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ML in Retail – Five compelling instances

We have already gotten acquainted to the reality that news about the accomplishments in the domain of machine learning implementation boils down to autonomous cars, speaking robots, and psychedelic images developed by neural networks. But, what if we look at the other side and observe how machine learning (ML) impacts an increasing number of fields, like retail. The advancements there are also shifting relentlessly. With ML, retailers can better undertake analysis and forecast purchasing behaviour. After all, present day’s consumers require maximum shopping convenience and the capability to purchase through differing channels.  

Consumer preferences are in a state of flux, however AI and Machine Learning in retail assist to predict and react to these changes in a timely fashion. Read on to get acquainted with the impact this tech has on the industry, and then with five fascinating instances of ML implementation in retail. 

Why does retail require machine learning? 

The capacity to forecast in the foundation for retailer’s survival. With artificial intelligence and automated ML tools, you can forecast how many goods will be required on a provided day in reaction to retail consumer’s demand, saving money and time. ML and AI can also be advantageous in planning for the optimization of purchases, inventory and sales. In this way you’ll be aware whether the present assortment is right with regards to retail consumer’s demand, if prices are set rightly, which stores require to organize the supply of specific goods, taking into account regional and other characteristics. 

Going by Juniper Research, retailer’s yearly expenditure on artificial intelligence will appreciate by 230% – from $3.6 billion in 2020 to $12 billion in just a couple of years from now. The primary direction of machine learning implementation within retail will be the leveraging of machine learning utilities for demand predictions. Revenues of products and services providers in the domain will attain $3 billion by the conclusion of the “reporting period” (an appreciation of 290% in contrast to $760 million in 2019) 

Within retail customer service, Juniper observes massive potential in chatbots that operate with consumers in the salesrooms. By 2023, bots assisting shoppers navigate the store will gain annual savings of $439 million for retailers ($7 million in 2019). They will make 22 billion contacts with visitors and will assist in making $112 billion worth of purchases.  

What does machine learning provide to retailers? 

Retailers should consider differing factors of individual department administration in geographically diverse areas to ensure a consistent supply slow with reduced expenses and reduced losses. The challenges encountered by the machine learning platform are as follows: 

  • Inventory and supply chain management with assortment planning; 
  • Consumer model analysis displaying invalid/scam requests; 
  • Client interaction analysis leveraging virtual assistants and chatbots; 
  • Execution of retail analytics on a scale to comprehend yearly growth. 
  • Personal recommendations leveraging collaborative filtration, content filtration, hybrid filtering, etc; 
  • Identification of item shortfalls in stores; 
  • Interpretation of text and images from invoices , packing lists, bills, etc. 

The following are five amazing instances of machine learning deployments in retail: 


In early-2018, the planet’s e-commerce giant Amazon initiated its first employee-less store Amazon Go for public utilization (before, for two years, the store with no cashiers and employees was available just to company staff members). It is not shocking the Amazon decided to try out advanced AI developments not just on the automated payment system. 

All sales departments of Amazon Go are outfitted with high-tech cameras with automatic object identification RFID (Radio Frequency Identification). Typically, such a system is harnessed in unmanned electric vehicles to track the behaviour of passengers within the cabin and automatic process of visual data by a computer. However, Amazon Go cameras went further and was harnessed to monitor retail consumer’s behaviour from the point they get into the store until they make the payment for their purchases. It might appear to be a tad creepy, but effective regardless. 

There are square-shaped cameras all across the ceiling of the store which maintain track of consumers. The primary objective of the cameras is to determine which items are of the most demand, which products are most typically returned to the shelves by the retail consumers, etc. 

The cameras are leveraged to monitor the buyer behaviour from the point the get into the store up to the point of payment. Also, Amazon Go cameras identify faces and decide the height, weight, skin colour and other physical attributes of retail consumers. Subsequently, the Artificial Intelligence linked to the video system, on the basis of all the data gathered, not just determines the most widespread products for particular customer groups, however additionally provides options for altering pricing policy. All this work is automated by a computer with no human intervention. 

The camera’s are additionally linked to the shop’s automated warehouse system and shelves outfitted with ‘Sensor fusion’ sensors. In case it is not feasible to identify the goods taken by the consumer, the camera identifies it in the warehouse system and coordinates with the and movement sensors, which are placed on every shelf. 

