Peter Armaly, VP Customer Success, ESG, interviews Lisa Palmer, Chief AI Strategist, AI Leaders
Peter: Let’s start with your focus of the last 10 years. When you think back through the timeline that saw you in financial services as a Chief Innovation Officer, then later as a leader in Gartner Executive Programs, then to Splunk as the Chief Technical Advisor… What are a few observations about how companies typically approach a) understanding their customers through data, and b) how well-managed, organized, and leveraged is that data?
LISA: Typically, customer data is silo’d with accessibility constraints so it’s difficult to gather useful insights. However, when it’s used well, it can provide the foundation for high-impact customer and employee experience improvements. Let me give an example, Zurich Insurance has certain seasons where they receive extreme volumes of customer support inquiries through email. Using their customer data, and natural language processing AI, they were able to handle this influx by categorizing, prioritizing, and in some cases, fully handling to closure with no human intervention. By introducing this data + AI + humans approach, Zurich elevated both its customer satisfaction and employee satisfaction.
Peter: But data is just one critical component of modern business, isn’t it? On its own, it’s useless… we know that… but when it is analyzed and placed within the proper context, data comes to life and for the curious, that’s when work can become exciting. Does that word “exciting” capture how you have felt about data over the last decade and if so, what do you think are some reasons that a person’s excitement about data can be easily deflated in the modern business world?
LISA: I love data. Way back when, I had only spent 2 months using my engineering skills when I learned our database structures, created some insightful reports, and ended up presenting them to the company’s CEO. At that point, my die was cast. I was hooked on data. So yes, I am excited about data! And as I’ve progressed through my career and become intrigued by AI, the criticality of data is clearer to me than ever.
Despite my enthusiasm, I am also realistic about the challenges that we face with data. One of the issues nearest to my heart is missing data. There’s a fabulous book by Caroline Criado Perez called Invisible Women: Data Bias in a World Designed for Men. This book was foundational for me in understanding the power of what is NOT in our datasets. She offers examples of women who are harmed by safety gear that is designed for men: Manufacturing accidents when women’s hands are pulled into equipment because their gloves are too big and women police officers whose protective gear gaps, leaving openings for bullets to access.
When we think about this in the context of Customer Success, what is the data that is missing for your customers? And what damage is being done by that? Given my years in enterprise sales, I immediately think of lost sales opportunities.
Peter: My opinion is that the rapid rise of AI is a natural progression in human history and in the human capacity for innovation. Even if you look at human history in just the last 50 to 70 years, it’s quite remarkable how quickly humans have innovated. AI’s development shouldn’t really come as a surprise to us given the exponential growth in higher educational institutions across the world that act both as incubators (but not exclusively) and as pathways for trained individuals who graduate and move into the business world where they find opportunities to make an impact through new technology and technological process creation. There are plenty of examples of exploitation of AI for bad, but there are equal numbers of examples of AI for good. We won’t tackle that topic today. What we want to talk about is whether businesses are prepared for that rapid rise of AI and, if not, is it primarily because they have not been able to adequately get their arms around the long-simmering challenges they have with data. In a recent article for Wharton, Scott Snyder, a senior fellow at Wharton’s Mack Institute for Innovation Management and Chief Digital Officer at EVERSANA, said this, “Despite the fact that 80% of all data will be controlled by enterprises by 2025, 40–90% of this data goes unused, or is “dark.” If the success of AI depends on not just data collection (which most companies have been doing forever), but also on data governance and data hygiene, what would you recommend that business leaders do first before exploring AI-based solutions?
LISA: That’s a great quote from Mr. Snyder and I suspect that the amount of dark data is closer to 90%, particularly given the rise of data from sensors. Based on my recent doctoral research of 46 real-world AI case studies, the identification of a specific issue that needs to be addressed is the key to success. This allows for laser focus on the data, people, process, and tech needed to create a successful outcome.
If we reflect on the Zurich email scenario, designing a solution for that particular use case allowed them to focus on only the data needed to address their specific need. Ultimately, they are able to scale out the use of what they’ve built, but they didn’t do it with scale in mind. Rather, they focused tightly on one particular problem. I’ve seen this practical success play out repeatedly.
Peter: There are two main drivers behind my desire to have you join me as a guest on this webinar.
