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We have been living in a post-COVID-19 world for more than a year and inevitably our lifestyles and habits have changed significantly during this time. Businesses that have been adversely affected by the pandemic have caused many of them to explore cost cutting measures and/or new ways to generate sales in order to survive. Meanwhile, some businesses, in particular those with online presence, have seen new opportunities during these volatile times. In this article, we will be looking at the retail industry and discussing how Conversational A.I. has helped e-Commerce businesses to reach the next level during the pandemic.

Retail in COVID-19

The retail industry had been going through a digital revolution even before COVID-19 struck. Driven by popular platforms like Shopify and Amazon, and improved by innovative hybrid frameworks like Alibaba’s “New Retail” model, e-Commerce has seen an unprecedented growth over the last two decades. While there have always been a segment of customers who prefer the traditional shopping experience of visiting brick & mortar retail outlets, in particular those within the Baby Boomer and Senior demographics, the trend for Gen-Xers, Millennials, and beyond, indicate an overwhelming preference for spending their money online.

Regardless, many industry analysts agreed that the death knell for traditional brick & mortar was too premature. Very few, if any, predicted that a Black Swan event like COVID-19 would force the entire world to change their buying habits overnight. Throughout 2020, national lockdowns and government issued social distancing measures have meant that online outlets have been the primary channel for most retail shoppers. This is supported by recently published numbers showing a sharp 13.4% rise in online payments in 2020 when compared to the previous year (The Asset, 2020). And with no clear solution in sight for the ongoing pandemic, it’s clear that COVID-19 has catalysed the need for traditional brick & mortar shops to explore and adopt new hybrid or online retail models.

E-Commerce and Chatbots

Customer Service excellence is a central component of any successful retail model. It helps brands to foster relationships and build trust, and good customer service can often be the key difference maker between a positive and negative shopping experience. Business owners are quickly discovering that customer service quality is equally important in online channels, and in some instances the consumer expectations are even higher than before. For example, traditional brick & mortar stores are only expected to operate within their general working hours, however the 24×7 nature of online channels mean that consumers expect to be able to reach these businesses at any time. Increasingly, businesses are looking towards ‘chatbots’ as the solution for these new challenges, with many of them having applied chatbots to their webpage or mobile apps in order to better engage with their customers and to provide around-the-clock customer support.

However, chatbots come with their limitations. Although the full potential for chatbots still remain untapped, early adopters are already experiencing some of the pains and limitations that exist. In particular, early and first generation chatbots are built using rule-based frameworks, resulting in consistent performance within closed environments, where the tasks are well-defined and the operators are trained to use the system, but almost completely unusable in most practical situations. In 2017, Facebook Messenger released its ‘Bots’ framework, allowing businesses to integrate chatbots to their business IM channel, but as these chatbots reportedly did not have a strong ability to understand general human enquiries, they ultimately failed to handle over 70% of total user requests throughout the year (Sun, 2017).

Improvements in Conversational A.I.

Since 2017, the underlying technology powering chatbots have improved massively. Not only has chatbots become better at understanding ‘intents’ from users’ statements, but the overall natural language understanding (NLU) framework has been dramatically improving year-over-year. In particular, chatbots increasingly have the ability to engage in multi-turn dialogues and to reference contextual information from a conversation to try and comprehend what the user is requesting for. In short, chatbots are getting smarter at understanding what we need and at executing these tasks, and as they become more capable of handling complex and unique requests it is likely that the trend of businesses adopting chatbots for customer service automation will continue into the foreseeable future.

It is also worth noting that these improvements are not exclusive to customer support but have immense potential and applications for customer engagement as well. As chatbots become better at tracking contextual information and user preferences, they can start to provide personalised marketing promotions, product recommendations, and even augment the way a service or message is curated for an individual user.