Let’s state the purchaser took the milk carton and started to read the compensation of the product, however, suddenly observed a familiar brand close by and returned the package to its place. Even during these few seconds of making the choice in Amazon Go will be tracked by the camera, sensor and inventory system, and the machine learning algorithms will draw the relevant conclusions. 

With AMS impressive revenue appreciation of 37% in Q2 2019 to $8.38 billion, the organization obviously has intentions to consolidate its swiftly growing machine learning capacities. 


Cosmetics retailer Sephora is pushing the limits with regards to innovation in the e-commerce space by developing a machine learning application that assists retail consumers detect specific shades my merely uploading a photo. The platform, which is the outcome of a partnership with ModiFace, a face analysis and visualization technology organization, is probably bound to have a profound impact that go further than the beauty industry. 

This tech will be an organic supplement to Sephora’s internet shopping experience and will simulate transactions, facilitating the consumer to visualize the advantages of product following the transaction. 

“We have been working with Sephora and other cosmetics companies for nearly a decade now,” and Parham Aarabi, CEO of ModiFace. “The issue of unveiling the product was a dominant challenge that can be partially addressed with AR evaluation. However, leveraging artificial intelligence to pick up shades and make suggestions of products prior to trying them on is a critical step. This has been being worked on for nearly half a decade, but we felt that the technology has ultimately become precise enough for a large-scale deployment.” 

The application harnesses ModiFace face recognition and visualization tech, which facilitates Sephora customers to upload photos to Facebook Messenger while chatting with Sephora Visual Artist bot. The tech then identifies the most compatible shade automatically and makes suggestions of existing products in Sephora’s stock leveraging the Artist Intelligence mechanism. The system renders a photo of a user donning a Sephora product visualizing the make-up, providing them a notion of what they will appear like without having be reliant on the customer’s imagination. 

“We found that individuals are leveraging this technology for intelligence,” Aarabi added. “For instance, they have a dress that they wish to match the exact same shade of lipstick to, or they identify a product in the store that they wish to Match to see similar shades from other brands. It’s very versatile in usefulness, and the amount of leveraging and degree of involvement definitely signify this.” The outcomes are already here, in Q1 of 2017, the Sephora digital conversion facilitated LVMH’s parent organization to improve revenue by 11%. Even during 2020’s financial crisis Sephora is doing really well, as it had then made the announcement that it intended to launch more than 100 new outlets. 


During 2018, the Swedish H&M division started leveraging machine learning to choose a range of stores. This way the organization hopes return retail consumers to compensate for the abysmal sales (ever in its history) in the 71-year history of H&M sales have been reducing for 10 consecutive quarters. 

Prior to that, the assortment of H&M stores was shaped by designers, writes the Wall Street Journal. H&M is the final clothing retailer to embrace technology to win over the consumers. For instance, Inditex (Zara, Bershka, Massimo Dutti, etc.) already leverage robots to make it simpler for retail consumers to choose online orders in stores, and Gap turns to Google Analytics data to monitor customer preferences. 

Analysts have expressed skepticism regarding H&M’s strategy. However, the tale of one H&M store in a prosperous district of Stockholm unveils that ML can really be advantageous. In this store, attention was paid to products for the entire family as the managers were oriented towards local residents. However, the analysis demonstrated that the majority of consumers were female and fashionable goods, such as flower skirts, sold shockingly well combined with expensive goods. H&M reviewed the store’s assortment and sales appreciated, even though so far, the organization refused to disclose just how much. The algorithms operate 24/7 and are modified to adapt to ongoing evolving consumer preferences and expectations. 

4.Apotek Hjartat 

Apotek Hjartat is Sweden’s forerunning chain of private pharmacies with approximately 390 drugstores and more than 3000 staff members employed in the organization. They operate pharmacies all across Sweden – in major cities, in rural regions in addition to sparsely populated regions. Throughout the AI utilization, the organization has enhanced its strategy by furnishing more precise pricing in contrast to competitors in both online and offline stores. 

