From scattered startup data to incubation  intelligence

hubraum is Deutsche Telekom’s tech incubator. It connects startups, ecosystem partners, and corporate teams to drive innovation and business collaboration.

Scouting activities for incubation programs are at the core of hubraum’s operations. However, data supporting these activities lacked structure and visibility.

1) Limited visibility into program performance 

There was no well-structured overview of attendance, engagement, or performance across key Programs.

2) Limited segmentation for outreach and nurturing 

Teams needed to:

• Select companies for the upcoming program outreach

• Identify contacts for nurturing campaigns

But lacked reliable segmentation and engagement-based prioritization.

3) No scoring 

There was no consistent way to evaluate:

• Which startups were most engaged

• Which companies qualified for follow-up

• Which participants became repeat attendees or potential Advocates

4) Data quality issues

The database contained:

• Duplicate records

• Inconsistent property usage in forms

• Incorrect company names

• Limited enrichment and incomplete firmographic data

This reduced trust in analytics and campaign targeting.

1) Introduced buyer personas and segmentation framework 

We created workflows with clear persona definitions and implemented:

• Persona-based segments

(Buyer persona assignment workflow in HubSpot - simplified version)

This workflow reads which Buyer Persona segment a contact belongs to (e.g., Partner) and automatically sets their Persona property to the matching value, keeping contacts correctly labelled.

• Segmentation logic for marketing and scouting teams

(Contact segmentation workflow in HubSpot- simplified version)

This workflow reads a contact's type (e.g., Startup) and automatically copies that value into their professional involvement field.

2) Implemented contact and company scoring 

Contact scoring based on:

• Engagement

• Persona alignment

Company scoring based on:

• Engagement

• Location

• Custom firmographic properties

We tested enrichment approaches (Clay vs. HubSpot smart properties) to improve scoring accuracy and data completeness.

3) Data cleaning and standardization 

We performed structured data cleanup:

• Merging duplicates

• Unifying form properties

• Correcting false company names

• Establishing consistent enrichment processes for new records

Additionally, we implemented the Data Quality Dashboard to monitor real-time data health.

(Data health dashboard in HubSpot)

4) Program and scouting dashboards 

We built:

• Programs dashboards for attendance and performance tracking

 (Program application dashboard in HubSpot) 

• Scouting dashboard for leadership visibility

 (Scouting dashboard in HubSpot)