life of a digital shopping journey as you shop for apparel, experiencing first-hand how technology advancements like machine learning and the Cortana Intelligence Suite, conversational bots, artificial intelligence, beacons, recommendation engines, and mobile solutions, can make true retail personalization a reality.

Convert an FAQ to a Chatbot

S h i S h — Sat, 17 Dec 2016 04:15:56 GMT

One of the exciting announcements this week was the latest addition to Microsoft Cognitive Services: QnA Maker. QnA Maker is a free, easy-to-use, REST API- and web-based service that trains AI to respond to users’ questions in a more natural, conversational way. To test it out, I decided to build a concept chatbot for L’Oreal. QnA Maker works in three steps: extraction, training and publishing. To start, feed it anything from existing FAQ URLs to documents and editorial content. I located one of the L’Oreal FAQ’s covering Cosmetics, Skincare, Hair Color and Haircare and used it as my training data.

QnA Maker locates & extracts all possible questions and answers from the link to the FAQ. The FAQ can also be provided using a .tsv, .pdf, .doc or .docx file. Check out the easy to follow steps at http://bit.ly/2hEPv3z

QnA Maker is available as a template on Azure Bot Service. Once I trained and published the QnA service, I used the Azure Bot Service to create a chatbot using the QnA template. Try out the chatbot by adding the L’OrealBot to Skype

Here is a video capture of the chatbot: https://youtu.be/ZlM78lYhJIk

Here are a few more examples of Chatbots that I built using the QnAMaker:

Some examples that I put together:

Video	Add to Skype	FAQ
http://bit.ly/2pa3GAr	http://bit.ly/2oN90Nl	http://bit.ly/2o6cDLH
http://bit.ly/2pa8w0y	http://bit.ly/2nVghI1	http://bit.ly/2paFSvU http://bit.ly/2oMZr0V http://bit.ly/2nM59LT http://bit.ly/2oh67ne http://bit.ly/2nmpXyx
http://bit.ly/2oRiCUn	http://bit.ly/2mTGERt	http://bit.ly/2oK52Wl http://bit.ly/2oK31tb http://bit.ly/2oK2vv8
http://bit.ly/2nTfaId	http://bit.ly/2o8LzON	http://bit.ly/2oKbaOf http://swa.is/2nmwFEG
http://bit.ly/2oaJMY7	http://bit.ly/2mZfYtN	http://bit.ly/2oK8a4n http://bit.ly/2nTgs5P http://bit.ly/2nTgw5N http://bit.ly/2o6c04F http://bit.ly/2oh8Ay0 http://bit.ly/2nM2Fxc
http://bit.ly/2o6vE0C	http://bit.ly/2odYj3j	http://bit.ly/2oaGAfc http://bit.ly/2oaIJHP http://bit.ly/2nLLhst
http://bit.ly/2nLYUrm	http://bit.ly/2oNi7NW	http://bit.ly/2pamXSe http://bit.ly/2panfZp
http://bit.ly/2papI61	http://bit.ly/2nqoIdZ	http://bit.ly/2paGYIf http://bit.ly/2oaJfp6 http://bit.ly/2nM4aLF http://bit.ly/2oKh5CK http://bit.ly/2ogZr8G http://bit.ly/2oRuYMt http://bit.ly/2o6lMnh
http://bit.ly/2oRTtJh	http://bit.ly/2nTAPQr	http://bit.ly/2nTAywT
	http://bit.ly/2nM43zQ	http://bit.ly/2oKAwLQ
http://bit.ly/2o6Ndxu	http://bit.ly/2lPsk7P	https://otto.me/2mqPPYe
http://bit.ly/2oNBE0P	http://bit.ly/2lRiiWe	http://bit.ly/2o6FgIL http://bit.ly/2oKAWBO
http://bit.ly/2paRqzk	http://bit.ly/2lL8f5D	http://bit.ly/2nTGW7u http://bit.ly/2nTK5nA http://bit.ly/2nn3scH http://bit.ly/2ohDFBJ http://bit.ly/2ob2u1Q http://bit.ly/2nTJhiz
https://t.co/bNqzRg7Rkr	http://bit.ly/2onmFrI	https://crate.us/2p73iWS http://bit.ly/2p6Y7q4 http://bit.ly/2p7eeUp http://bit.ly/2o71RE2 http://bit.ly/2opyjm5
http://bit.ly/2oePAxs	http://bit.ly/2pyTUbc	http://vuitton.lv/2pbVUK0 http://vuitton.lv/2nKh1TG
http://bit.ly/2oeIr0e	http://bit.ly/2obWNhT	http://bit.ly/2phAzvD http://bit.ly/2oyOS0D
http://bit.ly/2hv3VGP	http://bit.ly/2ifwxDp	http://estee.cm/2oznSwo http://bit.ly/2nQuvx5 http://bit.ly/2ozng9Z
http://bit.ly/2po8umt Concept: http://bit.ly/2pEyWYl	http://bit.ly/2hS1LMJ	http://bit.ly/2of1Kq5
http://bit.ly/2pohB6M	http://bit.ly/2lpmVsi	http://bit.ly/2ozobHk
http://bit.ly/2o6Ndxu	http://bit.ly/2lPsk7P	https://otto.me/2mqPPYe
	http://bit.ly/2poxqKK	http://bit.ly/2oLG2wW http://mcys.co/2nQfiMN http://bit.ly/2pkvmWS
http://bit.ly/2oeSWAE	http://bit.ly/2nizi91	http://bit.ly/2mLGN4l http://bit.ly/2nikHdE
	http://bit.ly/2pEt69z	http://bit.ly/2pkElr2 http://bit.ly/2oeDRPD
http://bit.ly/2pkOiF4	http://bit.ly/2hRXjia	http://bit.ly/2h2IZ7W http://bit.ly/2pkNhNc
http://bit.ly/2oeS1jx	http://bit.ly/2ib3Mbf	http://bit.ly/2oeWqTw http://bit.ly/2pEv1uH http://bit.ly/2pkFsac http://bit.ly/2ozgAsv http://bit.ly/2oeRle2
http://bit.ly/2pEmszR	http://bit.ly/2h0rK9e	http://thd.co/2nQtkxG http://bit.ly/2hOsxGY
http://bit.ly/2hE9vpp	http://bit.ly/2pkJXBG	http://bit.ly/2p6ihkf http://bit.ly/2oeE8lu

If you are heading over to the National Retail Federation’s Big Show 2017 in January, make sure to visit us at the Microsoft Booth (Booth #2803). Here you will be able to see for yourself how Chatbots can enable retailers to better engage with customers during the customer journey.

Understanding Advanced Analytics to Build a Stronger Retail Customer Experience

S h i S h — Sat, 22 Oct 2016 00:55:45 GMT

Thank you for those of you that joined by webinar yesterday: I loved the engagement and questions that came my way. For those of you that missed the live session, we have an on-demand recording available. In addition, I have also posted my slides to slideshare. We also shared an Ebook that you can download with more information on Advanced Analytics and Retail.

I would love to hear your feedback and comments via @5h15h