Apotek Hjartat have selected Revionics Competitive Insights AI development organization for the collaboration and generating a strategy to optimize price on the basis of machine learning in early 2017. They smartly leveraged these opportunities for better tracking of competitive prices and offers, while also improving their flexibility to react to changes in buyers and competitor’s behaviour. Their subsidiary Rimi Baltic has selected Revionics Price Optimization to enhance their interaction with clients and influence their business. 

“We are pleased to further grow our productive collaboration with Revionics by leveraging a data-driven strategy to undertake analysis the impact of our advertising,” stated Anders Nyberg. “This is crucial to our capacity to furnish insight, structure, and analysis to support strategic decisions and make sure that there is high ROI for our organization.” By taking up a science-driven strategy to pricing to obtain retail customer’s confidence, Apotek Hjartat is now able to shift to analysis of promotional campaigns to concentrate on those that produce more interest. Revionics facilitates them to identify targeted promotions that take into account seasonality and entice consumers, while averting disregard for other profitable products, which includes private brands, while enhancing business efficiency. 

On top of that, Hjartat leveraged artificial intelligence technology to promote products that assist people to give up smoking. In a city-square in Stockholm, the organization setup a screen with built-in smoke detectors. Each time a smoker props up in the recognition area, a young man on the screen begins to cough. The effectiveness of such deterrent marketing and advertising has been subject to controversy, but it is obvious that the leveraging of differing sensors is the future of Digital Signage (a tech for putting forth information from electronic media setup in public places). 


At Costco retail organization, machine learning is leveraged to upkeep the productivity and sustainability of its fresh food department. Costco made donations of all of its unsold or damaged products, and thus generated more than required fresh food. 

They have worked with the SAP organization to manage this problem leveraging a demand forecasting algorithm that helps administrators in ensuring the correct quantity of fresh products is always in stock. Costco’s baker administrators must be able to forecast the demand for all menu items they require to produce on an everyday basis. Before the SAP solution, these administrators had to setup a production plan on paper by review of sales and trend reports. They also had to undertake reviews of local events and the historical background of the fellow employees. Costco refers to it as “tribal knowledge”. There plans underwent updating everyday on the basis of the prior day’s leftovers, damaged or destroyed products, before the baking teams could commence their work in the mornings. The development of this solution was led by SAP AppHaus, an arm of SAP solutions. 

When Costco was poised to begin work on the new solution, they made an invitation to Jeff Lyons, senior Vice President of Fresh Foods, and several administrators and supervisors from their bakeries. These bread specialists at Costco are end-users of the solution, so AppHaus wanted their input on when consumers buy particular foods and how consistently they do it. They interviewed every staff member extensively and watched some of them during their work. When SAP had adequate data about the bakery administrator’s everyday tasks, they managed to develop a new “bakery of the future” with Costco staff members. It is a tablet application that displays information and ideas for bakery managers and digitizes manual processes. The application leverages machine learning to furnish a planning forecast for every item on the bakery menu. This forecast decides how much of each item is to be baked and is instantaneously adjusted for the passing residue and damaged items. 

Prospects for ML development in retail? 

Two and a half years ago, in January 2019, the U.S. hardware and software manufacturer IBM put out a report on the upcoming AI revolution in retail and consumer products, on the basis of a survey of 1,900 retailers in 23 nations. The study concentrates, amongst other things, on how automation can assist stores minimize the human factor and enhance retail consumer service. 

Going by the IBM survey, this year, more than 7/10ths of retail and consumer products companies will be leveraging smart automation utilities along their supply chains. Here are six domains where retailers intend to harness AI: 

  • Supply Chain Planning (85%) 
  • Marketing and advertising (75%) 
  • Demand forecasting (85%) 
  • Warehouse operations (73%) 
  • Consumer intelligence (79%) 
  • Pricing and promotion (73%) 

Perhaps it is time to learn how to leverage ML in order not to miss the moment when this tech will become the foundation for explosive growth. Even though it has already taken place, it’s time to rush. Retail is the domain in which the leveraging of machine learning is really worth the candles. Those organizations are reluctant to implement it are massively lagging behind with regards to key indicators, and the ones that are undertaking implementation are leapfrogging ahead, regardless of their position prior. It makes sense to get up to speed as actively as feasible. 

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