Change in the Retail Industry Last year we saw a huge meltdown in the Retail Industry characterised by bankruptcies announced by significant brands (Payless, Sports Authority) and significant store closings by household brands (Sears, JC Penneys, Macy’s, Radioshack). The fact that this happened when the economy was doing well raised questions on the possible reasons. While there were many, the key reasons that stood out was the rise of mobile and online shopping, and customer preferences for shop online, the ability to buy anywhere and get it delivered with customer’s choice of delivery option. Retailers that were heavily dependent on pure brick and mortar retail operations therefore bore the brunt of this disruptive wave in retail. This a study on how AI & Machine Learning for Retail can be mainstream in the near future. Statistics prove this – Amazon grew from 16 Billion to 80 Billion in North America from 2010 to 2016 compared to Sears 2016 revenue being 22 billion The obvious solution is Omnichannel Retail, however this comes with complexity that needs innovative thinking to solve. Artificial Intelligence and Machine Learning with the promise of innovation to help the humans overcome the complexity associated with an omnichannel retail operation therefore become a required technology rather than an option. The disruptive change in consumer preferences of shop anywhere, buy anywhere, and have products delivered as per choice, requires that the retail industry must have an omnichannel retail operation to be successful. Artificial Intelligence and Machine Learning provide the options that will help make this transition much easier. Omni Channel Retail There are many articles on best practices around Omni Channel Retail and I am not going to mention all of them. The following stand out in context of the need of having Artificial Intelligence and Machine Learning solutions in order to be able to implement these. Single unified view of the customer While this may be obvious, many companies find it hard to achieve the same. There are many key components that fall into the people, process and systems category that form the key enablers in achieving this. The complexity of managing the disparate set of customer related information that typically comes into the enterprise can only be handled with advanced automation concepts such automated classification into categories, key attributes and so on that can only be achieved with machine learning and artificial intelligence. Only with this can a retailer truly accomplish the marketing to customer order capture to order delivery cycle in a way that truly drives a compelling and superior customer experience that results in repeat customer sales and customer loyalty. Ability to have a marketing engine that is built and driven on omni-channel data Following up the ability of having a single and unified view of the customer is good. Augmenting that with an excellent repository of potential customers (contacts) with the ability to determine the right choice of offerings to market to using the most effective channel is a key differentiator for any retail organization.  Ability to have targeted recommendations for marketing in almost business real time is only possible with Artificial Intelligence and Machine Learning solutions. Variety of fulfilment options This is a must have for any good Omni Channel retail operation. While this may be obvious it presents an enormous amount of complexity in managing the Supply Chain. This complexity arises in determining the right levels of inventory to be either stocked in stores, distribution centers and warehouse while optimizing for cost and yet meeting the ability to fulfil customer orders as per customer expectations. This requires sophisticated demand planning, supply planning and distribution management solutions that no longer can be based on rule based algorithms which is the norm of most solutions in existence today. Artificial Intelligence and Machine Learning based solutions for demand and supply planning, optimizing inventory, intelligent routing are must haves to ensure that your retail operations and distribution work efficiently to meet customer’s expectations while being cost effective at the same time. Artificial Intelligence and Machine Learning (AI and ML) Artificial Intelligence and Machine Learning have existed 35-40 years ago but they were limited to those organisations (government, scientific, universities) that had access to huge computing power that was required to successfully build and leverage these solutions. Today with the advances in computing technology and the availability of both private and public cloud computing the ability to leverage Ai and ML solutions is getting more and more mainstream. This is because especially with the availability of public cloud platforms such as Google, AWS and Microsoft it is becoming possible to focus more on the development of the Ai and ML solution that solves the business use case then to have to focus first on creation of the foundational infrastructure to build such solutions on (the spread of effort between infrastructure and actual solution building typically was 90 to 10 percentage points). Google has pioneered the usage of Ai and ML to drive their own revenue generating solutions and now they have opened up the best in class public cloud platform for machine learning to the general public. This is called as the Google Cloud Platform. While a number of standard Ai and ML solutions are available (example Vision api, Speech api, Translate api, Natural language processing api and so on) it also enables the ability to have a custom Ai and ML solution that solves a specific business use case by leveraging Tensorflow (which is also open source and can run on any platform). AWS also has some form of Ai and ML components on its enterprise leading public cloud platform but these cannot be leveraged with the ease of which the GCP Ai and ML components can be. Similarly there are proprietary solutions from IBM (eg Watson) , Salesforce (Einstein) but these are either very proprietary (IBM) or solve a very narrow problem (Einstein). Recommendations Retail enterprises have to focus on the Omni Channel retail operation in order to compete successfully in today’s retail environment driven by search anywhere, shop anywhere, deliver anywhere consumer behavior. Retailers need to look at Artificial Intelligence and Machine Learning based solutions to help them achieve their Omni Channel operation needs specifically in the following high level uses cases: Capturing effective consumer and product sentiments Effective contact management for effective marketing campaigns in a omnichannel retail operation. Effective Demand Planning in a rapidly changing retail environment (example Fashion Retail) Effective Supply Planning in a omni channel supply chain Optimizing inventory in an omni channel retail supply chain Managing distribution for Customer Service at Optimized Cost in an omni channel retail supply chain Product Recommendations with dynamic Pricing to drive profits in an omnichannel retail operations   Finally, due to the ease of designing, developing and deploying the Ai and ML solutions it is recommended that enterprise look at leveraging Google Cloud Platform with its best in class Ai and ML capability for achieving desired business results with agility and speed. Google has partners it recommends that enterprises use when they are interested in deploying Ai and ML solutions on Google Cloud Platform. One such partner is Pluto7 which specializes in Ai and ML solutions for Retail, High Technology and Manufacturing Industries Read more on our website.

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