Executive Summary

This report provides a comprehensive analysis of building a Veeva support practice, focusing on staffing and knowledge management. It covers the historical and current context of Veeva Systems in the life sciences industry, describes the organizational models and team structures used for effective support, and outlines the specific roles, skills, and certifications required. The report also delves into best practices for knowledge base management (including tools, processes, and metrics), presents real-world case studies illustrating successful support practices (e.g., Veeva’s own use of Zendesk, and Agilent’s use of a KCS-based system), and discusses future trends such as AI-driven support and evolving customer expectations. Throughout, data and examples from industry sources (Atlassian, Zendesk, TechRadar, McKinsey, etc.) are used to back evidence-based recommendations. Key findings include the importance of a tiered support structure, integration of Knowledge-Centered Service (KCS) methods, continuous training (including Veeva certifications) for staff, and proactive use of knowledge management systems to increase first-contact resolution and customer satisfaction. Finally, the report highlights emerging directions in support, notably the growing role of AI automation and the critical need for human skills like empathy and data interpretation in the evolving support landscape (^[1]) (^[2]).

Introduction and Background

The Veeva Ecosystem and Industry Context

Veeva Systems is a leading provider of cloud-based software for the global life sciences industry, with a focus on regulatory compliance, clinical data, and commercial operations. Founded in 2007 by former Oracle executives, Veeva has grown rapidly; as of the mid-2020s it serves over 650 life sciences companies, from the world’s largest pharmaceutical firms to emerging biotech startups (^[3]). Its flagship products – like Veeva Vault for content management (e.g. clinical documents, quality systems) and Veeva CRM for sales/marketing — have become central to many organizations’ digital strategies. Veeva’s multi-tenant cloud model allows even small companies to access enterprise-class systems with strict FDA-level controls without heavy upfront infrastructure costs (^[4]). The platform is updated frequently (typically three releases per year), so customers continuously receive new features, but must also manage adoption and support.

Because life sciences is a heavily regulated, safety-critical industry, Veeva customers demand highly reliable, expert support. Downtime or misconfiguration can disrupt clinical trials or product launches. As a result, building a dedicated Veeva support practice – whether in-house or as a managed-service vendor – is critical to ensure customer success and long-term satisfaction. A support practice must offer technical troubleshooting, best-practice guidance, and ongoing maintenance for multiple Veeva products. Additionally, the complexities of life sciences processes (e.g. clinical development workflows, regulatory submissions, sample management, etc.) mean that support staff need both deep product knowledge and domain expertise (e.g. understanding of FDA regulations, GxP processes, or medical affairs).

At the same time, the broader customer service environment has been changing. Recent surveys (e.g. McKinsey’s 2022 State of Customer Care) highlight a “perfect storm” of rising support needs, talent shortages, and high customer expectations (^[5]). For instance, 61% of care leaders reported growing call volumes, and 58% expect them to rise further (^[6]). Training new support hires to full proficiency can take 3–6 months (^[7]). Meanwhile, customers now expect faster, more personalized service. TechRadar notes that 75% of customers want rapid (often immediate) responses, and that AI is automating basic queries, pushing human support roles to focus on empathy and strategic problem-solving (^[2]) (^[8]). These trends imply that a Veeva support practice must not only staff up sufficiently, but also organize itself to enable fast resolution and continuous learning.

Scope of the Report

This report explores how to build a robust Veeva support practice, with three central lenses: staffing, knowledge management, and team structure. It includes:

• Historical context and current state: overview of Veeva’s platform and its significance in life sciences; trends in technical support in regulated industries.
• Certifications and Skills: detailed breakdown of roles (e.g. Vault Administrators, CRM Specialists, Integration Developers, etc.) and the Veeva-specific and general skills/certifications they need.
• Knowledge Base and Content Management: best practices for building and maintaining an internal Knowledge Base (KB), including knowledge-capture processes (e.g. KCS), metrics, and tools (like Zendesk or Jira Service Management).
• Support Team Structure: typical organizational models (tiered support levels, roles of analysts/engineers/managers), plus guidance on reporting lines and escalation.
• Data-driven analysis: case studies (such as Veeva’s own Zendesk-based support operations and an Agilent KCS case) and industry metrics (e.g. how KB usage improves key performance indicators).
• Future directions: the impact of AI, remote work, and other emerging trends on Veeva support.

