Advancements in the retail and ecommerce sector have helped provide consumers with more tailor-made product recommendations and sophisticated guidance to eliminate friction throughout the shopping experience.
While having limited face-to-face interaction with customers and potential buyers, retailers have looked to the advanced capabilities embedded within conversational artificial intelligence (AI).
The last few years of the pandemic, which largely restricted physical interaction, meant that both retailers and consumers had to learn and adapt to digital communication tools. Conversational AI not only assists shoppers as they browse through the website, but it puts them in direct contact with the products and services they are looking for right from the start.
Instead of having to rely on more conventional chatbots, which saw a sharp rise during the early months of the pandemic, businesses can minimize mundane tasks while at the same time improving the shopping experience, saving them time and helping deep machine learning and natural language processing.
Researchers in the field of conversational AI found that by 2023 around 70% of chatbot conversations will be related to the retail sector.
As more brands look to transition online and competition in the market accelerates, the online customer experience will become a smoother and more delicate process that could ultimately prevent or minimize real-time engagement.
Conversational AI has moved beyond traditional chatbots such as those found at the bottom-right screen of some websites. Developments in the field of conversational AI, deep machine learning (DML) and language processing algorithms (LPA) have immensely improved within the last decade. Consumers have already become accustomed to the likes of Siri in iPhones and Amazon Alexa, which shows both the progress and difference conversational AI has made in our everyday lives.
With a whole host of innovative opportunities, ecommerce retailers and ecommerce technology will be able to enhance and improve the relationship between brands and consumers without encountering friction throughout most of the communication process.
To better understand these opportunities and what ecommerce retailers have done to improve the online shopping experience for consumers, shoppers and potential buyers, let's take a look at some of the challenges and benefits that conversational AI can bring to the table.
Conversational AI Needs to Appeal to Digital Consumers
Consumer trends are ever-changing, and in a dynamic landscape, this requires brands to find more digitally engaging methods that will help continuously improve the online shopping experience, highlight key offerings and remain a competitive player.
Globally, the number of digital buyers surpassed 2.14 billion at the end of 2021, which is up from the more than 1.66 billion recorded in 2016. The surge in digital shoppers alongside a growing tech-savvy population has meant that market competition has only become more challenging.
To face and overcome these challenges, online brands will need to appeal to the digital community through more personalized practices and efforts that could drive brand loyalty.
Instead of looking toward traditional solutions, which for some time included FAQ pages, chatbots, voicebots or AI assistants that were programmed using language processing methods to resolve client issues, brands can tap into the opportunities that lie within algorithmic data and information collection.
Conversational AI should be able to understand consumer questions, retrieve answers and deliver results adequately. This would mean that AI algorithms will be able to read shopper trends faster, pick up when a customer shops for specific items and help recommend shopper-specific products. Online brands and ecommerce retailers will also be able to set up shopper profiles to create measurable key data points.
With access to previous conversations and interactions, brands will be able to physically understand who their shoppers are. This would include the use of specific traits such as age, gender and location, among others. Ultimately, this would mean online retailers can build a more digitally fluid online interaction.
Having more digital natives and tech-savvy consumers while trading in a highly competitive market means that the focus for online retailers is not on how they can attract shoppers but rather on how they can retain them more effectively.
To better appeal to and retain shoppers, brands will need to focus on three key components:
- Creating customer-based support at entry points.
- Resolving customer queries without delay.
- Guiding and following customers throughout their online journeys.
The understanding here is to turn interested shoppers into paying shoppers while at the same time properly imprinting brand loyalty and ensuring a convenient shopping experience without the need for physical human interaction.
Related Article: How Will Conversational AI Transform Customer Experience?
Using Conversational AI to Create Predictive Models
It's already possible for AI and deep machine learning to pick up on consumer trends and behavior through the type of websites they visit, social media content they like and share, online profiles they interact with and even the keywords they search for.
As our software becomes increasingly good at spotting patterns, these digital protocols will be able to give online retailers insights based on consumer behavior.
