ML in the retail industry – Use cases, applications, and the future Part 1
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.
Industry heavyweights and early adopters of tech such as Amazon, Alibaba, and Ebay, have accomplished integration of AI tech throughout the cumulative sales cycle, ranging from the logistics of storage, to post-sales client service.
But, there is no requirement to be a major market player, or to be an online-only retailer to reap the benefits conferred by the radical innovation that is Machine Learning. In this guide by AICorespot, you will observe how ecommerce retailers and brick-and-mortar outlets of any size can potentially integrate ML technology to keep up the pace with their market competitors, by enhancing sales figures and minimizing expenditure.
Whether it be clothing or household essentials, the prospects within the retail domain are replete with potential. The use cases and applications put forth in this guide are merely a portion of the potential ML projects and act as instances of what can be accomplished presently within the retail domain. That being stated, several organizations have extremely unique requirements that could be fulfilled with big data.
Applications in ecommerce websites (Web-based retail)
Clients have a penchant for browsing through web-based catalogues as they feature aesthetic presentation and furnish plenty of data about the product they are interested in – this is in addition to suggesting other product categories, and similar products that they would potentially be interested in. Big data is a major factor in driving such recommendations, and so is Machine Learning. Even though automatic digital catalogue developers typically furnish adequate solutions, the harnessing of customized ML tech can considerably enhance the client experience to improve engagement and influence conversion numbers.
A framework that can engage in learning a majority of what is occurring in the market facilitates you to have enhanced data in comparison to your competitors in order to make enhanced decisions.
o Clients tend to look for visual content prior to a purchase being made.
o Keywords are not always a fool-proof way of searching for content.
o Visual search simplifies client searches and help them locate content.
o Images can be uploaded to hone in on particular items
o With the escalating amount of imagery being shared, ML algorithms can accomplish incredible results
o Market leaders such as Google, MS, and eBay have put forth Google Lens, Bing Visual Search and Image Search, respectively
‘If you do not have possession of this information as of yet, you should consider the fact that a ton of data can be collected through crawling the internet or by harnessing particular services. Further, a survey by 2016 conducted by Deloitte specified that 50% of shoppers and 58% of millennials would concur to share information in return retailers would provide customized offerings and services.
Hence, it would be viable to produce the particular solutions if you presently have an web store front or app. With regards to the volume of data required, even though more data has a tendency to provide improved outcomes, in some scenarios, small datasets enable perfectly manipulatable results.
Predicting Customer Behaviour
The objective of forecasting client behaviour is to predict how customers will act in the future on the basis of data of prior behaviours. These frameworks facilitate retailers to segregate clients and carry out customized marketing actions that are more efficient than generalized strategies. Further, taking actions on the basis of forecasted client requirements improves loyalty and retention.
A usual application is to forecast purchases. For instance, to have the knowledge about which clients are probably to commit to a purchase within the next week. More sophisticated forecasts might have to do with critical events in the lives of individuals. For instance, to forecast marriage or pregnancy, and then deliver customized offerings.
Forecasting the requirements of customers is a challenge where ML algorithms are of massive help.
Predictive models essentially require customer behaviour information. That is, for instance, purchase history or purchase trends, but it could potentially consist of social media activity and domain particular knowledge.
How frequently do you clients undertake transactions? Do they make purchases during sales time or prior to their birthdays? How many items do they usually purchase? What are they presently purchasing? What subjects are trending in social media circles? All this kind of data is leveraged by the models to forecast future behaviour.
And finally, retailers experience is very critical to choose business specific criterion and streamline the models.
Social Media: Customer Monitoring and Branding
Currently, social media is usually a lot more than networking platforms, and clients are harnessing it as a marketplace where they can purchase services and products.
Monitoring social media on a massive scale and collecting worthwhile insights is feasible owing to the potency of machine learning. Retailers can therefore obtain data with regards to what is propelling revenue, traffic, and engagement.
Through analysis and tracking of the flow of data, retailers can undertake optimization of the channel, target specific audiences, timing and content of their social media postings and marketing campaigns.
Monitoring mentions of a retailer to obtain insights is a known application. However, we can be grateful to image recognition, retailers can now observe how they are being depicted through the video and imagery shared on an everyday basis. Simultaneously, this tech can be leveraged to undertake analysis and act upon content produced by their competition.
Chatbots and Virtual Assistants (VAs)
Chatbots undertake interactions with clients and simulate human convos, taking them nearer to the shopping experience that buyers can get in a physical outlet.
They can furnish additional value at differing levels. For instance, chatbots can be leveraged to propel extra client purchases, to customize the client experience, to enhance searching capacities over your catalogue, or to manage a noteworthy portion of your client service.
What do I need to begin?
A dataset of samplings to be tackled by the bot is a very good input, however it is not compulsory. Regarding contact centres, FAQs are typically adequate input to produce a chatbot. As far as smart assistants or intelligent search systems are concerned, more data regarding your catalogue will be likely required. But then again, a chatbot can be produced in an incremental fashion, so it manage portion by portion your services and products.
The more critical thing is to be aware of your business and have the capacity to handle the requirements of your clients. Further, these are pretrained models for some type of communications that can be adapted to specific use cases by putting forth particular business knowledge.
This brings us to the conclusion of the first part of ML in the retail industry – use cases, applications, and the future. The second part of the blog will explore applications in brick-and-mortar retail.