Retable is a no-code AI-augmented data transformation tool for data and business analysts built natively for cloud data warehouses

Retable.ai

Hi, I’m Eugene Levy. I built three startups on AI and big data, so I feel entitled to confess. I am so fed up with the complexities of modern data stacks. ⛔️ The amount of technical garbage (ghm.. sorry.. knowledge) we, data people, are supposed to master is staggering - orchestrators, connectors, data warehouse, SQL in all its beauty. We are told to learn Python, GitHub, and the best practices they say - oh my… 🤦 Data is not code. Professional analytics is not Python\SQL coding. We value our time, and time to value. We enjoy working with numbers, digging for answers, doing digital investigations, and delivering value to our business folks, not wasting time on coding. 🤷 Look, all professional jobs got their tooling - data scientists got their notebooks. Developers got their IDEs, and UX designers are happy with Figma. Why don't we, data professionals, have one? Where is the data people’s tool? The one that stays with us for our whole careers. The one that empowers you and can be used outside the company. Companies own tools, we do not. Why? ⚔ So I set out on the path to change that and reimagine what true professional visual data transformation tools should look like - that is how Retable.ai was born. 🥂 If you work with data you will want to see what we've been building in stealth for a year now. 🎁 We started from the first principles - make data pipelines and analyses portable and reusable. No matter where you work, locally on a desktop, in the cloud, or on top of a fancy data warehouse - you build it once, and it runs everywhere. 🌎 🎁 💪 We have 70 seats in our Early Adopter Program! Retable.ai is as easy as Excel. Visual and accessible but powerful, as a true IDE, with no limits on data size, and fully automatable. https://www.retable.ai/eap 🎁 Finally. Why bother clicking buttons at all? Talk to our powerful AI - it does all that dirty boring prep work for you. 🤖 That’s what I call the data people’s tool. But wait! It would not be a people's tool if it is not free, right? So we made it… 💲 You can try it right now but we have a limited amount of seats 👉 Data analytics is going big and mainstream. Data people deserve the best tooling. Come join us to shape it together. https://www.retable.ai/eap

Reimagining What Visual Data Transformation Tools Should Look Like

Reimaginando el aspecto que deberían tener las herramientas de transformación de datos visuales

Réinventer à quoi devraient ressembler les outils de transformation visuelle des données

