OpenText Core Experience Insights - Marq Integration and Automation

Common Integration Use Cases Between OpenText Core Experience Insights and Marq

OpenText Core Experience Insights helps organizations measure how employees or customers interact with content and digital experiences, while Marq is used to create, manage, and distribute branded content and templates at scale. Together, they can connect content creation with performance analytics so teams can improve adoption, consistency, and business impact.

1. Measure performance of branded content templates

Data flow: Marq - OpenText Core Experience Insights

Track how often Marq-created templates, brochures, sales sheets, or internal communications are opened, shared, and completed by users. OpenText Core Experience Insights can surface which template types drive the highest engagement and which assets are underused, helping marketing and enablement teams refine content libraries based on actual usage.

2. Optimize employee communications and internal campaigns

Data flow: Marq - OpenText Core Experience Insights

When HR, internal communications, or operations teams publish newsletters, policy updates, or onboarding materials in Marq, usage data can be sent to OpenText Core Experience Insights to measure readership, completion rates, and repeat access. This helps identify whether employees are engaging with critical communications and where follow-up is needed.

3. Improve sales enablement content adoption

Data flow: Marq - OpenText Core Experience Insights

Sales teams often rely on branded one-pagers, pitch decks, and proposal templates created in Marq. By analyzing usage patterns in OpenText Core Experience Insights, enablement leaders can determine which assets are most frequently used by sales reps, which regions or teams are adopting them, and which materials may need redesign or retirement.

4. Link content engagement to campaign effectiveness

Data flow: Marq - OpenText Core Experience Insights

Marketing teams can distribute campaign assets created in Marq and use OpenText Core Experience Insights to monitor engagement across channels and audiences. This supports campaign analysis by showing which content formats generate the strongest interaction, enabling better decisions on messaging, layout, and distribution strategy.

5. Identify training and onboarding content gaps

Data flow: Marq - OpenText Core Experience Insights

Organizations can use Marq to produce onboarding guides, training handouts, and process documentation, then use OpenText Core Experience Insights to see which materials are accessed most often and where users drop off. Learning and development teams can use this insight to simplify confusing content, add missing guidance, or create more effective follow-up materials.

6. Govern content standardization across departments

Data flow: Bi-directional

Marq can serve as the controlled source for approved templates, while OpenText Core Experience Insights provides visibility into how those templates are actually used across departments. This combination helps content governance teams enforce brand consistency, identify unauthorized variations, and prioritize updates to the most heavily used assets.

7. Support continuous improvement for customer-facing materials

Data flow: Marq - OpenText Core Experience Insights

Customer success, support, and account teams can publish customer-facing guides, renewal materials, and service communications in Marq and track engagement in OpenText Core Experience Insights. The analytics can reveal whether customers are interacting with the right materials at the right time, helping teams improve clarity, timing, and retention outcomes.

8. Prioritize content updates based on real usage data

Data flow: OpenText Core Experience Insights - Marq

OpenText Core Experience Insights can identify high-traffic or low-performing content assets, and those findings can be sent back to Marq teams responsible for content creation. This enables content owners to update templates, revise messaging, or retire outdated assets based on actual engagement data rather than assumptions.