All claims are supported by citations from authoritative sources (industry reports, case studies, vendor documentation). The intended audience is executives and practice leaders planning or improving a Veeva support service organization, as well as knowledge managers and support analysts who want an in-depth understanding of the topic.

Required Certifications and Skills

A high-performing Veeva support team requires a range of technical and domain skills. Veeva offers industry-recognized certifications for its products, which ensure staff have proven competence. In practice, support organizations typically require employees to hold relevant Veeva certifications (or to earn them on the job). Common certifications and skill areas include:

• Veeva Vault Platform Administrator: Validated by the official Vault Platform Administrator Exam, this credential confirms ability to configure Veeva Vault data models, UI, and workflows (^[9]). The exam covers topics like defining custom objects and fields, designing lifecycles and workflows, setting up security and user roles, and troubleshooting). It is aimed at system administrators and consultants who conduct daily Vault operations (^[9]) (^[9]). Support staff who manage Vault environments should be certified or equivalently experienced.
• Veeva Vault Developer/Architect: While Veeva’s education site highlights training courses (e.g. “Develop Custom Code Solutions for Vault”) rather than a single “exam”, typical developer roles require knowledge of the Veeva Vault API, Vault Query Language (VQL), Vault Metadata Definition Language (MDL), and the Vault Java SDK (^[10]) (^[11]). Developers integrate Vault with other systems and build custom features. A strong Java and REST API background is expected.(For example, Veeva’s Technical Consultant role lists “4+ years of Java” and familiarity with Vault’s Java SDK and web services (^[12]) (^[13]).)
• Veeva CRM (CLM) Specialist: For companies using Veeva CRM (prescription tracking, sample management, closed-loop marketing), support staff should have expertise in CRM modules like Veeva CLM, Medical (Multichannel), and Veeva OpenData. Veeva offers training courses (e.g. “Administer Vault CRM” for CRM configuration (^[14])) and exams on CRM administration (organization setup, CLM presentations, multichannel operations). Familiarity with marketing automation/medical information processes is important.
• Integration and Data Migration: Knowledge of data integration tools (e.g. Informatica, MuleSoft) and ETL processes is often needed. Specifically, Veeva projects frequently involve migrating legacy content into Vault or synchronizing Veeva CRM with other systems. For example, the Veeva Vault Migration Certification requires completion of Vault Admin training plus migration experience (^[15]). Skills in SQL, XML/JSON, and middleware platforms add value.
• Quality and Compliance Domain Knowledge: Since Vault often supports Quality (e.g. regulatory documents, CAPA processes) and Clinical (studies data) functions, support engineers should understand GxP terminology and regulatory workflows (e.g. 21 CFR Part 11, electronic signatures). This domain expertise cannot be learned overnight, so hiring (or training) from life sciences backgrounds (pharma, biotech) is common.
• General Technical Skills: Concurrently, foundational IT skills are needed: familiarity with SaaS architectures, networking basics, SQL/databases, web technologies (HTML, JavaScript), and user support tools (ticketing systems, remote support software). Understanding ITIL/ITSM concepts (incidents, service requests, change control) is also advantageous, as many Veeva customers operate to ITIL standards.
• Soft Skills and Business Communication: Support roles demand clear communication, problem-solving, and customer service orientation. In the era of AI-assisted support, subject-matter experts will handle more complex inquiries requiring empathy and critical thinking rather than rote fixes (^[2]) (^[16]). Support staff often interact with diverse internal and external stakeholders, so training and presentation abilities are key. Language skills may be required for global firms (e.g. English plus one European or Asian language (^[17])).

Crucially, securing staff with recognized credentials builds customer confidence. As one Veeva partner certification guide notes, a certified Vault Administrator is a prerequisite for specialized partnership levels (^[15]). Support teams should encourage ongoing learning (including maintaining certifications — Veeva requires annual recertification exams (^[18])) to keep up with frequent product releases.

The table below summarizes typical Veeva support roles and their associated skills/certifications:

(Sources: Veeva training materials (^[9]) (^[10]); Veeva partner certification criteria (^[15]); industry job postings (^[12]) (^[19]))

Knowledge Base and Knowledge Management

Knowledge management (KM) is a cornerstone of an effective support practice. A well-maintained knowledge base (KB) both empowers customers (self-service) and speeds support operations by giving agents immediate access to solutions. In the context of Veeva support, best practices include continuous content creation, rigorous review workflows, and alignment with customer needs.

