Much has been written about the potential of AI systems to automate customer engagements at scale, but it can also be used to scale customer personalization through secure backend integrations and machine learning.
As technologies evolve, so do user expectations for engaging with their favorite companies and brands. When it comes to meeting or exceeding those expectations, advanced Artificial Intelligence (AI) solutions will be invaluable for their ability to not only automate but to personalize customer engagements at scale.
A survey by customer experience (CX) consultant Walker Information asked 400 business professionals to rank a series of customer expectations in regards to business impact – and the results illustrated the need to add AI for enhanced engagements. According to survey respondents, the three most important customer expectations were ease, speed and personalization (taking the number three, two, and one slots respectively). A state-of-the-art conversational AI solution such as Amelia can satisfy all these expectations by delivering 24/7 on-demand access to information and resolutions, while subsequently (and most importantly) tailoring experiences for individual users.
Much has been written about how automation can scale engagements and increase information access (see here, here and here), but in this blog we will detail the ways that conversational AI solutions can be used to scale personalization.
Imagine if all of your customer service agents had every detail about your current offerings committed to memory. Now imagine that these same agents also memorized your customers’ complete buying histories as well. These all-knowing service agents could answer all customer questions, and provide useful information based on past purchases, interactions and transactions. However, this isn’t some far-off fantasy — these agents exist today. Modern Intelligent Virtual Agents (IVAs) such as Amelia use secure back-end integrations to tap into corporate and proprietary databases in order to deliver comprehensive customer service. For example:
Customer: I bought a yellow shirt at this location in spring, but my dog ripped a hole in the sleeve. Do you have any more in stock?
IVA: I’m sorry to hear that happened to your shirt. I’ve located the yellow shirt you purchased in May. Unfortunately, we don’t have that shirt in your size at this location. However, we do have one in stock the Springfield outlet. Would you like me to have it shipped to that location for you?
With IVAs’ advanced learning and machine-based memory, this kind of interaction could be repeated across various scenarios — a returning customer ordering a food delivery would never need to repeat their home address or payment information, a clothing retailer would have a customer's measurements at the ready, and a pharmacy would have all patient allergies on file when filling new prescriptions.
All this user data can allow IVAs to automatically tailor customer journeys in order to drive sales and enhance the CX. For example, if a customer purchases double the usual quantity of baby supplies (e.g., two cribs, two rockers, etc.), an AI system can be taught to discern that the customer is expecting twins without having been specifically told so. The system could then use this information to offer the customer relevant items (e.g., double strollers, books on raising twins, twin-themed clothing, etc.). Similarly, the AI systems could use other purchasing clues to infer additional customer needs.
Machine learning allows AI systems to optimize up- and cross-selling opportunities, as well as automatically generate highly personalized communications in future interactions to keep customers further engaged (e.g., to continue with twins example, the same customer might receive an email about a deal on a toddler-sized bunk bed in a couple of years).
An IVA for Everyone
Beyond IVAs providing personalized service agents, imagine the potential of having each customer paired with a salesperson best suited for them through AI.
While all customers should be treated equally, they shouldn’t all be treated in exactly the same manner, or provided the exact same offers. For example, a salesperson at a furniture store might showcase different items to a recent college grad than they would to a middle-aged executive. A top salesperson might even converse with different customers in different ways, depending on their spending histories and other factors. In the future, companies may be able to personalize conversational engagements for different customer segments, if not right down to the individual.
Indeed, we have already seen some systems provide different conversational experiences based on users’ preferred languages. As these systems become more common, brands may choose to alter their IVA to suit the preferences of individual customers. The technology could also be used to tailor the conversational experience so that the IVA would use different phraseology or even have a different voice or accent.
Although some of this functionality is down the line, it demonstrates how AI technology could be used by enterprises to provide users with comprehensive and highly-personalized customer experiences, engendering greater customer loyalty and generating additional business.