The balance of power is shifting from offline to online retail: how is AI fortifying this move?

By Sindhuja Balaji

Highlights

NASSCOM Research, NASSCOM CoE DSAI along with Ernst & Young today released a report titled "Indian Retail: AI Imperative to Data-Led Growth" focusing on AI opportunities in India's retail sector. Here are the key highlights and findings

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The ongoing COVID crisis has revealed some remarkable shifts in behaviours, attitudes and approach to doing business. One of the industry sectors to be deeply impacted, and one which also stands to benefit the most from a technology-driven makeover is retail. With a majority of Indian retail existing offline, and online retail slowly gaining customer loyalty, it is a lucrative time for retail companies to focus on faster and better technologies to improve their revenue streams and enhance customer base. 

NASSCOM Research, NASSCOM CoE DSAI along with Ernst & Young today released a report titled Indian Retail: AI Imperative to Data-Led Growth focusing on AI opportunities in India's retail sector. This report also works as a playbook, primed to help enterprises and startups build on an AI-based action plan to accelerate AI adoption. Retail in India has a massive customer base, is highly dynamic and diverse, and poised for growth. Indian retail is among the top five markets globally, expected to reach a value of US$1.4 trillion by 2024. There are several factors at play for Indian retail to redefine its persona; the growth of technology solutions being key. 

Key Takeaways From The Report: 

  • By 2024, organised retail and e-commerce could grow by 3X, bringing data-led opportunities in retail to the forefront. The unorganized retail is expected to grow by only 40%. 
  • With broken supply chains, reduced discretionary spend, and gaining prominence of contactless delivery, COVID-19 is further necessitating digitization and putting pressure on operational efficiency
  • Digital enterprises adopting AI based solutions such as demand forecasting, customer segmentation, ensuring constant supply of products, and reacting to demand signals real time are expected to have a significant edge to survive and thrive in the long term
  • Customer experience, operations and distribution & logistics are the top priorities for AI implementation
  • Prioritising of use cases based on business impact and implementation capabilities are critical in enterprise AI 
  • Product technology companies, startups, technology service providers must collaborate to build optimum capabilities and enhance adoption 
  • AI charter defined based on company’s strategy and vision can enable long-term and sustainable business benefits

Transformation of Retail in India Due To AI

While the industry is stil ruled by the unorganized sector with an 85% market share ( as of 2019), new age retailers such as organized and e-commerce market are expected to grow three times the current size by 2024. In this time, the unorganised sector could grow only by 40%. With the advent of new age retailers, recent government regulations surrounding FDI and GST, the balance of power is gradually shifting from local convenience stores to ecommerce and retail giants. This is also expected to generate massive amounts of consumer data throughout the retail value chain across various touchpoints that brings data-led opportunities in retail to the forefront. Rising numbers of millennial shoppers, rise in aspirations, proliferation of technology and spending capacity will contribute to the growth of the unorganised sector. New marketing formats and the omnipresence of online retail pose new challenges to retailers today. To stay relevant in the industry, retail enterprises are adopting newer operating models to shift the focus from just price to price + digital that enables data-driven decisions systems using AI.

Opportunity to Transform with AI - COVID & Beyond: 

One of the most significant changes brought about in retail by COVID19 was exposing the disconnected supply and demand infrastructure. Since the country went into a lockdown, it triggered panic buying especially for some items over others, making it very challenging for retailers to manage inventory, predict consumer behaviour, manage workforce requirements. In addition, a new challenge that retailers had to face was delivering items direct to home since the possibility of their buyer purchasing from a store was brought to nil practically overnight. It was at this time that it become evident that companies that had long pivoted to an AI-first strategy or had established themselves with AI-guided offerings as their cornerstone, were able to respond much faster to the urgencies brought about by COVID19. 

Looking ahead, in a post COVID world, data driven decisions will play a crucial role in building an intelligent enterprise - with a focus on demand forecasting, contactless purchasing, customer segmentation and operational efficiency. 