Benefits of Knowledge Management in Support

A robust KM practice delivers multiple benefits: faster resolution times, increased first-contact resolution, consistency of answers, and lower operational costs. Industry analyses quantify these gains: one study of KCS (Knowledge-Centered Support) implementation showed 30–50% increases in first-contact resolution rates and 50–60% improvements in time-to-resolution (^[20]). Similarly, a knowledge system can accelerate new-agent onboarding (up to 70% faster time-to-proficiency) and reduce support volume by enabling self-service (^[20]). For Veeva customers juggling complex compliance queries, these improvements can translate into significant value.

Atlassian (Jira Service Management) emphasizes that capturing and surfacing relevant knowledge at the right time “allows teams to respond to and resolve issues quickly, provide consistent answers, and enable self service” (^[21]). For example, if a customer submits a service request about a failed Vault workflow, the support portal can automatically suggest existing KB articles related to similar keywords (^[22]). This not only helps the customer self-solve routine problems (e.g. missing user permissions) but also helps support agents avoid reinventing solutions. As Atlassian notes, agents empowered with good articles “spend less time on requests” and have a “blueprint for continuous improvement” (^[23]). In crises like production outages, documented incident playbooks and runbooks (a facet of KM) ensure faster mitigation and postmortem learning (^[24]).

Zendesk’s public case study of Veeva’s internal support team illustrates these benefits. Before formal KM processes, Veeva’s agents might “handle tickets and then, maybe on a Friday…create knowledge articles on the side” (^[25]). After implementing Knowledge-Centered Service (KCS) workflows with Zendesk Guide, Veeva cut article publishing time from 30 days to under a week (^[26]). Agents who demonstrate knowledge-writing competency were promoted through a three-tiered KCS program; they earned badges and increased autonomy, driving rapid content creation (^[27]). Veeva integrated KB creation into the ticketing flow using Zendesk’s Knowledge Capture app, so that on resolving an issue an agent could immediately draft and tag an article (^[28]). The result was a constantly-growing KB (over 2,500 articles) and very high support metrics (97% CSAT, 99% SLA adherence) (^[29]) (^[26]). These findings confirm that embedding KM into daily support work dramatically improves efficiency and customer outcomes.

Knowledge Base Structure and Processes

Key practices for the support KB include:

• Tier 0 (Self-Service): Creating a public or internal help center where customers can search FAQs, user guides, and release notes. By Atlassian’s model, this is “level zero” support where users themselves find answers via the knowledge base and AI assistants (^[30]). As [2] noted, every time an agent creates a reusable solution for a ticket, it should be published to the KB so future users can self-serve (^[31]). A well-indexed search engine (with tags/categories aligned to Veeva modules) is crucial for customers to locate relevant info.
• Tiered Article Review Workflow: Introduce a review process to maintain quality. For example, Veeva originally used a label-based workflow but later switched to “Team Publishing” in Zendesk Guide, enabling assign-and-notify reviews (^[32]). In practice, each new KB draft is reviewed by a subject-matter expert (SME) before publication. Over time, experienced agents gain trust to publish directly (as per Veeva’s KCS tiers) (^[27]). This ensures consistency and accuracy.
• Classification and Metadata: Articles should be organized by product (Vault, CRM), by function (e.g. “User Administration”, “Integration”), and by ticket topic. In the Zendesk case, managers perform a “gap analysis” report showing ticket counts vs. article counts per product, then coach teams to write more content in under-served areas (^[33]). A similar practice in any KM system helps prioritize knowledge creation where it’s most needed.
• Continuous Updates: Veeva releases new features quarterly. The KB must be updated post-release to cover changes. This may involve change-management coordination: when a release note says a feature is enhanced, a support article should explain end-user actions or configuration steps. As TechRadar suggests for “agentic AI” readiness, consolidating fragmented knowledge into a single source and assigning SME ownership is critical (^[34]). Similarly, designate ownership (e.g. each major product area has a knowledge coach) to keep content current.
• Measurement and Improvement: Track metrics like KB article views, article usefulness ratings, and ticket deflection rates (tickets avoided due to KB searches). Many support tools (Zendesk, Jira Service Desk) have built-in KB analytics. Veeva’s support team measured publication latency and prioritized quick turn-around; by instituting formal workflows they achieved sub-48-hour publishing for most articles (^[32]). Also measure customer outcomes: higher self-service usage should correlate with fewer low-level tickets and higher CSAT (^[35]) (^[20]).
• Tool Integration: Ideally, the KB is integrated with the support ticketing system. Zendesk’s Knowledge Capture app, for instance, lets agents create or search articles directly within a ticket and link answers automatically (^[28]). Similarly, Jira Service Management can pull Confluence articles into tickets. Such integration ensures agents always have the latest info at hand.

