How AI Can Help Retailers Thrive in 2021 and Beyond

Due to the COVID-19 pandemic, retailers are poised to bring in a great deal of revenue through their e-commerce platforms. Not only can artificial intelligence (AI) effectively help keep e-commerce stores up-and-running during peak shopping seasons and thereafter, but it can also provide effective sales recommendations and timely automated customer support.

 Use AI to Automate Your Most Frequently Encountered Customer Support Issues

A recent ManageEngine remote work online behavior report revealed that the majority of consumers are quite amenable to chatbot-based support. In fact, roughly three out of every four (76 percent) respondents found chatbot-based support to be either "satisfactory" or "excellent." If your e-commerce site has an effective automated support system in place, you can save money on support costs without compromising on quality. After your algorithm has been effectively trained to recognize the most common problems that customers encounter, the machine learning (ML)-based support responses can be automated.

As a caveat, ML-based customer support has been found to be received quite differently depending on the age of the consumer. Generally speaking, younger consumers are more comfortable engaging with chatbot-based support than older consumers. In the ManageEngine study, most Gen Zers and millennials were able to use chatbots to effectively solve their issues; however, nearly half (48 percent) of those customers aged 65 and over complained that their issue wasn't resolved in a timely manner. This suggests that automated customer support tools are more effective for those retail outlets that cater toward younger generations. That said, across the board, nearly all customers have come to appreciate — and in some cases, expect — well-run automated customer support tools. 

Offer Intelligent, Automated Recommendations

According to the aforementioned study, most consumers (66 percent) trust an AI tool's recommendations. By collecting data and training your algorithm, you can recognize shopping patterns, automate your recommendations, and, in turn, increase your sales conversion rate.

Again, younger consumers do find automated recommendations to be more reliable than older consumers. Only 15 percent of all the respondents said they would "never" trust a recommendation from AI; however, 25 percent of 55-64 year-olds, and 32 percent of those aged 65-plus said they wouldn't trust the recommendations of AI. Thus, if your audience demographic skews younger, you can expect AI-based recommendations to move the sales conversion needle upwards.

Of course, the quality of a recommendation is entirely dependent upon the quality of your algorithm. As long as your algorithm has processed enough properly labeled — and vetted — data, your AI-based recommendations should be solid, and your conversion rate should benefit.

Use ML-Based Analytics to Bolster Your E-Commerce Site's Overall Performance

To keep your sites up and running with increased traffic levels, you can use machine learning to shorten your IT personnel's mean time to resolution (MTTR). ML-powered analytics tools help identify issues before there's a problem. Firstly, the tool assesses what your company's baseline activity generally looks like — e.g., how many visitors usually come, at what time, and from what geographic location — then it sends your IT team an alert if there's any anomalous behavior. Even in the worst-case scenario of a data breach or a website crash, you'll be able to resolve the issue quickly through the help of automated alerts from a ML-based analytics tool.

As another quick caveat, these ML-based solutions are only as good as the data that you provide the algorithm. It's important to periodically check the results of your algorithm to ensure accuracy. Also, it goes without saying that you need to account for seasonality as well.

John Donegan is an enterprise analyst at ManageEngine, the IT management division of Zoho Corp. John covers infosec and cybersecurity, addressing both the technology-related issues and their impact on business.