In last week’s blog, we focused on how you can create natural, intuitive AI-powered CX interactions, and wrote about how you can shift from proactive to reactive engagement and leverage AI to drive ongoing insights.
This week, we’ll take a look at ChatGPT’s answer to the next question in our series: What’s the next best frontier for AI to create value in CX beyond reducing call volumes and automating work from call center agents? As usual, we’ll outline our own thoughts on striking this balance, after quickly sharing some thoughts on how well ChatGPT did answering the question.
Chat GPT’s Answer (repeated here, verbatim from ChatGPT):
While reducing call volumes and automating work from call center agents are important use cases for AI in CX, there are many other frontiers where AI can create value. Here are some examples:
Personalization at scale: AI can be used to analyze customer data and behavior to create highly personalized experiences for customers at scale. This can include personalized product recommendations, customized marketing messages, and tailored customer support.
Predictive analytics: AI can be used to analyze customer data to identify patterns and make predictions about future behavior. This can help businesses anticipate customer needs and proactively offer solutions, improving the overall customer experience.
Natural language processing: Natural language processing (NLP) allows machines to understand and respond to human language, enabling more natural and intuitive interactions with customers. This can include chatbots and voice assistants that can help customers with their needs in a more efficient and personalized way.
Visual recognition: AI-powered visual recognition technology can be used to analyze images and videos to identify customer needs and provide personalized recommendations. For example, a retailer could use visual recognition to recommend products to customers based on the clothes they are wearing in a photo.
Emotional intelligence: AI can be used to analyze customer data to understand emotional states and provide tailored responses. For example, an AI-powered chatbot could recognize when a customer is feeling frustrated and provide more empathetic support.
Augmented reality: AI-powered augmented reality technology can be used to provide customers with immersive experiences that help them better understand and engage with products or services. For example, a furniture retailer could use augmented reality to allow customers to see what furniture would look like in their home before making a purchase.
Overall, the next frontier for AI in CX is to create even more personalized, intuitive, and immersive experiences that better anticipate and meet customer needs. By leveraging AI in new and innovative ways, businesses can stay ahead of the competition and deliver exceptional customer experiences.
How Did ChatGPT Do?
Overall, ChatGPT did well laying out several ways you can apply AI outside of call centers. Its point about using AI to understand customer emotion is insightful and very powerful as a place to focus your efforts to align your CX with your brand strategy, as are its mentions of predictive analytics and personalization as ways of creating value using AI. However, as with prior questions in this series, ChatGPT’s answer does not call out the specific use cases you can focus on in much detail, though it would if you got into an extended conversation with it. In particular, there are a number of use cases in marketing and sales that are valuable frontiers beyond service-oriented ones. Taking a functional lens to dive deeper into these use cases vs. a more tech and analytics-centric approach to where the frontiers are would have made ChatGPT’s answer more pragmatic and useful.
Opportunities in Marketing, Sales, and Service
Creating opportunities for AI to enhance your company’s marketing, sales, and service capabilities means considering a broader pipeline of use cases beyond call centers in marketing and sales, while continuing to drive value from the next wave of services use cases too. Moreover, your customer service-oriented interactions - whether via calls with live agents or chatbots - are also a valuable source of data that can be applied for use cases in marketing and sales when combined with other data sets such as ratings and reviews, social sentiment, messaging, open-ended survey questions, etc. Qualtrics recognized the importance of bringing together these high value data sets to enable a broader set of CX use cases when it acquired Clarabridge, which was one of the pioneers in this space (since rebranded Qualtrics XM Discover). Companies using AI to improve marketing, sales, or service practices will benefit greatly from leveraging this bounty of unstructured data, including ingesting your open-ended questions from your surveys into your AI models, too. In many cases, your investments in these capabilities can be self-funding through savings in research and testing costs, in addition to other operational savings from reduced call volumes and other cost of quality opportunities (see my Reimagining Insights blog series in collaboration with Lou Carbone for more on these opportunities to create a growth flywheel by scaling AI-driven insights, leveraging cost savings to fund your investment in these growth enabling capabilities).