विज़ुअल डेटा ट्रांसफ़ॉर्मेशन टूल को कैसा दिखना चाहिए, इसकी फिर से कल्पना करना

ビジュアル データ トランスフォーメーション ツールのあるべき姿を再考する

Reimaginando como as ferramentas de transformação de dados visuais devem ser

Hình dung lại các công cụ chuyển đổi dữ liệu trực quan sẽ trông như thế nào

重新构想可视化数据转换工具的外观

With the introduction of lockdown measures and brick-and-mortar stores closing their doors, retailers’ reliance on has intensified. The push to online and the need to attract and retain customers has made retail brands invest more in digital advertising and bring livestream shopping to their marketing mixes. ecommerce solutions A hot trend, livestream shopping makes online shopping more engaging and experiential while taking conversions way up. In this article, we’ll examine the phenomenon of livestream shopping, evaluate its impact on the retail sector, and zoom in on out-of-the-box and custom live video shopping tools. What is livestream shopping? The concept of live shopping goes back to the 80s and builds on the idea of video shopping. You are likely to remember QVC’s Lisa Robertson casually chatting with the viewers while demonstrating jewelry or HSN's Bobby Ray Carter showcasing fashion pieces with the viewers tuning in, seeing what’s for sale, and calling in to make a purchase. Livestream shopping — using video streaming tools to demonstrate products, communicate with an audience, and sell, all online — is a natural offshoot of this original TV-oriented model. Combining the best of ecommerce, video streaming, and social media, live commerce lets people shop anytime and from anywhere, encourages real-time communication, and sparks interactivity, appealing to today’s tech-savvy, sit-at-home customersLivestream shopping can take different forms, from live events and auctions to online marketplaces and influencer streams. Irrespective of the format, viewers can tune into a live shopping session, leave comments, ask questions, and communicate with a host or with one another. And once they are ready to purchase, they can click a link embedded in a shoppable video and proceed to checkout. Live shopping provides for personalization, too. Although one-to-many live streams, when products are marketed to mass online audiences, are more popular, brands can pay special treatment to their repeat buyers through one-to-one livestream sessions. With private dialogues between a host and a customer, brands can produce higher conversion rates and average order values and increase the customers’ chance of returning. How does livestream shopping work? Suppose you want to tap into livestream shopping. In that case, you have three options: 1. Leverage social media’s streaming functionality conditions and predicting patient outcomes 2. Market your products on an existing video commerce platform or a marketplace with streaming features 3. Opt for a custom livestream shopping solution 1. Social media The most popular and perhaps most straightforward way to start with livestream shopping is going live on popular social media apps. Luckily, most social media platforms have introduced livestream and ecommerce functionality in response to the video shopping trend. Instagram, for instance, has added built-in checkout capabilities throughout its apps, including Instagram Live, which, by the way, has seen a 70% increase in the number of viewers in 2020. Facebook, too, is developing native livestream shopping tools. In turn, TikTok is partnering with ecommerce giant Walmart for the latter to market their products online. The critical advantage of turning to social media is the possibility to team up with influencers, who play a crucial role in the success of livestream shopping, right where their audiences are. A study from Adweek reports that 49% of respondents actively follow social media influencers and based upon an influencer’s recommendation. So, having influencers as hosts may help in developing trust and loyalty among customers. However, as social media platforms generate income via media buy, they focus on being social. So, ecommerce functionality is still limited to select brands. As a result, shoppers have to be routed to other platforms or websites to finalize purchases, which disrupts the whole retailtainment experience. have made a purchase 2. Out-of-the-box live shopping apps Another way to start capitalizing on the video shopping trend involves joining an existing livestream shopping platform or an ecommerce marketplace with livestreaming features. And these span from video commerce giants like Amazon Live and Taobao, live shopping startups like Bambuser and Spin, and niche live commerce apps like NTWRK. The essential advantage is that most out-of-the-box video shopping apps feature built-in integrations with popular ecommerce platforms and payment gateways as compared to the previous option. So, users don’t have to be routed to other sites to complete a purchase and can enjoy an uninterrupted video shopping experience. The fundamental limitation is that some livestream shopping platforms, especially larger ones and those with established audiences, expect a brand to meet a range of requirements, like having a steady business history, and charge commissions. Amazon Live, for instance, charges up to 10% on each purchase. Another downside is rising competition — with millions of livestream views, it can be tricky for retailers to stand out. 3. Custom live video shopping applications Retailers who seek to make video shopping an essential part of their marketing kit may consider developing a custom retail solution with livestream functionality. Going custom is undoubtedly more effort-intensive than the previous two options. But a custom app can let retailers take maximum advantage of the video shopping trend with tailored