If you’re wondering about all the ways you can use AI in eCommerce customer support, you’re not alone. Artificial intelligence is a red-hot topic lately, and for good reason.
Thanks to OpenAI, ChatGPT and the like, AI is evolving at a rapid pace. Today’s AI is smarter, more nuanced, and more accurate than ever before. But, understanding the ways you can use it in the real world requires digging a little deeper into the inner workings of AI.
High-performance customer support teams provide many benefits to any eCommerce business. These advantages include higher sales, better retention, more loyalty and trust, improved recognition and brand awareness, better reviews leading to the ability to charge higher prices, fewer returns and complaints, and more. With next-generation AI seeing mass adoption across the online sales sector, what are the best ways for you to maximize the impact on your business? Keep reading to discover our top 10 ways to use AI in eCommerce customer support.
Identifying Where AI Can Add Rapid Value
Before we get started with considering how to get the best use from AI, it is important to identify what problems you are hoping to solve. For nearly 80% of consumers, there are three critical elements that they value when interacting with a business. These include: speed, convenience, and friendly and knowledgeable service.
Customers want fast responses, via whatever channel they are most comfortable with. They want those responses to be empathetic and helpful.
With the increasing need to sell across multiple channels, from marketplaces such as Amazon, Walmart, and eBay; webstores such as Shopify, WooCommerce, and Magento/Adobe; and increasingly to social channels such as Facebook, Instagram, and WhatsApp; support teams are under increasing pressure to deliver on customer expectations and meet SLAs.
The good news is that those customers are willing to pay more for a good customer experience (CX). In fact, they will pay an average of 13% more when there is better service from a seller. Investing in AI to improve support is therefore worth it to achieve that type of margin increase.
AI has been used within eCommerce customer support functions for a number of years. However, so-called Generative AI is leading to a step-change in capabilities. This type of AI is pre-trained on large data sets and has been made readily available to end-users and software developers through open APIs. This means that support teams can now expect to have AI-powered solutions on hand to support their day-to-day processes.
Already, we’re seeing use cases emerge which are focused on improving response times across unlimited channels. Furthermore, these efficiency enhancements are coupled with improved sentiment analysis and knowledge, empowering teams to provide faster and smarter responses to customers.
AI is good at three things of real value to online sellers:
Automation
AI can automate repetitive tasks, reducing human effort and increasing efficiency. Artificial intelligence can be leveraged by teams to automate responses to routine queries, enable self-service, and provide comprehensive out-of-hours support.
Natural Language Processing
AI can process human language, enabling rapid “understanding” of human messages. This means we can access more reliable classification and summaries of customer queries, plus sentiment analysis and even foreign language translations.
Recommendation Systems
AI can analyze human preferences and behaviors to provide personalized recommendations. With next-gen AI, we can now find a “better” answer to customers’ questions, personalize those responses, and quickly identify when to escalate to a human.
By analyzing each of these AI capabilities, we can get a better understanding of where support teams reap almost-instant benefits from adopting AI-powered support solutions.
Top 10 Use Cases for AI-powered eCommerce Customer Support
1. Provide faster responses to routine queries
We know that more than half (51%) of eCommerce customer queries typically relate to delivery status updates (30%) and returns processing (21%). (Source, eDesk, 2023)
In most instances, these query types can be handled effectively through automation. In fact, we’re seeing sellers resolve upwards of 70% of these types of routine queries using AI-powered automations.
Including other types of queries, the net result is that sellers can expect to resolve between 40-50% of queries instantly through automation. This means that customers get the answer they need at speed. It also frees up teams to focus on higher value tasks, while still keeping customers happy. We’ve seen support agents achieve 4X improvements in response times when fully utilizing AI and automation. (eDesk, 2023)
2. Classify customer queries
AI language processing means that automated classification of incoming customer messages is now much more reliable and granular. Generative AI models are trained on many terabytes of data. This means, that even before they are used within your organization, they have unprecedented capability to “understand” the purpose and intent behind a customer’s message.
We’re seeing many new classifications emerge when using AI to support initial ticket analysis. For example, AI does a great job of distinguishing between different types of order queries such as “damaged items,” “missing items,” “wrong item,” “item not as described,” “item doesn’t fit,” etc. This increased sophistication means that queries can be instantly and reliably routed to the right process. Reporting also becomes more useful as specific issues with particular products and channels are more easily identified.