While there is still plenty of room for improvement, it is undeniable that the e-Commerce industry is already starting to experience the benefits of adopting chatbots in their customer service operations. Research suggests that as businesses transition towards A.I. powered chatbots, they are able to cut customer support costs by as much as $8 million per year (Gilchrist, 2017). Additionally, it is estimated that customer engagement through A.I. powered chatbots can reduce churn rate, improve up-sell and acquisition by 10 to 15 percent (Kalkum et al., 2020). With the rapid improvement of Conversational A.I., these next generation chatbots will soon become the main agent for customer relationship management.

Nike and A.I.

Nike is one example of many multinational corporations that have heavily invested in adopting A.I. to great success, improving customer engagement and personalising consumer experiences. In 2018 and 2019, Nike acquired Zodiac and Celect, respectively, and incorporated their predictive analytic solutions within their Shopping Assistant chatbot. Specifically, these solutions allowed Nike’s Shopping Assistant to analyse preferences and user behaviour history to recommend products and services.

Heidi O’Neill, former President of Nike Direct, spearheading Nike’s direct retail and e-commerce business around the world, speaking with the Wall Street Journal, had this to say about Nike’s efforts with AI in retail: “what’s important from a Nike shopping experience is that with machine learning and AI, we’re able to have every digital experience at Nike be unique and personal” (Safdar, 2019). This fanatical dedication for personalising the `Nike shopping experience` is a critical factor to Nike’s continued success, evidenced by the fact that Nike Direct contributed US$12b to Nike’s revenue in 2020, representing approximately 30% of total revenues (Shahbandeh, 2020).

The Age of New Retail After COVID-19

Although the pandemic may have forced people to adapt to the digital paradigm, it would be premature to discard the traditional brick & mortar model. What we expect to see in the coming decade is a shift towards adoption of a hybrid approach. The “New Retail” concept, introduced by Jack Ma, the founder of Alibaba, in 2016 is a model that seeks to blend the best of the physical and digital world to create a ubiquitous shopping experience. For example, comparing product features and prices is a common consumer behaviour before making purchases, or physically feeling and touching products is another common behaviour. Through the use of a ubiquitous shopping experience, Alibaba allows consumers to shop according to their preferred behaviours and preferences, and this model has seen incredible success thus far.

Inevitably, technology is the centrepiece of a successful New Retail model and as we continue to develop more sophisticated A.I., we can expect even more flexibility and personalisation within the shopping experience. AliMe, Alibaba’s customer service chatbot, widely credited as a key factor for Alibaba’s record-breaking Single’s Day sales numbers in previous years, handled 9 million queries in 2017, increasing to 300 million queries in 2019 (Dong, April 2020). The continuing improvements to AliMe serve as Alibaba’s testament to the transformative powers of Conversational A.I. within the retail industry.

Conclusion

As customers have changed their shopping habits from going to shopping centres to shopping online due to COVID-19, businesses should also adjust their strategies in order to create better engagement with their customers. However, basic chatbot is not enough to handle diverse and complicated human conversation in order to build personalised consumer experience, Conversation A.I. will be the upgrade that e-Commerce businesses needed to compete during this pandemic, and hopefully kick starting New Retail.

Reference

Dong, A. (2020 April 20). AI chatbot behind Alibaba’s $38 billion Single’s Day sales miracle. Digital Initiative. Harvard Business School.


Gilchrist, K. (2017, May 9). Chatbots expected to cut business costs by $8 billion by 2022. CNBC.


Kalkum F., Kleinstein B., Lewandowski D., and Raabe, J. (2022, June 22). Technology and innovation: Building the superhuman agent. McKinsey & Company.


Safdar, K. (2019, May 13). Nike’s Strategy to Get a Lot More Personal With Its Customers. The Wall Street Journal.


Shahbandeh, M. (2020, November 23). Nike’s revenue worldwide from 2005 to 2020. Statista.


Sun, L. (2017, February 28.) Facebook Inc’s Chatbots Hit a 70% Failure Rate. The Motley Fool.


The Asset. (2020, July 20). Covid-19 boosts Hong Kong e-commerce market