CASE STUDY
How Montgomery Homes Improved Sales Team Performance
Industry: Real Estate & Construction
Solution Area: Sales Responsiveness & Cycle Time Optimization
Tools Used: HubSpot, Fivetran, Microsoft SQL Server, Power BI
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:
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Which executives respond the fastest—and does that drive higher conversion?
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Where are delays happening—before Tender or after Tender?
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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.
Dashboard Walkthrough
Purpose
Improve sales responsiveness and cycle times—because faster motion correlates with higher close rates.
What’s on this page
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Velocity KPIs: Median days New Enquiry→First Response, New Enquiry→Tender, New Enquiry→Deposit, Tender→Deposit, Deposit→Final Changes Submitted.
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30-day responsiveness leaderboard: Median First-Response Time by Sales Executive (goal line shown).
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Monthly velocity trends: Enquiry→Tender and Tender→Deposit over time.
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Allocation heatmap: New Enquiries allocated each month by Executive.
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Cycle length visuals:
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By Executive (NE→Tender, Tender→Deposit, NE→Deposit)
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By Region (same cuts)
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Activity log (last 6 months): Engagement counts to spot pipeline inactivity.
Key questions it answers
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Who responds fastest, and how does that translate to deposits?
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Where are the bottlenecks—before Tender or after Tender?
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Are certain regions or execs carrying longer cycles?
Decisions & actions
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Set/coach to first-response SLAs; route new enquiries to the fastest reps.
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Address post-tender stalls with nurture tasks and follow-up cadences.
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Rebalance territory/lead allocation using the allocation heatmap.
Representative metrics
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Median time between stages computed on deal cohorts (HubSpot timestamps) to reduce outlier noise.
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SLA breach rate = % of New Enquiries with First Response > X hours.
Technical Architecture & Process
1) Extract & Load with Fivetran
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Connector: HubSpot → SQL Server (destination).
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Objects synced: contacts, companies, deals, pipelines, deal_pipelines, engagements (calls/emails/meetings), marketing_emails, campaigns, web_analytics.
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Sync mode: Incremental (high-watermark on updatedAt), history/soft deletes via _fivetran_synced, _fivetran_deleted.
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Schedule: 15–60 mins.
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Landing schemas/tables:
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hubspot_raw.contacts, hubspot_raw.deals, hubspot_raw.companies, hubspot_raw.contact_company, hubspot_raw.engagements_*
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fivetran_audit._metadata
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Result: clean, append-safe raw HubSpot data in SQL Server with CDC.
2) Model in SQL Server (unify leads → opps → proposals → conversions)
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Dimensions: dim_contact, dim_company, dim_campaign, dim_date.
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Facts: fact_lead, fact_opportunity, fact_activity, fact_revenue.
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Keys: HubSpot native IDs; surrogate keys for dims.
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Unification View:
Result: a consistent SQL model tying leads → opps → proposals → won deals.
Power BI Functionality Used
Drill-down & drill-through
from region-level to executive-level responsiveness.
Heatmaps & leaderboards
to spotlight cycle bottlenecks.
Median cohort calculations
(HubSpot timestamps) to reduce outlier noise.
RLS filters
for role-based views.
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.