functionality, high-quality streaming, advanced analytics, and maybe even some extra features, like AI-powered video content personalization. If you do opt for a we recommend starting with an MVP. However, it is essential to plan for the future and make sure your app will handle the growing number of shoppers. For that, make time to choose a reliable content delivery network (CDN). Looking for the perfect CDN, make sure it has a robust server infrastructure close to your target locations and pay attention to the bandwidth. With the basics in place, you can think about MVP features. The essentials to include in the first app version are: custom livestream shopping app, User registration and authorization Online streaming as such Live chatting Checkout and payment Server-side stream processing With the subsequent iterations, you can add everything from different signup options, personal customer profiles, search options, the possibility to save streams as on-demand videos, advanced analytics, streaming content personalization, and more What’s the livestream shopping market size? The current livestream shopping trend has its roots in China. China’s ecommerce giants started holding live shopping events hosted by local celebrities back in the 2010s. The US has begun to catch up with the trend, too. The US livestreaming market is expected to by the end of 2021 and hit $25 billion by 2023, according to Coresight Research. The market value is still incomparable to that in China, where it comprised $150 billion in 2020 and is expected to reach $300 billion in 2021. The companies that look prominent on the US livestream shopping market today include: reach $11 billion Launched in 2018, the platform has a 2-million follower base, which is constantly growing. According to Bloomberg, since its premiere, TalkShopLive’s sales have increased sevenfold TalkShopLive. . Having started as a directory for American retailers to reach Chinese travelers, the platform has turned into a profitable livestream shopping endeavor that has recently expanded into and is gaining popularity on the American market ShopShops . The mobile app uses live shows to sell limited edition products. The company has seen its sales double in 2020, with some shows reaching $1 million in sales in approximately 10 minutes NTWRK The ecommerce giant has rolled out its video streaming service with lifestyle, fitness, and cooking streams Amazon. The company has introduced Shoploop — a video shopping platform that features 90-second shoppable stories Google. Its Live Shopping feature allows creators and brands to sell products during Instagram Live broadcasts. The functionality is available to business accounts Instagram. An explosion of digital media and video content consumption, an increasing impact celebrities and influencers have on shopping decisions, and the push to online boosted by the COVID-19 pandemic allow projecting that more US retail brands will move on the live shopping trend in the near future. Companies profiting from livestream shopping today Nordstrom Nordstrom, an American department store chain, launched its own in 2020. Following the launch, the brand hosted over 50 virtual events, translating the store experience into the digital world, and plans on carrying out their livestream shopping endeavors through 2021. During each event, customers can shop the fashion products mentioned in the video and participate in a live chat. The company’s efforts are paying off. In 2020, 54% of the brand’s sales came from digital channels, while virtual styling accounted for 30% of all styling appointments. shoppable livestream channel CAIA Cosmetics, Lyko CAIA Cosmetics and Lyko, Swedish cosmetic and hair care brands, have partnered with Bambuser, a video commerce startup, to power their marketing with live shopping. CAIA Cosmetics, for instance, holds make-up tutorials with the brand’s founder Bianca Ingrosso and claims to be very satisfied with the customers’ response. The , with 17% of the viewers participating in the live chat and 60% — engaging through likes. The first live stream ended up with a 5% conversion rate, which is higher than the brand’s website conversions. Lyko, in turn, chose to kick off their live broadcasts by offering tips and gifts during the holiday season. The brand had an average viewing time of 16.5 minutes, 17% chatting and 29% liking engagement, and a 22% conversion rate. average stream viewing time comprises 11 minutes Quivr Quivr, a coffee and tea manufacturer, has tried social media to market their products via video streams but eventually landed on Amazon Live as their video commerce platform. Quivr’s co-founder Ash Crawford says that the of about 150% the next 24 hours after a live stream. brand sees a guaranteed increase in sales Livestream shopping trends and predictions The possibilities for further evolution and expansion of livestream shopping are numerous. Introducing AI, for example, can help retailers create highly personalized channels of live, interactive content created or curated specifically for a given customer. and VR, in turn, would be of help in creating immersive video shopping experiences for sit-at-home customers, for example, transporting them to the front row of a fashion show with a host ready to answer questions. Leveraging AR In the shorter perspective, brands will continue to extend their reach to more customers through live streams, analysts say. Considering the drastic change in shopping habits and the push to online spaces that are expected to last, livestream shopping is likely to extend in the post-pandemic world. Livestream shopping offers an opportunity to bring the best of physical shopping to online customers. If you’re ready to seize the opportunity, contact the ITRex team!

