Katarina Railko has spent her career at the intersection of hospitality, entertainment, and large-scale events, translating real-world guest behavior into digital strategies that move the needle. Drawing on years in travel and tourism, she now advises hoteliers on unifying tech stacks, activating AI inside day-to-day workflows, and protecting brand equity while accelerating direct revenue. In this conversation with Alex Taillon, she demystifies how a single, hospitality-focused platform—refined over more than 25 years—brings CMS, analytics, reservations, planning, and reputation management together, and how teams actually use it from the first login to the first booking.
The Vizergy Marketing System centralizes CMS, analytics, reservations, plans, and reputation services. Can you walk me through a real client rollout step by step, share the timeline and team roles, and quantify how centralizing tools changed direct bookings, ROAS, and time-to-publish?
We start with discovery: a working session to map every tool they touch—CMS, ad platforms, reservation engine, reputation feeds—and reconcile it against the one-platform model. From there, we run parallel tracks: data and analytics set up a hospitality-focused Adobe Analytics implementation, while web and content migrate priority pages and booking paths into the CMS. Marketing plan management and reputation services plug in next so teams have a single login for day-to-day operations. In Episode 4 of our interview series, we talk about how this approach cuts “tab fatigue” and shortens the distance from idea to execution. While I won’t cite client-proprietary numbers, the measurable wins show up in the platform’s own reporting: faster time-to-publish visible in workflow logs, ROAS tracked alongside reservations and room nights, and a steady shift in channel mix toward direct. The biggest surprise for most teams is psychological—once you can see visits, bookings, and click-to-call side by side, decisions feel less like guesswork and more like operating a single machine.
You integrate with Adobe, Google, Microsoft, Facebook, and SynXis. Can you describe one implementation where these integrations unlocked something you couldn’t do before, detail the data flow end to end, and share the impact on revenue, channel mix, or bounce rate?
One multi-property client struggled to tie media spend to reservation outcomes because the journey crossed 4–5 systems. We stitched Adobe Analytics to Google Ads, Bing (Microsoft) Ads, Facebook Ads, and SynXis so the click-through, on-site behavior, and reservation data landed in one warehouse. The flow is clean: ad platforms pass campaign and audience parameters, the CMS appends content and placement context, Adobe resolves sessions and events, and SynXis closes the loop with reservation and room night details. Channel-based reporting inside the platform then exposes which audiences and placements are feeding higher-value bookings, not just clicks. The qualitative impact was immediate: they shifted budget toward sources that consistently produced reservations rather than vanity visits, and bounce rate dropped on pages where creative and CMS modules were tuned to match the exact audience we were paying to acquire.
Your CMS has enterprise features for multi-property management from a single login. How do large management companies use this daily, which workflows save the most time, and what governance or approval paths keep brand standards intact?
For management groups, the single-login view is everything. Content leads can schedule an offer across a portfolio in one pass, ops can push a reviews feed or a booking widget update to every site, and analytics can benchmark properties without exporting a dozen CSVs. Time savings come from portfolio-level modules: the Offers application with automated landing pages, global components for headers/footers, and shared media libraries that prevent duplicate work. Governance is built into the workflow—role-based permissions, required approvers for sensitive areas like homepage and booking flows, and locked patterns for typography and color so brand stays consistent even as content flexes. It’s not just faster; it’s safer, because the guardrails live inside the CMS instead of in a PDF brand guide on someone’s desktop.
You emphasize compliance, proprietary security, and a single platform. Can you outline your security model, note how data is segmented and audited, and share an example where this approach prevented an issue or passed a tough client compliance check?
The platform is proprietary end to end, so we can enforce consistent controls rather than stitching policies across third parties. Data is segmented by client and property, with role-based access layered on top; audit logs capture every change—from page edits to offer schedules—so reviews are straightforward. Centralization reduces risk because fewer tools mean fewer weak links, and compliance reviews aren’t a scavenger hunt across vendors. A recent enterprise client with strict procurement requirements ran a full audit against our change logs, permissioning, and encryption posture, and passed without remediation—largely because the “one platform” model gave them a single set of controls and a single source of truth.
On AI content and image generation, how do you train prompts to match a brand’s voice, what guardrails prevent off-brand outputs, and which metrics—time-to-first-draft, edit rate, or conversion lift—show the biggest gains?