Opportunities in Marketing
Just like AI has been applied to mine insights from your service interactions, there is also a significant body of learnings on how to leverage other unstructured data like ratings and reviews and social sentiment as a high value source of insights. Sources to mine for insights include syndicated platforms for reviews like Bazaarvoice, as well as sites that help you tap into social sentiment about your brand such as Qualtrics, Sprinklr or Brandwatch (for more on this topic, check out my article that was featured on the cover of strategy+business, The Social Life of Brands).
AI has gained traction in companies’ marketing practices because companies can leverage AI to optimize paid media campaigns as well as next best content for owned media channels such as email, social feeds, your mobile app, or your website. The right content containing the right calls to action along the customer journey reinforces usage, engagement, and ultimately, brand advocacy. The ultimate goal is to turn customers into brand advocates and improve customer lifetime value. There are a growing set of journey orchestration and marketing automation platforms that can be used to drive these use cases, such as Optimove, Adobe, and Salesforce.
Creating optimized content that resonates with customers means understanding their emotions first. Knowing how the customer feels about themselves along their customer journey is essential for your human centered design investments to yield the highest ROI. Brands that can reduce the gap between the emotions they want customers to feel as part of their brand strategy with the customer’s actual emotions during the experience are the ones most capable of creating the most engaging experiences. To measure this alignment consider using an emotional motif in combination with AI-driven analytics of your unstructured data (which Lou Carbone and I wrote about in the Reimagining Insights series here).
Opportunities in Sales
Another high value frontier for applying AI beyond call centers is virtual sales interactions, which are increasingly important to BtoB companies, whether for remote sales interactions, virtual trade shows, or digitized experiences for their innovation centers or design studios to influence specifications and project expertise into the market. This trend has continued post-pandemic, meaning companies previously reliant on in-person sales models need to devise ways to deliver the quality of an in-person experience, virtually.
The breadth of AI’s ability to capture feedback spans across many sources of unstructured data, including increasingly, video applications such as Zoom or Teams calls, and not just voice-to-text or chatbots. When leveraged properly, these insights can help support an ongoing quality management cycle to drive value from your use cases. For more on this topic, see this interview I did with Farzad Aref from Farlinium, one of the strategic partners for JourneySpark Consulting, in this collection of videos.
Opportunities in Service
To truly enhance a company’s service capabilities, look beyond call centers and toward implementing an always-on approach. An always-on approach goes further than using AI chatbots to fill the gaps during non-operating hours. It leverages proactive outreach and voice AI capabilities, helping solve customer issues without having to email a sales rep to set up a virtual meeting or call the call center.
For example, companies can deliver a high-touch experience to those customers that aren’t able to travel to visit their innovation center or attend a trade show. They can also mitigate the competitive advantage competitors previously had from having sales offices closer to customers or a larger sales organization by extending their reach and the quality of the expertise they deliver through use of AI to optimize the “words that work” and help deliver the right content to customers along their customer journey. These approaches to use AI are often best done as a complement to virtual or in person interactions with your sales team, rather than as a complete substitute for them. You can increase your “low, no touch” engagement with customers, but even getting to >50% low, no touch is a significant stretch goal for most organizations.
For more on the opportunities and challenges of applying AI across these use cases, see also the blog series I did with Greg Kihlström, the first of which can be found here.
Up Next Week
In next week’s blog, we’ll be writing about how AI can be used to go beyond traditional survey-based approaches that are used for relationship based or transactional listening for moments that matter. We’ll explore how some of the advances discussed here for mining unstructured data and understanding your customers’ emotions along their journey can provide better, more actionable insights than historical survey-based approaches to gathering experience data. I’ll continue posting weekly for this AI series every Wednesday covering each of the ten questions. I’ll also continue to post one of my book reviews for my favorite ten business books every Friday. If you’d like to jump ahead to see all ten reviews now, click here to check them out.
We’re looking forward to continuing the conversation!