An Introduction to Livestream Shopping

13 Retail Technologies That Could Help Brick-and-Mortar Businesses

Is Shopping In-Store An Experience Of The Past?

Technology is improving as we speak. Robots exist, IoT is here to support your business, AI can help you amaze your customers, and VR can help you travel in a different world. Keeping up with trends and tech is important. You know it and your customers know it. In this article, we are going to show you how you can include robots in your strategy. But before we get started, let's see how Retail has changed with the help of technology. in-store marketing How Has Technology Changed Retail? eCommerce platforms innovate as much as possible. Personalized ads, great customer experience, analyzing data, and implementing campaigns that focus on the customer. How about brick and mortar shops? The fact that customers tried online shopping and had a great experience makes them want more from physical stores. Shoppers expect retailers to offer them the same benefits or even better. You might be asking yourself: If customers love shopping online why are they still shopping in stores? Well, people love to see, touch, and smell the products before purchasing them. Plus, shopping has become an experience over the years and is becoming a trend. retailtainment While brick and mortar shops don’t innovate as fast as eCommerce platforms do, technology has had an amazing impact on Retail. Do you remember how your store looked 5 years ago? No self check out, no click and collect, no digital displays, no possibility to scan a QR code, no connecting between the online and the offline environment. This is how tech changed retail: SELF-CHECKOUT Technology has changed the way people scan and pay for products. Shoppers can now scan their own products and pay with cash, card, smartphone, or smartwatch inside the store. IT REDUCED THE COSTS Technology managed to reduce the costs for retailers. Fewer employees means less money spent on salaries. While it might seem like an IoT device or robot is much more expensive, your investment is recovered in a few months. IT MADE EMPLOYEES MORE EFFICIENT These devices are here to help employees perform certain tasks that are too difficult (lifting heavy products and moving them around the warehouse) or too repetitive (checking out stock and refiling shelves with products). IT HELPED IMPROVE CUSTOMER SERVICE can also be used to help customers navigate inside the store. This way, it can help you improve the quality of your customer service. Robots And it is only the beginning! By 2025, there will be IoT devices connected all over the world. 75.4 billion Technology doesn’t only take care of your stock or reduce your costs, it can also be included in your in-store marketing! How can you use technology in your in-store marketing strategy? IoT devices can do much more than filling up your shelves or moving merchandise around the store, they can also have a big impact on your in-store marketing strategy. Apart from promotions, samplings, events, and old-school POP displays, tech devices need to be included in your in-store marketing strategy. Let us show you some examples of how technology can be included in your in-store marketing strategy. IN-STORE MARKETING ROBOTS Yes, there are robots that can put products on the shelves, but did you know that there are robots that can make your products talk, sing, and move? Tokinomo uses light, sound, and motion technology to bring your products to life! Our in-store marketing robot can help you promote your products and surprise your customers as they stroll away on an aisle. Once they reach your product, Tokinomo will sense the movement and activate itself. You can choose what your products tell customers, how it moves, and how fast it talks or sings. It’s all up to you! The best part? On our cloud-based platform, you can change the audio message to communicate a new promotion or match a recent event. You can also collect, in real-time, important data to see exactly how your campaign is performing. AUGMENTED REALITY Want to promote your products? Why don’t you make your customers go on a scavenger hunt? With the help of AR, your customers could enter a maze and search for products directly on their phone while being in a store. Just like Pokemon Go, people will love to look for hidden treasures while they are grocery shopping. There are various ways in which you can include technology in your in-store marketing strategy. Make products dance, make customers hunt for treasures, offer shoppers an experience, not just products. Previously published at https://www.tokinomo.com/blog/how-to-use-technology-in-your-in-store-marketing-strategy