We start with a brand voice pack: tone descriptors, do/don’t lists, and 3–5 “golden” examples pulled from the client’s own site. Those become structured prompt primers inside the platform so content creators don’t have to reinvent cues every time they draft a page. Guardrails include banned phrases, reading-level targets, and templated structures that map to hospitality patterns like offers, rooms, and dining—so outputs align with brand and page intent. The most consistent lift shows up in time-to-first-draft—teams move from blank page to publishable copy in a single working session—and edit rate trends down as the system learns from approved revisions. We also see stronger continuity between page copy and urgency messaging when both are generated and edited in one place.
Your computer vision auto-generates alt text and tags for ADA needs. Can you explain the tagging workflow, how editors review and approve it, and share before-and-after accessibility scores, page speed changes, or organic traffic shifts?
When an image is uploaded, computer vision suggests descriptive alt text and tags tied to hospitality contexts—rooms, amenities, dining, or events. Editors review suggestions in-line, accept or tweak them, and publish with a single click; the CMS then applies the tags consistently across modules like galleries and offers. The benefit is twofold: accessibility compliance improves because nothing ships without alt text, and search engines get clearer signals about content. We’ve watched ADA scanning scores inside the platform rise as more images flow through this workflow, and page speed remains stable because the tagging happens at upload, not via a heavy client-side script.
AI Report Insights flags channel shifts and referral spikes. How does a hotelier act on one of these alerts in practice, which dashboards or filters they open next, and what performance swing (visits, reservations, ROAS) you’ve seen after timely action?
An alert might read: “Referral traffic spiking from a local event site; reservations not yet following.” The next move is to open the channel-based reporting view, filter by that referral, and inspect landing pages with CMS Page Analysis—are we matching intent with a relevant offer or room type? If not, the team can deploy an offer landing page through the Offers app and surface it via personalization for that audience. Because visits, reservations, and ROAS appear together, you can watch the effect in near real time. Timely action typically stabilizes ROAS and closes the gap between traffic surges and bookings, especially around expos and conferences where Katarina’s events background becomes a practical advantage.
CMS personalization supports targeted messaging. Could you detail a full personalization scenario—audience rules, creative variants, and placement—then share test design, sample size, and the lift in CTR, bookings, or average order value?
A common scenario is geo + referral intent. Audience rules might target users within driving distance who arrive from Google or a partner site with event context. Creative variants include a localized hero banner, urgency messaging in the booking widget, and an offer block for a weekend package. We place these in high-visibility modules—hero, mid-page offer, and exit-intent lightbox—so guests see consistent cues. Testing is straightforward: split by audience eligibility and hold out a control within the same traffic sources; then monitor CTR and bookings with Adobe’s hospitality-focused events. Even without quoting figures, the platform-level reporting makes it clear when the personalized variant outperforms the control across clicks and reservations.
Your booking widgets, urgency messaging, and the Offers app with automated landing pages aim at direct revenue. How do you sequence these on a page, what triggers you use, and which combination has produced the strongest increase in conversion rate?
Sequence matters. We anchor the booking widget above the fold, pair it with subtle urgency—limited rooms or time-bound offers—and support it with an Offers landing section mid-page. Triggers include scroll depth for reinforcing messages and exit intent for a final nudge that aligns with the hero offer, not a random popup. The combination that works most reliably is a clear hero promise, a visible booking widget, and an automated offer page that mirrors the messaging; it keeps the guest in a single narrative from first glance to confirmation. The consistency is what lifts conversion—every module, including reviews and video, points to the same action.
You provide website speed and ADA scanning with a CDN and page speed monitoring. Can you outline the optimization playbook you follow, the order of fixes you deploy, and the typical improvements you see in Core Web Vitals and organic rankings?
We start with measurement—page speed monitoring flags templates and assets that need love. Then we tackle the big rocks: image optimization, critical CSS, script deferral, and CDN caching rules tuned for hospitality content like galleries and menus. In parallel, we run ADA scans to catch contrast, keyboard navigation, and alt text gaps; the computer vision tagging helps here. Core Web Vitals improve because we shorten the critical path, and organic visibility follows as search engines reward faster, more accessible experiences. The playbook is repeatable—measure, optimize, validate—and the gains hold because they’re baked into the CMS, not one-off fixes.
