>>February (Page 2)

This is the second part of the blog series on ML in retail, its use cases, applications, and the future. This first part explores applications within the online retail space.  

Retail Inventory and Stocking 

Optimization of inventory scheduling/planning and predictive maintenance is a critical issue and a really vital logistic concern for retailers.  

  • Forecasting Inventory Requirements 

ML algorithms can harness purchase data to forecast inventory requirements in real-time. On the basis of the specific day of the week, the events in the vicinity, the season, social media information, and the client’s historical behaviour, these algorithms can furnish an everyday dashboard of suggested offerings to a purchasing manager. 

  • The Power of Computer Vision 

Brick-and-mortar retailers can reap benefits from the impressive latest outcomes on computer vision. The new strategies within the space could be leveraged to produce real-time, precise estimations of the products in a specific store. With this data, an ML algorithm could provide notifications to store administrators of not-expected patterns of inventory information that could be on account of theft or an unprecedented increase in the demand for particular products. 

Another application is to harness imagery to undertake analysis of the utilization of shelf spacing and detection of sub-optimal configurations. A really good instance of this tech is LoweBot, the Lowe’s autonomous retail service robot, that, asides from assisting the client to shop, consistently survey inventory and provide feedback on a real-time basis to the store staff members. 

What is required to begin? 

The inventory planning models require customer behaviour data. That is, for instance, shopping history or purchase trends, however it could additionally include social media activity and domain particular knowledge. 

Computer vision algorithms require images for processing. They can be sourced from security cameras setup in the store or be taken by staff members. 

 

  Behavioural Tracking through Video Analytics 

A nice thing with regards to brick-and-mortar outlets is that the behaviour and interaction of human agents with products can produce worthwhile insights in a manner that web-based retail just cannot do. Computer vision algorithms can identify faces and person’s traits like gender or range of age, producing invaluable exploitable information. 

  • Analysing Navigational Routes 

Where to place differing items is an issue of criticality for brick-and-mortar retailers, who are always on the lookout for extra ways to comprehend the client’s pathway to making a purchase. 

Computer vision algorithms can undertake tracking of client’s journey in stores to comprehend how they have interactions with it. These algorithms can identify the walk patterns and the direction of the gaze of the clients. Retailers can leverage this data restructure store layouts or to measure the interest level in their products. They can additionally identify locations that have a ton of traffic and visual attention. 

Do senior citizens do more shopping on weekdays? Do teens have a tendency to cover just a portion of the store, for instance, the front portion. Does the store have a higher visit rate in the winter? Variables like age, day of the week or season could be leveraged to produce insights that assist to dynamically alter product placements and develop effective promotions. 

  • Theft Prevention 
  • Theft prevention is a typical issue in retail with a robust ROI, where ML tech can go beyond the usual utilization of video cameras to identify shoplifters. 
  • Facial recognition algorithms that can undergo training to identify known shoplifters when they get into the store. 
  • Walmart evaluated this tech way back in 2015 as an anti-theft mechanism. 
  • In the same manner, computer vision can identify if somebody chooses an item, it could identify if somebody hides an items in their jacket or backpack. 
  • Further, the same strategy can provide alerts in real-time security personnel or administrators and send to them video excerpts so they make judgments by themselves prior to confrontation of the individual in the shop. 
  • Gesture Recognition and Product Tracking 

Brick-and-mortar retailers typically have no data with regards to the items clients pick up, look at, and place back on the shelf after picking it up. They do not have any data either way about what clients look at next.  

A computer vision algorithm can undertake monitoring of shoppers facial and hand gestures to predict how successful an item is. This type of applications produces priceless data with regards to how many times an item is selected from the shelves, placed back on the shelf or in the shopping cart, or purchased. 

What is required to begin? 

If your outlet is outfitted with security cams with a specific image quality, then you already have all that is required to undertake implementation of the solutions prior specified. 

 

 

Conclusion 

That brings us to the end of the second part of this blog series. In the next part of this blog series, we will be looking at the future of Machine Learning in the Retail Industry. 

AI/ML are trending innovations within the retail space. ecommerce, specifically, has taken a shine to AI/ML-driven tech, driving recommendations, and similar products to the ones they’ve previously browsed on the online storefront. Brick-and-mortar stores have witnessed the advent of the “Grab and go” philosophy, where friction-less, contact-less, queue-less shopping has become the new trending innovation.

This is the seventh and final part of the multi-part blog series on emergent tech within the financial and banking spaces, that are poised to take the industry forward. The final part of this blog series rounds up these emergent technologies that are having an impact on the financial/banking space, presenting a bird’s eye view perspective on these innovative technologies.

This is the continuation of the multi-part blog series on Emergent tech that are poised to drive the financial and banking domain in the immediate future. The fifth part of this blog took a look at advantages of RPA within the finance, the difference between AI-driven RPA and conventional RPA, the key capacities for cognitive automation, and the part of low-code development.

Human error within the financial space has the outcome of 25,000 hours of preventable rework, as an average per enterprise and expenses $878,000 annually. Robotic Process Automation (RPA) is one of the obvious solutions to this issue. Going by a research carried out by McKinsey, approximately 6/10ths of occupations can help in automation of more than 3/10ths of activities with RPA.