How To Use Technology To Enhance Your In-Store Marketing Strategy

Stores are changing. We see it happening before our eyes, even if we don’t always realize it. Little by little, they are becoming just one extra step in an increasingly complex customer journey. Thanks to digitalisation and retail automation, . The quality of the experience, a feeling of belonging and recognition, the comfort of the purchase… all these parameters now matter as much as sales per square meter, and must therefore submit themselves to the optimizations prescribed by Data Science and its “intelligent algorithms” ( ). the store is no longer an end in itself, but a mean of serving the needs of the brand at large aka artificial Intelligence in the form of machine learning and deep learning The use of artificial intelligence is, above all, . Indeed, e-commerce players did not wait on anyone: note, for example, the adaptation of online search results to the end customer, or the recommendations made based on a digital profile. These two aspects are impossible for brick and mortar (for now). However, physical commerce has its own strengths. Olfactory, visual, auditory, etc. data can be used to give the consumer a feeling of having experienced something unique and made specifically for them. In addition to customer relationship improvements, artificial intelligence also makes it possible to seek the resolution of problems that have long represented a burden for retailers : better inventory management, optimization of store space, optimization of employee time… a competitive necessity We present below a complete look at deep learning/ machine learning use cases implemented to create the , supported by real-life examples. store of the future 1. Adapting the store and its inventory to better serve customers It’s a known fact that e-commerce actors can optimize their websites in real time using dynamic statistics. This allows them to define the most effective strategies according to the resources available and predefined customers segmentation. Like any physical space, the store does not have this luxury. However, this does not prevent the optimization of physical spaces, thanks to insights gleaned from intelligent algorithms. Back in the days (less than 20 years ago), we’d hire students to follow and count customers in specific areas of the store. Thankfully, these times are over. Heat-maps, average route diagrams, time spent on screens, various ratios in relation to total attendance, correlations… the cameras in store and computer vision algorithms now provide actionable tools based on images. Today, heat-mapping and activity recognition solutions help not only to position promotions, but also to create entire marketing strategies, and to measure the performance of each department, as well as that of product placements. Solutions offered by the likes of can analyze store videos to give retailers data on the number of people in their store, the path they take once inside and where they linger. This helps marketers identify popular locations, allowing them to change the layout of furnishings, displays, advertising or staff to better serve their customers and increase revenue. periodic RetailFlux As technologies evolve, we are also starting to hear about “ ”: these tools, created by start-ups such as , , and , allow us to estimate the age and gender of people passing in front of a camera, thus giving stores access to a whole new granularity of analyzes. This aspect is paramount to the rationalisation now expected of marketers and category managers. demographic recognition DeepVision AI MyStore-e RetailDeep RetailNext Although these cameras are often hung from the ceiling, this is not always the case: (in partnership with ), for example, recently to create a network of “smart” displays that brands can use to target ads to specific types of customers. The doors act as a digital merchandising platform that depicts food and beverages in their best light, but also as an in-store billboard that can show ads to consumers who are approaching, based on variables such as approximate age, gender and current weather. Cameras and sensors inside the connected coolers can also determine which items buyers have picked up or viewed, giving advertisers insight into how their promotions work on the screen, and quickly notifying a retailer if a product is no longer in stock. Walgreens Cooler Screens integrated cameras, sensors and digital screens in the doors of its stores’ coolers The key question thus shifts from “where” and “how many” to “who”, “when”, “how often”, “how long” and “for how many cookies?”. 2. Forecasting to increase profits These data, mixed with those from check-outs and loyalty programs, are key to and creating store clusters, which in turn improves retailers’ supply chains. By better predicting what products will do well in a certain area, machine learning algorithms from startups such as can reduce dead stock, help optimise pricing (and profits), and increase customer loyalty (people obviously tend to enjoy finding the right product mix in their nearest store). forecasting demand Symphony RetailAI Indeed, unsold stock might be one of the retail industry’s biggest handicaps: . Reducing this number is key to the industry’s long-term survival : every dollar spent on what becomes dead inventory is valuable money that could have been put towards training talent, better R&D, or, most obviously, brand new smart algorithms. unused inventory costs U.S retailers about $50 a year billion Forecasting also helps retailers optimisise their promotions : the less dead stocks are in the warehouse, the more strategic promotions can be, instead of being merely reactionary. Many pricing will particularly appreciate this aspect, as it will make their job a lot easier, and a lot less thankless. aficionados 3. Personnalisation to promote an in-store experience In the same way that a website can adapt in real time to end users, an increased granularity of computer vision is also possible in stores, allowing it to