![]() ![]() We unlock the power of data ontology, providing a unique, industry-specific data model for every business. ![]() We provide actionable insights that reduce customer effort and optimize costs. We bridge the data gap, ensuring that your customer service data isn't isolated-it's shared, utilized, and acted upon. ![]() Instead, we provide a unified platform that harmonizes data from every corner of your service ecosystem. We're not bound by the limitations of the Franken-stack. ServiceMob is not just another tool it's a paradigm shift. It calls for a platform that transcends the Franken-stack, a platform like serviceMob. This world is within reach, but it requires a revolution in how we approach service analytics. Imagine a world where data flows seamlessly, where customer service teams, data scientists, executives, and other business units share a common understanding. A solution must emerge that provides insights across the entire customer support journey, from the first point of contact to issue resolution. It's about breaking free from the constraints of isolated systems and forging a unified, data-driven approach. So, what's the way out of this labyrinthine mess? The solution lies in recognizing that the future of customer service analytics must be decoupled from the tech stack. Instead, it becomes a wild goose chase, with customers making multiple contacts to solve a single issue. The fragmented data within the Franken-stack makes it nearly impossible to measure resolution effectively. Resolution, which should be the Holy Grail of customer service, is often a mere illusion. The Quest for Resolution: The metric of 'Resolution' is a prime example. They lack the depth to provide insights into how service impacts the customer's journey and effort. However, they overlook a crucial metric-the customer's effort. They excel at managing channels and ensuring process adherence. The Blind Spot in Service Analytics: The reality is that most existing customer service analytics tools focus on just two areas: Contact Management and Quality Assurance. Why? Because our analytics, rooted in the Franken-stack, aren't effectively addressing the core issues driving these contacts. The technological mirage is real-despite all the data and AI, customer contacts aren't vanishing. Without the right acumen to navigate the nuances of customer service, we're essentially flying blind.Ĭontact Demand that Won't Die: Despite the promise of technological advancement, customer service operations continue to grapple with rising contact demand. Data wrangling becomes the norm, and true analytics takes a back seat. Many data science teams, despite their brilliance, often find themselves grappling with the complexity of service data. The result? Critical customer insights are fragmented, lost in translation, and ultimately, underutilized.Ī Historical Lack of Acumen: The conversation we've had throughout this article underscores a glaring issue-limited domain expertise in service analytics. This data disparity is like speaking different dialects in a world that should be unified by a common language. Systems often function as isolated islands, each with its own data model and language. But why, you may ask? Let's unravel the mysteries within this tech labyrinth and discover why it's not set up to help executives improve resolution and reduce customer effort.ĭata Disarray: One of the primary culprits in the tech horror story of customer service is the lack of data integration. While individually powerful, this convoluted ecosystem is far from the seamless, integrated powerhouse it needs to be. This metaphorical monster is a tangled web of systems, from CRMs to CCaaS, AI solutions to chatbots, IVRs to WFM, and QA tools, among others. In the ever-evolving landscape of customer service, executives face a daunting challenge-taming the Franken-stack. ![]()
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