Automated schema and SEO features are built in, including support for AI overview results. What schema types you deploy for hotels, how you validate them, and can you share examples where search visibility or AI-overview placement materially improved?
We deploy hospitality-relevant schema—hotel, local business, offers, events, and reviews—so search engines understand context without guesswork. Validation runs in-platform and against external testing tools, and we monitor coverage in search console alongside our own reporting. Support for AI overview results means structuring content to answer broad and specific queries with the right entities and attributes, which the CMS templates encourage. Visibility improves when the technical foundation is consistent across pages—search sees a coherent hotel, not a patchwork of pages. We’ve seen clients surface more frequently where AI summaries rely on structured data, particularly for offers and amenities.
Reporting blends Adobe Analytics with Google Ads, Bing Ads, Facebook Ads, and Google Business Profile in a warehouse. Can you map the data pipeline, highlight the channel-based reporting you rely on weekly, and share a case where this view redirected budget profitably?
The pipeline pulls raw hits from Adobe’s hospitality-focused implementation, merges them with campaign data from Google, Microsoft, and Facebook, and augments with Google Business Profile interactions—all in a central warehouse. The platform’s channel-based reporting then rolls up visits, reservations, room nights, revenue, ROAS, and click-to-call by source. Weekly, we look for discrepancies between click volume and reservation yield, and for properties where Google Business Profile calls correlate with on-site conversions. In one case, that view justified a pivot from a high-click but low-yield channel to sources that consistently closed with room nights, improving the overall ROAS picture without increasing total spend.
Internally, teams use AI and Microsoft Copilot for content, competitor research, and data analysis. How do you train staff, enforce safe usage, and measure productivity gains, and can you share a story where AI changed a project’s outcome?
Training is departmental and continuous—content learns prompt craftsmanship and brand voice packs; media teams focus on analysis and pattern detection; account teams practice summarizing multi-source insights. Safe usage is policy plus platform: we keep sensitive data inside secured systems and rely on the proprietary stack so prompts and outputs don’t leak. Productivity is measured in cycle time—brief to draft, analysis to recommendation, and approval to publish—and in quality markers like edit rate and rework. One project that turned on AI was a rapid re-theme for a seasonal campaign; the AI-generated content and image tags got us from concept to live pages in a single working day, and the on-brand guardrails meant approvals were swift.
CMS Page Analysis gives page-level performance. How do teams diagnose a slipping page, which metrics they check first, and what step-by-step fixes—copy, layout, internal links, or offers—most often recover rankings and conversions?
We start with Page Analysis to see traffic sources, engagement, and conversion events specific to that URL. If organic is down, we inspect title/description alignment and schema coverage; if paid or referral is underperforming, we check message match and above-the-fold layout. Fixes roll out in this order: clarify copy and headings, restructure layout for scannability, add internal links from relevant pages, and align an offer to the page’s real intent. Because the CMS ties these changes to analytics, we can watch visits, CTR, and bookings rebound without guessing which tweak moved the needle.
You’ve been early to adopt AI but selective. How do you evaluate new AI features, what criteria (quality, bias, privacy, maintainability) you score, and can you describe a capability you rejected and why, plus one you greenlit that paid off?
We score candidates on four axes: output quality, bias risk, privacy posture, and long-term maintainability inside a proprietary platform. Anything that threatens brand voice or mishandles guest data is out, even if the demo is shiny. We rejected a “hands-off” auto-personalization concept that bypassed approvals; it clashed with our governance model and risked off-brand experiences. We greenlit computer vision for alt text because it solved a specific compliance gap, fit neatly into the upload workflow, and made editors faster without taking control away. That balance—assist, don’t overreach—is how we stay early and safe.
Do you have any advice for our readers?
Treat your marketing stack like a hotel lobby: one entrance, clear signage, and everything guests need within a few steps. Start by centralizing the essentials—CMS, analytics, reservations, and offers—so your team can see the whole journey in one place. Lean on AI where it saves time and enforces quality, especially for content drafting, image tagging, and insights triage, but keep humans in the approval loop. Finally, measure what matters to hospitality—visits, reservations, room nights, revenue, ROAS, and click-to-call—and let that channel-based view guide your next move.