target individuals. However, these algorithms are based on more elements than the ones presented above, and are thus more complex/less reliable. To work at a personal level, the algorithms need a mix of demographic recognition, loyalty code identification, and augmented reality, often integrated into smart objects such as mirrors. Although they cannot ( ) be implemented on a large scale, these solutions exemplify a profound change in the way stores sell. We are moving from the sale of product to the sale of experiences, where the physical offer becomes a by-product. This is the concept of . Low prices and an extensive catalog are no longer enough for customers, who can find such a value proposition online. An authentic brand experience becomes key to survival : the store is a storehouse of engaging experiences, ideas and interactions. yet shoppertainment The use cases are of course numerous ( ): during , presented a connected mirror which links visual recognition data and the stores product database. In the case of an optical store for example, the mirror can recognize the model tested and display product or marketing information concerning it. The sellers also have statistics on the use of the mirror in real time: they know that the person who has tried a certain type of glasses has been there for some time or hesitates between two pairs. This facilitates the work of the seller who can thus advise the customer on the products which really interest them. even if they often border on the sci-fi technobabble side of the AI equator 2019’s NRF Google has for its part allied itself with to test , while acts more like a board which encourages its customers to engage with the community created and maintained by the brand. H&M Microsoft a mirror allowing to take selfies thanks to voice commands mirror Lululemon’s Smart mirrors can of course be placed at different intervals of the purchasing process: is located in the fitting room to transform the often frustrating experience of trying out clothes. Buyers can interact with the mirror to change the lighting in their fitting room and can select different sizes or colors for their outfits, which an employee will get. The mirror also recommends other items that would go well with what is being tried. ’s Ralph Lauren Cosmetic companies have also adopted these solutions: the uses an intelligent algorithm which mixes the gender, age, appearance and style of the person looking at it in order to make recommendations. It even claims to differentiate between people wearing neutral or bright colors, daring or conservative styles and clothes with floral and geometric patterns to name a few. smart mirror Sephora Through deep learning, we are also seeing a new technique emerge : . It is the ability of computers to recognize, interpret, and possibly stimulate emotions. It is indeed possible to identify gestures such as head and body movements, while a voice’s tone can also speak volumes about an individual’s emotional state. These insights can be used in the store so as not to generate an inconvenience for a customer who clearly does not need to be helped or bothered. These technologies are nevertheless new (only offers retail use cases) and intrusive: it is therefore preferable not to comment yet on future use cases. affective computing Releyeble 4. Making the shopping experience smoother for the customer Mirrors, augmented reality, virtual reality … they rarely respond to real pain points for retailers and their customers. And we know these pain points by heart : checkout length, quick product localization and inventory management… those should be priorities for stores looking for ways to use machine learning and deep learning solutions. Reducing friction at checkout In China, for example, customers of certain s can, thanks to technology, , after having linked an image of their face to a digital payment system or bank account. The American chain has also in some of its restaurants: the first time customers order using in-stores kiosks, they are invited to link their faces to their account using ( ’s) in order to benefit from numerous advantages. Payment by bank card is still necessary, but the company intends to switch to payment by facial recognition if the initial test phase is successful. KFC Alipay make a purchase by placing themselves in front of a POS equipped with cameras Caliburger tested the idea of ​​facial recognition NEC ’s facial recognition software NeoFace Fears over cybersecurity could however prevent this kind of solution from seeing the light of day on a large scale. Indeed, customers are more and more jealous of their personal data (and rightly so): according to a study, only are ready to submit to facial recognition in stores. For recognition by mobile application, . Wavestone 11% of consumers this figure rises to 40% Other, more viable, ways to use computer vision to make checkout more fluid are therefore being considered. We are by now all familiar with ’s automated stores ( ), which allow customers with a Prime account to enter the store with a code on their phones, do their shopping, and exit the store without going through a checkout. An algorithm having “followed” the customer around, the amount of purchases is automatically debited, and an invoice is sent by email. Testing of this technology is also , in partnership with . Amazon Go not too familiar, one hopes underway at Casino XXII There are many start-ups in this space: , , … all claim to help companies eliminate the checkout of customers. China, meanwhile, is casually reworking the very concept of the store through the . Standard Cognition Zippin Trigo Vision by Bingo-Box Auchan Reducing stock-outs All these cameras can be used to see more than customers: many solutions for monitoring shelves have indeed emerged. They offer to send an alert to employees in the event of a shortage, which allows for a prompt response. This is key for stores: in North America each year (~4% of revenues). Not only that, but stock-outs can also actively drive customers into