As pressure mounts to differentiate with superb customer service via interactive channels, more brands are turning to artificial intelligence (AI) to help deliver that service at scale. Technology researcher Gartner predicts that by 2021, seven out of 10 companies will rely on AI to boost productivity. Consulting firm McKinsey found that 47 percent of companies have already implemented some form of AI; within the retail sector, 52 percent of companies have implemented AI to help with marketing and sales, while 23 percent are using machine intelligence to enhance customer service.
Thanks to big data processing capabilities and innovations in machine learning, more vendors are touting AI-enhanced solutions to meet the needs of merchants that are scrambling to implement intelligent solutions. However, it’s been a bumpy ride so far. Not all AI tools are created equal; some feature rigid algorithms that can’t take into account individual business rules, while others fail to integrate well with other systems. Additionally, execution depends largely on the quality of data being input to “teach” algorithms how to behave. Stories of poor AI-driven experiences are commonplace. Some are merely annoying, such as persistent remarketing ads for products already purchased, while others are more sinister, such as instances of algorithms displaying racial and gender bias.
These missteps partly explain why so far shoppers are still wary of purely automated interactions. In order to deliver customer experiences that seamlessly blend AI-powered information and recommendations with human insight, merchants must do their utmost to ensure automated services deliver on their promises. To maximize the success of AI implementations, merchants should consider the following:
Avoid the 'Black Box'
Merchants should build accountability and transparency into AI-powered offerings, whether they're internal or from a third-party vendor. That means creating internal guidelines for data governance and ensuring that AI-driven results are explainable and provable. External vendors’ tool sets should accommodate business rules, segments and other pre-existing constraints. Given that merging data with analytics is companies’ No. 1 AI data priority, according to consultant PwC, technology vendors should also provide help interpreting activity and results so that brands can build meaningful AI metrics. As business goals change, merchants should have the means to adjust algorithms and set new guardrails for AI interpretation.
Start With the Right Set of Solid Data
AI tools are only as good as the data the machines interpret, so merchants must collect, parse, label and cleanse information before it reaches the AI layer. Selecting the right data points is also crucial; sellers should focus on the input that’s most meaningful to the AI-enhanced task at hand. For example, in-store traffic patterns are less relevant to an AI implementation that tracks shipments of online orders for home delivery, but that same data may be crucial for reducing delays and confusion surrounding buy online, pick up in-store (BOPIS) services.
Be Transparent About Human/AI Transitions
Merchants should clearly delineate the types of interactions AI-powered services can handle, and then identify and test a variety of escalating situations to ensure a seamless transition to human help. And since shoppers react negatively to machines masquerading as humans, according to SAP, chatbots and avatars should be explicitly identified as such, with handoffs to humans clearly flagged.
'Making It' With AI in 2020
As the technology improves to close the gaps, brands with quality AI solutions built on foundations of sound data will begin to realize critical gains in 2020. For others, the seams will begin to show, and customers may defect if they lose patience with poor automated experiences.
Fang Cheng is the CEO and co-founder of Linc Global, a customer care automation platform that helps brands differentiate themselves with automated services and experiences across the channels shoppers prefer.
Fang Cheng is the CEO and co-founder of Linc Global, a customer care automation platform that helps brands differentiate themselves with automated services and experiences across the channels shoppers prefer. Linc Global has served over 15 percent of U.S. shoppers, creating a competitive advantage, reducing customer service costs, and turning service interactions into new engagement and revenue. Linc Global's clients include Carter’s, eBags, Stein Mart, Lamps Plus, JustFab.com, Tarte, Hugo Boss, Vineyard Vines, and P&G Shop.
With a passion and relentlessness for improving the customer experience, Fang brought together a seasoned team of technologists and product-minded people to empower brands with the ability to serve and engage shoppers and to drive profitable growth in the face of rising competition and customer expectations. With a Ph.D. in bioinformatics from NYU, Fang previously co-founded a business acquired by Amazon, and prior to that, she worked as a hedge fund manager.