Knowledge Management Frameworks

Supporting all these processes are structured KM methodologies. One widely endorsed approach is Knowledge-Centered Service (KCS), which emphasizes creating knowledge as a by-product of solving issues, and promoting active KM participation among support staff. Veeva’s example is explicitly KCS-based: agents “gain experience and prove they can create accurate articles” to advance tiers (^[27]). As Atlassian explains, KCS drives continuous learning by having people document solutions as they become experts, improving first-contact resolution and speeding defect identification (^[26]) (^[20]). Implementing KCS typically involves training all agents in writing KB articles and linking them to incidents, so knowledge maintenance is embedded in daily support activities.

Beyond KCS, general knowledge-management literature (e.g. ITIL’s KM practice) highlights the value of a formal knowledge strategy, roles like “Knowledge Manager,” and culture of sharing. A report on Knowledge Management in organizations stresses consolidating “tacit” (expert) knowledge and making it explicit in repositories (^[4]) (^[34]). In practice, a support practice may hold periodic “knowledge reviews” or postmortems where solutions from major incidents are translated into KB content, ensuring institutional learning.

Support Team Structure

Effectively supporting Veeva products requires a clear support organization. The most common model is a tiered support hierarchy (sometimes in three tiers, plus a “tier zero” for self-service). This helps ensure the right expertise handles each problem and provides a career ladder for support staff.

• Tier 0 (Self-Service): As noted above, this is the public-facing knowledge base and automated assistants. Investment here can deflect a significant portion of trivial tickets (e.g. password resets, known bug workarounds) away from live support (^[30]) (^[22]).
• Tier 1 (General Support / Help Desk): The first human level. Tier 1 agents handle routine, low-severity issues (password resets, basic application use questions, account access, navigation help) (^[36]) (^[37]). They use scripts or the KB (Tier 0 resources) to resolve common problems. A Tier 1 agent needs enough familiarity with Veeva to walk users through procedures, but does not need deep technical expertise. Atlassian notes that Tier 1 only requires “staff with minimal experience,” focusing on isolated issues of limited impact (^[36]). For example, if a medical affairs user can’t upload a file in PromoMats, a Tier 1 analyst might follow a KB article guiding them step-by-step. If stuck, the agent escalates.
• Tier 2 (Technical Support): Tier 2 engineers are the second line, skilled professionals who handle more complex technical problems that Tier 1 cannot resolve (^[38]). This includes in-depth Vault configuration issues (broken lifecycles, integration troubles), CRM behavior not covered by standard docs, or performance issues requiring logs analysis. Tier 2 personnel often have Veeva certifications and deeper product knowledge. They maintain the department’s “comprehensive knowledge base” and even coach Tier 1 on new features (^[39]). In practice, Tier 2 might reproduce an issue in a scratch org, consult with Veeva’s engineering, or deploy patches.
• Tier 3 (Expert/Engineering): This top tier handles the most complex or critical problems. Tier 3 may consist of the original implementation team, senior architects, or even direct escalation to the Veeva vendor’s engineers. They work on critical failures, advanced customization debugging, or writing new code to fix system-level bugs. Tier 3 specialists typically have intimate knowledge of the Veeva platform internals and often assist with performance tuning, complex integration, and security audits. They rarely escalate further (often interfacing with Veeva product support if needed).

This tiered approach is well-known in IT support. As one industry summary puts it, Tier 1 is "first point of contact for basic issues," Tier 2 "resolves escalated, more complex issues," and Tier 3 "handles critical, highly complex technical problems" (^[40]). Zendesk similarly emphasizes that a tiered system optimizes efficiency and employee career progression (^[41]). Tiered models also align with ITIL incident management: by matching issue severity/complexity to appropriate skill level, tickets move through the pipeline smoothly.