Why Changing With The Times Is Imperative: 

An example of a global retailer that has kept itself in line with technology-triggered changes explains why this is an effective strategy. Between 2008-14, the organisation understood the long-term play of e-commerce and began building capabilities that included a separate business unit for online retail, a technology center for online store innovation and building a value chain for online orders. From 2015-18, it consolidated its efforts in demand forecasting, automated order management, cashier-less platforms, understanding customer behaviour and more. By 2019, AI has been established as a strategic priority across the value chain with omni channel instore pickup, digital scheduling, robotic stock out detection and more. The company doubled its e-commerce revenue during 2017-2020 and increased its digital inventory by 6X enabling accurate demand forecasting targeting its wide customer base. 

Challenges in Retail & Prioritising Challenges Using AI: 

The retail sector is facing complexities like never before. Customer profiles are becoming increasingly complex while global retail players entering the Indian market could shift the supply dynamics, creating whole new demand segments. Can AI help? Yes, apparently so. Be it planning and procurement, sales and marketing, distribution and logistics, in store and online operation, or customer services, AI can help with product assortment using a range of datasets, micro segmentation strategies for customised customer communication, image recognition at warehouses & van route optimisation, product suggestions based on user history and chatbots. Depending on which area poses a bigger challenge to an enterprise, they can apply AI capabilities to address the bottleneck. Enterprises are required to invest in AI solutions that specifically target their weakest performing areas across value chain to benefit from the efficiency gains and financial impact.

Provider & Retail Enterprise Dynamics in Retail AI: 

A collaborative approach is the way ahead for a core retail enterprise to reap benefits in a tech-first world. Understandably, most retail companies would not possess capabilities that are suited to this world, so working with product technology companies, service providers and startups in tandem is the preferred path. Major product companies include Google, Salesforce, Microsoft and SAP that offer platform-based AI solutions, verticalised to focus on industry specific implementations. Technology service providers are companies like Cognizant, Infosys, Wipro, Tech Mahindra and TCS, which are also bullish on a platform based approach and for whom managed services for AI deployment are becoming popular. Retail AI startups form the last faction of this triumvirate who are representative of the customer - with solutions for targeted marketing, inventory optimisation and customer engagement primarily, and include companies like Wesense.ai, Intellibot, Veda Labs and Yellow Messenger. 

Product technology companies are consciously moving towards end to end solutions, are catering to front end of the retail value chain and even augmenting their worth with full stack capabilities, bringing infrastructure, data, intelligence and applications to the fore. They offer highly verticalised products for enhanced client satisfaction. For instance, IBM's infrastructure layer is the IBM Cloud, with the AI platform Watson and an application layer including IBM Sterling Supply Chain Suite (with delivery transaction, inventory visibility, store engagement, order management, fulfilment optimiser and price quote configuration), IBM Watson Assistant & Salesforce Customer Engagement Platform. In order to enhance the product portfolio, acquisitions are taking place with a focus on speech, NLP, NLG and computer vision. Since 2010, nearly 50 + AI startups were acquired in retail and CPG. And finally, a targeted emphasis on reskilling and acqui-hiring is most common in AI capacity building. 

Product technology companies are now engaging long-term with retail enterprises through strategic partnerships; such as Walmart has entered a five-year strategic partnership with Microsoft to accelerate AI, Cloud and IoT capabilities; while Marks & Spencer has partnered with Microsoft to drive integration of AI technologies across the retail value chain of M&S. 

Technology service providers have their own AI platform as a differentiated value proposition for taking their AI offerings to market. Service providers are flexible to leverage either their own platform or use a third party platform (from a non-competing provider). Unlike large product companies that have their own full stack solution from infrastructure to data and intelligence layer, most service providers have strategic partners to provision a full stack capability. Retail enterprises, on the other hand, are cautiously signing up with service providers as they seek value in outcome when it comes to AI capabilities. 