3. Summarize long and/or complex customer queries
Significant time savings can now be achieved by customer support agents thanks to AI-generated summaries of incoming customer queries. These summaries can extract the most pertinent information from a customer’s message and present them in a single sentence or two. This capability adds even more value when a ticket evolves into a series of messages back and forth between the customer and the business. This allows agents to spot commonly-asked queries or understand the ticket at a glance.
An ongoing burden for support teams is what is often labeled the “handover.” This is where agents coming off shift are required to summarize open conversations in order to provide a brief to incoming agents. Once again, AI summaries greatly alleviate this manual burden. For around one-third (32%) of support leaders globally, this summarizing capability is one of the more instant benefits being realized with the adoption of AI.
4. Assess customer sentiment
AI language processing models have proven adept at identifying and classifying human sentiment. By understanding a customer’s sentiment, whether they are satisfied, frustrated, angry, or confused, eCommerce support teams can tailor their responses accordingly. This is particularly important if the sentiment analysis is combined with the lifetime value or segmentation status of that customer. An unhappy VIP customer may need to be escalated quickly to a senior team member for a very individualized approach. Sentiment analysis can provide valuable insights into the urgency and severity of the problem at hand.
In addition, happy customers are more likely to leave negative reviews which can have a detrimental impact on brand reputation. Customer sentiment data is important for both customer support and marketing teams. Increasingly, support software solutions are integrating with CRM (customer relationship management) solutions. This helps to ensure a unified approach to customer communication across all channels.
5. Get suggested responses & resolutions
The combination of query classification, content summarization, and sentiment analysis means that AI is now able to provide suggested resolutions instantly. AI suggestion effectiveness is dependent on the amount of context provided to the algorithms. For example, including customer history, order, and delivery status together with business policies all ensure that the suggested resolution is appropriate and accurate. There are typically two approaches to managing responses following intelligent analysis of incoming queries.
Firstly, pre-scripted templates can be surfaced based on the outcome of the initial classification. This helps to provide the highest level of consistency, quality, and accuracy. The pre-scripted template approach ensures that the correct resolution is suggested and that the language and tone are in keeping with the brand.
A second approach is to point the AI model at your knowledge base and allow it to make all the connections between the query and your company policies. (This approach assumes your knowledge base is comprehensive and up to date.)
In either case, suggested resolutions can greatly reduce the training effort of team members. They also help to maintain high levels of consistent quality, thus leading to happier customer and team members.
6. Deliver personalized responses at scale
Many eCommerce merchants are concerned that switching on automation will reduce their ability to provide a truly personal customer experience. This is undoubtedly the case, however in very many instances, customers value speed, convenience, and a truly helpful response over platitudes. Leveraging AI to ensure that all the available information from and about a customer is taken into account when crafting a resolution is a game-changer for the industry.
Personalization is being effectively achieved by firstly ensuring that full context is taken into consideration. This includes customer purchase history, current order history, delivery status and tracking, customer conversation history, and sentiment. Suggested resolutions can be highly personalized with the combination of well-crafted, pre-determined templates that effectively incorporate personalization snippets. Snippets can include a customer’s name, delivery address, order details, tracking numbers, and delivery status. This level of personalization together with the relevant policy can, in most cases, instantly provide customers with the information they need to address their query.
7. Translate queries and responses into any language
AI language translation models have made significant progress in recent years and have achieved impressive results. They even often surpass traditional machine translation systems like Google Translate in terms of accuracy and fluency. AI’s ability to interpret natural human communication in any language is coupled with the ability to communicate in natural language with layers of empathy. Typically, we are now also seeing a wider scope of languages being supported by AI’s language models.
For many support teams who are servicing customers in multiple languages, the challenge of multilingual support is not insignificant. For many, it has meant outsourcing to third parties. However, this often holds back expansion into new markets.
But, thanks to generative AI’s higher quality and reliability of translation, customer queries can be translated in real time into an agent’s language. The agent can then reply in their own language, with the reassurance that the response to the customer will be in the customer’s chosen language. This means global sellers can deliver a high standard of communication and clarity on all sides.
8. Escalate to humans
To realize the full value from AI-powered customer support solutions, online sellers are gradually migrating more and more query types to “handsfree” mode. This means that for certain query types, resolution is being achieved completely by AI and automation. Simple queries such as order status, delivery status, returns processing, and cancellations can readily be acknowledged and processed by AI. AI systems can ensure that all the necessary customer information has been gathered. This leads to seamless processing of the request. We’re seeing on average 46% of queries already being resolved without human intervention (eDesk, 2023).
