I’m thrilled to sit down with Aisha Amaira, a seasoned MarTech expert whose passion for blending technology with marketing has transformed how businesses uncover vital customer insights. With her deep expertise in CRM marketing technology and customer data platforms, Aisha has a unique perspective on how innovation can address critical challenges in customer experience. In our conversation, we dive into the subtle yet damaging concept of “slow failing” in customer service, explore the pitfalls of fragmented data and siloed interactions, and discuss how businesses can harness comprehensive data strategies to turn hidden inefficiencies into opportunities for improvement.
How would you define “slow failing” in the realm of customer service, and what makes it different from more obvious business struggles?
“Slow failing” is this insidious process where a business gradually loses customers due to underlying issues in their service model that they don’t even see. Unlike a sudden crisis—say, a major product recall or a PR disaster—slow failing is subtle. It’s the result of small, cumulative frustrations that erode customer loyalty over time, like repeated poor experiences or inefficiencies that go unnoticed. Businesses might be hitting their basic metrics, but they’re missing the bigger picture of customer dissatisfaction because they’re not connecting the dots across their data.
What are some reasons businesses often fail to recognize they’re in a state of slow failing?
A lot of it comes down to blind spots in how they handle information. Many companies operate with fragmented systems where data from different touchpoints—like a website chat and a follow-up phone call—aren’t linked. This means they can’t see the full scope of a customer’s journey or spot patterns of frustration. On top of that, there’s often a disconnect between departments or teams, so no one’s looking at the holistic experience. They might think everything’s fine because one metric looks good, but they’re missing the warning signs elsewhere.
Can you break down what data silos are and how they contribute to customer frustration in contact centers?
Data silos happen when information from different channels or systems is stored separately without integration. For example, a customer’s email complaint might sit in one database, while their call history is in another, and there’s no way to connect the two. This creates a disjointed experience where a customer has to repeat their issue every time they switch channels, which is incredibly frustrating. It also means agents lack context, so they can’t address the root problem effectively, leaving customers feeling unheard.
Why do you think certain customer interactions, like abandoned calls or repeat attempts, often get overlooked in traditional contact center reporting?
Traditional reporting tends to focus on successful interactions—calls that connect to an agent or chats that resolve an issue. Things like abandoned calls or repeat dials are often seen as background noise and not worth tracking in depth. Many legacy systems just aren’t designed to capture or analyze these moments, so they slip through the cracks. But these interactions are critical because they signal friction—maybe the wait time is too long or the IVR menu is confusing—and ignoring them means missing out on why customers are struggling.
The idea of metrics like Average Speed of Answer versus Net Promoter Score comes up a lot. How can focusing on these metrics sometimes miss the mark on true customer satisfaction?
Metrics like Average Speed of Answer prioritize efficiency over quality. You might answer calls quickly, but if the resolution isn’t effective, the customer still leaves unhappy. On the other hand, survey-based metrics like Net Promoter Score only capture feedback from a small subset of customers who bother to respond, often at a specific moment in time. They don’t reflect the broader experience or ongoing issues. Both can give a false sense of security if you’re not digging deeper into the actual customer journey and all its touchpoints.
What’s the value in capturing every single customer interaction, even those that don’t result in a successful contact?
Capturing every interaction—whether it’s an abandoned call, a failed chat, or a repeat attempt—gives you a complete picture of where customers are hitting roadblocks. For instance, if you log that a lot of callers drop off at a specific point in the IVR, you can investigate whether the menu is unclear or wait times are too long. This data helps you pinpoint friction and make targeted improvements, ultimately reducing customer effort and boosting satisfaction. It’s about understanding the full story, not just the happy endings.
Can you explain what a real-time data pipeline is and how it can transform customer experience in a contact center?
A real-time data pipeline is essentially a system that connects all your customer touchpoints—voice calls, chats, emails, IVR interactions—and streams that data into a unified view as it happens. Think of it as plumbing that links everything together so you’re not looking at isolated pieces. This setup allows businesses to see the entire customer journey in the moment, spot issues instantly, and act on them—like escalating a frustrated customer to the right agent before they give up. It’s a game-changer for proactive service and accurate performance tracking.
Looking ahead, what’s your forecast for how businesses will tackle slow failing and improve customer experience in the coming years?
I believe we’ll see a big shift toward integrated, real-time data solutions as businesses realize they can’t afford to operate in silos anymore. The focus will be on capturing every interaction and using advanced analytics to predict and prevent customer frustration before it escalates. Technologies like AI will play a huge role in analyzing patterns and automating responses to common pain points. Ultimately, companies that invest in understanding the full customer journey—and act on those insights—will be the ones who turn slow failing into sustainable success.