Finally, retail AI startups are helping retailers step up their digital play by enabling them to deliver exceptional customer experience and at the same time, optimizing their internal operations. Store operations is an upcoming application area being explored using technologies such as video analytics, customer sentiment analysis and market intelligence. For instance, a company like Jumper.ai is a B2B2C platform that leverages social media activity and turns it into a point of sale, with a 20X growth in customer engagement and 58% conversion rate from engagement to sale. Another example is Wesense.ai, which makes an existing CCTV "intelligent" enough to deliver store-specific insights, and they have reported a 15% increase in conversion, optimal store layout and reduced waiting time. 

Perception of AI in Retail: 

Understanding the universe of AI applications, retail ecosystem and solutions that provider ecosystem players are provisioning is only a part of the journey. Knowing where retail enterprises in India stand w.r.t industry and/or global players is crucial for choosing the next step in AI journey. Therefore, to gauge the evolution of retail enterprises, and where they are in the AI journey, a survey was conducted targeting 500+ CXOs working with Indian enterprises, GCCs and large start-ups. The survey covers enterprise of all sizes (small, mid, large) bringing in a comprehensive view of AI in India’s retail industry. The insights presented in the report were derived from both the survey and in-depth qualitative interviews with CXOs of major retail enterprises operating in India. 

Here are some insights from retail CXOs: 

  • Customer experience, operational efficiency and revenue growth are priority areas for AI in retail: For more than 50% of the retail CxOs surveyed, customer experience, revenue growth and operational efficiency were the top three areas where AI can add maximum value. It was observed that AI in general is being leveraged or planned to be leveraged in core functions such as operations, customer service, marketing and coms, production, etc. over the support functions such as HR, finance, strategy, IT, quality, R&D and risk management in retail enterprises.
  • Value articulation and low ecosystem maturity are key challenges during AI deployment: Top three challenges across sectors are low maturity of external AI ecosystem, inability to quantify the benefits of AI and inadequate number of use cases. Multiple and disparate datasets is a unique challenge in retail falling in the top five challenges. Retail enterprises that have implemented AI solutions (both pilots and at scale) have cultural and behavioural impediments as one of their top three challenges
  • Data security and algorithmic auditability are key AI implementation risks for retail enterprises: Data security takes the top spot for AI implementation risks – protection from cyber attacks, institutionalizing access control, multiple levels of encryption, regular audits are the typical concerns under data security. The CxO interviews revealed that perceived risks to brand reputation associated with AI go beyond expected job losses and labour impact to cover concerns on data privacy, hacking, safety of AI-powered machines and ethical biases in algorithms, etc. 
  • Majority of the retail enterprises who implemented AI are still not entirely satisfied with their AI implementations: Only 8% of the retail enterprises (much less as compared to 18% across sectors) are highly satisfied with their AI implementations so far, and more than 60% are somewhat / moderately satisfied. Inability to quantify the benefits of AI, inadequate number of use-cases were cited during the CxO interviews as contributing factors resulting in relatively lower satisfaction from existing AI implementations
  • Reskilling existing talent is highly effective in retail enterprises that implemented AI solutions: Enterprises across verticals consider hiring new talent as the most effective strategy to build AI capability. In retail, however, reskilling is considered more effective. This trend is increasingly observed in retail enterprises that have implemented AI solutions already

Traversing the Maturity Curve: AI Adoption Is A Journey; Not A Destination

Enterprises are required to identify the lagging areas and strategically address them in order to derive maximum value from their AI implementations. The nine strategic bold plays provided in this section can help fast track the enterprise AI journey.

The AI wave is already here. Companies that are bold enough to ride these industry headwinds stand a good chance to emerge successful and even set some new precedents in the world of retail.


To read the entire report, click here

About the author

Sindhuja Balaji

Senior Content Writer

Sindhuja Balaji is a Senior Content Writer with India AI. She has 10 years of experience as a journalist in print, digital & television media, covering technology, business, culture and city affairs. Prior to joining India AI, she led Content, Social Media & PR Outreach initiatives for the NASSCOM Center of Excellence for IoT & AI. She particularly enjoys exploring the potential of advanced technologies and their impact on the economy, business & policy development

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