In addition to the support technicians, the support organization usually includes leadership and specialized roles:

• Support Manager / Director: Oversees the entire support practice, sets SLAs (service-level agreements), monitors KPIs, and liaises with senior customers. They design escalation policies and ensure alignment with business goals. In Veeva’s case, the manager would coordinate with both customers and with Veeva’s internal product teams to communicate roadmaps and known issues (^[42]).
• Knowledge Manager / KM Coach (already mentioned): Owns the knowledge management process (see previous section). They ensure quality of the KB, analyze gaps, and coach agents on KCS methods.
• Team Leads / Technical Leads: Senior support engineers may serve as Tier 2/3 leads, mentoring newbies and reviewing major escalations. They often interface between support and project teams (e.g. telling implementation engineers about recurring ticket patterns).
• Customer Success Manager (CSM): In many Veeva engagements (especially those under managed services), a CSM or account manager ensures the customer’s broader goals are met. They may handle high-level issues, coordinate with professional services for major upgrades, and guide change management. (While not strictly “support” staff, CSMs often work closely with the support practice cross-functionally.)
• Training/Education Specialist: Some organizations have a role focused on end-user training (classroom or eLearning). This role can reduce support tickets by preemptively teaching users. Veeva also offers its own Education Services, and support practices may leverage or partner with them.

Team structure also depends on geography and customer scale. Large global customers may require 24x7 “follow-the-sun” support, staffed by teams in multiple time zones. The support practice might have regional pods or partner contracts to provide this round-the-clock service. Alternatively, a centralized offshore team can handle Tier 1/Tier 2 tickets after hours with escalation back to Tier 3 in a primary region when needed. Modern tools (chat, videoconference, shared KB) make distributed support feasible, but clear roles and handoff protocols are essential.

Below is a simplified table illustrating a typical tiered structure and responsibilities:

(Tier definitions adapted from industry sources (^[41]) (^[36]) (^[39]).)

This structure helps ensure that routine tickets are handled quickly at lower tiers, while specialized staff focus their time on complex issues. Career paths are clearer: a Tier 1 agent may train and progress to Tier 2 (often after passing Veeva certifications), then potentially to Tier 3 engineering or solution architect roles. Such progression was formalized at Veeva via their KCS tiers (^[27]), and it also drives employee retention and satisfaction.

Data and Metrics in Support and KM

A data-driven approach is crucial for optimizing a support practice. Key performance indicators (KPIs) assess team performance, guide staffing needs, and demonstrate ROI of KM initiatives. Common metrics include:

• First-Contact Resolution (FCR): Percentage of tickets resolved without escalation (measured by tickets closed at Tier 1). Higher FCR indicates effective Tier 1 training and KB usage. As noted, KCS adoption can boost FCR by 30–50% (^[43]).
• Average Resolution Time (ART) or Mean Time to Resolve (MTTR): Measures how long, on average, a ticket remains open. Effective knowledge practices (and higher Tier 1 FCR) reduce MTTR. The case study above reported multi-day publishing turnaround vs. 1–2 days after KM improvements (^[26]), which would shorten resolution delays.
• Customer Satisfaction (CSAT): Survey scores from customers after ticket closure. Zendesk’s Veeva example achieved ~97–99% in CSAT/SLA adherence (^[35]), showing that high-quality KM and support processes directly correlate with satisfaction.
• Ticket Deflection Rate: Proportionate reduction in tickets due to KB availability. While exact quantification is complex (as multiple factors affect ticket volume), an increase in self-service usage usually means "fewer tickets filed." Tools can track purchases of knowledge-base usage vs. ticket count. The ROI analysis from several reports suggests that robust KB can reduce inbound queries significantly (^[44]) (^[43]).
• KB Article Quality Metrics: e.g. percentage of KB articles with positive feedback, or frequency of KB article updates. This helps KM staff know which articles need revision. Some organizations tag each knowledge article as “Solution Verified” when an agent successfully applies it, creating feedback loops.
• Training and Certification Rates: Internally track the number of team members with required Veeva certs and how often recertification is due. Because Veeva’s platform evolves, certification maintenance is part of quality assurance for the support team.
• Staffing Efficiency: KPIs like tickets per support FTE, or agent utilization rates, can guide resourcing decisions. If ticket volume spikes (e.g. after a major software release) and backlog grows, that signals the need for more Tier 1 or Tier 2 staff. Conversely, low backlog with many idle agents may indicate overstaffing.