the arms of competition: 24% of Amazon’s revenue at a local retailer. There are many examples of such solutions: in France, works with . In the US, has been working on this concept since last year, as has ABInbev with Interestingly, ’s solution offers a similar process, but the camera is in the hands of individuals in order to take the photos that will be analyzed by the algorithms. In China, meanwhile, ( ) is pushing the border of augmented stores more than anywhere else in the world. stockouts represent more than $129 billion in lost sales comes from customers who have experienced a stock-out Angus AI Les Mousquetaires Walmart Focal Systems . Yoobic Hema ’s store of the future Alibaba Shopping advices through Voice technology Of course, images aren’t the only things that can be analyzed in store; voice also has a role to play in streamlining customer journeys. This under-appreciated method of shopping is due for a small revolution: 13% of all households in the United States owned a smart speaker in 2017, per . That number is predicted to rise to 55% by 2022. OC&C Strategy Consultants The fact that is also a leader in voice technologies shows how serious the Seattle giant is in terms of its brick-and-mortar domination (having already conquered virtual spaces). The brand’s , launched in 2019 work with to answer any questions it understands while a customer is on the move. More interestingly for retail, it also informs the user if the closest Whole Foods ( ) has an item a customer is looking for. Once they are informed and in the store, the Echo Buds can direct them to the right aisle. You can imagine not only guiding you to an item, but if you tell it that you want to make lasagna, it could also guide you through a store, giving you the quickest way to pick up all the necessary ingredients. ( ). Amazon Echo Buds Alexa Amazon owns Whole Foods Alexa The future is ear get it? Virtual assistants are indeed on the rise. , for example, has partnered with American retailer to . By mixing artificial intelligence, voice-activated technology and LED lights on the shelves, helps buyers choose the perfect whiskey bottle. Three bottles are recommended after a quick conversation, and the relevant shelves light up to lead the customer to the preferred bottles. If customers already have a brand in mind, the assistant can recommend other brands or bottles with similar flavor profiles. The whole experience lasts no more than 2 minutes. The voice assistant makes it a pleasant and informative experience, with a mix of banter and useful information. The Mars Agency BevMo! test , a digital whiskey purchase assistant SmartAisle SmartAisle From NLP to virtual assistants, the two examples above show that, if used well, Voice technology can free more employee time, and give key data to retailers. Robotic automation The discussion on improving and streamlining processes would not be complete without a discussion around robotics. These objects, long relegated to science fiction, are now showing their usefulness in stores around the world. Although robotics is not in itself a subcategory of artificial intelligence, robots roaming the aisles use notions of computer vision and NLP. Just like , is here too at the cutting edge of technology: robots (called “Auto-S”), which are designed to scan items on the shelves to help with price accuracy and restocking, are already present in 1000 of their stores. These six feet tall devices contain 15 cameras each, which scan shelves and send alerts to employees in real time. This frees workers from the need to focus on repeatable, predictable and manual tasks, giving them time to focus more on sales and customer service. Amazon Walmart Bossa Nova has also introduced robots that clean floors, unload and sort items from trucks and pick up orders in stores. It is interesting to note that this niche is quickly becoming highly competitive, ’s robots have been deployed in store across America, with the same value proposition as , while ’s unveiled in 2016 a robot that can understand and respond to simple customer questions. Post-coronavirus, in the coming months. Walmart Simbe Schnuck Bossa Nova Lowe it is almost certain that the movement towards robotics will accelerate 5. Loss prevention “Shrinkage” (theft) has an enormous cost: (2.1% of annual turnover in the distribution sector), weighing heavy on the margins of distributors already highly pressurized by price wars. Security therefore becomes a pressing need. And because of costs, so does automation. This can take many forms. Augmented cameras, for example, can identify if a product has been hidden, and alert a human. This would, however, produce a lot of false positives due to the physical impossibility of an all-knowing camera. Companies such as or claim to be able to avoid this problem by alerting someone only if the behavior of a visitor is highly suspicious. Solutions such as also offer to detect “sweethearting” (an employee pretending to make a transaction, but in fact giving a product to an acquaintance without payment). It is important to remember that a large percentage of store thefts go through employees. The ROI of these solutions is easy to calculate: stores know exactly how much they lose from theft and errors. As such, this use case is likely to be one of the first to be implemented. €49 billion per year on a European scale Vaak DeepCam AI StopLift Conclusion In view of all these developments, and despite their many positives for both retailers and customers, it is essential that customers question retailers about who has access to data and how it is used. It goes without saying that transparency must be the watchword of any use of personal data in order to guarantee consumers the preservation of their private life. If you’re eager to get going with your very own corporate A.I project, I recommend jumping straight to my latest article on the matter : . 10 Steps to your very own Corporate Artificial Intelligence project This article was originally on my blog, The Pourquoi Pas. Come say hi!

