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How Montgomery Homes Improved Sales Team Performance

  • Writer: GrowthBI
    GrowthBI
  • Nov 28
  • 2 min read

Industry: Real Estate & Construction

Solution Area: Sales Responsiveness & Cycle Time Optimization

Tools Used: HubSpot, Fivetran, Microsoft SQL Server, Power BI


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Business Impact

8–10 hours/week saved​ by replacing manual activity logs with automated velocity tracking.


​25% faster response times improving enquiry-to-deposit conversion rates.​


​Higher close rates ​achieved by targeting bottlenecks, leading to an estimated $180K+ uplift in annual deposits.


The Challenge

Sales executives were handling a large volume of enquiries but managers lacked visibility into response times and bottlenecks in the sales cycle. Key gaps included:


  • Which executives respond the fastest—and does that drive higher conversion?

  • Where are delays happening—before Tender or after Tender?

  • Are certain regions consistently carrying longer sales cycles?


Our Solution

We built a centralized SQL data model powered by Fivetran (HubSpot → SQL Server) and layered it into interactive Power BI dashboards. The solution provided a 360° view of sales and marketing performance.

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Dashboard Walkthrough

Purpose

Improve sales responsiveness and cycle times—because faster motion correlates with higher close rates.


What’s on this page

Velocity KPIs: Median days New Enquiry→First Response, New Enquiry→Tender, New Enquiry→Deposit, Tender→Deposit, Deposit→Final Changes Submitted.


30-day responsiveness leaderboard: Median First-Response Time by Sales Executive (goal line shown).


Monthly velocity trends: Enquiry→Tender and Tender→Deposit over time.


Allocation heatmap: New Enquiries allocated each month by Executive.


Cycle length visuals:

By Executive (NE→Tender, Tender→Deposit, NE→Deposit)

By Region (same cuts)


Activity log (last 6 months): Engagement counts to spot pipeline inactivity.




Key questions it answers

  • Who responds fastest, and how does that translate to deposits?

  • Where are the bottlenecks—before Tender or after Tender?

  • Are certain regions or execs carrying longer cycles?


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Decisions & actions


  • Set/coach to first-response SLAs; route new enquiries to the fastest reps.

  • Address post-tender stalls with nurture tasks and follow-up cadences.

  • Rebalance territory/lead allocation using the allocation heatmap.



Representative metrics


Median time between stages computed on deal cohorts (HubSpot timestamps) to reduce outlier noise.


SLA breach rate = % of New Enquiries with First Response > X hours.



Technical Architecture & Process

1) Extract & Load with Fivetran


Connector: HubSpot → SQL Server (destination).


Objects synced: contacts, companies, deals, pipelines, deal_pipelines, engagements (calls/emails/meetings), marketing_emails, campaigns, web_analytics.


Sync mode: Incremental (high-watermark on updatedAt), history/soft deletes via _fivetran_synced, _fivetran_deleted.


Schedule: 15–60 mins.


Landing schemas/tables:


hubspot_raw.contacts, hubspot_raw.deals, hubspot_raw.companies, hubspot_raw.contact_company, hubspot_raw.engagements_*


fivetran_audit._metadata


Result: clean, append-safe raw HubSpot data in SQL Server with CDC.


2) Model in SQL Server (unify leads → opps → proposals → conversions)


Dimensions: dim_contact, dim_company, dim_campaign, dim_date.


Facts: fact_lead, fact_opportunity, fact_activity, fact_revenue.


Keys: HubSpot native IDs; surrogate keys for dims.


Unification View:



Power BI Functionality Used


Conclusion

The sales velocity dashboard revealed exactly where cycle times slowed down—whether at enquiry, tender, or deposit stage. Leaderboards and cycle length visuals provided accountability for each executive and region. By identifying bottlenecks and enforcing SLAs, the client reduced delays and accelerated revenue realization.

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