Lastly, success stories such as the Agilent case show broad business impacts: Agilent saw a sixfold increase in support efficiency, 95% CSAT, and 25% cost reduction after deploying a KM-centric platform (^[45]). While Agilent is not in life sciences R&D, this underscores generalizable trends: investing in knowledge and automation delivers quantifiable returns.

Case Studies and Examples

Veeva’s Internal Support (Zendesk Case Study)

Veeva’s own customer support organization provides a powerful example. In 2015 Veeva implemented Zendesk Support and Guide as its ticketing and knowledge platforms (^[3]). The support leaders reported dramatic improvements: by building knowledge capture into the ticket workflow and adding a multi-tier publishing process, Veeva cut article publishing time from 30 days to under 1 week (^[26]). Agents began creating KB content as they solved tickets (instead of deferring it). A final optimization using Zendesk’s Team Publishing allowed articles to go live in “1–2 days” (^[32]). Over time, agents were trained in KCS: they moved up through three levels of content authoring competency, earning badges and autonomy (^[27]). The knowledge capture integration also let them easily generate and update articles without leaving the ticket interface (^[28]).

These process changes had measurable effects: with over 2,500 knowledge articles, Veeva resolved 35,000 support tickets per month, and achieved 97% customer satisfaction (CSAT) and 99% SLA adherence (^[1]) (^[35]). This case underlines that a focus on KM can transform a support practice even in a complex enterprise environment.

Agilent Laboratories (Life Sciences Diagnostics)

Another instructive example comes from Agilent Technologies, which provides instruments and software for life sciences and diagnostics. Agilent faced a growing support volume across multiple channels and needed 24/7 service while reducing costs. They partnered with a vendor (Sobot) to implement an omnichannel support system built on KCS principles (^[46]) (^[47]). The solution unified all customer messages (email, chat, social) into a single interface and added an AI-powered chatbot linked to an evolving knowledge base (^[46]) (^[47]). Routine questions became automated: the chatbot “learned from each case” and updated the KB, deflecting simpler issues without human intervention (^[46]) (^[47]).

The results were striking: Agilent reported a sixfold improvement in support efficiency, costs down 25%, and CSAT up to 95% (^[45]). The AI chatbot handled a large share of basic tickets, freeing human agents for complex problems. The total effect was so significant that Agilent noted a substantial reduction in staffing needs for the same (or even greater) workload (^[45]). In summary, by heavily investing in knowledge tooling and AI, Agilent transformed their support model into a competitive advantage. (While Agilent’s domain is diagnostics, the principle applies broadly: a well-managed knowledge base with smart automation can dramatically boost support performance while controlling personnel costs.)

Other industry reports and case studies consistently corroborate these findings. For example, customer-support organizations implementing KCS or similar frameworks often see 30–60% faster incident resolution and improved service-level attainment (^[20]) (^[45]). Organizations should study these examples for practical ideas on structuring their workflows and choosing technology.

Implications and Future Directions

Looking forward, several trends will influence how a Veeva support practice should evolve.

