GTM Efficiency Drivers

Purpose

Provide a driver-based view of go-to-market efficiency, attributing changes in customer acquisition cost to underlying sales spend, marketing spend, and customer volume dynamics.

This model is designed as an explanatory analytics layer, enabling:

Attribution of CAC inflation or deflation
Separation of spend-driven vs growth-driven efficiency changes
Clear reconciliation between CAC levels and CAC movements

It shifts the analysis from metric tracking to cause analysis.

Business Logic

Start from GTM Efficiency
Join with Customer Count View
Compute period-over-period deltas using LAG
Attribute CAC change into:
- Sales cost changes
- Marketing cost changes
- New customer volume effects
Ensure driver components reconcile to total CAC change
Exclude the first period where lagged values are unavailable

Output Contract

Definitions

Column	Description
date	Date of the snapshot (`YYYY-MM-DD`)
value_type	Indicator such as `actual`, `budget`
sales_cost	CAC change driven by sales spend variation
mkt_cost	CAC change driven by marketing spend variation
new_customers	CAC change driven by customer volume changes
cac_delta	Total period-over-period change in blended CAC

Sample & Implementation

Drivers of changes in blended sales & marketing CAC over time.
date	value_type	sales_cost	mkt_cost	new_customers	cac_delta
2025-09-01	budget	0.0000	0.0000	-0.0000	0.0000
2025-10-01	budget	0.0000	0.0000	-0.0000	0.0000
2025-11-01	budget	0.0000	0.0000	-0.0000	0.0000
2025-12-01	budget	0.0000	0.0000	-0.0000	0.0000
2026-01-01	budget	98.7166	77.6500	-0.0000	176.3666
2026-02-01	budget	0.0000	0.0000	-0.0000	0.0000
2026-03-01	budget	0.0000	0.0000	-0.0000	0.0000

{{config(materialized='view')}}


with gtm_efficiency as (
    select * from {{ ref('gtm_efficiency') }}
),

customer_count_view as (
    select * from {{ref('customer_count_view')}}
),

calculated_values as (
    select
        gtm.date,
        gtm.value_type,
        (gtm.sales_cost - LAG(gtm.sales_cost) OVER (PARTITION BY gtm.value_type ORDER BY gtm.date)) / ccv.new_customers as sales_cost,
        (gtm.mkt_cost - LAG(gtm.mkt_cost) OVER (PARTITION BY gtm.value_type ORDER BY gtm.date)) / ccv.new_customers as mkt_cost,
        -((gtm.sales_cost + gtm.mkt_cost) / LAG(ccv.new_customers) OVER (PARTITION BY ccv.value_type ORDER BY ccv.date) - (gtm.sales_cost + gtm.mkt_cost) / ccv.new_customers) as new_customers,
        (gtm.sales_mkt_cac - LAG(gtm.sales_mkt_cac) OVER (PARTITION BY gtm.value_type ORDER BY gtm.date) ) as cac_delta
    from gtm_efficiency gtm
    left join customer_count_view ccv on gtm.date = ccv.date and gtm.value_type = ccv.value_type
)

select *
from calculated_values
where sales_cost IS NOT NULL

Dependencies

Transformations: GTM Efficiency and Customer Count View