It's not at all possible that these insights will be completely accurate in some cases. It does, however, lend itself to building predictive models, which could help to further advance the online retail experience.
Building predictive models can help to:
- Automate common customer queries and responses
- Provide more shoppers with specific results upon searching through online offers
- Advise customers on related product offerings
- Automate faster processing at checkout or returns
- Deploying guidance based on product and customer preference
With the help of artificial intelligence, ecommerce brands can build predictive models that can closely relate to changing consumer behavior. As online users start to follow new trends based on social media platforms or other digital native communication channels, retailers can adjust their customer experience to focus on them.
While this is a constantly changing process, having more predictive models that can help deliver accurate results time and again, retailers will be able to leverage the opportunities to fill customer-related needs without falling behind on overhyped trends outside their scope of interest. This is part of the many reasons why conversational AI and real-time feedback from users are crucial to creating customer-tailored recommendations.
In a nutshell, we see how these practices can help improve cross-selling and up-selling as they analyze consumer trends in the broader digital sphere, track a customer's previous spending habits and preferences and monitor queries or issues raised with customer support.
Although building predictive models is seemingly harder and more complex than simply implementing conversational AI within the online shopping experience, it should remain a crucial factor worth considering that can help keep brands ahead within the competitive marketplace.
The Challenges Facing Conversational AI
While we are well aware of the technological benefits housed within conversational AI, there are numerous challenges ecommerce retailers will still need to face. Difficulties can range across platforms and retailers, as they largely depend on the level of AI software used.
Already we see a tremendous amount of backlash forming around the use of AI that looks to capture consumer information to help build more user-centric algorithms. We see this in things such as social media feeds that are constantly changing as soon as we start interacting with a specific type of profile, brand or online personality.
This resonates with the larger picture that represents difficulties for many ecommerce retailers looking to gain more online exposure and build hyper-personalized customer experiences.
Some of the limitations within conversational AI include:
- Obtaining data and information will need to be done through user consent.
- Data collected through voice assistants need to be properly stored and processed.
- Retailers will need to be more transparent about their data collection practices.
- AI is human-centered and focuses on the broader collection of data to simply build an algorithm.
- Consumers are left to the demise of a computerized program to resolve their queries.
- In some instances, AI and natural language processing are unable to resolve issues, leading to increased customer dissatisfaction.
- Before enabling us to take full control, the software will first need to scope shopper trends to build an algorithm that will enhance the overall retail experience.
- Implementing the software comes at a cost burden to many retailers.
- Retailers may not be able to remake the funds spent on implementing these systems.
- As the platform grows and expands, further improvements and modifications will be needed to help cope with the increased volume of site visitors.
No Universal Antidote
- Just because it works for one retailer doesn't mean it will work for all of them.
- AI requires complex features and insights to function properly.
- In some cases, conversational AI will only replace mundane tasks but could still require human intervention.
Among these challenges and limitations, it becomes clear how conversational artificial intelligence still requires further improvements to become more centered around the physical human experience.
While many customers tend to feel separated from the brand or online store when interacting with chatbots or voice assistants, greater dissatisfaction from customers would lead to some brands and online retailers stepping in and resolving issues themselves rather than relying on artificial intelligence.
Related Article: Top Conversational AI Metrics for CX Professionals
Final Thoughts: Is AI Right for Your Brand?
Will artificial intelligence give ecommerce retailers tremendous benefits? It's still not able to replace the full human element that helps it develop and expand to what it is today.
There are several ways through which artificial intelligence software, deep machine learning and natural language processing have helped shape a more profound understanding of the online shopping experience. Through various capabilities and complex algorithms, these systems can build and deliver customer focus insights that can further initiate a more personalized shopping experience.
Despite its dominant online presence and robust benefits, brands and online retailers will need to consider the long-term potential rather than focusing on near-term results. Regardless of which side of the aisle you may find yourself in support of conversational AI, it's clear how this software has come to permanently revolutionize our way of work, communication and online shopping.