1) In my view, many Customer Success organizations are perched on a fulcrum. If they continue to execute their existing models of engagement that rely mostly on people as the face of communication with customers, these organizations will begin to fail and will eventually fall off the edge in the wrong direction. Many of us believe that the path to a more viable future for CS lies in its willingness to go in the other direction, towards an embrace of digital as the leading face of information for customers, by adopting more modern technologies to deliver more personalized and impactful service. That’s one driver.
2) The other is that you have such a rich background in data analytics and data management, and you are an advanced business thinker about the ethical use of artificial intelligence, and I believe that Customer Success professionals will need to grow their skills in the direction of those categories.
If CS makes a move towards more fully embracing deeper data analytics to create actionable insights, and perhaps even AI, what are a few considerations CS leaders should be aware of. What are the perils?
LISA: I believe that CS absolutely needs to embrace data and AI. Frankly, I believe that not doing so is creating both customer retention risk AND it’s negatively impacting revenue growth opportunities. For example, what if CS had a recommendation engine that included not only internally known elements about the customer but also added external data. Imagine that on phone or video interactions, using that recommendation engine, the CS professional could speak to the client about what competitors are doing, that they are aware of through public and proactive data mining, and how deploying tech that they already own could help them to compete.
Peter: Another quote… I recently attended a lecture. A book launch and a lecture, actually. The three authors of the book, Power and Prediction, spoke about artificial intelligence in the context of business and one of the things they said was, “The rise of AI is shifting prediction from humans to machines… this sets the stage for a flourishing of new decisions that will have profound implications for system-level innovation.” I don’t know about whether you’re a natural optimist. I am and when I read that quote, I am excited at the possibilities it communicates to me… Do you see a day in the near future when AI will penetrate organizations to the extent that the authors hint at with this quote? Do you see AI being able to work its way into low-level processes that teams like Customer Success utilize to deliver their service to customers?
LISA: Without a doubt, I’m optimistic! In my research study, I identified 3 primary ways that AI is already creating business value in enterprise environments: Internal operations processes, creative new ways to drive revenue, and risk avoidance. All of these scenarios could apply to Customer Success.
1) Applying AI to processes as Zurich did,
2) Identifying cross-sell opportunities that would allow CS to work directly with sales to grow revenue, and
3) Identifying at-risk renewals. As an example, in one of my case studies, they were able to successfully predict which customers would renew and even which ones would pay on time. They then prioritized CS time accordingly. This is a perfect example of data + AI + humans creating value.
Peter: In your LinkedIn bio, one of the things you say about yourself is this. “Leading with customer success as my true north, I'm passionate about the ethical application of technology, particularly data and AI. I believe that purpose and profits can - and should - co-exist!”. I love that sentiment and feel you’ve captured in a short sentence two concepts that many customer success professionals find extremely challenging to hold in their heads at the same time. (QUESTION)… Can you elaborate on that a bit more for the audience? If purpose (i.e. driving great customer outcomes) is possible at the same time a company can be profitable, what are some of the behaviors required of leaders? How do they ensure their teams strike that necessary balance?
LISA: Many years ago, the B2C push for ethical and sustainable behaviors from brands began. Today, that expectation has bled into the B2B world. Buyers are willing to spend more money with “trustworthy” brands (10%+). Further, statistically, women are more concerned with these behaviors and they comprise 53% of B2B buyers under the age of 30 today and nearly all under 25. This means that procurement teams are expected to be heavily female in 10 years.
What does this tell us? That making customer desires for positive worldly impact a priority equates to revenue. We are no longer in a world where committing to ethical and sustainable practices is an expensive and altruistic act for companies. Instead, we are now in a time where failing to meet customers’ ethical and sustainability expectations will directly negatively impact revenue.
So, what can leaders do? If you’re getting heavy profit-only pressure, data is your friend - as it usually is!
1) Does your company have a stated external position on purpose? If so, you need to learn it and befriend your internal champions.
2) Research your market and your customers (or find an internal team who has already compiled this information), understand their expectations for your behavior, compile and present this data to ensure that you’re raising awareness of those in power. You may be surprised to learn that your own procurement teams have similar required expectations about the vendors that they buy from!
With Customer Success so close to the customer every day, you need to be holding the banner high on this topic. The bottom line is that no one wants to be the next organization fielding a public relations disaster due to unethical or unsustainable behavior in today’s societal business environment.