ReadingWhat has changed since Zendesk and Salesforce Service Cloud started

What has changed since Zendesk and Salesforce Service Cloud started


Over the past decade, the world of customer service and support CRM has undergone a significant transformation. Companies that once defined the landscape, such as Zendesk and Salesforce ServiceCloud, are now navigating a new era marked by seismic shifts in both business and technology.

Today’s customer, whether B2B or B2C, is increasingly sophisticated in their expectations. For that reason, excellent customer service and support continue to be a critical input to business success. The benchmark for an outstanding customer support experience is higher than ever before, and organizations — especially in SaaS and hi-tech — require solutions that are built to solve complex workflows.

There are five major changes that loosen the stranglehold Zendesk and Salesforce ServiceCloud have maintained in the world of customer service and support. These changes are encouraging businesses to redefine the way they approach customer experience.

This is the inaugural post in a series of blogs about the changes that have taken place in our industry in the past decade.

Business models and enterprise complexity

From public cloud to subscription realities

  • The rise of public cloud gave rise to subscription models, which dramatically changed the way businesses sell software, support customers, measure engagement, and realize revenue. This gave rise to customer success and growth engineering as distinct departments this past decade, and more so because Support CRMs looked the other way ("not my problem").
  • The era of cheap money has ushered in an abundance of SaaS solutions, leading to fractured organizations grappling with SaaS sprawl, reminiscent of the on-prem infrastructure complexity that SaaS initially sought to resolve. SaaS is now the new “hardware”.
  • These SaaS "boxes" have introduced integration challenges that IT continues to grapple with (remember 2005?). The scourge that Salesforce set out to eliminate — massive multi-year business software rollouts, multi-million dollar professional services projects, bundled software that is barely integrated — is what they've come to become twenty years later!
  • Legacy support CRMs now face acquisition by Private Equity (PE) firms or are bundled by Salesforce. Either way, the result is price gouging during renewals. The effect of this complexity is that such bundles are either overlooked by Support Engineers (SEs) and Product Managers (PMs) or deemed unaffordable for support teams.

Customer and employee communication

Mobile, collaboration, and the post-COVID landscape

  • Mobile technologies, collaboration tools like Slack, and the global shift to remote work due to COVID-19 have completely changed communication dynamics and further decentralized teams.
  • VoIP telephony led to the demise of call center software in support systems and paved the way for modern, messaging-based customer support on platforms like WhatsApp and Slack.
  • In a world of instant-everything, customers now expect instant responses. All communication — and the software on which it occurs — must be low-latency. Without websockets and invisible CDNs (content delivery networks), it is almost impossible to make communication consumer-grade.
  • The use of AI for voice recordings and front-office intelligence and the rise of WebRTC — commodity audio and video — for customer communication, has made it imperative for support folks to either buy these intelligence tools or spend tens of minutes on every intervention to create zoom meetings for simple collaboration.

Programming languages and developer experience

GitHub, Cloud-Based CI/CD, and the Power of Python and Node.js

  • The rise of GitHub gave rise to browser-based developer collaboration, and also engendered cloud-based CI/CD. All this has dramatically changed the way developers work and collaborate with support and devops engineers. This movement led to pervasive microservice-based architectures, cloud-based observability, feature flags, and eventually product analytics. All this meant that there are now way better insights into software performance, making it easier than ever to tie code quality to customer experience and business outcomes.
  • The past decade saw a sea change in browser-side languages and open source frameworks, from HTML5 to JavaScript to ReactJS and visualization libraries. And from 5-lines of code to embed chatbots to feature tracking to super-easy session analytics for understanding user interactions.
  • The rise of Python and Node.js in the cloud, open source distributed streaming, cloud-hosted data streaming, multi-tenant pub-sub, and serverless (lambdas) compute to help build lightweight IFTTTs (if-then-else-that automations). And lately, GenAI-powered code-gen for building complex workflows. None of these are leverageable by Zendesk or Salesforce, because their architecture and choice of language predates these technologies. And one must possess proprietary Salesforce language skills to customize and develop anything on top.

Data and AI

From Cloud DWs to Natural Language querying

  • The proliferation of object storage and elastic compute in the public cloud gave rise to cloud data warehouses, which provide a dynamic and scalable foundation for storing, managing, and processing vast volumes of data.
  • Developer-grade data pipelines for extract-transform-load (ETL), lightweight columnar-compressed SQL engines, and the introduction of powerful compute engines (WASM) on the browser continue to disrupt traditional analytics and reporting.
  • Developer-grade CDNs, native Electron apps, and micro-tenant data ponds on the browser will modernize the landscape of data, as data processing moves from centralized architectures to the edge, making analytics accessible and affordable for all.
  • Text2SQL with GPT has enabled natural language querying capabilities, empowering anyone and everyone to derive insights that were previously only accessible to data team members.

From GPT to Copilots: Modernizing search in customer support

  • GenAI — LLMs, AI middleware (LangChain | Llama Index), diffusion models, Retrieval Augmented Generation (RAG) et al. — will take years to simplify for the channel and implementation partner community, as these abstractions are changing fast and bleeding edge in nature.
  • Chatbots and vector databases have become integral components to business software, thereby enhancing the efficiency of support systems.
  • Knowledge bases (KBs) have emerged as central repositories for deflecting customer requests, forming a more engaging and knowledge-rich replacement to traditional message boards at a fraction of the response time.
  • Lightweight authoring tools, complete with rich text editors and inline commenting features, have streamlined content creation. GenAI further accelerates this content creation process.
  • Semantic search in the enterprise facilitates effortless information retrieval, providing support engineers and agents with AI copilots for more effective problem-solving.

These are shifting sands — in interest rates, business models, new department expenses (vitamin departments vs. painkillers), user empowerment (with data), and productivity (with AI) — of our times, and customer support must evolve beyond tickets, L1, and L2. These folks are our frontline, no less than sales, and they take the bullets from customers. We need to bolster them with innovation, insights, and appreciation. In the subsequent blogs of this series, we will delve deeper into each of these pillars and explain how they elevate support to have a seat at the table with product and engineering.