• AI and Automation: As noted earlier, AI now handles many routine interactions: Salesforce’s CEO revealed that the company replaced 4,000 support jobs with AI “Agentforce” agents, which now handle roughly half of all customer interactions (^[8]). In the Veeva context, AI chatbots (trained on the KB) might soon field questions about common Vault configurations or CRM features. Automated systems can pre-triage tickets, suggest solutions to agents in real time, and even initiate self-service triggers (e.g. detecting a known error pattern and emailing the user a KB link). However, as TechRadar emphasizes, human agents will remain crucial for complex or sensitive cases, and their roles will shift toward empathy, creativity, and strategic advisory (^[2]). Practically, a Veeva support practice should plan for AI integration (e.g. maintaining clean, structured KB content to feed chatbots) while investing in training staff for roles that AI cannot fulfill (like CX design, analytics, or personalized consulting).
• Knowledge Consolidation: Future “agentic AI” systems rely on high-quality knowledge bases (^[34]). Enterprises are advised to unify fragmented knowledge across systems (documentation, wikis, previous ticket archives) into a coherent KM framework with strong governance (^[34]). For a Veeva support team, this means breaking down silos (e.g. ensuring that product updates noted in one document are quickly integrated into the main KB and training materials). Agile integration of legacy and new info is vital, as [83] suggests: ensuring real-time synchronization and open standards for knowledge exchange will keep support knowledge relevant and comprehensive.
• Data-Driven Support: With powerful analytics, support teams can become proactive. For instance, by mining Veeva usage logs or error tracking, a team could predict common issues (e.g. a Vault migration bug after an upgrade) and push relevant FAQs to users before they even ask. The workforce analytics from the McKinsey survey (e.g. long training times (^[7])) also argue for investing in support employee experience: applying lean and continuous learning in support can reduce the onboarding time and burnout that plague care teams.
• Remote and Flexible Work: The pandemic has shifted many support roles to remote or hybrid models. McKinsey noted that 85% of customer care workforces can work from home (^[48]). A Veeva support practice can tap global talent pools (e.g. hiring Veeva-certified admins in different countries). However, this requires strong digital collaboration tools and a focus on building team culture to mitigate issues like isolation or overwork. Rotations and “follow-the-sun” scheduling help ensure 24/7 coverage without burning out any one team.
• Integration with Product Development: Veeva’s frequent releases make change management a critical support task. Support teams should be aligned with product teams: participating in release beta testing, updating KBs before release, and feeding customer feedback into product roadmaps. This devops-like collaboration ensures support doesn’t become overwhelmed by surprises, and it also turns support agents into advocates for beneficial features.

Conclusion

Building a Veeva support practice is a multifaceted endeavor requiring strategic staffing, rigorous knowledge management, and adaptable organizational structure. This report has identified that:

• Staffing: Key roles include system administrators, developers, consultants, and support analysts, all of whom require Veeva-specific knowledge and certifications (^[9]) (^[10]). Soft skills (communication, empathy) and industry domain expertise are equally important for delivering service excellence (^[2]) (^[19]). Support teams benefit from clear career paths (e.g. tiered KCS levels (^[27])) and continuous training (Veeva exams, product updates).
• Knowledge Management: A dynamic, searchable knowledge base is essential. Best practices (like embedding KCS methods, having a defined review process, and measuring KB impact) pay off in higher first-contact resolution and customer satisfaction (^[26]) (^[20]). Case studies (Veeva’s Zendesk usage, Agilent’s AI chatbot) illustrate that disciplined KM yields dramatic efficiency gains. Support leaders should adopt analytics to identify KB gaps and refine content continuously.
• Support Structure: Organizing support in tiers (self-service, levels 1–3) with well-defined roles ensures efficient issue routing and leverages agent expertise appropriately (^[36]) (^[38]). Leadership roles (Support Managers, Knowledge Managers) are critical for setting standards and driving improvement. Global coordination and clear escalation paths enhance reliability and customer trust.
• Technological Trends: The advent of AI and advanced service platforms is reshaping support. The future support team will likely mix AI agents handling routine queries and human experts focusing on complex problem solving and customer experience (^[8]) (^[2]). Knowledge systems must evolve to feed AI with accurate, structured information (^[34]). Organizations that proactively integrate these technologies, while reskilling staff, will lead the industry.

In summary, a successful Veeva support practice is built on well-chosen people, empowered by a strong body of knowledge, structured by best-practice service models, and informed by data and innovation. By following the strategies outlined here—grounded in real-world examples and expert recommendations—life sciences companies and service partners can deliver the timely, expert support that Veeva customers expect, today and in the future.

References:

• Zendesk Customer Story: Veeva’s Support Operations (2025) (^[1]) (^[26]) (^[27]).
• Atlassian ITSM Knowledge Management Guides (^[49]) (^[23]).
• Atlassian Support Tiers Overview (^[41]) (^[36]).
• McKinsey, “The state of customer care in 2022” (2022) (^[5]) (^[7]).
• Sobot, “Top Knowledge-Centered Service Case Studies” (2025) (^[20]) (^[45]).
• Veeva Education Services — Certification Details (Vault Admin Exam) (^[9]).
• Veeva Partner Migration Certification Requirements (^[15]).
• Techradar (Oct 2025): “Salesforce CEO says it cut 4,000 support jobs…” (^[8]).
• Techradar (Oct 2025): “Will AI mean the end of the road for my customer service career?” (^[2]).
• Techradar (Aug 2025): “How enterprises can transition their knowledge …for Agentic AI” (^[34]).