In today's fast-paced, customer-centric business landscape, support leaders are increasingly turning to data and AI to drive efficiency, uncover insights, and deliver exceptional experiences. We had the pleasure of speaking with Nicole Brown, Head of Customer Care at GotPhoto.com, a leading volume photography platform. Our conversation explored Nicole's philosophy on the strategic role of support, her approach to leveraging data and AI to empower her team, and her tips for getting started with AI even on a small scale. Here are the key takeaways from our discussion:
Q: Can you share your overarching philosophy on the role of customer care within an organization?
Nicole: I really believe that support is the blood pressure for the organization. As the head of customer care, it's our job to be the customer advocate. We're the ones taking in all that customer sentiment day in and day out. But it's not enough to just absorb it - we have to put the data behind it and bring those insights back to the rest of the organization, whether it's product, sales, marketing, or beyond.
I think a lot of times the missing piece is that data element. That's the language of the C-suite. If we want to drive real change and get the resources we need to improve things for customers, reduce churn, invest in the right tools - we have to be able to quantify the impact and build that business case.
Data is also so crucial for my own team - for validating the work of our frontline agents, helping them understand our priorities and roadmap, and getting their buy-in on where we're headed. We can't fix everything at once, so I use data a lot to say "Here are the key issues we're tackling this quarter, this is how it ladders up to our bigger goals, and this is why it matters." It really helps connect the dots for them and give purpose to the changes we're making.
Q: You mentioned having a background in robotic process automation (RPA) from a previous role. Can you share more about how that experience shaped your perspective on the potential of AI in support?
Nicole: Absolutely. In my prior organization, we had a lot of repetitive, manual technical work that was costing us millions of dollars a year. Things like dispatching field technicians to fix equipment issues. We were able to implement an RPA solution that automated a lot of those tasks and delivered a strong ROI very quickly.
But what really struck me beyond the efficiency gains was the impact it had on my team's engagement and satisfaction. Suddenly they weren't bogged down in all this mundane work and could focus on more meaningful, customer-facing activities. We were able to upskill them and get them working on higher-value problems, that was the highlight of the project.
That was a huge light bulb moment for me in terms of the potential for AI to not just streamline processes, but really transform the day-to-day experience of support professionals and create room for them to grow. As leaders, we might have the initial vision, but once the team starts to see it in action, they come up with the best ideas. They find all these other use cases we never would have thought of. That's when it really takes on a life of its own and becomes woven into the fabric of how we work.
Q: For support leaders who are just getting started with exploring AI, what advice would you give them? Where should they focus first?
Nicole: The advice you will always hear about getting started with AI is to start looking at those tasks you do every day or every week that are repetitive and time-consuming. Things like reporting, data entry, content generation. There's probably a generative AI tool that could help automate some of that and give you back some valuable time.
But I also encourage people to think about those projects that have been sitting on the backburner that they can't quite seem to get off the ground. Is there a way to incorporate AI as a tool to drive some momentum there? Maybe it's a thought partner to brainstorm ideas with, or a way to knock out a first pass at something to get the ball rolling.
An example I love - my team had all these different customer facing templates, but they were in all these different voices and tones. It was an inconsistent experience. And standardizing them was one of those projects that kept getting deprioritized. So we are putting them in a custom chatbot I built with GPT-4 and are having it enhance everything in a unified brand voice. Suddenly this project that was on the backburner became this fun, interactive experience of testing prompts and seeing what the AI came back with. Now it's this virtuous cycle where the more they use it, the more excited they get, and the more ideas they bring for how to apply it to other areas.
Another big opportunity I see is using AI to fill capability gaps while you're waiting on headcount or tooling. We don't have a formal QA program right now to evaluate agent interactions. But I'm having my team use a chatbot to analyze call transcripts and surface coaching opportunities. It's not a perfect replacement for a robust QA system, but it's a way to start leveling up our performance and consistency with the resources we have today.
So my advice would be - don't wait for the perfect use case or the big budget initiative. Look for those quick wins where AI can help you punch above your weight and start weaving it into the culture of continuous improvement on your team. You'll be amazed at how quickly it catches on and the creativity it unlocks in your people.
Q: One thing you touched on earlier was the importance of tying support initiatives back to tangible business outcomes. Can you share an example of how you've approached measuring the ROI of some of your AI and automation efforts?
Nicole: One framework I like to use is looking at how much we're currently spending on a particular process, both in terms of hard costs and time. So when we implemented that RPA solution I mentioned, I looked at what we were paying for each truck roll to a retail location - around $100 per dispatch. Then I factored in the time my team was spending on those activities and translated that into a labor cost.
From there, I looked at the volume of those incidents we were handling in a given month and projected that out to get an annualized cost. That gave me a baseline of what we were spending today. Then I looked at what it would cost us to implement the RPA - the licensing fees, the developer hours to build and maintain it, and any infrastructure costs. And I modeled out what our new cost per incident would be.
We were able to automate over 75% of those incidents, so the savings were substantial, 250k in just a few months. And that's not even counting the soft benefits like increased customer satisfaction, reduced churn, and improved agent morale and retention. But having those hard dollar figures to point to made it so much easier to get buy-in and justify the continued investment.
I think the key is really understanding your current state and being able to paint a clear before and after picture. And the more you can tie it to those metrics the business cares about - cost savings, revenue growth, NPS, CSAT - the easier it becomes to prioritize and scale those efforts over time.
Conclusion
Our conversation with Nicole underscored that while data and AI can be powerful tools for any support organization, the real magic lies in embedding them into the culture and daily habits of frontline teams. By democratizing access to data, empowering agents to be active participants in process improvement, and always anchoring initiatives to clear customer and business outcomes, CX leaders can harness the full potential of these technologies to drive transformative change.
A few key principles emerged that can serve as a north star for any support professional looking to become more data and AI-driven in their approach:
- Partner early and often with cross-functional stakeholders to ensure alignment on goals, metrics, and prioritization
- Invest in the data foundation and governance needed for teams to easily self-serve the insights they need to make better decisions
- Start small with AI by finding low-effort, high-impact ways to automate repetitive tasks and let teams experience the value firsthand
- Identify the leading indicators that will help you measure and articulate the ROI of your efforts in terms that resonate with executive audiences
- Embrace a culture of continuous learning and experimentation, where every team member feels empowered to suggest new applications for data and AI in their work
As GotPhoto.com continues on its mission to revolutionize the volume photography industry, Nicole's forward-thinking approach to customer care offers a compelling model for any business seeking to scale efficiently while still delivering world-class experiences. With a steadfast commitment to data-driven decision making, an eye always toward agent experience and enablement, and a spirit of relentless iteration, she is well-positioned to keep raising the bar for what support can contribute to the bottom line - one insight and automation at a time.
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