An important component in ensuring that customer acceptance of AI-powered support is knowing when to escalate to a human. Support teams should monitor all AI interactions and potentially retain overall ability to close out queries. In addition, sellers should be transparent with customers that their initial response is from AI, and give them the option to request human support. Training and configuring your AI solution to identify when to automatically escalate complex or emotionally charged customer conversations to your team is an important part of ensuring you continue to deliver exceptional customer experience.
9. Provide out-of-hours support
44% of support leaders who are already deploying AI in their organization are realizing the ability to provide comprehensive support 24/7. (Source: Intercom, 2023) Customers are shopping and interacting with businesses at all times of the day and across multiple time-zones. Expectations on response times are getting shorter, with 64% of consumers stating that they expect real-time support, regardless of the channel; voice, chat, or email. (Salesforce, 2019)
Rather than deploying generic out-of-office automated responses, merchants can now with some reliability seek to resolve customer queries outside of office hours. Clearly, not all questions can be resolved fully by AI, but acknowledging customer queries, responding appropriately in an automated manner, and setting expectations are massive steps forward in providing a meaningful service to customers.
10. Manage peaks effectively
Many eCommerce businesses are challenged with staffing up and maintaining service levels during peak times. For Amazon sellers for example, this is especially difficult during short-burst peaks such as the Prime Day period where query volumes can increase by over 50% for an intense period. (eDesk, 2023)
For most sellers, Black Friday, Cyber Monday, and the extended holiday season present difficulties in staff sourcing and training.
Relying on AI automations can greatly reduce the requirements to scale up and train new team members. With more standardized processes and pre-written answers, the training burden is much lighter and shorter during these already hectic periods. Newer employees can be automatically assigned less critical customer queries, allowing more experienced team members to focus on urgent and complex queries.
Getting Started with AI-Enabled eCommerce Customer Support
Online retailers who are embracing AI and automation are reporting 4X improvements in customer support efficiencies. They are able to handle more tickets in the same timeframes as previously, to provide faster responses to customers, achieve and beat their SLAs, and provide more consistent and high quality resolutions to customers. All of this is ultimately leading to better customer satisfaction, better reviews, more purchases, and the ability to charge more.
To get started, support leaders should start by identifying the gaps in their current performance. This will help them to align solutions with their current needs. Here at eDesk, we’ve provided a handy eCommerce Customer Support Scorecard, which will enable support managers to self-assess their performance and benchmark it against the best in the industry. Download your free copy of this scorecard here.
When considering solutions, use this checklist to identify key features that will be important to your business:
- Automated classifications
- Sentiment analysis
- Context (customer and order history) analysis
- Summarization of queries and conversations
- Suggested response capabilities
- Customizable and personalizable templates
- Automated resolution capability
- Escalations and routing
- Multilingual translation
Some things you might want to check, in order to manage potential risks associated with your AI-enablement rollout might include:
Privacy and Security
Understand how your support solution provides your data through the AI APIs. For example, here at eDesk, no personal information is shared with AI models and no data is retained by the models.
AI Mistakes
This technology is still relatively new and errors can emerge through contextual misinterpretation and inadvertent biases. Developing well-constructed, personalized templates will guarantee highest quality and consistency responses.
Human Touch
Many consumers still expect human intervention on their queries. Be transparent with customers about your usage of AI and provide options to easily and quickly escalate to your support team.
Top 10 Ways to Use AI in eCommerce Customer Support: Final Thoughts
AI and automation are set to change the face of eCommerce customer support over the next couple of years. We’ve seen that they can have a dramatically positive impact on both internal operations and customer satisfaction. The future looks bright for this technology in our sector, for those who embrace it.
Increasingly, AI will play a role in the real-time resolution of the many routine customer queries. In parallel, our support teams will be increasingly supported by AI, helping them to provide quicker and more helpful responses to queries and relieving them of monotonous routine manual activities. Everyone can benefit from a considered rollout of AI and automation across eCommerce customer support. If you would like to learn more about eDesk’s approach to AI, please don’t hesitate to get in touch.
Ready to start using an AI-powered customer support system today? Try eDesk for free!
