OpenText DAM (OTMM) - Loci Integration and Automation

Common Integration Use Cases Between OpenText DAM (OTMM) and Loci

OpenText DAM (OTMM) is used to centrally manage rich media such as product images, campaign assets, museum collections, and broadcast video. Loci adds AI-driven content recommendations based on user behavior and content analysis. Together, they can connect asset management with personalized content delivery, helping organizations surface the right media to the right audience, improve engagement, and reduce manual content selection.

1. AI-Powered Asset Recommendations for Marketing Teams

Direction: OpenText DAM (OTMM) to Loci

Marketing teams can use OTMM as the source of approved campaign assets, while Loci analyzes asset metadata, usage patterns, and audience behavior to recommend the most relevant images and videos for a specific campaign, channel, or segment. For example, when a marketer is building a seasonal campaign, Loci can suggest high-performing visuals from OTMM based on prior engagement and content similarity.

Business value: Speeds up campaign assembly, improves content relevance, and reduces time spent searching for assets.

2. Personalized Product Content Recommendations in Digital Commerce

Direction: Bi-directional

OTMM supplies product images and videos to commerce or product content platforms, while Loci uses customer behavior and content interaction data to recommend the most relevant product media. If a shopper views a product category repeatedly, Loci can surface related product videos, alternate images, or lifestyle visuals managed in OTMM to increase conversion.

Business value: Improves product discovery, supports upsell and cross-sell, and increases engagement on product detail pages.

3. Contextual Content Recommendations for Museum and Heritage Experiences

Direction: OpenText DAM (OTMM) to Loci

Museums and heritage organizations can store digital photos and videos of collections in OTMM, then use Loci to recommend related artifacts, exhibits, or media based on visitor interests and browsing history. For example, if a visitor explores ancient pottery content, Loci can recommend related collection images, curator videos, or themed exhibit pages from the DAM-connected content library.

Business value: Increases visitor engagement, supports richer digital storytelling, and helps audiences discover more relevant collection content.

4. Event and Campaign Asset Personalization Across Channels

Direction: OpenText DAM (OTMM) to Loci

OTMM can manage videos and images from company events, trade shows, and marketing campaigns, while Loci recommends the most relevant event highlights or campaign assets for different audience groups. For example, a sales portal can show event recap videos to prospects who engaged with a specific industry topic, while internal teams see assets aligned to their region or business unit.

Business value: Improves content reuse across teams, increases relevance of distributed assets, and supports targeted engagement.

5. Channel-Specific Content Selection for Distribution Teams

Direction: Bi-directional

Distribution teams can pull approved product images and broadcast assets from OTMM, while Loci uses channel performance data to recommend which assets should be prioritized for each outlet such as web, mobile, email, or streaming platforms. If a certain video thumbnail or product image performs better on mobile, Loci can recommend similar assets from OTMM for future distribution.

Business value: Optimizes asset selection by channel, improves content performance, and reduces manual testing effort.

6. AI-Assisted Content Tagging and Recommendation Enrichment

Direction: OpenText DAM (OTMM) to Loci

OTMM metadata such as product category, campaign, rights information, and media type can be sent to Loci to improve recommendation quality. Loci can then use this enriched content profile to match assets to user intent more accurately. For example, a video tagged as a product launch asset can be recommended to users who previously engaged with launch-related content.

Business value: Improves recommendation accuracy, strengthens metadata usage, and increases the value of existing DAM governance.

7. Performance Feedback Loop for Asset Optimization

Direction: Loci to OpenText DAM (OTMM)

Loci can send engagement analytics back to OTMM, showing which assets are most recommended, clicked, viewed, or converted. DAM administrators and content owners can use this feedback to retire underperforming assets, promote high-performing media, and guide future creative production. For example, if one product video consistently drives higher engagement, OTMM can flag similar assets for broader reuse.

Business value: Creates a closed-loop content optimization process, supports better asset governance, and informs future production investment.

8. Role-Based Content Experiences for Internal and External Portals

Direction: Bi-directional

OTMM provides the approved media library, while Loci personalizes what each user sees in portals such as partner sites, employee hubs, or customer self-service environments. A distributor may see product images and sell sheets relevant to their region, while a museum donor may see curated collection videos aligned to their interests. Loci uses behavior and context to recommend the most relevant OTMM assets in real time.

Business value: Delivers tailored user experiences, improves portal engagement, and ensures content is both relevant and governed.