How the Retail Industry is Implementing Machine Learning and Deep Learning 

Ah, AI. It's getting everywhere, isn't it? It has us fascinated, and sometimes maybe a little spooked. Time for the truth: teams that don’t leverage AI will fall behind. As a (or engineering leader), part of your job is now leading this drive toward safe, thoughtful, and impactful AI adoption. CTO I haven’t written a comprehensive list. I don’t think anyone actually needs one. What I have done in this article is chosen the “best-in-class” tools for each area of software delivery (or at least those which currently have AI tools built for them that are capable of having an impact). Let’s go and look at my AI for software development picks. ‍ The AI “revolution” It’s a cliche, but it isn’t an exaggeration. AI revolutionizing software engineering, with a wide range of machine learning algorithms and AI-based tools and technologies emerging that can help developers improve their workflow and boost efficiency. is AI has genuinely practical applications for a wide range of tasks, from developing more accurate test cases to creating more efficient code. Developers can use AI-enabled tools to automate many of the tasks involved in software engineering, freeing up more time to focus on core development tasks. AI-based software testing is another exciting area of development. With the help of neural networks, developers can test their code more thoroughly and identify potential bugs and errors before they become significant issues. It’s also a fast lane to improve the developer experience. AI engineering enables your team to do more of what they actually enjoy in the development process. The future of software development is looking brighter than ever, thanks to the incredible power of AI and deep learning. With the help of these technologies, developers can streamline their workflows, improve their code quality, and stay ahead of the competition with less human intervention. GitHub Copilot X: Your [Future] AI Pair Programmer This one is easily the best-known on our list, and the most hyped. The catch? When it arrives, it will probably be the most-adopted AI tool in software engineering. It isn’t out yet. You might be familiar with , and its features like Copilot chat. X is the smarter successor, built on GPT-4. It’s pitched as your AI pair programmer, leveled up. It integrates into most parts of your engineers’ workflows. GitHub Copilot ‍ Here are a few things it should be able to do: Explain pieces of code Fix errors Generate unit tests Write pull request templates Write CLI commands My bet is that when this arrives, it will streamline most aspects of software delivery – any part of the lifecycle directly dealing with the code. Sourcegraph Cody – Write code faster with AI Can’t wait for Copilot X? Try Sourcegraph Cody. It can help you read, write and understand code much faster. They say 10x faster. Cody reads and comprehends your entire codebase along with your code graph and company docs, and can answer questions about it. It’s in Beta (like many AI tools right now) and doesn’t always get things right, but it’s clearly a priority product for the Sourcegraph team, and they say its results get better every day. Try Cody here Or try: – Another alternative. Can do autocomplete, write blocks of code, and prompt-drive development. Can’t do testing yet at the time of writing. Mutable.ai – Specialising in test-writing Codium.ai ‍ Readable – Never write comments again You want clear, consistent documentation. Readable AI automates the process of generating comments for your source code. Poor (or absent) docs can be the bane of engineers’ lives – it makes it especially difficult for engineers new to a codebase or a team, wasting a tonne of time. Readable AI should significantly reduce developers' time writing comments, allowing them to concentrate on more complex tasks and improve overall productivity. It’s compatible with the IDEs your team already uses, like VSCode, Visual Studio, IntelliJ, and PyCharm, and it can read most languages. Try Readable here ‍ Adrenaline AI: Understand your code For most engineering teams, spending what feels like a lifetime poring over Stack Overflow is an everyday reality. What if, instead, your engineers could just ask an expert who had total comprehension of the codebase, and get an immediate answer? That’s what Adrenaline is trying to do. It can explain how features work, locate where it’s implemented, or guide them through debugging. It’s built using static analysis, vector search, and advanced language models. Try Adrenaline free ‍ Grit.io: Automated Technical Debt Management What if you could automate code migrations? What if you could let AI handle dependency upgrades? Grit.io is an automated technical debt management tool designed to do this easily. It can auto-generate pull requests that handle the nitty-gritty by adhering to the best practices, and it has a constant radar that detects regressions. They claim migrations can be completed 10x faster. If that’s even half-true (I didn’t verify this), dealing with these kinds of tech debt will no longer be a significant drain on time and resources. Try Grit here ‍ Codeball AI: Supercharge Your Code Review Process Tired of spending countless hours reviewing pull requests? Codeball AI is here to save the day! This AI-powered code review tool evaluates pull requests, highlights risks, and can even approve low-risk PRs with your organization’s configuration. Ultimately, it should mean you can ship faster and with greater confidence. It’s great for spotting risky code changes – it’s been trained on millions of code contributions. Trained on millions of code contributions, Codeball AI can recognize risky code changes and provide actionable, team-specific insights. It works with GitHub Actions and supports 20+ programming languages. Try Codeball here ‍ That’s it! Well, there you have it. AI is changing the game. I’m absolutely convinced that this is just a mere taste of what is to come. AI for software engineering is here. Keeping up with the change will be for many tech teams, and potentially the businesses they’re a part of. Get it right, and I think it’s entirely possible to find numerous practical potential uses of AI that really can turn “10x-ing” from an anecdote into a reality. make or break

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